code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[list[int]] = [[0 for _ in range(lowercase__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
SCREAMING_SNAKE_CASE__ : Any = 1
for n in range(m + 1 ):
for k in range(1 , lowercase__ ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
SCREAMING_SNAKE_CASE__ : Dict = int(input("Enter a number: ").strip())
print(partition(n))
except ValueError:
print("Please enter a number.")
else:
try:
SCREAMING_SNAKE_CASE__ : int = int(sys.argv[1])
print(partition(n))
except ValueError:
print("Please pass a number.")
| 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class snake_case :
def __init__( self : str , a_ : List[str] , a_ : Tuple=13 , a_ : Dict=30 , a_ : Optional[int]=2 , a_ : Tuple=3 , a_ : Dict=True , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[str]=5 , a_ : Any=4 , a_ : Dict=37 , a_ : Dict="gelu" , a_ : int=0.1 , a_ : Optional[Any]=0.1 , a_ : Any=10 , a_ : List[str]=0.02 , a_ : Any=3 , a_ : List[str]=None , a_ : Optional[int]=2 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : int = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
SCREAMING_SNAKE_CASE__ : Optional[int] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_patches + 2
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase( self : List[str] , a_ : List[str] , a_ : Optional[Any] , a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = DeiTModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : List[str] , a_ : List[str] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = DeiTForMaskedImageModeling(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DeiTForMaskedImageModeling(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : int = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = DeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class(a_ )
SCREAMING_SNAKE_CASE__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a_ )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def __lowercase( self : str , a_ : str , a_ : Tuple , a_ : Union[str, Any]=False )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE__ : Tuple = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
model.gradient_checkpointing_enable()
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a_ ),
*get_values(a_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
SCREAMING_SNAKE_CASE__ : int = problem_type['title']
SCREAMING_SNAKE_CASE__ : Tuple = problem_type['num_labels']
SCREAMING_SNAKE_CASE__ : str = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
SCREAMING_SNAKE_CASE__ : Any = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a_ ) as warning_list:
SCREAMING_SNAKE_CASE__ : str = model(**a_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def __lowercase( self : int )-> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(images=a_ , return_tensors='pt' ).to(a_ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**a_ )
# verify the logits
SCREAMING_SNAKE_CASE__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
SCREAMING_SNAKE_CASE__ : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = inputs.pixel_values.to(a_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
| 636 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
}
SCREAMING_SNAKE_CASE__ : int = Dataset.from_dict(lowercase__ )
return dataset
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = get_dataset()
SCREAMING_SNAKE_CASE__ : List[Any] = make_duplicate_clusters(a_ , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def __lowercase( self : int )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_dataset()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = deduplicate_dataset(a_ )
self.assertEqual(len(a_ ) , 2 )
print(a_ )
self.assertEqual(duplicate_clusters[0][0]['copies'] , 2 )
self.assertEqual(duplicate_clusters[0][0]['is_extreme'] , a_ )
| 636 | import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = KandinskyVaaInpaintPipeline
lowercase_ = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
lowercase_ = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
lowercase_ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowercase_ = False
@property
def __lowercase( self : Optional[int] )-> str:
"""simple docstring"""
return 32
@property
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
return 32
@property
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
return self.time_input_dim
@property
def __lowercase( self : Tuple )-> Optional[int]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
return 100
@property
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = UNetaDConditionModel(**a_ )
return model
@property
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase( self : List[str] )-> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_unet
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[int] = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=a_ , set_alpha_to_one=a_ , steps_offset=1 , prediction_type='epsilon' , thresholding=a_ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowercase( self : List[str] , a_ : Any , a_ : Dict=0 )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(a_ ) ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
a_ )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(a_ ) ).to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = Image.fromarray(np.uinta(a_ ) ).convert('RGB' ).resize((256, 256) )
# create mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.ones((64, 64) , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = 0
if str(a_ ).startswith('mps' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.manual_seed(a_ )
else:
SCREAMING_SNAKE_CASE__ : List[str] = torch.Generator(device=a_ ).manual_seed(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = 'cpu'
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict = self.pipeline_class(**a_ )
SCREAMING_SNAKE_CASE__ : List[str] = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = pipe(**self.get_dummy_inputs(a_ ) )
SCREAMING_SNAKE_CASE__ : int = output.images
SCREAMING_SNAKE_CASE__ : Any = pipe(
**self.get_dummy_inputs(a_ ) , return_dict=a_ , )[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
print(F'''image.shape {image.shape}''' )
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array(
[0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
def __lowercase( self : List[str] )-> str:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def __lowercase( self : str )-> int:
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
SCREAMING_SNAKE_CASE__ : List[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
SCREAMING_SNAKE_CASE__ : Any = np.ones((768, 768) , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = 'a hat'
SCREAMING_SNAKE_CASE__ : int = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(a_ )
SCREAMING_SNAKE_CASE__ : int = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipeline.to(a_ )
pipeline.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.Generator(device='cpu' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = pipe_prior(
a_ , generator=a_ , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : str = pipeline(
image=a_ , mask_image=a_ , image_embeds=a_ , negative_image_embeds=a_ , generator=a_ , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
SCREAMING_SNAKE_CASE__ : Dict = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(a_ , a_ )
| 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE__ : Tuple = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {'0': '0', '1': '1'}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = '', ''
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ )
for i in range(len(lowercase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
SCREAMING_SNAKE_CASE__ : int = lexicon[curr_string]
result += last_match_id
SCREAMING_SNAKE_CASE__ : str = last_match_id + '0'
if math.loga(lowercase__ ).is_integer():
SCREAMING_SNAKE_CASE__ : List[str] = {}
for curr_key in list(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = lexicon.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = new_lex
SCREAMING_SNAKE_CASE__ : Any = last_match_id + '1'
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ''
return result
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 8
try:
with open(lowercase__ , 'wb' ) as opened_file:
SCREAMING_SNAKE_CASE__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase__ ) , lowercase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = data_bits[counter:]
SCREAMING_SNAKE_CASE__ : int = data_bits[counter + 1 :]
return data_bits
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = read_file_binary(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = remove_prefix(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = decompress_data(lowercase__ )
write_file_binary(lowercase__ , lowercase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 636 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'layoutlmv3'
def __init__( self : Dict , a_ : List[Any]=5_0265 , a_ : Optional[int]=768 , a_ : List[Any]=12 , a_ : Any=12 , a_ : List[str]=3072 , a_ : int="gelu" , a_ : Any=0.1 , a_ : int=0.1 , a_ : Optional[Any]=512 , a_ : Any=2 , a_ : Optional[int]=0.02 , a_ : Tuple=1e-5 , a_ : Any=1 , a_ : Tuple=0 , a_ : Union[str, Any]=2 , a_ : List[Any]=1024 , a_ : Dict=128 , a_ : Tuple=128 , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[Any]=128 , a_ : Union[str, Any]=64 , a_ : Tuple=256 , a_ : Union[str, Any]=True , a_ : Optional[Any]=True , a_ : List[Any]=True , a_ : List[str]=224 , a_ : Union[str, Any]=3 , a_ : Dict=16 , a_ : Tuple=None , **a_ : str , )-> Optional[int]:
"""simple docstring"""
super().__init__(
vocab_size=a_ , hidden_size=a_ , num_hidden_layers=a_ , num_attention_heads=a_ , intermediate_size=a_ , hidden_act=a_ , hidden_dropout_prob=a_ , attention_probs_dropout_prob=a_ , max_position_embeddings=a_ , type_vocab_size=a_ , initializer_range=a_ , layer_norm_eps=a_ , pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = max_ad_position_embeddings
SCREAMING_SNAKE_CASE__ : List[str] = coordinate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = shape_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = has_relative_attention_bias
SCREAMING_SNAKE_CASE__ : str = rel_pos_bins
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_rel_pos
SCREAMING_SNAKE_CASE__ : Any = has_spatial_attention_bias
SCREAMING_SNAKE_CASE__ : Tuple = rel_ad_pos_bins
SCREAMING_SNAKE_CASE__ : int = max_rel_ad_pos
SCREAMING_SNAKE_CASE__ : Optional[int] = text_embed
SCREAMING_SNAKE_CASE__ : Optional[int] = visual_embed
SCREAMING_SNAKE_CASE__ : Any = input_size
SCREAMING_SNAKE_CASE__ : Tuple = num_channels
SCREAMING_SNAKE_CASE__ : List[str] = patch_size
SCREAMING_SNAKE_CASE__ : int = classifier_dropout
class snake_case ( UpperCamelCase_ ):
lowercase_ = version.parse('1.12' )
@property
def __lowercase( self : int )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
else:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels'}),
] )
@property
def __lowercase( self : Union[str, Any] )-> float:
"""simple docstring"""
return 1e-5
@property
def __lowercase( self : Optional[int] )-> int:
"""simple docstring"""
return 12
def __lowercase( self : Optional[int] , a_ : "ProcessorMixin" , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional["TensorType"] = None , a_ : int = 3 , a_ : int = 40 , a_ : int = 40 , )-> Mapping[str, Any]:
"""simple docstring"""
setattr(processor.image_processor , 'apply_ocr' , a_ )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Tuple = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Dict = processor.tokenizer.num_special_tokens_to_add(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=a_ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE__ : List[str] = [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [[[48, 84, 73, 128]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
SCREAMING_SNAKE_CASE__ : List[str] = self._generate_dummy_images(a_ , a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = dict(
processor(
a_ , text=a_ , boxes=a_ , return_tensors=a_ , ) )
return inputs
| 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'AutoTokenizer'
lowercase_ = ['tokenizer']
lowercase_ = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self : Union[str, Any] , a_ : List[Any] , a_ : Dict=None )-> Any:
"""simple docstring"""
super().__init__(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = speaker_embeddings
@classmethod
def __lowercase( cls : List[str] , a_ : Union[str, Any] , a_ : Any="speaker_embeddings_path.json" , **a_ : int )-> List[str]:
"""simple docstring"""
if speaker_embeddings_dict_path is not None:
SCREAMING_SNAKE_CASE__ : Dict = get_file_from_repo(
a_ , a_ , subfolder=kwargs.pop('subfolder' , a_ ) , cache_dir=kwargs.pop('cache_dir' , a_ ) , force_download=kwargs.pop('force_download' , a_ ) , proxies=kwargs.pop('proxies' , a_ ) , resume_download=kwargs.pop('resume_download' , a_ ) , local_files_only=kwargs.pop('local_files_only' , a_ ) , use_auth_token=kwargs.pop('use_auth_token' , a_ ) , revision=kwargs.pop('revision' , a_ ) , )
if speaker_embeddings_path is None:
logger.warning(
F'''`{os.path.join(a_ , a_ )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
SCREAMING_SNAKE_CASE__ : int = None
else:
with open(a_ ) as speaker_embeddings_json:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(a_ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : Tuple = AutoTokenizer.from_pretrained(a_ , **a_ )
return cls(tokenizer=a_ , speaker_embeddings=a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple , a_ : Dict="speaker_embeddings_path.json" , a_ : int="speaker_embeddings" , a_ : bool = False , **a_ : Tuple , )-> Optional[Any]:
"""simple docstring"""
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(a_ , a_ , 'v2' ) , exist_ok=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = {}
SCREAMING_SNAKE_CASE__ : List[Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_voice_preset(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict['repo_or_path'] , a_ , F'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=a_ , )
SCREAMING_SNAKE_CASE__ : int = os.path.join(a_ , F'''{prompt_key}_{key}.npy''' )
SCREAMING_SNAKE_CASE__ : str = tmp_dict
with open(os.path.join(a_ , a_ ) , 'w' ) as fp:
json.dump(a_ , a_ )
super().save_pretrained(a_ , a_ , **a_ )
def __lowercase( self : List[Any] , a_ : str = None , **a_ : Optional[int] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.speaker_embeddings[voice_preset]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
SCREAMING_SNAKE_CASE__ : Tuple = get_file_from_repo(
self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] , subfolder=kwargs.pop('subfolder' , a_ ) , cache_dir=kwargs.pop('cache_dir' , a_ ) , force_download=kwargs.pop('force_download' , a_ ) , proxies=kwargs.pop('proxies' , a_ ) , resume_download=kwargs.pop('resume_download' , a_ ) , local_files_only=kwargs.pop('local_files_only' , a_ ) , use_auth_token=kwargs.pop('use_auth_token' , a_ ) , revision=kwargs.pop('revision' , a_ ) , )
if path is None:
raise ValueError(
F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
SCREAMING_SNAKE_CASE__ : Tuple = np.load(a_ )
return voice_preset_dict
def __lowercase( self : List[Any] , a_ : Optional[dict] = None )-> List[str]:
"""simple docstring"""
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self : Optional[Any] , a_ : Dict=None , a_ : Optional[Any]=None , a_ : Tuple="pt" , a_ : Union[str, Any]=256 , a_ : str=False , a_ : List[Any]=True , a_ : int=False , **a_ : List[str] , )-> Optional[Any]:
"""simple docstring"""
if voice_preset is not None and not isinstance(a_ , a_ ):
if (
isinstance(a_ , a_ )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
SCREAMING_SNAKE_CASE__ : int = self._load_voice_preset(a_ )
else:
if isinstance(a_ , a_ ) and not voice_preset.endswith('.npz' ):
SCREAMING_SNAKE_CASE__ : int = voice_preset + '.npz'
SCREAMING_SNAKE_CASE__ : Tuple = np.load(a_ )
if voice_preset is not None:
self._validate_voice_preset_dict(a_ , **a_ )
SCREAMING_SNAKE_CASE__ : str = BatchFeature(data=a_ , tensor_type=a_ )
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer(
a_ , return_tensors=a_ , padding='max_length' , max_length=a_ , return_attention_mask=a_ , return_token_type_ids=a_ , add_special_tokens=a_ , **a_ , )
if voice_preset is not None:
SCREAMING_SNAKE_CASE__ : Dict = voice_preset
return encoded_text
| 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def __lowercase( self : Any , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] , a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '</s>'
SCREAMING_SNAKE_CASE__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(a_ ) , 1103 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
SCREAMING_SNAKE_CASE__ : Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
SCREAMING_SNAKE_CASE__ : int = 'To ensure a smooth flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ['This is going to be way too long.' * 150, 'short example']
SCREAMING_SNAKE_CASE__ : int = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def __lowercase( self : Any )-> str:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[int] = PegasusTokenizer(a_ , offset=0 , mask_token_sent=a_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def __lowercase( self : List[str] , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple )-> str:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : str = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
@require_torch
def __lowercase( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ['This is going to be way too long.' * 1000, 'short example']
SCREAMING_SNAKE_CASE__ : Optional[int] = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : str = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 636 | 1 |
import os
import sys
import unittest
SCREAMING_SNAKE_CASE__ : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
SCREAMING_SNAKE_CASE__ : str = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
SCREAMING_SNAKE_CASE__ : Dict = os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class snake_case ( unittest.TestCase ):
def __lowercase( self : str )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = get_test_to_tester_mapping(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = get_test_to_tester_mapping(a_ )
SCREAMING_SNAKE_CASE__ : Any = {'BertModelTest': 'BertModelTester'}
SCREAMING_SNAKE_CASE__ : int = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
def __lowercase( self : Optional[int] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = get_model_to_test_mapping(a_ )
SCREAMING_SNAKE_CASE__ : Any = get_model_to_test_mapping(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
SCREAMING_SNAKE_CASE__ : int = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
def __lowercase( self : List[str] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = get_model_to_tester_mapping(a_ )
SCREAMING_SNAKE_CASE__ : int = get_model_to_tester_mapping(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
SCREAMING_SNAKE_CASE__ : Dict = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
self.assertEqual(get_test_info.to_json(a_ ) , a_ )
| 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 636 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class snake_case :
@staticmethod
def __lowercase( *a_ : Union[str, Any] , **a_ : int )-> Tuple:
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class snake_case ( unittest.TestCase ):
lowercase_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def __lowercase( self : str , a_ : List[str] , a_ : int , a_ : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ObjectDetectionPipeline(model=a_ , image_processor=a_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __lowercase( self : Any , a_ : Tuple , a_ : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png' , threshold=0.0 )
self.assertGreater(len(a_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a_ , {
'score': ANY(a_ ),
'label': ANY(a_ ),
'box': {'xmin': ANY(a_ ), 'ymin': ANY(a_ ), 'xmax': ANY(a_ ), 'ymax': ANY(a_ )},
} , )
import datasets
SCREAMING_SNAKE_CASE__ : Tuple = datasets.load_dataset('hf-internal-testing/fixtures_image_utils' , 'image' , split='test' )
SCREAMING_SNAKE_CASE__ : List[str] = [
Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
'http://images.cocodataset.org/val2017/000000039769.jpg',
# RGBA
dataset[0]['file'],
# LA
dataset[1]['file'],
# L
dataset[2]['file'],
]
SCREAMING_SNAKE_CASE__ : str = object_detector(a_ , threshold=0.0 )
self.assertEqual(len(a_ ) , len(a_ ) )
for outputs in batch_outputs:
self.assertGreater(len(a_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a_ , {
'score': ANY(a_ ),
'label': ANY(a_ ),
'box': {'xmin': ANY(a_ ), 'ymin': ANY(a_ ), 'xmax': ANY(a_ ), 'ymax': ANY(a_ )},
} , )
@require_tf
@unittest.skip('Object detection not implemented in TF' )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
pass
@require_torch
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = 'hf-internal-testing/tiny-detr-mobilenetsv3'
SCREAMING_SNAKE_CASE__ : Optional[Any] = AutoModelForObjectDetection.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Any = AutoFeatureExtractor.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : str = ObjectDetectionPipeline(model=a_ , feature_extractor=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' , threshold=0.0 )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
] , )
SCREAMING_SNAKE_CASE__ : List[str] = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
],
[
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
{'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
],
] , )
@require_torch
@slow
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'facebook/detr-resnet-50'
SCREAMING_SNAKE_CASE__ : List[str] = AutoModelForObjectDetection.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoFeatureExtractor.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = ObjectDetectionPipeline(model=a_ , feature_extractor=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
] )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
] , )
@require_torch
@slow
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'facebook/detr-resnet-50'
SCREAMING_SNAKE_CASE__ : Optional[int] = pipeline('object-detection' , model=a_ )
SCREAMING_SNAKE_CASE__ : Dict = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = object_detector(
[
'http://images.cocodataset.org/val2017/000000039769.jpg',
'http://images.cocodataset.org/val2017/000000039769.jpg',
] )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
[
{'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
{'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
{'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
],
] , )
@require_torch
@slow
def __lowercase( self : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.9985
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'facebook/detr-resnet-50'
SCREAMING_SNAKE_CASE__ : Any = pipeline('object-detection' , model=a_ )
SCREAMING_SNAKE_CASE__ : Dict = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' , threshold=a_ )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
{'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = 'Narsil/layoutlmv3-finetuned-funsd'
SCREAMING_SNAKE_CASE__ : str = 0.9993
SCREAMING_SNAKE_CASE__ : List[str] = pipeline('object-detection' , model=a_ , threshold=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = object_detector(
'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png' )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
{'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
] , )
| 636 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
lowercase_ = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self : Dict , a_ : int , a_ : int , a_ : Optional[int] = None , a_ : int = 5_0257 , a_ : int = 1024 , a_ : int = 768 , a_ : int = 12 , a_ : int = 12 , a_ : Optional[int] = None , a_ : str = "gelu_new" , a_ : float = 0.1 , a_ : float = 0.1 , a_ : float = 0.1 , a_ : float = 1e-5 , a_ : float = 0.02 , a_ : bool = True , a_ : bool = True , a_ : bool = False , a_ : bool = False , )-> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
F''' `n_embd`: {n_embd} are not equal.''' )
SCREAMING_SNAKE_CASE__ : Tuple = prefix_inner_dim
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prefix_hidden_dim
SCREAMING_SNAKE_CASE__ : List[Any] = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
SCREAMING_SNAKE_CASE__ : Optional[int] = (
nn.Linear(self.prefix_hidden_dim , a_ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
SCREAMING_SNAKE_CASE__ : Tuple = GPTaConfig(
vocab_size=a_ , n_positions=a_ , n_embd=a_ , n_layer=a_ , n_head=a_ , n_inner=a_ , activation_function=a_ , resid_pdrop=a_ , embd_pdrop=a_ , attn_pdrop=a_ , layer_norm_epsilon=a_ , initializer_range=a_ , scale_attn_weights=a_ , use_cache=a_ , scale_attn_by_inverse_layer_idx=a_ , reorder_and_upcast_attn=a_ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaLMHeadModel(a_ )
def __lowercase( self : Any , a_ : torch.Tensor , a_ : torch.Tensor , a_ : Optional[torch.Tensor] = None , a_ : Optional[torch.Tensor] = None , )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.transformer.transformer.wte(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.encode_prefix(a_ )
SCREAMING_SNAKE_CASE__ : str = self.decode_prefix(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
SCREAMING_SNAKE_CASE__ : Tuple = torch.cat((dummy_token, input_ids) , dim=1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.transformer(inputs_embeds=a_ , labels=a_ , attention_mask=a_ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def __lowercase( self : Optional[int] , a_ : int , a_ : torch.device )-> torch.Tensor:
"""simple docstring"""
return torch.zeros(a_ , self.prefix_length , dtype=torch.intaa , device=a_ )
def __lowercase( self : Tuple , a_ : Tuple )-> Dict:
"""simple docstring"""
return self.encode_prefix(a_ )
@torch.no_grad()
def __lowercase( self : Dict , a_ : Optional[int] , a_ : Optional[int] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = torch.split(a_ , 1 , dim=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Dict = []
for feature in features:
SCREAMING_SNAKE_CASE__ : List[Any] = self.decode_prefix(feature.to(a_ ) ) # back to the clip feature
# Only support beam search for now
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.generate_beam(
input_embeds=a_ , device=a_ , eos_token_id=a_ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.stack(a_ )
SCREAMING_SNAKE_CASE__ : int = torch.stack(a_ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def __lowercase( self : Dict , a_ : List[str]=None , a_ : str=None , a_ : Union[str, Any]=None , a_ : int = 5 , a_ : int = 67 , a_ : float = 1.0 , a_ : Optional[int] = None , )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : List[Any] = torch.ones(a_ , device=a_ , dtype=torch.int )
SCREAMING_SNAKE_CASE__ : Any = torch.zeros(a_ , device=a_ , dtype=torch.bool )
if input_embeds is not None:
SCREAMING_SNAKE_CASE__ : List[Any] = input_embeds
else:
SCREAMING_SNAKE_CASE__ : Any = self.transformer.transformer.wte(a_ )
for i in range(a_ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.transformer(inputs_embeds=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits
SCREAMING_SNAKE_CASE__ : Any = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
SCREAMING_SNAKE_CASE__ : List[str] = logits.softmax(-1 ).log()
if scores is None:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = logits.topk(a_ , -1 )
SCREAMING_SNAKE_CASE__ : int = generated.expand(a_ , *generated.shape[1:] )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
SCREAMING_SNAKE_CASE__ : str = next_tokens
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokens.expand(a_ , *tokens.shape[1:] )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cat((tokens, next_tokens) , dim=1 )
else:
SCREAMING_SNAKE_CASE__ : Any = -float(np.inf )
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Dict = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
SCREAMING_SNAKE_CASE__ : List[str] = scores_sum / seq_lengths[:, None]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = scores_sum_average.view(-1 ).topk(a_ , -1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = next_tokens // scores_sum.shape[1]
SCREAMING_SNAKE_CASE__ : Tuple = seq_lengths[next_tokens_source]
SCREAMING_SNAKE_CASE__ : Tuple = next_tokens % scores_sum.shape[1]
SCREAMING_SNAKE_CASE__ : Any = next_tokens.unsqueeze(1 )
SCREAMING_SNAKE_CASE__ : List[str] = tokens[next_tokens_source]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat((tokens, next_tokens) , dim=1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = generated[next_tokens_source]
SCREAMING_SNAKE_CASE__ : List[str] = scores_sum_average * seq_lengths
SCREAMING_SNAKE_CASE__ : List[Any] = is_stopped[next_tokens_source]
SCREAMING_SNAKE_CASE__ : int = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
SCREAMING_SNAKE_CASE__ : Tuple = torch.cat((generated, next_token_embed) , dim=1 )
SCREAMING_SNAKE_CASE__ : Tuple = is_stopped + next_tokens.eq(a_ ).squeeze()
if is_stopped.all():
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scores / seq_lengths
SCREAMING_SNAKE_CASE__ : List[str] = scores.argsort(descending=a_ )
# tokens tensors are already padded to max_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = [tokens[i] for i in order]
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.stack(a_ , dim=0 )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | 1 |
from __future__ import annotations
import numpy as np
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = np.shape(lowercase__ )
if rows != columns:
SCREAMING_SNAKE_CASE__ : Dict = (
'\'table\' has to be of square shaped array but got a '
f'''{rows}x{columns} array:\n{table}'''
)
raise ValueError(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.zeros((rows, columns) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.zeros((rows, columns) )
for i in range(lowercase__ ):
for j in range(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sum(lower[i][k] * upper[k][j] for k in range(lowercase__ ) )
if upper[j][j] == 0:
raise ArithmeticError('No LU decomposition exists' )
SCREAMING_SNAKE_CASE__ : Optional[int] = (table[i][j] - total) / upper[j][j]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
for j in range(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = sum(lower[i][k] * upper[k][j] for k in range(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | 1 |
def _a ( ):
'''simple docstring'''
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
SCREAMING_SNAKE_CASE__ : Tuple = generate_large_matrix()
SCREAMING_SNAKE_CASE__ : List[Any] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
assert all(row == sorted(lowercase__ , reverse=lowercase__ ) for row in grid )
assert all(list(lowercase__ ) == sorted(lowercase__ , reverse=lowercase__ ) for col in zip(*lowercase__ ) )
def _a ( lowercase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE__ : str = (left + right) // 2
SCREAMING_SNAKE_CASE__ : str = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] = mid + 1
else:
SCREAMING_SNAKE_CASE__ : Any = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase__ )
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Any = len(grid[0] )
for i in range(len(lowercase__ ) ):
SCREAMING_SNAKE_CASE__ : Tuple = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase__ ) * len(grid[0] )) - total
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
return len([number for row in grid for number in row if number < 0] )
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 0
for row in grid:
for i, number in enumerate(lowercase__ ):
if number < 0:
total += len(lowercase__ ) - i
break
return total
def _a ( ):
'''simple docstring'''
from timeit import timeit
print('Running benchmarks' )
SCREAMING_SNAKE_CASE__ : int = (
'from __main__ import count_negatives_binary_search, '
'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE__ : List[str] = timeit(f'''{func}(grid=grid)''' , setup=lowercase__ , number=5_00 )
print(f'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 636 | 1 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _a ( lowercase__ : Optional[int] , lowercase__ : Optional[int] , lowercase__ : Optional[int]=0 ):
'''simple docstring'''
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = '.' * max(0 , spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
SCREAMING_SNAKE_CASE__ : Optional[Any] = fmt.format(lowercase__ )
# Print and recurse (if needed).
if isinstance(lowercase__ , lowercase__ ):
if msg is not None:
print(lowercase__ )
for k in val.keys():
recursive_print(lowercase__ , val[k] , spaces + 2 )
elif isinstance(lowercase__ , torch.Tensor ):
print(lowercase__ , ':' , val.size() )
else:
print(lowercase__ , ':' , lowercase__ )
def _a ( lowercase__ : Optional[Any] , lowercase__ : List[Any] , lowercase__ : Optional[int] , lowercase__ : List[Any] , lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = param.view(*lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple = param.view(*lowercase__ )
return param
def _a ( lowercase__ : Optional[Any] , lowercase__ : Dict , lowercase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict.get('args' , lowercase__ )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : str = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : str = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : Tuple = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : List[Any] = input_state_dict['checkpoint_version']
else:
SCREAMING_SNAKE_CASE__ : Dict = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Tuple = input_state_dict['model']
# The language model.
SCREAMING_SNAKE_CASE__ : Dict = model['language_model']
# The embeddings.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm['embedding']
# The word embeddings.
SCREAMING_SNAKE_CASE__ : str = embeddings['word_embeddings']['weight']
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : List[str] = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : List[Any] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : List[str] = embeddings['position_embeddings']['weight']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : int = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : Dict = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Any = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'attention.dense': '.attn.c_proj.',
'self_attention.dense': '.attn.c_proj.',
'mlp.dense_h_to_4h': '.mlp.c_fc.',
'mlp.dense_4h_to_h': '.mlp.c_proj.',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : Optional[int] = layer_re.match(lowercase__ )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = f'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
SCREAMING_SNAKE_CASE__ : Dict = 'ln_1' if op_name.startswith('input' ) else 'ln_2'
SCREAMING_SNAKE_CASE__ : Any = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Tuple = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Optional[Any] = masked_bias
SCREAMING_SNAKE_CASE__ : Union[str, Any] = fix_query_key_value_ordering(lowercase__ , lowercase__ , 3 , lowercase__ , lowercase__ )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : Optional[Any] = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : List[str] = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = fix_query_key_value_ordering(lowercase__ , lowercase__ , 3 , lowercase__ , lowercase__ )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : Optional[int] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : List[str] = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Optional[int] = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Tuple = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Optional[int] = transformer['final_layernorm.weight']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer['final_layernorm.bias']
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = word_embeddings
# It should be done!
return output_state_dict
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' , action='store_true' )
parser.add_argument(
'path_to_checkpoint' , type=lowercase__ , help='Path to the checkpoint file (.zip archive or direct .pt file)' , )
parser.add_argument(
'--config_file' , default='' , type=lowercase__ , help='An optional config json file describing the pre-trained model.' , )
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : str = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint , 'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.load(lowercase__ , map_location='cpu' )
else:
SCREAMING_SNAKE_CASE__ : Any = torch.load(args.path_to_checkpoint , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict.get('args' , lowercase__ )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : List[Any] = 'gelu_fast'
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Any = 'gelu_new'
else:
SCREAMING_SNAKE_CASE__ : Dict = 'gelu'
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'gelu_new'
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : int = GPTaConfig(
vocab_size=5_02_57 , n_positions=10_24 , n_embd=10_24 , n_layer=24 , n_head=16 , n_inner=40_96 , activation_function=lowercase__ , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type='cls_index' , summary_use_proj=lowercase__ , summary_activation=lowercase__ , summary_proj_to_labels=lowercase__ , summary_first_dropout=0.1 , scale_attn_weights=lowercase__ , use_cache=lowercase__ , bos_token_id=5_02_56 , eos_token_id=5_02_56 , )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ['GPT2LMHeadModel']
# Convert.
print('Converting' )
SCREAMING_SNAKE_CASE__ : Dict = convert_megatron_checkpoint(lowercase__ , lowercase__ , lowercase__ )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(lowercase__ , lowercase__ )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'gpt2'
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : str = ds_args.tokenizer_name_or_path
else:
raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Any = 'gpt2'
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoTokenizer.from_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = type(lowercase__ ).__name__
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(lowercase__ )
# Save tokenizer based on args
print(f'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(lowercase__ )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : str = os.path.join(lowercase__ , 'pytorch_model.bin' )
print(f'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(lowercase__ , lowercase__ )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__ : str = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self : Optional[int] , *lowercase__ : int , **lowercase__ : Tuple ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 636 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
SCREAMING_SNAKE_CASE__ : List[Any] = 8
def _a ( lowercase__ : List[str] , lowercase__ : str=BITS ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = x.device
SCREAMING_SNAKE_CASE__ : int = (x * 2_55).int().clamp(0 , 2_55 )
SCREAMING_SNAKE_CASE__ : int = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowercase__ )
SCREAMING_SNAKE_CASE__ : str = rearrange(lowercase__ , 'd -> d 1 1' )
SCREAMING_SNAKE_CASE__ : List[str] = rearrange(lowercase__ , 'b c h w -> b c 1 h w' )
SCREAMING_SNAKE_CASE__ : List[Any] = ((x & mask) != 0).float()
SCREAMING_SNAKE_CASE__ : Dict = rearrange(lowercase__ , 'b c d h w -> b (c d) h w' )
SCREAMING_SNAKE_CASE__ : Any = bits * 2 - 1
return bits
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any]=BITS ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = x.device
SCREAMING_SNAKE_CASE__ : List[Any] = (x > 0).int()
SCREAMING_SNAKE_CASE__ : int = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowercase__ , dtype=torch.intaa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rearrange(lowercase__ , 'd -> d 1 1' )
SCREAMING_SNAKE_CASE__ : Optional[int] = rearrange(lowercase__ , 'b (c d) h w -> b c d h w' , d=8 )
SCREAMING_SNAKE_CASE__ : Dict = reduce(x * mask , 'b c d h w -> b c h w' , 'sum' )
return (dec / 2_55).clamp(0.0 , 1.0 )
def _a ( self : Any , lowercase__ : torch.FloatTensor , lowercase__ : int , lowercase__ : torch.FloatTensor , lowercase__ : float = 0.0 , lowercase__ : bool = True , lowercase__ : List[str]=None , lowercase__ : bool = True , ):
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
SCREAMING_SNAKE_CASE__ : List[Any] = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.alphas_cumprod[timestep]
SCREAMING_SNAKE_CASE__ : Dict = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
SCREAMING_SNAKE_CASE__ : Optional[int] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE__ : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
SCREAMING_SNAKE_CASE__ : Optional[int] = self.bit_scale
if self.config.clip_sample:
SCREAMING_SNAKE_CASE__ : Any = torch.clamp(lowercase__ , -scale , lowercase__ )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
SCREAMING_SNAKE_CASE__ : Any = self._get_variance(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
SCREAMING_SNAKE_CASE__ : int = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE__ : Any = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE__ : str = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_output.device if torch.is_tensor(lowercase__ ) else 'cpu'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.randn(model_output.shape , dtype=model_output.dtype , generator=lowercase__ ).to(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = self._get_variance(lowercase__ , lowercase__ ) ** 0.5 * eta * noise
SCREAMING_SNAKE_CASE__ : str = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=lowercase__ , pred_original_sample=lowercase__ )
def _a ( self : List[Any] , lowercase__ : torch.FloatTensor , lowercase__ : int , lowercase__ : torch.FloatTensor , lowercase__ : Optional[int]="epsilon" , lowercase__ : Any=None , lowercase__ : bool = True , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = torch.split(lowercase__ , sample.shape[1] , dim=1 )
else:
SCREAMING_SNAKE_CASE__ : Dict = None
# 1. compute alphas, betas
SCREAMING_SNAKE_CASE__ : Tuple = self.alphas_cumprod[t]
SCREAMING_SNAKE_CASE__ : str = self.alphas_cumprod[t - 1] if t > 0 else self.one
SCREAMING_SNAKE_CASE__ : int = 1 - alpha_prod_t
SCREAMING_SNAKE_CASE__ : int = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
SCREAMING_SNAKE_CASE__ : Optional[Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
SCREAMING_SNAKE_CASE__ : List[str] = model_output
else:
raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )
# 3. Clip "predicted x_0"
SCREAMING_SNAKE_CASE__ : str = self.bit_scale
if self.config.clip_sample:
SCREAMING_SNAKE_CASE__ : Any = torch.clamp(lowercase__ , -scale , lowercase__ )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE__ : str = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
SCREAMING_SNAKE_CASE__ : Dict = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
SCREAMING_SNAKE_CASE__ : Dict = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
SCREAMING_SNAKE_CASE__ : Tuple = 0
if t > 0:
SCREAMING_SNAKE_CASE__ : Any = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=lowercase__ ).to(model_output.device )
SCREAMING_SNAKE_CASE__ : Any = (self._get_variance(lowercase__ , predicted_variance=lowercase__ ) ** 0.5) * noise
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=lowercase__ , pred_original_sample=lowercase__ )
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[Any] , a_ : UNetaDConditionModel , a_ : Union[DDIMScheduler, DDPMScheduler] , a_ : Optional[float] = 1.0 , )-> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[int] = bit_scale
SCREAMING_SNAKE_CASE__ : List[str] = (
ddim_bit_scheduler_step if isinstance(a_ , a_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=a_ , scheduler=a_ )
@torch.no_grad()
def __call__( self : int , a_ : Optional[int] = 256 , a_ : Optional[int] = 256 , a_ : Optional[int] = 50 , a_ : Optional[torch.Generator] = None , a_ : Optional[int] = 1 , a_ : Optional[str] = "pil" , a_ : bool = True , **a_ : Union[str, Any] , )-> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=a_ , )
SCREAMING_SNAKE_CASE__ : List[Any] = decimal_to_bits(a_ ) * self.bit_scale
SCREAMING_SNAKE_CASE__ : Optional[int] = latents.to(self.device )
self.scheduler.set_timesteps(a_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
SCREAMING_SNAKE_CASE__ : Any = self.unet(a_ , a_ ).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE__ : int = self.scheduler.step(a_ , a_ , a_ ).prev_sample
SCREAMING_SNAKE_CASE__ : Any = bits_to_decimal(a_ )
if output_type == "pil":
SCREAMING_SNAKE_CASE__ : str = self.numpy_to_pil(a_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a_ )
| 636 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _a ( lowercase__ : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = XGBClassifier()
classifier.fit(lowercase__ , lowercase__ )
return classifier
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = load_iris()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = data_handling(lowercase__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = train_test_split(
lowercase__ , lowercase__ , test_size=0.25 )
SCREAMING_SNAKE_CASE__ : Tuple = iris['target_names']
# Create an XGBoost Classifier from the training data
SCREAMING_SNAKE_CASE__ : Tuple = xgboost(lowercase__ , lowercase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowercase__ , lowercase__ , lowercase__ , display_labels=lowercase__ , cmap='Blues' , normalize='true' , )
plt.title('Normalized Confusion Matrix - IRIS Dataset' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(lowercase__ )
or left < -len(lowercase__ )
or right >= len(lowercase__ )
or right < -len(lowercase__ )
):
raise IndexError('list index out of range' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ : int = find_max(lowercase__ , lowercase__ , lowercase__ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ : Tuple = find_max(lowercase__ , mid + 1 , lowercase__ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 636 | 1 |
def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
SCREAMING_SNAKE_CASE__ : int = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = str(bin(lowercase__ ) )[2:]
SCREAMING_SNAKE_CASE__ : List[Any] = max(len(lowercase__ ) , len(lowercase__ ) )
return "0b" + "".join(
str(int('1' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowercase__ ) , b_binary.zfill(lowercase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class snake_case ( unittest.TestCase ):
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE__ : Optional[int] = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(a_ ) , torch_builtin(a_ ) ) )
self.assertFalse(torch.allclose(gelu_python(a_ ) , gelu_new(a_ ) ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
SCREAMING_SNAKE_CASE__ : List[str] = get_activation('gelu' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_activation('gelu_10' )
SCREAMING_SNAKE_CASE__ : List[Any] = torch_builtin(a_ )
SCREAMING_SNAKE_CASE__ : Dict = geluaa(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(a_ ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def __lowercase( self : Optional[int] )-> List[Any]:
"""simple docstring"""
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(a_ ):
get_activation('bogus' )
with self.assertRaises(a_ ):
get_activation(a_ )
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = get_activation('gelu' )
SCREAMING_SNAKE_CASE__ : Tuple = 1
SCREAMING_SNAKE_CASE__ : int = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Tuple = acta.a
| 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[int] , a_ : Dict=7 , a_ : Any=3 , a_ : Any=18 , a_ : int=30 , a_ : int=400 , a_ : List[Any]=None , a_ : int=True , a_ : int=True , a_ : Dict=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'height': 20, 'width': 20}
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : List[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_convert_rgb
SCREAMING_SNAKE_CASE__ : List[str] = [512, 1024, 2048, 4096]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(a_ , stream=a_ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = PixaStructImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.prepare_dummy_image()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE__ : List[Any] = 2048
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(a_ , return_tensors='pt' , max_patches=a_ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
SCREAMING_SNAKE_CASE__ : int = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
SCREAMING_SNAKE_CASE__ : List[Any] = 'Hello'
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processor(
a_ , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : int = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = PixaStructImageProcessingTester(self , num_channels=4 )
SCREAMING_SNAKE_CASE__ : Dict = 3
@property
def __lowercase( self : Any )-> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 636 | 1 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if isinstance(lowercase__ , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class snake_case :
def __lowercase( self : Union[str, Any] , a_ : Optional[Any] , a_ : str )-> Tuple:
"""simple docstring"""
pass
def __lowercase( self : List[Any] )-> Optional[Any]:
"""simple docstring"""
pass
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
pass
def __lowercase( self : int , a_ : np.ndarray , a_ : np.ndarray , a_ : float )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = np.abs((a - b) ).max()
self.assertLessEqual(a_ , a_ , F'''Difference between torch and flax is {diff} (>= {tol}).''' )
def __lowercase( self : Union[str, Any] , a_ : str , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Any , a_ : str=None , **a_ : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = VisionTextDualEncoderConfig.from_vision_text_configs(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = FlaxVisionTextDualEncoderModel(a_ )
SCREAMING_SNAKE_CASE__ : str = model(input_ids=a_ , pixel_values=a_ , attention_mask=a_ )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], config.projection_dim) )
def __lowercase( self : Any , a_ : Any , a_ : str , a_ : Union[str, Any] , a_ : List[str] , a_ : Optional[Any]=None , **a_ : Optional[int] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.get_vision_text_model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Dict = {'vision_model': vision_model, 'text_model': text_model}
SCREAMING_SNAKE_CASE__ : List[str] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=a_ , pixel_values=a_ , attention_mask=a_ )
self.assertEqual(output['text_embeds'].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['image_embeds'].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __lowercase( self : Union[str, Any] , a_ : int , a_ : Any , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any=None , **a_ : str )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.get_vision_text_model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : str = {'vision_model': vision_model, 'text_model': text_model}
SCREAMING_SNAKE_CASE__ : Dict = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model(input_ids=a_ , pixel_values=a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(input_ids=a_ , pixel_values=a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = after_output[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(a_ , 1e-3 )
def __lowercase( self : List[Any] , a_ : int , a_ : Dict , a_ : str , a_ : Tuple , a_ : Any=None , **a_ : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.get_vision_text_model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'vision_model': vision_model, 'text_model': text_model}
SCREAMING_SNAKE_CASE__ : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**a_ )
SCREAMING_SNAKE_CASE__ : int = model(
input_ids=a_ , pixel_values=a_ , attention_mask=a_ , output_attentions=a_ )
SCREAMING_SNAKE_CASE__ : str = output.vision_model_output.attentions
self.assertEqual(len(a_ ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE__ : int = to_atuple(vision_model.config.image_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = to_atuple(vision_model.config.patch_size )
SCREAMING_SNAKE_CASE__ : List[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE__ : List[str] = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
SCREAMING_SNAKE_CASE__ : Dict = output.text_model_output.attentions
self.assertEqual(len(a_ ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __lowercase( self : List[Any] , a_ : Tuple , a_ : List[Any] , a_ : Optional[int] )-> Tuple:
"""simple docstring"""
pt_model.to(a_ )
pt_model.eval()
# prepare inputs
SCREAMING_SNAKE_CASE__ : Optional[int] = inputs_dict
SCREAMING_SNAKE_CASE__ : str = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int = pt_model(**a_ ).to_tuple()
SCREAMING_SNAKE_CASE__ : Optional[int] = fx_model(**a_ ).to_tuple()
self.assertEqual(len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(a_ , pt_output.numpy() , 4e-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : int = FlaxVisionTextDualEncoderModel.from_pretrained(a_ , from_pt=a_ )
SCREAMING_SNAKE_CASE__ : str = fx_model_loaded(**a_ ).to_tuple()
self.assertEqual(len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(a_ , pt_output.numpy() , 4e-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = VisionTextDualEncoderModel.from_pretrained(a_ , from_flax=a_ )
pt_model_loaded.to(a_ )
pt_model_loaded.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] = pt_model_loaded(**a_ ).to_tuple()
self.assertEqual(len(a_ ) , len(a_ ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(a_ , pt_output_loaded.numpy() , 4e-2 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int , a_ : Dict )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = VisionTextDualEncoderConfig.from_vision_text_configs(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = VisionTextDualEncoderModel(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = FlaxVisionTextDualEncoderModel(a_ )
SCREAMING_SNAKE_CASE__ : str = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , a_ )
SCREAMING_SNAKE_CASE__ : str = fx_state
self.check_pt_flax_equivalence(a_ , a_ , a_ )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : str , a_ : Any )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = VisionTextDualEncoderConfig.from_vision_text_configs(a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = VisionTextDualEncoderModel(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = FlaxVisionTextDualEncoderModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = load_flax_weights_in_pytorch_model(a_ , fx_model.params )
self.check_pt_flax_equivalence(a_ , a_ , a_ )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**a_ )
def __lowercase( self : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**a_ )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
self.check_save_load(**a_ )
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**a_ )
@is_pt_flax_cross_test
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = config_inputs_dict.pop('vision_config' )
SCREAMING_SNAKE_CASE__ : List[str] = config_inputs_dict.pop('text_config' )
SCREAMING_SNAKE_CASE__ : List[Any] = config_inputs_dict
self.check_equivalence_pt_to_flax(a_ , a_ , a_ )
self.check_equivalence_flax_to_pt(a_ , a_ , a_ )
@slow
def __lowercase( self : Union[str, Any] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.get_pretrained_model_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = model_a(**a_ )
SCREAMING_SNAKE_CASE__ : Tuple = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxVisionTextDualEncoderModel.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : int = model_a(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = after_outputs[0]
SCREAMING_SNAKE_CASE__ : int = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(a_ , 1e-5 )
@require_flax
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-vit' , 'hf-internal-testing/tiny-bert' , vision_from_pt=a_ , text_from_pt=a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = 13
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([batch_size, 4] )
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def __lowercase( self : List[str] , a_ : Optional[Any] , a_ : Tuple )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = FlaxViTModel(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = FlaxBertModel(a_ )
return vision_model, text_model
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxViTModelTester(self )
SCREAMING_SNAKE_CASE__ : int = FlaxBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple = vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = vision_config_and_inputs
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
'hf-internal-testing/tiny-random-clip' , 'hf-internal-testing/tiny-bert' , vision_from_pt=a_ , text_from_pt=a_ , )
SCREAMING_SNAKE_CASE__ : List[Any] = 13
SCREAMING_SNAKE_CASE__ : List[str] = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([batch_size, 4] )
SCREAMING_SNAKE_CASE__ : int = {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}
return model, inputs
def __lowercase( self : Union[str, Any] , a_ : Tuple , a_ : Dict )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = FlaxCLIPVisionModel(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxBertModel(a_ )
return vision_model, text_model
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = FlaxCLIPVisionModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple = FlaxBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = clip_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Optional[Any] = bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = vision_config_and_inputs
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Optional[int] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian' , logit_scale_init_value=1.0 )
SCREAMING_SNAKE_CASE__ : Any = VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian' )
SCREAMING_SNAKE_CASE__ : Optional[int] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
SCREAMING_SNAKE_CASE__ : Any = processor(
text=['una foto di un gatto', 'una foto di un cane'] , images=a_ , padding=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : str = model(**a_ )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
SCREAMING_SNAKE_CASE__ : List[str] = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image , a_ , atol=1e-3 ) )
| 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {} # {vertex:distance}
def __lt__( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
return self.key < other.key
def __repr__( self : Any )-> Dict:
"""simple docstring"""
return self.id
def __lowercase( self : Optional[Any] , a_ : int )-> List[str]:
"""simple docstring"""
self.neighbors.append(a_ )
def __lowercase( self : int , a_ : int , a_ : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = weight
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowercase__ )
graph[b - 1].add_edge(graph[a - 1] , lowercase__ )
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
for u in graph:
SCREAMING_SNAKE_CASE__ : Dict = math.inf
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = graph[:]
while q:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(lowercase__ )
q.remove(lowercase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : int = u
SCREAMING_SNAKE_CASE__ : Any = u.edges[v.id]
for i in range(1 , len(lowercase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
for u in graph:
SCREAMING_SNAKE_CASE__ : List[str] = math.inf
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = list(lowercase__ )
hq.heapify(lowercase__ )
while h:
SCREAMING_SNAKE_CASE__ : Optional[int] = hq.heappop(lowercase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : List[str] = u
SCREAMING_SNAKE_CASE__ : Dict = u.edges[v.id]
hq.heapify(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[Any] , *a_ : Tuple , **a_ : Tuple )-> None:
"""simple docstring"""
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , a_ , )
super().__init__(*a_ , **a_ )
| 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Any = [
"SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
"SEWForCTC",
"SEWForSequenceClassification",
"SEWModel",
"SEWPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE__ : int = radians(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = angle_in_radians
SCREAMING_SNAKE_CASE__ : Optional[int] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = -1
for _ in range(lowercase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase__ , lowercase__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 636 | 1 |
from __future__ import annotations
class snake_case :
def __init__( self : Optional[int] , a_ : Dict=None )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = data
SCREAMING_SNAKE_CASE__ : List[str] = None
def __repr__( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self
while temp:
string_rep.append(F'''{temp.data}''' )
SCREAMING_SNAKE_CASE__ : List[str] = temp.next
return "->".join(a_ )
def _a ( lowercase__ : list ):
'''simple docstring'''
if not elements_list:
raise Exception('The Elements List is empty' )
SCREAMING_SNAKE_CASE__ : Optional[int] = Node(elements_list[0] )
for i in range(1 , len(lowercase__ ) ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Node(elements_list[i] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = current.next
return head
def _a ( lowercase__ : Node ):
'''simple docstring'''
if head_node is not None and isinstance(lowercase__ , lowercase__ ):
print_reverse(head_node.next )
print(head_node.data )
def _a ( ):
'''simple docstring'''
from doctest import testmod
testmod()
SCREAMING_SNAKE_CASE__ : Optional[Any] = make_linked_list([14, 52, 14, 12, 43] )
print('Linked List:' )
print(lowercase__ )
print('Elements in Reverse:' )
print_reverse(lowercase__ )
if __name__ == "__main__":
main()
| 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE__ : Tuple = range(3 , int(math.sqrt(lowercase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _a ( lowercase__ : List[str] , lowercase__ : Any=1 , **lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = factor * value
SCREAMING_SNAKE_CASE__ : Dict = value
while not is_prime(lowercase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase__ )
return value
| 636 | 1 |
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = [0] * len(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
# use last results for better performance - dynamic programming
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
SCREAMING_SNAKE_CASE__ : List[str] = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
SCREAMING_SNAKE_CASE__ : Tuple = j
return prefix_result
def _a ( lowercase__ : str ):
'''simple docstring'''
return max(prefix_function(lowercase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class snake_case :
def __init__( self : str , a_ : List[str] , a_ : Tuple=13 , a_ : Dict=30 , a_ : Optional[int]=2 , a_ : Tuple=3 , a_ : Dict=True , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[str]=5 , a_ : Any=4 , a_ : Dict=37 , a_ : Dict="gelu" , a_ : int=0.1 , a_ : Optional[Any]=0.1 , a_ : Any=10 , a_ : List[str]=0.02 , a_ : Any=3 , a_ : List[str]=None , a_ : Optional[int]=2 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : int = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
SCREAMING_SNAKE_CASE__ : Optional[int] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_patches + 2
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase( self : List[str] , a_ : List[str] , a_ : Optional[Any] , a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = DeiTModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : List[str] , a_ : List[str] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = DeiTForMaskedImageModeling(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DeiTForMaskedImageModeling(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : int = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = DeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class(a_ )
SCREAMING_SNAKE_CASE__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a_ )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def __lowercase( self : str , a_ : str , a_ : Tuple , a_ : Union[str, Any]=False )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE__ : Tuple = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
model.gradient_checkpointing_enable()
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a_ ),
*get_values(a_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
SCREAMING_SNAKE_CASE__ : int = problem_type['title']
SCREAMING_SNAKE_CASE__ : Tuple = problem_type['num_labels']
SCREAMING_SNAKE_CASE__ : str = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
SCREAMING_SNAKE_CASE__ : Any = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a_ ) as warning_list:
SCREAMING_SNAKE_CASE__ : str = model(**a_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def __lowercase( self : int )-> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(images=a_ , return_tensors='pt' ).to(a_ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**a_ )
# verify the logits
SCREAMING_SNAKE_CASE__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
SCREAMING_SNAKE_CASE__ : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = inputs.pixel_values.to(a_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
| 636 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[Any] , *a_ : Optional[int] , **a_ : Optional[Any] )-> None:
"""simple docstring"""
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , a_ , )
super().__init__(*a_ , **a_ )
| 636 | import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertConfig",
"MobileBertOnnxConfig",
],
"tokenization_mobilebert": ["MobileBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = ["MobileBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
"MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileBertForMaskedLM",
"MobileBertForMultipleChoice",
"MobileBertForNextSentencePrediction",
"MobileBertForPreTraining",
"MobileBertForQuestionAnswering",
"MobileBertForSequenceClassification",
"MobileBertForTokenClassification",
"MobileBertLayer",
"MobileBertModel",
"MobileBertPreTrainedModel",
"load_tf_weights_in_mobilebert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
"TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileBertForMaskedLM",
"TFMobileBertForMultipleChoice",
"TFMobileBertForNextSentencePrediction",
"TFMobileBertForPreTraining",
"TFMobileBertForQuestionAnswering",
"TFMobileBertForSequenceClassification",
"TFMobileBertForTokenClassification",
"TFMobileBertMainLayer",
"TFMobileBertModel",
"TFMobileBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 636 | import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE__ : str = "\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n >>> from diffusers.utils import load_image\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior.to(\"cuda\")\n\n >>> prompt = \"A red cartoon frog, 4k\"\n >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n ... )\n >>> pipe.to(\"cuda\")\n\n >>> init_image = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/frog.png\"\n ... )\n\n >>> image = pipe(\n ... image=init_image,\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... strength=0.2,\n ... ).images\n\n >>> image[0].save(\"red_frog.png\")\n ```\n"
def _a ( lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : int=8 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
SCREAMING_SNAKE_CASE__ : List[str] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def _a ( lowercase__ : str , lowercase__ : List[str]=5_12 , lowercase__ : Dict=5_12 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
SCREAMING_SNAKE_CASE__ : Tuple = np.array(pil_image.convert('RGB' ) )
SCREAMING_SNAKE_CASE__ : Dict = arr.astype(np.floataa ) / 127.5 - 1
SCREAMING_SNAKE_CASE__ : str = np.transpose(lowercase__ , [2, 0, 1] )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.from_numpy(lowercase__ ).unsqueeze(0 )
return image
class snake_case ( UpperCamelCase_ ):
def __init__( self : Dict , a_ : UNetaDConditionModel , a_ : DDPMScheduler , a_ : VQModel , )-> List[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=a_ , scheduler=a_ , movq=a_ , )
SCREAMING_SNAKE_CASE__ : Any = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowercase( self : Tuple , a_ : List[Any] , a_ : int , a_ : Tuple )-> List[str]:
"""simple docstring"""
# get the original timestep using init_timestep
SCREAMING_SNAKE_CASE__ : Optional[int] = min(int(num_inference_steps * strength ) , a_ )
SCREAMING_SNAKE_CASE__ : Dict = max(num_inference_steps - init_timestep , 0 )
SCREAMING_SNAKE_CASE__ : Dict = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __lowercase( self : Dict , a_ : Any , a_ : List[Any] , a_ : Union[str, Any] , a_ : Optional[Any] , a_ : List[str] , a_ : int , a_ : List[Any]=None )-> List[str]:
"""simple docstring"""
if not isinstance(a_ , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a_ )}''' )
SCREAMING_SNAKE_CASE__ : Dict = image.to(device=a_ , dtype=a_ )
SCREAMING_SNAKE_CASE__ : Dict = batch_size * num_images_per_prompt
if image.shape[1] == 4:
SCREAMING_SNAKE_CASE__ : Optional[int] = image
else:
if isinstance(a_ , a_ ) and len(a_ ) != batch_size:
raise ValueError(
F'''You have passed a list of generators of length {len(a_ )}, but requested an effective batch'''
F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
elif isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(a_ )
]
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cat(a_ , dim=0 )
else:
SCREAMING_SNAKE_CASE__ : str = self.movq.encode(a_ ).latent_dist.sample(a_ )
SCREAMING_SNAKE_CASE__ : int = self.movq.config.scaling_factor * init_latents
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cat([init_latents] , dim=0 )
SCREAMING_SNAKE_CASE__ : Dict = init_latents.shape
SCREAMING_SNAKE_CASE__ : str = randn_tensor(a_ , generator=a_ , device=a_ , dtype=a_ )
# get latents
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler.add_noise(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Dict = init_latents
return latents
def __lowercase( self : Union[str, Any] , a_ : List[Any]=0 )-> Dict:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
SCREAMING_SNAKE_CASE__ : Any = torch.device(F'''cuda:{gpu_id}''' )
SCREAMING_SNAKE_CASE__ : List[str] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(a_ , a_ )
def __lowercase( self : List[Any] , a_ : Optional[int]=0 )-> List[str]:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
SCREAMING_SNAKE_CASE__ : str = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=a_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE__ : Optional[int] = None
for cpu_offloaded_model in [self.unet, self.movq]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = cpu_offload_with_hook(a_ , a_ , prev_module_hook=a_ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(a_ , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(a_ )
def __call__( self : Any , a_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a_ : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] , a_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , a_ : int = 512 , a_ : int = 512 , a_ : int = 100 , a_ : float = 4.0 , a_ : float = 0.3 , a_ : int = 1 , a_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , a_ : Optional[str] = "pil" , a_ : bool = True , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._execution_device
SCREAMING_SNAKE_CASE__ : Any = guidance_scale > 1.0
if isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat(a_ , dim=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_embeds.shape[0]
if isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cat(a_ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_embeds.repeat_interleave(a_ , dim=0 )
SCREAMING_SNAKE_CASE__ : Dict = negative_image_embeds.repeat_interleave(a_ , dim=0 )
SCREAMING_SNAKE_CASE__ : Tuple = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=a_ )
if not isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = [image]
if not all(isinstance(a_ , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
F'''Input is in incorrect format: {[type(a_ ) for i in image]}. Currently, we only support PIL image and pytorch tensor''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cat([prepare_image(a_ , a_ , a_ ) for i in image] , dim=0 )
SCREAMING_SNAKE_CASE__ : Tuple = image.to(dtype=image_embeds.dtype , device=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.movq.encode(a_ )['latents']
SCREAMING_SNAKE_CASE__ : str = latents.repeat_interleave(a_ , dim=0 )
self.scheduler.set_timesteps(a_ , device=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_timesteps(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = timesteps[:1].repeat(batch_size * num_images_per_prompt )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = downscale_height_and_width(a_ , a_ , self.movq_scale_factor )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_latents(
a_ , a_ , a_ , a_ , image_embeds.dtype , a_ , a_ )
for i, t in enumerate(self.progress_bar(a_ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'image_embeds': image_embeds}
SCREAMING_SNAKE_CASE__ : Tuple = self.unet(
sample=a_ , timestep=a_ , encoder_hidden_states=a_ , added_cond_kwargs=a_ , return_dict=a_ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE__ : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.scheduler.step(
a_ , a_ , a_ , generator=a_ , )[0]
# post-processing
SCREAMING_SNAKE_CASE__ : List[str] = self.movq.decode(a_ , force_not_quantize=a_ )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
SCREAMING_SNAKE_CASE__ : Dict = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE__ : List[str] = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ : Optional[int] = self.numpy_to_pil(a_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=a_ )
| 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE__ : Tuple = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {'0': '0', '1': '1'}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = '', ''
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ )
for i in range(len(lowercase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
SCREAMING_SNAKE_CASE__ : int = lexicon[curr_string]
result += last_match_id
SCREAMING_SNAKE_CASE__ : str = last_match_id + '0'
if math.loga(lowercase__ ).is_integer():
SCREAMING_SNAKE_CASE__ : List[str] = {}
for curr_key in list(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = lexicon.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = new_lex
SCREAMING_SNAKE_CASE__ : Any = last_match_id + '1'
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ''
return result
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 8
try:
with open(lowercase__ , 'wb' ) as opened_file:
SCREAMING_SNAKE_CASE__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase__ ) , lowercase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = data_bits[counter:]
SCREAMING_SNAKE_CASE__ : int = data_bits[counter + 1 :]
return data_bits
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = read_file_binary(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = remove_prefix(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = decompress_data(lowercase__ )
write_file_binary(lowercase__ , lowercase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 636 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class snake_case ( unittest.TestCase ):
def __lowercase( self : int )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : List[str] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
SCREAMING_SNAKE_CASE__ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'do_resize': True,
'size': {'height': 224, 'width': 224},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 18},
'do_normalize': True,
'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
'do_convert_rgb': True,
}
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(self.tmpdirname , a_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(a_ , a_ )
def __lowercase( self : Dict , **a_ : int )-> Optional[int]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : List[str] , **a_ : int )-> int:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : str , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : List[Any] = [Image.fromarray(np.moveaxis(a_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[int] = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Dict = ChineseCLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , a_ )
self.assertIsInstance(processor_fast.tokenizer , a_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , a_ )
self.assertIsInstance(processor_fast.image_processor , a_ )
def __lowercase( self : str )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor(do_normalize=a_ )
SCREAMING_SNAKE_CASE__ : int = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=a_ )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , a_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , a_ )
def __lowercase( self : List[str] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Any = processor(images=a_ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
SCREAMING_SNAKE_CASE__ : Any = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE__ : int = processor(text=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer(a_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
SCREAMING_SNAKE_CASE__ : Dict = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processor(text=a_ , images=a_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(a_ ):
processor()
def __lowercase( self : Tuple )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : Tuple = processor.batch_decode(a_ )
SCREAMING_SNAKE_CASE__ : str = tokenizer.batch_decode(a_ )
self.assertListEqual(a_ , a_ )
def __lowercase( self : Optional[int] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = ChineseCLIPProcessor(tokenizer=a_ , image_processor=a_ )
SCREAMING_SNAKE_CASE__ : Any = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : int = processor(text=a_ , images=a_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | 1 |
def _a ( lowercase__ : list , lowercase__ : list , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
if any(p < 0 for p in profit ):
raise ValueError('Profit can not be negative.' )
if any(w < 0 for w in weight ):
raise ValueError('Weight can not be negative.' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
SCREAMING_SNAKE_CASE__ : List[Any] = [p / w for p, w in zip(lowercase__ , lowercase__ )]
# Creating a copy of the list and sorting profit/weight in ascending order
SCREAMING_SNAKE_CASE__ : Tuple = sorted(lowercase__ )
# declaring useful variables
SCREAMING_SNAKE_CASE__ : List[str] = len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
SCREAMING_SNAKE_CASE__ : str = sorted_profit_by_weight[length - i - 1]
SCREAMING_SNAKE_CASE__ : Tuple = profit_by_weight.index(lowercase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"Input profits, weights, and then max_weight (all positive ints) separated by "
"spaces."
)
SCREAMING_SNAKE_CASE__ : List[str] = [int(x) for x in input("Input profits separated by spaces: ").split()]
SCREAMING_SNAKE_CASE__ : Tuple = [int(x) for x in input("Input weights separated by spaces: ").split()]
SCREAMING_SNAKE_CASE__ : str = int(input("Max weight allowed: "))
# Function Call
calc_profit(profit, weight, max_weight)
| 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def __lowercase( self : Any , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] , a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '</s>'
SCREAMING_SNAKE_CASE__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(a_ ) , 1103 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
SCREAMING_SNAKE_CASE__ : Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
SCREAMING_SNAKE_CASE__ : int = 'To ensure a smooth flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ['This is going to be way too long.' * 150, 'short example']
SCREAMING_SNAKE_CASE__ : int = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def __lowercase( self : Any )-> str:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[int] = PegasusTokenizer(a_ , offset=0 , mask_token_sent=a_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def __lowercase( self : List[str] , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple )-> str:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : str = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
@require_torch
def __lowercase( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ['This is going to be way too long.' * 1000, 'short example']
SCREAMING_SNAKE_CASE__ : Optional[int] = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : str = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 636 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
"xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
"xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
"xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
"xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
"xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
"xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
"xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
"xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
"xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'xlm'
lowercase_ = {
'hidden_size': 'emb_dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
'n_words': 'vocab_size', # For backward compatibility
}
def __init__( self : List[Any] , a_ : Tuple=3_0145 , a_ : Union[str, Any]=2048 , a_ : int=12 , a_ : List[Any]=16 , a_ : str=0.1 , a_ : Union[str, Any]=0.1 , a_ : Optional[int]=True , a_ : Dict=False , a_ : Optional[Any]=False , a_ : Optional[Any]=False , a_ : List[Any]=1 , a_ : int=True , a_ : Dict=512 , a_ : List[str]=2048**-0.5 , a_ : Tuple=1e-1_2 , a_ : Optional[int]=0.02 , a_ : Any=0 , a_ : Optional[int]=1 , a_ : Union[str, Any]=2 , a_ : List[Any]=3 , a_ : List[str]=5 , a_ : Optional[int]=True , a_ : List[str]="first" , a_ : int=True , a_ : Union[str, Any]=None , a_ : Optional[int]=True , a_ : Tuple=0.1 , a_ : Any=5 , a_ : List[str]=5 , a_ : List[str]=0 , a_ : Any=0 , a_ : Dict=2 , a_ : List[Any]=0 , **a_ : Optional[int] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = emb_dim
SCREAMING_SNAKE_CASE__ : Dict = n_layers
SCREAMING_SNAKE_CASE__ : Any = n_heads
SCREAMING_SNAKE_CASE__ : int = dropout
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE__ : Any = gelu_activation
SCREAMING_SNAKE_CASE__ : List[Any] = sinusoidal_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = causal
SCREAMING_SNAKE_CASE__ : Union[str, Any] = asm
SCREAMING_SNAKE_CASE__ : int = n_langs
SCREAMING_SNAKE_CASE__ : Any = use_lang_emb
SCREAMING_SNAKE_CASE__ : int = layer_norm_eps
SCREAMING_SNAKE_CASE__ : str = bos_index
SCREAMING_SNAKE_CASE__ : Tuple = eos_index
SCREAMING_SNAKE_CASE__ : str = pad_index
SCREAMING_SNAKE_CASE__ : Union[str, Any] = unk_index
SCREAMING_SNAKE_CASE__ : str = mask_index
SCREAMING_SNAKE_CASE__ : Dict = is_encoder
SCREAMING_SNAKE_CASE__ : str = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = embed_init_std
SCREAMING_SNAKE_CASE__ : List[str] = init_std
SCREAMING_SNAKE_CASE__ : Dict = summary_type
SCREAMING_SNAKE_CASE__ : Optional[Any] = summary_use_proj
SCREAMING_SNAKE_CASE__ : Optional[Any] = summary_activation
SCREAMING_SNAKE_CASE__ : str = summary_proj_to_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = summary_first_dropout
SCREAMING_SNAKE_CASE__ : Tuple = start_n_top
SCREAMING_SNAKE_CASE__ : Tuple = end_n_top
SCREAMING_SNAKE_CASE__ : List[Any] = mask_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] = lang_id
if "n_words" in kwargs:
SCREAMING_SNAKE_CASE__ : str = kwargs['n_words']
super().__init__(pad_token_id=a_ , bos_token_id=a_ , **a_ )
class snake_case ( UpperCamelCase_ ):
@property
def __lowercase( self : List[Any] )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : int = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 636 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def __lowercase( self : Optional[int] )-> List[Any]:
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
SCREAMING_SNAKE_CASE__ : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('sample_euler' )
SCREAMING_SNAKE_CASE__ : Any = 'A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE__ : Tuple = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
SCREAMING_SNAKE_CASE__ : Dict = output.images
SCREAMING_SNAKE_CASE__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Tuple = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __lowercase( self : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
SCREAMING_SNAKE_CASE__ : str = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('sample_euler' )
SCREAMING_SNAKE_CASE__ : Any = 'A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = sd_pipe([prompt] , generator=a_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
SCREAMING_SNAKE_CASE__ : Tuple = output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def __lowercase( self : int )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
SCREAMING_SNAKE_CASE__ : List[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
SCREAMING_SNAKE_CASE__ : Optional[int] = 'A painting of a squirrel eating a burger'
SCREAMING_SNAKE_CASE__ : Tuple = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str = sd_pipe(
[prompt] , generator=a_ , guidance_scale=7.5 , num_inference_steps=15 , output_type='np' , use_karras_sigmas=a_ , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = output.images
SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array(
[0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 636 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {"vocab_file": "spiece.model"}
SCREAMING_SNAKE_CASE__ : int = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
}
}
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
# Segments (not really needed)
SCREAMING_SNAKE_CASE__ : str = 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : Any = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Tuple = 4
class snake_case ( UpperCamelCase_ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = 'left'
def __init__( self : List[Any] , a_ : Union[str, Any] , a_ : int=False , a_ : Optional[Any]=True , a_ : Dict=False , a_ : str="<s>" , a_ : Optional[Any]="</s>" , a_ : int="<unk>" , a_ : Optional[Any]="<sep>" , a_ : Dict="<pad>" , a_ : Any="<cls>" , a_ : int="<mask>" , a_ : Optional[Any]=["<eop>", "<eod>"] , a_ : Optional[Dict[str, Any]] = None , **a_ : Tuple , )-> None:
"""simple docstring"""
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : List[str] = AddedToken(a_ , lstrip=a_ , rstrip=a_ ) if isinstance(a_ , a_ ) else mask_token
SCREAMING_SNAKE_CASE__ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=a_ , remove_space=a_ , keep_accents=a_ , bos_token=a_ , eos_token=a_ , unk_token=a_ , sep_token=a_ , pad_token=a_ , cls_token=a_ , mask_token=a_ , additional_special_tokens=a_ , sp_model_kwargs=self.sp_model_kwargs , **a_ , )
SCREAMING_SNAKE_CASE__ : Dict = 3
SCREAMING_SNAKE_CASE__ : int = do_lower_case
SCREAMING_SNAKE_CASE__ : Optional[int] = remove_space
SCREAMING_SNAKE_CASE__ : Tuple = keep_accents
SCREAMING_SNAKE_CASE__ : Tuple = vocab_file
SCREAMING_SNAKE_CASE__ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a_ )
@property
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return len(self.sp_model )
def __lowercase( self : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = {self.convert_ids_to_tokens(a_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : int = None
return state
def __setstate__( self : Dict , a_ : Tuple )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
SCREAMING_SNAKE_CASE__ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowercase( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
if self.remove_space:
SCREAMING_SNAKE_CASE__ : Dict = ' '.join(inputs.strip().split() )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = inputs
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.replace('``' , '"' ).replace('\'\'' , '"' )
if not self.keep_accents:
SCREAMING_SNAKE_CASE__ : Any = unicodedata.normalize('NFKD' , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''.join([c for c in outputs if not unicodedata.combining(a_ )] )
if self.do_lower_case:
SCREAMING_SNAKE_CASE__ : Dict = outputs.lower()
return outputs
def __lowercase( self : Any , a_ : str )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.preprocess_text(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.sp_model.encode(a_ , out_type=a_ )
SCREAMING_SNAKE_CASE__ : Any = []
for piece in pieces:
if len(a_ ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
SCREAMING_SNAKE_CASE__ : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(a_ , '' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
SCREAMING_SNAKE_CASE__ : Dict = cur_pieces[1:]
else:
SCREAMING_SNAKE_CASE__ : Dict = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(a_ )
else:
new_pieces.append(a_ )
return new_pieces
def __lowercase( self : List[str] , a_ : Union[str, Any] )-> List[str]:
"""simple docstring"""
return self.sp_model.PieceToId(a_ )
def __lowercase( self : Optional[Any] , a_ : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
return self.sp_model.IdToPiece(a_ )
def __lowercase( self : str , a_ : Any )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = ''.join(a_ ).replace(a_ , ' ' ).strip()
return out_string
def __lowercase( self : Dict , a_ : List[int] , a_ : bool = False , a_ : bool = None , a_ : bool = True , **a_ : Dict , )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = kwargs.pop('use_source_tokenizer' , a_ )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(a_ , skip_special_tokens=a_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE__ : Dict = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(a_ ) )
SCREAMING_SNAKE_CASE__ : Any = []
sub_texts.append(a_ )
else:
current_sub_text.append(a_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(a_ ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
SCREAMING_SNAKE_CASE__ : Optional[int] = ''.join(a_ )
SCREAMING_SNAKE_CASE__ : str = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE__ : List[str] = self.clean_up_tokenization(a_ )
return clean_text
else:
return text
def __lowercase( self : Any , a_ : List[int] , a_ : Optional[List[int]] = None )-> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowercase( self : str , a_ : List[int] , a_ : Optional[List[int]] = None , a_ : bool = False )-> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a_ , token_ids_a=a_ , already_has_special_tokens=a_ )
if token_ids_a is not None:
return ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1, 1]
return ([0] * len(a_ )) + [1, 1]
def __lowercase( self : Union[str, Any] , a_ : List[int] , a_ : Optional[List[int]] = None )-> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowercase( self : Union[str, Any] , a_ : str , a_ : Optional[str] = None )-> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(a_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Any = os.path.join(
a_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a_ )
elif not os.path.isfile(self.vocab_file ):
with open(a_ , 'wb' ) as fi:
SCREAMING_SNAKE_CASE__ : List[str] = self.sp_model.serialized_model_proto()
fi.write(a_ )
return (out_vocab_file,)
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | 1 |
from copy import deepcopy
class snake_case :
def __init__( self : Tuple , a_ : list[int] | None = None , a_ : int | None = None )-> None:
"""simple docstring"""
if arr is None and size is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = size
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * size
elif arr is not None:
self.init(a_ )
else:
raise ValueError('Either arr or size must be specified' )
def __lowercase( self : Optional[Any] , a_ : list[int] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = len(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = deepcopy(a_ )
for i in range(1 , self.size ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self.next_(a_ )
if j < self.size:
self.tree[j] += self.tree[i]
def __lowercase( self : Dict )-> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE__ : List[Any] = self.next_(a_ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def __lowercase( a_ : int )-> int:
"""simple docstring"""
return index + (index & (-index))
@staticmethod
def __lowercase( a_ : int )-> int:
"""simple docstring"""
return index - (index & (-index))
def __lowercase( self : Optional[Any] , a_ : int , a_ : int )-> None:
"""simple docstring"""
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
SCREAMING_SNAKE_CASE__ : Any = self.next_(a_ )
def __lowercase( self : Union[str, Any] , a_ : int , a_ : int )-> None:
"""simple docstring"""
self.add(a_ , value - self.get(a_ ) )
def __lowercase( self : int , a_ : int )-> int:
"""simple docstring"""
if right == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[str] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
SCREAMING_SNAKE_CASE__ : str = self.prev(a_ )
return result
def __lowercase( self : Any , a_ : int , a_ : int )-> int:
"""simple docstring"""
return self.prefix(a_ ) - self.prefix(a_ )
def __lowercase( self : str , a_ : int )-> int:
"""simple docstring"""
return self.query(a_ , index + 1 )
def __lowercase( self : Tuple , a_ : int )-> int:
"""simple docstring"""
value -= self.tree[0]
if value < 0:
return -1
SCREAMING_SNAKE_CASE__ : List[Any] = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class snake_case :
def __init__( self : List[str] , a_ : Tuple , a_ : Any=13 , a_ : Any=7 , a_ : int=True , a_ : Tuple=True , a_ : Dict=True , a_ : str=True , a_ : str=99 , a_ : Tuple=32 , a_ : Dict=2 , a_ : Tuple=4 , a_ : List[Any]=37 , a_ : Optional[Any]="gelu" , a_ : Union[str, Any]=0.1 , a_ : Optional[Any]=0.1 , a_ : int=512 , a_ : List[Any]=16 , a_ : Tuple=2 , a_ : Union[str, Any]=0.02 , a_ : Union[str, Any]=False , a_ : List[str]=True , a_ : str="None" , a_ : Union[str, Any]=3 , a_ : List[Any]=4 , a_ : List[str]=None , )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = parent
SCREAMING_SNAKE_CASE__ : str = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = seq_length
SCREAMING_SNAKE_CASE__ : List[str] = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = hidden_size
SCREAMING_SNAKE_CASE__ : int = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Tuple = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Dict = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
SCREAMING_SNAKE_CASE__ : Any = relative_attention
SCREAMING_SNAKE_CASE__ : List[str] = position_biased_input
SCREAMING_SNAKE_CASE__ : List[str] = pos_att_type
SCREAMING_SNAKE_CASE__ : Optional[int] = scope
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Any = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : List[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : List[Any] = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=a_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Any , a_ : Union[str, Any] , a_ : List[Any] , a_ : Dict , a_ : str , a_ : Union[str, Any] , a_ : Optional[Any] , a_ : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = TFDebertaVaModel(config=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Optional[int] = [input_ids, input_mask]
SCREAMING_SNAKE_CASE__ : Any = model(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : int , a_ : Optional[Any] , a_ : int , a_ : Dict , a_ : int , a_ : str , a_ : Any , a_ : Any )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = TFDebertaVaForMaskedLM(config=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : int , a_ : Optional[Any] , a_ : int , a_ : Union[str, Any] , a_ : Optional[Any] , a_ : Tuple , a_ : str , a_ : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : List[Any] = TFDebertaVaForSequenceClassification(config=a_ )
SCREAMING_SNAKE_CASE__ : Dict = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : int , a_ : Any , a_ : Optional[int] , a_ : Any , a_ : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFDebertaVaForTokenClassification(config=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE__ : Any = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Dict , a_ : Tuple , a_ : int , a_ : int , a_ : Union[str, Any] , a_ : str , a_ : Optional[int] , a_ : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFDebertaVaForQuestionAnswering(config=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase_ = (
{
'feature-extraction': TFDebertaVaModel,
'fill-mask': TFDebertaVaForMaskedLM,
'question-answering': TFDebertaVaForQuestionAnswering,
'text-classification': TFDebertaVaForSequenceClassification,
'token-classification': TFDebertaVaForTokenClassification,
'zero-shot': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase_ = False
lowercase_ = False
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFDebertaVaModelTester(self )
SCREAMING_SNAKE_CASE__ : List[str] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*a_ )
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a_ )
def __lowercase( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*a_ )
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a_ )
@slow
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(a_ )
@require_tf
class snake_case ( unittest.TestCase ):
@unittest.skip(reason='Model not available yet' )
def __lowercase( self : str )-> List[str]:
"""simple docstring"""
pass
@slow
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
SCREAMING_SNAKE_CASE__ : Dict = tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] )
SCREAMING_SNAKE_CASE__ : List[str] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.constant(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , a_ , atol=1e-4 )
| 636 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | 1 |
class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def _a ( ):
'''simple docstring'''
if os.name == "nt":
SCREAMING_SNAKE_CASE__ : Dict = CursorInfo()
SCREAMING_SNAKE_CASE__ : Optional[int] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def _a ( ):
'''simple docstring'''
if os.name == "nt":
SCREAMING_SNAKE_CASE__ : Any = CursorInfo()
SCREAMING_SNAKE_CASE__ : str = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : Dict = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowercase__ , ctypes.byref(lowercase__ ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def _a ( ):
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 636 | 1 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = ProphetNetTokenizer
lowercase_ = False
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ : str = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def __lowercase( self : Union[str, Any] , a_ : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = 'UNwant\u00E9d,running'
SCREAMING_SNAKE_CASE__ : str = 'unwanted, running'
return input_text, output_text
def __lowercase( self : Tuple )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(a_ , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ ) , [9, 6, 7, 12, 10, 11] )
def __lowercase( self : Optional[int] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __lowercase( self : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BasicTokenizer(do_lower_case=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : str )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : Tuple )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BasicTokenizer(do_lower_case=a_ , strip_accents=a_ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase( self : Tuple )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BasicTokenizer(do_lower_case=a_ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
SCREAMING_SNAKE_CASE__ : str = {}
for i, token in enumerate(a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = i
SCREAMING_SNAKE_CASE__ : List[Any] = WordpieceTokenizer(vocab=a_ , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
@require_torch
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
SCREAMING_SNAKE_CASE__ : Optional[int] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
SCREAMING_SNAKE_CASE__ : Optional[int] = [1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(a_ , padding=a_ , return_tensors='pt' )
self.assertIsInstance(a_ , a_ )
SCREAMING_SNAKE_CASE__ : str = list(batch.input_ids.numpy()[0] )
self.assertListEqual(a_ , a_ )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def __lowercase( self : Union[str, Any] )-> str:
"""simple docstring"""
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def __lowercase( self : str )-> Dict:
"""simple docstring"""
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased' )
SCREAMING_SNAKE_CASE__ : str = tokenizer.encode('sequence builders' , add_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.encode('multi-sequence build' , add_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer.build_inputs_with_special_tokens(a_ )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.build_inputs_with_special_tokens(a_ , a_ )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102]
| 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__ : str = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self : Optional[int] , *lowercase__ : int , **lowercase__ : Tuple ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 636 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Any = logging.getLogger(__name__)
@dataclass(frozen=UpperCamelCase_ )
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = None
lowercase_ = None
lowercase_ = None
@dataclass(frozen=UpperCamelCase_ )
class snake_case :
lowercase_ = 42
lowercase_ = None
lowercase_ = None
lowercase_ = None
lowercase_ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class snake_case ( UpperCamelCase_ ):
lowercase_ = 42
def __init__( self : int , a_ : str , a_ : PreTrainedTokenizer , a_ : str , a_ : Optional[int] = None , a_ : str=False , a_ : bool = False , )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = hans_processors[task]()
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(
a_ , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(a_ ) , a_ , ) , )
SCREAMING_SNAKE_CASE__ : Tuple = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE__ : Optional[Any] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE__ : Tuple = cached_features_file + '.lock'
with FileLock(a_ ):
if os.path.exists(a_ ) and not overwrite_cache:
logger.info(F'''Loading features from cached file {cached_features_file}''' )
SCREAMING_SNAKE_CASE__ : Dict = torch.load(a_ )
else:
logger.info(F'''Creating features from dataset file at {data_dir}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = (
processor.get_dev_examples(a_ ) if evaluate else processor.get_train_examples(a_ )
)
logger.info('Training examples: %s' , len(a_ ) )
SCREAMING_SNAKE_CASE__ : Tuple = hans_convert_examples_to_features(a_ , a_ , a_ , a_ )
logger.info('Saving features into cached file %s' , a_ )
torch.save(self.features , a_ )
def __len__( self : str )-> Union[str, Any]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : str , a_ : List[str] )-> InputFeatures:
"""simple docstring"""
return self.features[i]
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
return self.label_list
if is_tf_available():
import tensorflow as tf
class snake_case :
lowercase_ = 42
def __init__( self : Optional[int] , a_ : str , a_ : PreTrainedTokenizer , a_ : str , a_ : Optional[int] = 128 , a_ : List[str]=False , a_ : bool = False , )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = hans_processors[task]()
SCREAMING_SNAKE_CASE__ : Optional[int] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = label_list[2], label_list[1]
SCREAMING_SNAKE_CASE__ : Dict = label_list
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor.get_dev_examples(a_ ) if evaluate else processor.get_train_examples(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = hans_convert_examples_to_features(a_ , a_ , a_ , a_ )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 1_0000 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(a_ )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
SCREAMING_SNAKE_CASE__ : str = tf.data.Dataset.from_generator(
a_ , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
return self.dataset
def __len__( self : Dict )-> Dict:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Optional[Any] , a_ : str )-> InputFeatures:
"""simple docstring"""
return self.features[i]
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
return self.label_list
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : Dict )-> Tuple:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(a_ , 'heuristics_train_set.txt' ) ) , 'train' )
def __lowercase( self : Dict , a_ : str )-> Optional[int]:
"""simple docstring"""
return self._create_examples(self._read_tsv(os.path.join(a_ , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def __lowercase( self : int )-> Any:
"""simple docstring"""
return ["contradiction", "entailment", "neutral"]
def __lowercase( self : Any , a_ : Optional[Any] , a_ : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = []
for i, line in enumerate(a_ ):
if i == 0:
continue
SCREAMING_SNAKE_CASE__ : List[str] = '%s-%s' % (set_type, line[0])
SCREAMING_SNAKE_CASE__ : Any = line[5]
SCREAMING_SNAKE_CASE__ : Optional[int] = line[6]
SCREAMING_SNAKE_CASE__ : int = line[7][2:] if line[7].startswith('ex' ) else line[7]
SCREAMING_SNAKE_CASE__ : Any = line[0]
examples.append(InputExample(guid=a_ , text_a=a_ , text_b=a_ , label=a_ , pairID=a_ ) )
return examples
def _a ( lowercase__ : List[InputExample] , lowercase__ : List[str] , lowercase__ : int , lowercase__ : PreTrainedTokenizer , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {label: i for i, label in enumerate(lowercase__ )}
SCREAMING_SNAKE_CASE__ : Dict = []
for ex_index, example in tqdm.tqdm(enumerate(lowercase__ ) , desc='convert examples to features' ):
if ex_index % 1_00_00 == 0:
logger.info('Writing example %d' % (ex_index) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=lowercase__ , max_length=lowercase__ , padding='max_length' , truncation=lowercase__ , return_overflowing_tokens=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = label_map[example.label] if example.label in label_map else 0
SCREAMING_SNAKE_CASE__ : Any = int(example.pairID )
features.append(InputFeatures(**lowercase__ , label=lowercase__ , pairID=lowercase__ ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(f'''guid: {example}''' )
logger.info(f'''features: {features[i]}''' )
return features
SCREAMING_SNAKE_CASE__ : str = {
"hans": 3,
}
SCREAMING_SNAKE_CASE__ : Tuple = {
"hans": HansProcessor,
}
| 636 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Dict = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'mgp-str'
def __init__( self : Dict , a_ : Optional[int]=[32, 128] , a_ : List[str]=4 , a_ : List[Any]=3 , a_ : Union[str, Any]=27 , a_ : Optional[Any]=38 , a_ : Any=5_0257 , a_ : Tuple=3_0522 , a_ : Any=768 , a_ : Union[str, Any]=12 , a_ : List[Any]=12 , a_ : Tuple=4.0 , a_ : Union[str, Any]=True , a_ : Optional[Any]=False , a_ : Tuple=1e-5 , a_ : List[Any]=0.0 , a_ : Any=0.0 , a_ : int=0.0 , a_ : Optional[int]=False , a_ : int=0.02 , **a_ : List[str] , )-> Any:
"""simple docstring"""
super().__init__(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = image_size
SCREAMING_SNAKE_CASE__ : List[Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_token_length
SCREAMING_SNAKE_CASE__ : Tuple = num_character_labels
SCREAMING_SNAKE_CASE__ : List[Any] = num_bpe_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_wordpiece_labels
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : List[str] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = mlp_ratio
SCREAMING_SNAKE_CASE__ : Dict = distilled
SCREAMING_SNAKE_CASE__ : str = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = drop_rate
SCREAMING_SNAKE_CASE__ : List[str] = qkv_bias
SCREAMING_SNAKE_CASE__ : Dict = attn_drop_rate
SCREAMING_SNAKE_CASE__ : List[str] = drop_path_rate
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_aa_attentions
SCREAMING_SNAKE_CASE__ : List[Any] = initializer_range
| 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(lowercase__ )
or left < -len(lowercase__ )
or right >= len(lowercase__ )
or right < -len(lowercase__ )
):
raise IndexError('list index out of range' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ : int = find_max(lowercase__ , lowercase__ , lowercase__ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ : Tuple = find_max(lowercase__ , mid + 1 , lowercase__ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 636 | 1 |
from collections import defaultdict
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowercase__ )
if ret % 2 == 0:
cuts.append(lowercase__ )
return ret
def _a ( ):
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 10, 9
SCREAMING_SNAKE_CASE__ : int = defaultdict(list)
SCREAMING_SNAKE_CASE__ : dict[int, bool] = {}
SCREAMING_SNAKE_CASE__ : list[int] = []
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class snake_case ( unittest.TestCase ):
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = ['a', 'b', 'c']
# Defaults to last layer if both are None
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_aligned_output_features_output_indices(a_ , a_ , a_ )
self.assertEqual(a_ , ['c'] )
self.assertEqual(a_ , [2] )
# Out indices set to match out features
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = get_aligned_output_features_output_indices(['a', 'c'] , a_ , a_ )
self.assertEqual(a_ , ['a', 'c'] )
self.assertEqual(a_ , [0, 2] )
# Out features set to match out indices
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = get_aligned_output_features_output_indices(a_ , [0, 2] , a_ )
self.assertEqual(a_ , ['a', 'c'] )
self.assertEqual(a_ , [0, 2] )
# Out features selected from negative indices
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = get_aligned_output_features_output_indices(a_ , [-3, -1] , a_ )
self.assertEqual(a_ , ['a', 'c'] )
self.assertEqual(a_ , [-3, -1] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
# Stage names must be set
with self.assertRaises(a_ ):
verify_out_features_out_indices(['a', 'b'] , (0, 1) , a_ )
# Out features must be a list
with self.assertRaises(a_ ):
verify_out_features_out_indices(('a', 'b') , (0, 1) , ['a', 'b'] )
# Out features must be a subset of stage names
with self.assertRaises(a_ ):
verify_out_features_out_indices(['a', 'b'] , (0, 1) , ['a'] )
# Out indices must be a list or tuple
with self.assertRaises(a_ ):
verify_out_features_out_indices(a_ , 0 , ['a', 'b'] )
# Out indices must be a subset of stage names
with self.assertRaises(a_ ):
verify_out_features_out_indices(a_ , (0, 1) , ['a'] )
# Out features and out indices must be the same length
with self.assertRaises(a_ ):
verify_out_features_out_indices(['a', 'b'] , (0,) , ['a', 'b', 'c'] )
# Out features should match out indices
with self.assertRaises(a_ ):
verify_out_features_out_indices(['a', 'b'] , (0, 2) , ['a', 'b', 'c'] )
# Out features and out indices should be in order
with self.assertRaises(a_ ):
verify_out_features_out_indices(['b', 'a'] , (0, 1) , ['a', 'b'] )
# Check passes with valid inputs
verify_out_features_out_indices(['a', 'b', 'd'] , (0, 1, -1) , ['a', 'b', 'c', 'd'] )
def __lowercase( self : List[str] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BackboneMixin()
SCREAMING_SNAKE_CASE__ : int = ['a', 'b', 'c']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ['a', 'c']
SCREAMING_SNAKE_CASE__ : Any = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ['a', 'c'] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
SCREAMING_SNAKE_CASE__ : Optional[int] = ['a', 'b']
self.assertEqual(backbone.out_features , ['a', 'b'] )
self.assertEqual(backbone.out_indices , [0, 1] )
SCREAMING_SNAKE_CASE__ : Dict = [-3, -1]
self.assertEqual(backbone.out_features , ['a', 'c'] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[int] , a_ : Dict=7 , a_ : Any=3 , a_ : Any=18 , a_ : int=30 , a_ : int=400 , a_ : List[Any]=None , a_ : int=True , a_ : int=True , a_ : Dict=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'height': 20, 'width': 20}
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : List[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_convert_rgb
SCREAMING_SNAKE_CASE__ : List[str] = [512, 1024, 2048, 4096]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(a_ , stream=a_ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = PixaStructImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.prepare_dummy_image()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE__ : List[Any] = 2048
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(a_ , return_tensors='pt' , max_patches=a_ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
SCREAMING_SNAKE_CASE__ : int = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
SCREAMING_SNAKE_CASE__ : List[Any] = 'Hello'
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processor(
a_ , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : int = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = PixaStructImageProcessingTester(self , num_channels=4 )
SCREAMING_SNAKE_CASE__ : Dict = 3
@property
def __lowercase( self : Any )-> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 636 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {} # {vertex:distance}
def __lt__( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
return self.key < other.key
def __repr__( self : Any )-> Dict:
"""simple docstring"""
return self.id
def __lowercase( self : Optional[Any] , a_ : int )-> List[str]:
"""simple docstring"""
self.neighbors.append(a_ )
def __lowercase( self : int , a_ : int , a_ : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = weight
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowercase__ )
graph[b - 1].add_edge(graph[a - 1] , lowercase__ )
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
for u in graph:
SCREAMING_SNAKE_CASE__ : Dict = math.inf
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = graph[:]
while q:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(lowercase__ )
q.remove(lowercase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : int = u
SCREAMING_SNAKE_CASE__ : Any = u.edges[v.id]
for i in range(1 , len(lowercase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
for u in graph:
SCREAMING_SNAKE_CASE__ : List[str] = math.inf
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = list(lowercase__ )
hq.heapify(lowercase__ )
while h:
SCREAMING_SNAKE_CASE__ : Optional[int] = hq.heappop(lowercase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : List[str] = u
SCREAMING_SNAKE_CASE__ : Dict = u.edges[v.id]
hq.heapify(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | 1 |
import gc
import threading
import time
import psutil
import torch
class snake_case :
def __init__( self : Dict )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = psutil.Process()
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def __lowercase( self : Dict )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = -1
while True:
SCREAMING_SNAKE_CASE__ : Optional[int] = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = True
SCREAMING_SNAKE_CASE__ : Optional[int] = threading.Thread(target=self.peak_monitor )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
self.thread.start()
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
self.thread.join()
return self.cpu_memory_peak
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PeakCPUMemory()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = {'time': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
SCREAMING_SNAKE_CASE__ : Tuple = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
SCREAMING_SNAKE_CASE__ : Dict = torch.cuda.memory_allocated(lowercase__ )
torch.cuda.reset_peak_memory_stats()
return measures
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = {'time': time.time() - start_measures['time']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
SCREAMING_SNAKE_CASE__ : int = (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20
# GPU mem
for i in range(torch.cuda.device_count() ):
SCREAMING_SNAKE_CASE__ : Dict = (torch.cuda.memory_allocated(lowercase__ ) - start_measures[str(lowercase__ )]) / 2**20
SCREAMING_SNAKE_CASE__ : List[Any] = (torch.cuda.max_memory_allocated(lowercase__ ) - start_measures[str(lowercase__ )]) / 2**20
return measures
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
print(f'''{description}:''' )
print(f'''- Time: {measures['time']:.2f}s''' )
for i in range(torch.cuda.device_count() ):
print(f'''- GPU {i} allocated: {measures[str(lowercase__ )]:.2f}MiB''' )
SCREAMING_SNAKE_CASE__ : Any = measures[f'''{i}-peak''']
print(f'''- GPU {i} peak: {peak:.2f}MiB''' )
print(f'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''' )
print(f'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''' )
| 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 636 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
"google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
"google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
"google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'owlvit_text_model'
def __init__( self : str , a_ : Optional[int]=4_9408 , a_ : int=512 , a_ : Dict=2048 , a_ : Dict=12 , a_ : List[str]=8 , a_ : Dict=16 , a_ : Optional[Any]="quick_gelu" , a_ : List[Any]=1e-5 , a_ : Any=0.0 , a_ : Optional[Any]=0.02 , a_ : List[str]=1.0 , a_ : Any=0 , a_ : List[str]=4_9406 , a_ : Optional[Any]=4_9407 , **a_ : Dict , )-> int:
"""simple docstring"""
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
SCREAMING_SNAKE_CASE__ : Tuple = vocab_size
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[int] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : str = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict = attention_dropout
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
@classmethod
def __lowercase( cls : Dict , a_ : Union[str, os.PathLike] , **a_ : Any )-> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = cls.get_config_dict(a_ , **a_ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
SCREAMING_SNAKE_CASE__ : Any = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(a_ , **a_ )
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'owlvit_vision_model'
def __init__( self : Optional[int] , a_ : Union[str, Any]=768 , a_ : Optional[Any]=3072 , a_ : Optional[Any]=12 , a_ : Tuple=12 , a_ : int=3 , a_ : int=768 , a_ : Optional[int]=32 , a_ : str="quick_gelu" , a_ : int=1e-5 , a_ : List[Any]=0.0 , a_ : Union[str, Any]=0.02 , a_ : Optional[int]=1.0 , **a_ : Dict , )-> Optional[int]:
"""simple docstring"""
super().__init__(**a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : Dict = image_size
SCREAMING_SNAKE_CASE__ : Any = patch_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Tuple = attention_dropout
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = initializer_factor
@classmethod
def __lowercase( cls : Optional[Any] , a_ : Union[str, os.PathLike] , **a_ : str )-> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = cls.get_config_dict(a_ , **a_ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('model_type' ) == "owlvit":
SCREAMING_SNAKE_CASE__ : Dict = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(a_ , **a_ )
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'owlvit'
lowercase_ = True
def __init__( self : Tuple , a_ : Union[str, Any]=None , a_ : Any=None , a_ : Dict=512 , a_ : int=2.6592 , a_ : int=True , **a_ : int , )-> str:
"""simple docstring"""
super().__init__(**a_ )
if text_config is None:
SCREAMING_SNAKE_CASE__ : Any = {}
logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.' )
if vision_config is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = {}
logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.' )
SCREAMING_SNAKE_CASE__ : str = OwlViTTextConfig(**a_ )
SCREAMING_SNAKE_CASE__ : List[str] = OwlViTVisionConfig(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = projection_dim
SCREAMING_SNAKE_CASE__ : Optional[int] = logit_scale_init_value
SCREAMING_SNAKE_CASE__ : str = return_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1.0
@classmethod
def __lowercase( cls : int , a_ : Union[str, os.PathLike] , **a_ : str )-> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = cls.get_config_dict(a_ , **a_ )
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(a_ , **a_ )
@classmethod
def __lowercase( cls : int , a_ : Dict , a_ : Dict , **a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = {}
SCREAMING_SNAKE_CASE__ : str = text_config
SCREAMING_SNAKE_CASE__ : int = vision_config
return cls.from_dict(a_ , **a_ )
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.text_config.to_dict()
SCREAMING_SNAKE_CASE__ : List[str] = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.__class__.model_type
return output
class snake_case ( UpperCamelCase_ ):
@property
def __lowercase( self : Optional[Any] )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
] )
@property
def __lowercase( self : List[Any] )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('logits_per_image', {0: 'batch'}),
('logits_per_text', {0: 'batch'}),
('text_embeds', {0: 'batch'}),
('image_embeds', {0: 'batch'}),
] )
@property
def __lowercase( self : int )-> float:
"""simple docstring"""
return 1e-4
def __lowercase( self : Union[str, Any] , a_ : "ProcessorMixin" , a_ : int = -1 , a_ : int = -1 , a_ : Optional["TensorType"] = None , )-> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = super().generate_dummy_inputs(
processor.tokenizer , batch_size=a_ , seq_length=a_ , framework=a_ )
SCREAMING_SNAKE_CASE__ : Any = super().generate_dummy_inputs(
processor.image_processor , batch_size=a_ , framework=a_ )
return {**text_input_dict, **image_input_dict}
@property
def __lowercase( self : int )-> int:
"""simple docstring"""
return 14
| 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE__ : int = radians(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = angle_in_radians
SCREAMING_SNAKE_CASE__ : Optional[int] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = -1
for _ in range(lowercase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase__ , lowercase__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 636 | 1 |
from typing import Any
import numpy as np
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return np.array_equal(lowercase__ , matrix.conjugate().T )
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = v.conjugate().T
SCREAMING_SNAKE_CASE__ : Dict = v_star.dot(lowercase__ )
assert isinstance(lowercase__ , np.ndarray )
return (v_star_dot.dot(lowercase__ )) / (v_star.dot(lowercase__ ))
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array([[1], [2], [3]] )
assert is_hermitian(lowercase__ ), f'''{a} is not hermitian.'''
print(rayleigh_quotient(lowercase__ , lowercase__ ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(lowercase__ ), f'''{a} is not hermitian.'''
assert rayleigh_quotient(lowercase__ , lowercase__ ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE__ : Tuple = range(3 , int(math.sqrt(lowercase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _a ( lowercase__ : List[str] , lowercase__ : Any=1 , **lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = factor * value
SCREAMING_SNAKE_CASE__ : Dict = value
while not is_prime(lowercase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase__ )
return value
| 636 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _a ( lowercase__ : int ):
'''simple docstring'''
random.seed(lowercase__ )
np.random.seed(lowercase__ )
torch.manual_seed(lowercase__ )
torch.cuda.manual_seed_all(lowercase__ )
# ^^ safe to call this function even if cuda is not available
class snake_case :
def __init__( self : Any , a_ : Iterable[torch.nn.Parameter] , a_ : float = 0.9999 , a_ : float = 0.0 , a_ : int = 0 , a_ : bool = False , a_ : Union[float, int] = 1.0 , a_ : Union[float, int] = 2 / 3 , a_ : Optional[Any] = None , a_ : Dict[str, Any] = None , **a_ : Optional[int] , )-> Dict:
"""simple docstring"""
if isinstance(a_ , torch.nn.Module ):
SCREAMING_SNAKE_CASE__ : str = (
'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
'Please pass the parameters of the module instead.'
)
deprecate(
'passing a `torch.nn.Module` to `ExponentialMovingAverage`' , '1.0.0' , a_ , standard_warn=a_ , )
SCREAMING_SNAKE_CASE__ : Dict = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
SCREAMING_SNAKE_CASE__ : Any = True
if kwargs.get('max_value' , a_ ) is not None:
SCREAMING_SNAKE_CASE__ : Any = 'The `max_value` argument is deprecated. Please use `decay` instead.'
deprecate('max_value' , '1.0.0' , a_ , standard_warn=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs['max_value']
if kwargs.get('min_value' , a_ ) is not None:
SCREAMING_SNAKE_CASE__ : List[str] = 'The `min_value` argument is deprecated. Please use `min_decay` instead.'
deprecate('min_value' , '1.0.0' , a_ , standard_warn=a_ )
SCREAMING_SNAKE_CASE__ : int = kwargs['min_value']
SCREAMING_SNAKE_CASE__ : Optional[int] = list(a_ )
SCREAMING_SNAKE_CASE__ : Dict = [p.clone().detach() for p in parameters]
if kwargs.get('device' , a_ ) is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'The `device` argument is deprecated. Please use `to` instead.'
deprecate('device' , '1.0.0' , a_ , standard_warn=a_ )
self.to(device=kwargs['device'] )
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : Optional[int] = decay
SCREAMING_SNAKE_CASE__ : Optional[Any] = min_decay
SCREAMING_SNAKE_CASE__ : int = update_after_step
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_ema_warmup
SCREAMING_SNAKE_CASE__ : Union[str, Any] = inv_gamma
SCREAMING_SNAKE_CASE__ : str = power
SCREAMING_SNAKE_CASE__ : str = 0
SCREAMING_SNAKE_CASE__ : List[str] = None # set in `step()`
SCREAMING_SNAKE_CASE__ : Tuple = model_cls
SCREAMING_SNAKE_CASE__ : Optional[Any] = model_config
@classmethod
def __lowercase( cls : Any , a_ : List[Any] , a_ : str )-> "EMAModel":
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = model_cls.load_config(a_ , return_unused_kwargs=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model_cls.from_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = cls(model.parameters() , model_cls=a_ , model_config=model.config )
ema_model.load_state_dict(a_ )
return ema_model
def __lowercase( self : List[str] , a_ : List[Any] )-> List[Any]:
"""simple docstring"""
if self.model_cls is None:
raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )
if self.model_config is None:
raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )
SCREAMING_SNAKE_CASE__ : Tuple = self.model_cls.from_config(self.model_config )
SCREAMING_SNAKE_CASE__ : List[str] = self.state_dict()
state_dict.pop('shadow_params' , a_ )
model.register_to_config(**a_ )
self.copy_to(model.parameters() )
model.save_pretrained(a_ )
def __lowercase( self : Optional[int] , a_ : int )-> float:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
SCREAMING_SNAKE_CASE__ : int = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
SCREAMING_SNAKE_CASE__ : List[Any] = (1 + step) / (10 + step)
SCREAMING_SNAKE_CASE__ : str = min(a_ , self.decay )
# make sure decay is not smaller than min_decay
SCREAMING_SNAKE_CASE__ : int = max(a_ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def __lowercase( self : int , a_ : Iterable[torch.nn.Parameter] )-> Optional[Any]:
"""simple docstring"""
if isinstance(a_ , torch.nn.Module ):
SCREAMING_SNAKE_CASE__ : Any = (
'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
'Please pass the parameters of the module instead.'
)
deprecate(
'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`' , '1.0.0' , a_ , standard_warn=a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = parameters.parameters()
SCREAMING_SNAKE_CASE__ : Optional[Any] = list(a_ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
SCREAMING_SNAKE_CASE__ : Dict = self.get_decay(self.optimization_step )
SCREAMING_SNAKE_CASE__ : Optional[int] = decay
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 - decay
SCREAMING_SNAKE_CASE__ : str = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , a_ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
SCREAMING_SNAKE_CASE__ : Optional[int] = deepspeed.zero.GatheredParameters(a_ , modifier_rank=a_ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(a_ )
def __lowercase( self : Union[str, Any] , a_ : Iterable[torch.nn.Parameter] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = list(a_ )
for s_param, param in zip(self.shadow_params , a_ ):
param.data.copy_(s_param.to(param.device ).data )
def __lowercase( self : Optional[int] , a_ : Any=None , a_ : Dict=None )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
p.to(device=a_ , dtype=a_ ) if p.is_floating_point() else p.to(device=a_ )
for p in self.shadow_params
]
def __lowercase( self : Union[str, Any] )-> dict:
"""simple docstring"""
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def __lowercase( self : Optional[Any] , a_ : Iterable[torch.nn.Parameter] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = [param.detach().cpu().clone() for param in parameters]
def __lowercase( self : Dict , a_ : Iterable[torch.nn.Parameter] )-> None:
"""simple docstring"""
if self.temp_stored_params is None:
raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
for c_param, param in zip(self.temp_stored_params , a_ ):
param.data.copy_(c_param.data )
# Better memory-wise.
SCREAMING_SNAKE_CASE__ : Optional[int] = None
def __lowercase( self : Any , a_ : dict )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = copy.deepcopy(a_ )
SCREAMING_SNAKE_CASE__ : int = state_dict.get('decay' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('Decay must be between 0 and 1' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.get('min_decay' , self.min_decay )
if not isinstance(self.min_decay , a_ ):
raise ValueError('Invalid min_decay' )
SCREAMING_SNAKE_CASE__ : int = state_dict.get('optimization_step' , self.optimization_step )
if not isinstance(self.optimization_step , a_ ):
raise ValueError('Invalid optimization_step' )
SCREAMING_SNAKE_CASE__ : Tuple = state_dict.get('update_after_step' , self.update_after_step )
if not isinstance(self.update_after_step , a_ ):
raise ValueError('Invalid update_after_step' )
SCREAMING_SNAKE_CASE__ : int = state_dict.get('use_ema_warmup' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , a_ ):
raise ValueError('Invalid use_ema_warmup' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.get('inv_gamma' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('Invalid inv_gamma' )
SCREAMING_SNAKE_CASE__ : str = state_dict.get('power' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('Invalid power' )
SCREAMING_SNAKE_CASE__ : str = state_dict.get('shadow_params' , a_ )
if shadow_params is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = shadow_params
if not isinstance(self.shadow_params , a_ ):
raise ValueError('shadow_params must be a list' )
if not all(isinstance(a_ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('shadow_params must all be Tensors' )
| 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class snake_case :
def __init__( self : str , a_ : List[str] , a_ : Tuple=13 , a_ : Dict=30 , a_ : Optional[int]=2 , a_ : Tuple=3 , a_ : Dict=True , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[str]=5 , a_ : Any=4 , a_ : Dict=37 , a_ : Dict="gelu" , a_ : int=0.1 , a_ : Optional[Any]=0.1 , a_ : Any=10 , a_ : List[str]=0.02 , a_ : Any=3 , a_ : List[str]=None , a_ : Optional[int]=2 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : int = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
SCREAMING_SNAKE_CASE__ : Optional[int] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_patches + 2
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase( self : List[str] , a_ : List[str] , a_ : Optional[Any] , a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = DeiTModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : List[str] , a_ : List[str] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = DeiTForMaskedImageModeling(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DeiTForMaskedImageModeling(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : int = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = DeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class(a_ )
SCREAMING_SNAKE_CASE__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a_ )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def __lowercase( self : str , a_ : str , a_ : Tuple , a_ : Union[str, Any]=False )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE__ : Tuple = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
model.gradient_checkpointing_enable()
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a_ ),
*get_values(a_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
SCREAMING_SNAKE_CASE__ : int = problem_type['title']
SCREAMING_SNAKE_CASE__ : Tuple = problem_type['num_labels']
SCREAMING_SNAKE_CASE__ : str = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
SCREAMING_SNAKE_CASE__ : Any = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a_ ) as warning_list:
SCREAMING_SNAKE_CASE__ : str = model(**a_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def __lowercase( self : int )-> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(images=a_ , return_tensors='pt' ).to(a_ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**a_ )
# verify the logits
SCREAMING_SNAKE_CASE__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
SCREAMING_SNAKE_CASE__ : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = inputs.pixel_values.to(a_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
| 636 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = StableDiffusionInstructPixaPixPipeline
lowercase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowercase( self : str )-> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
SCREAMING_SNAKE_CASE__ : Optional[int] = PNDMScheduler(skip_prk_steps=a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CLIPTextModel(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase( self : Optional[Any] , a_ : Union[str, Any] , a_ : List[str]=0 )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(a_ ) ).to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = Image.fromarray(np.uinta(a_ ) ).convert('RGB' )
if str(a_ ).startswith('mps' ):
SCREAMING_SNAKE_CASE__ : List[str] = torch.manual_seed(a_ )
else:
SCREAMING_SNAKE_CASE__ : str = torch.Generator(device=a_ ).manual_seed(a_ )
SCREAMING_SNAKE_CASE__ : str = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def __lowercase( self : Any )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[int] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Tuple = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Any = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = 'french fries'
SCREAMING_SNAKE_CASE__ : Tuple = sd_pipe(**a_ , negative_prompt=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = output.images
SCREAMING_SNAKE_CASE__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Dict = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : str = [inputs['prompt']] * 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array(inputs['image'] ).astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.from_numpy(a_ ).unsqueeze(0 ).to(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image / 2 + 0.5
SCREAMING_SNAKE_CASE__ : str = image.permute(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = image.repeat(2 , 1 , 1 , 1 )
SCREAMING_SNAKE_CASE__ : Dict = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : List[str] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : str )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' )
SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Any = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Optional[int] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[Any] = [round(a_ , 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(a_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : int )-> str:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def __lowercase( self : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = VaeImageProcessor(do_resize=a_ , do_normalize=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(**self.get_dummy_inputs_by_type(a_ , input_image_type='pt' ) )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = components['vae']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_inputs_by_type(a_ , input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
SCREAMING_SNAKE_CASE__ : Optional[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
SCREAMING_SNAKE_CASE__ : Any = pipe(**a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = np.abs(out - out_latents_inputs ).max()
self.assertLess(a_ , 1e-4 , 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def __lowercase( self : Optional[int] )-> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase( self : int , a_ : Union[str, Any]=0 )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.manual_seed(a_ )
SCREAMING_SNAKE_CASE__ : Dict = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
SCREAMING_SNAKE_CASE__ : str = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Any = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : str = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_inputs()
SCREAMING_SNAKE_CASE__ : int = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Dict = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = 0
def callback_fn(a_ : int , a_ : int , a_ : torch.FloatTensor ) -> None:
SCREAMING_SNAKE_CASE__ : str = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
SCREAMING_SNAKE_CASE__ : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE__ : Any = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Tuple = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE__ : Optional[int] = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : str = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Any = self.get_inputs()
pipe(**a_ , callback=a_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def __lowercase( self : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
SCREAMING_SNAKE_CASE__ : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE__ : int = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe(**a_ )
SCREAMING_SNAKE_CASE__ : Tuple = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def __lowercase( self : int )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
SCREAMING_SNAKE_CASE__ : int = inputs['image'].resize((504, 504) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'timbrooks/instruct-pix2pix'
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
a_ , safety_checker=a_ , )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe(**a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
SCREAMING_SNAKE_CASE__ : int = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 636 | import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _a ( lowercase__ : bytes , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = f'''{sampling_rate}'''
SCREAMING_SNAKE_CASE__ : Optional[int] = '1'
SCREAMING_SNAKE_CASE__ : int = 'f32le'
SCREAMING_SNAKE_CASE__ : int = [
'ffmpeg',
'-i',
'pipe:0',
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
try:
with subprocess.Popen(lowercase__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
SCREAMING_SNAKE_CASE__ : Dict = ffmpeg_process.communicate(lowercase__ )
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to load audio files from filename' ) from error
SCREAMING_SNAKE_CASE__ : Tuple = output_stream[0]
SCREAMING_SNAKE_CASE__ : Any = np.frombuffer(lowercase__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError('Malformed soundfile' )
return audio
def _a ( lowercase__ : int , lowercase__ : float , lowercase__ : str = "f32le" , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = f'''{sampling_rate}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = '1'
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE__ : Optional[Any] = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE__ : List[str] = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
SCREAMING_SNAKE_CASE__ : List[str] = platform.system()
if system == "Linux":
SCREAMING_SNAKE_CASE__ : Any = 'alsa'
SCREAMING_SNAKE_CASE__ : int = 'default'
elif system == "Darwin":
SCREAMING_SNAKE_CASE__ : str = 'avfoundation'
SCREAMING_SNAKE_CASE__ : Dict = ':0'
elif system == "Windows":
SCREAMING_SNAKE_CASE__ : Any = 'dshow'
SCREAMING_SNAKE_CASE__ : Any = 'default'
SCREAMING_SNAKE_CASE__ : List[str] = [
'ffmpeg',
'-f',
format_,
'-i',
input_,
'-ac',
ac,
'-ar',
ar,
'-f',
format_for_conversion,
'-fflags',
'nobuffer',
'-hide_banner',
'-loglevel',
'quiet',
'pipe:1',
]
SCREAMING_SNAKE_CASE__ : Any = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
SCREAMING_SNAKE_CASE__ : List[Any] = _ffmpeg_stream(lowercase__ , lowercase__ )
for item in iterator:
yield item
def _a ( lowercase__ : int , lowercase__ : float , lowercase__ : Optional[int] = None , lowercase__ : Optional[Union[Tuple[float, float], float]] = None , lowercase__ : str = "f32le" , ):
'''simple docstring'''
if stream_chunk_s is not None:
SCREAMING_SNAKE_CASE__ : Dict = stream_chunk_s
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = chunk_length_s
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ffmpeg_microphone(lowercase__ , lowercase__ , format_for_conversion=lowercase__ )
if format_for_conversion == "s16le":
SCREAMING_SNAKE_CASE__ : Tuple = np.intaa
SCREAMING_SNAKE_CASE__ : Any = 2
elif format_for_conversion == "f32le":
SCREAMING_SNAKE_CASE__ : Any = np.floataa
SCREAMING_SNAKE_CASE__ : int = 4
else:
raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )
if stride_length_s is None:
SCREAMING_SNAKE_CASE__ : str = chunk_length_s / 6
SCREAMING_SNAKE_CASE__ : str = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(lowercase__ , (int, float) ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = [stride_length_s, stride_length_s]
SCREAMING_SNAKE_CASE__ : Optional[int] = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
SCREAMING_SNAKE_CASE__ : str = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
SCREAMING_SNAKE_CASE__ : Any = datetime.datetime.now()
SCREAMING_SNAKE_CASE__ : Any = datetime.timedelta(seconds=lowercase__ )
for item in chunk_bytes_iter(lowercase__ , lowercase__ , stride=(stride_left, stride_right) , stream=lowercase__ ):
# Put everything back in numpy scale
SCREAMING_SNAKE_CASE__ : Any = np.frombuffer(item['raw'] , dtype=lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = (
item['stride'][0] // size_of_sample,
item['stride'][1] // size_of_sample,
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def _a ( lowercase__ : Any , lowercase__ : int , lowercase__ : Tuple[int, int] , lowercase__ : bool = False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = b''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
for raw in iterator:
acc += raw
if stream and len(lowercase__ ) < chunk_len:
SCREAMING_SNAKE_CASE__ : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(lowercase__ ) >= chunk_len:
# We are flushing the accumulator
SCREAMING_SNAKE_CASE__ : List[Any] = (_stride_left, stride_right)
SCREAMING_SNAKE_CASE__ : List[str] = {'raw': acc[:chunk_len], 'stride': stride}
if stream:
SCREAMING_SNAKE_CASE__ : List[str] = False
yield item
SCREAMING_SNAKE_CASE__ : Optional[Any] = stride_left
SCREAMING_SNAKE_CASE__ : Tuple = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(lowercase__ ) > stride_left:
SCREAMING_SNAKE_CASE__ : Tuple = {'raw': acc, 'stride': (_stride_left, 0)}
if stream:
SCREAMING_SNAKE_CASE__ : Tuple = False
yield item
def _a ( lowercase__ : List[Any] , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2**24 # 16Mo
try:
with subprocess.Popen(lowercase__ , stdout=subprocess.PIPE , bufsize=lowercase__ ) as ffmpeg_process:
while True:
SCREAMING_SNAKE_CASE__ : int = ffmpeg_process.stdout.read(lowercase__ )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('ffmpeg was not found but is required to stream audio files from filename' ) from error
| 636 | import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE__ : Optional[Any] = abspath(join(dirname(__file__), "src"))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="ignore", category=FutureWarning)
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
config.addinivalue_line(
'markers' , 'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested' )
config.addinivalue_line(
'markers' , 'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested' )
config.addinivalue_line('markers' , 'is_pipeline_test: mark test to run only when pipelines are tested' )
config.addinivalue_line('markers' , 'is_staging_test: mark test to run only in the staging environment' )
config.addinivalue_line('markers' , 'accelerate_tests: mark test that require accelerate' )
config.addinivalue_line('markers' , 'tool_tests: mark the tool tests that are run on their specific schedule' )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase__ )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE__ : str = terminalreporter.config.getoption('--make-reports' )
if make_reports:
pytest_terminal_summary_main(lowercase__ , id=lowercase__ )
def _a ( lowercase__ : Dict , lowercase__ : str ):
'''simple docstring'''
if exitstatus == 5:
SCREAMING_SNAKE_CASE__ : List[str] = 0
# Doctest custom flag to ignore output.
SCREAMING_SNAKE_CASE__ : Any = doctest.register_optionflag("IGNORE_RESULT")
SCREAMING_SNAKE_CASE__ : Any = doctest.OutputChecker
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Any , a_ : str , a_ : Dict , a_ : List[Any] )-> Dict:
"""simple docstring"""
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : str = CustomOutputChecker
SCREAMING_SNAKE_CASE__ : Tuple = HfDoctestModule
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfDocTestParser
| 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE__ : Tuple = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {'0': '0', '1': '1'}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = '', ''
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ )
for i in range(len(lowercase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
SCREAMING_SNAKE_CASE__ : int = lexicon[curr_string]
result += last_match_id
SCREAMING_SNAKE_CASE__ : str = last_match_id + '0'
if math.loga(lowercase__ ).is_integer():
SCREAMING_SNAKE_CASE__ : List[str] = {}
for curr_key in list(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = lexicon.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = new_lex
SCREAMING_SNAKE_CASE__ : Any = last_match_id + '1'
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ''
return result
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 8
try:
with open(lowercase__ , 'wb' ) as opened_file:
SCREAMING_SNAKE_CASE__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase__ ) , lowercase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = data_bits[counter:]
SCREAMING_SNAKE_CASE__ : int = data_bits[counter + 1 :]
return data_bits
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = read_file_binary(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = remove_prefix(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = decompress_data(lowercase__ )
write_file_binary(lowercase__ , lowercase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 636 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['pixel_values']
def __init__( self : int , a_ : bool = True , a_ : Dict[str, int] = None , a_ : int = 0.9 , a_ : PILImageResampling = PILImageResampling.BICUBIC , a_ : bool = True , a_ : Dict[str, int] = None , a_ : Union[int, float] = 1 / 255 , a_ : bool = True , a_ : bool = True , a_ : Optional[Union[float, List[float]]] = None , a_ : Optional[Union[float, List[float]]] = None , **a_ : List[Any] , )-> None:
"""simple docstring"""
super().__init__(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'shortest_edge': 224}
SCREAMING_SNAKE_CASE__ : Tuple = get_size_dict(a_ , default_to_square=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
SCREAMING_SNAKE_CASE__ : str = get_size_dict(a_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ : List[Any] = do_resize
SCREAMING_SNAKE_CASE__ : Optional[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = crop_pct
SCREAMING_SNAKE_CASE__ : Union[str, Any] = resample
SCREAMING_SNAKE_CASE__ : Tuple = do_center_crop
SCREAMING_SNAKE_CASE__ : Optional[Any] = crop_size
SCREAMING_SNAKE_CASE__ : Any = do_rescale
SCREAMING_SNAKE_CASE__ : List[Any] = rescale_factor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_normalize
SCREAMING_SNAKE_CASE__ : List[str] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
SCREAMING_SNAKE_CASE__ : Any = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __lowercase( self : Union[str, Any] , a_ : np.ndarray , a_ : Dict[str, int] , a_ : Optional[float] = None , a_ : PILImageResampling = PILImageResampling.BICUBIC , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : Dict , )-> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = get_size_dict(a_ , default_to_square=a_ )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
if crop_pct is not None:
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ : Dict = int(size['shortest_edge'] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(size['height'] / crop_pct )
else:
SCREAMING_SNAKE_CASE__ : Any = (int(size['height'] / crop_pct ), int(size['width'] / crop_pct ))
else:
raise ValueError('Invalid size for resize: {}'.format(a_ ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = get_resize_output_image_size(a_ , size=a_ , default_to_square=a_ )
else:
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ : str = get_resize_output_image_size(a_ , size=size['shortest_edge'] , default_to_square=a_ )
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (size['height'], size['width'])
else:
raise ValueError('Invalid size for resize: {}'.format(a_ ) )
return resize(a_ , size=a_ , resample=a_ , data_format=a_ , **a_ )
def __lowercase( self : List[str] , a_ : np.ndarray , a_ : Dict[str, int] , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : Dict , )-> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = get_size_dict(a_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(a_ , size=(size['height'], size['width']) , data_format=a_ , **a_ )
def __lowercase( self : str , a_ : np.ndarray , a_ : Union[int, float] , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : List[Any] , )-> str:
"""simple docstring"""
return rescale(a_ , scale=a_ , data_format=a_ , **a_ )
def __lowercase( self : List[Any] , a_ : np.ndarray , a_ : Union[float, List[float]] , a_ : Union[float, List[float]] , a_ : Optional[Union[str, ChannelDimension]] = None , **a_ : List[Any] , )-> np.ndarray:
"""simple docstring"""
return normalize(a_ , mean=a_ , std=a_ , data_format=a_ , **a_ )
def __lowercase( self : Optional[int] , a_ : ImageInput , a_ : bool = None , a_ : Dict[str, int] = None , a_ : int = None , a_ : PILImageResampling = None , a_ : bool = None , a_ : Dict[str, int] = None , a_ : bool = None , a_ : float = None , a_ : bool = None , a_ : Optional[Union[float, List[float]]] = None , a_ : Optional[Union[float, List[float]]] = None , a_ : Optional[Union[str, TensorType]] = None , a_ : ChannelDimension = ChannelDimension.FIRST , **a_ : str , )-> PIL.Image.Image:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ : str = crop_pct if crop_pct is not None else self.crop_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ : str = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ : Any = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ : Union[str, Any] = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ : List[str] = get_size_dict(a_ , default_to_square=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ : Any = get_size_dict(a_ , param_name='crop_size' )
SCREAMING_SNAKE_CASE__ : int = make_list_of_images(a_ )
if not valid_images(a_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_pct is None:
raise ValueError('Crop_pct must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : Optional[Any] = [to_numpy_array(a_ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ : int = [self.resize(image=a_ , size=a_ , crop_pct=a_ , resample=a_ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE__ : str = [self.center_crop(image=a_ , size=a_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ : Any = [self.rescale(image=a_ , scale=a_ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.normalize(image=a_ , mean=a_ , std=a_ ) for image in images]
SCREAMING_SNAKE_CASE__ : Optional[int] = [to_channel_dimension_format(a_ , a_ ) for image in images]
SCREAMING_SNAKE_CASE__ : Any = {'pixel_values': images}
return BatchFeature(data=a_ , tensor_type=a_ )
| 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def __lowercase( self : Any , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] , a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '</s>'
SCREAMING_SNAKE_CASE__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(a_ ) , 1103 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
SCREAMING_SNAKE_CASE__ : Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
SCREAMING_SNAKE_CASE__ : int = 'To ensure a smooth flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ['This is going to be way too long.' * 150, 'short example']
SCREAMING_SNAKE_CASE__ : int = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def __lowercase( self : Any )-> str:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[int] = PegasusTokenizer(a_ , offset=0 , mask_token_sent=a_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def __lowercase( self : List[str] , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple )-> str:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : str = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
@require_torch
def __lowercase( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ['This is going to be way too long.' * 1000, 'short example']
SCREAMING_SNAKE_CASE__ : Optional[int] = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : str = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 636 | 1 |
from manim import *
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE__ : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : int = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : List[str] = VGroup(*a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = VGroup(*a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : int = VGroup(a_ , a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : Tuple = Text('CPU' , font_size=24 )
SCREAMING_SNAKE_CASE__ : Dict = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(a_ )
SCREAMING_SNAKE_CASE__ : str = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = VGroup(*a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Text('GPU' , font_size=24 )
SCREAMING_SNAKE_CASE__ : Dict = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
gpu.move_to([-1, -1, 0] )
self.add(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = VGroup(*a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : int = Text('Model' , font_size=24 )
SCREAMING_SNAKE_CASE__ : str = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
model.move_to([3, -1.0, 0] )
self.add(a_ )
SCREAMING_SNAKE_CASE__ : int = []
for i, rect in enumerate(a_ ):
rect.set_stroke(a_ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE__ : Optional[Any] = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(a_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=a_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=a_ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=a_ , buff=0.0 )
self.add(a_ )
cpu_targs.append(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE__ : Optional[Any] = VGroup(*a_ ).arrange(a_ , buff=0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Text('Loaded Checkpoint' , font_size=24 )
SCREAMING_SNAKE_CASE__ : Dict = Group(a_ , a_ ).arrange(a_ , aligned_edge=a_ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE__ : str = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE__ : str = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(a_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE__ : int = MarkupText(
F'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(a_ ) , Write(a_ ) )
self.play(Write(a_ , run_time=1 ) , Create(a_ , run_time=1 ) )
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : str = []
for i, rect in enumerate(a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = fill.copy().set_fill(a_ , opacity=0.7 )
target.move_to(a_ )
first_animations.append(GrowFromCenter(a_ , run_time=1 ) )
SCREAMING_SNAKE_CASE__ : List[str] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(a_ , run_time=1.5 ) )
self.play(*a_ )
self.play(*a_ )
self.wait()
| 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 636 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'perceiver'
def __init__( self : Tuple , a_ : int=256 , a_ : Dict=1280 , a_ : Optional[int]=768 , a_ : Union[str, Any]=1 , a_ : Optional[Any]=26 , a_ : Union[str, Any]=8 , a_ : Dict=8 , a_ : Union[str, Any]=None , a_ : Tuple=None , a_ : List[Any]="kv" , a_ : Dict=1 , a_ : Union[str, Any]=1 , a_ : Tuple="gelu" , a_ : Any=0.1 , a_ : Tuple=0.02 , a_ : Dict=1e-1_2 , a_ : Tuple=True , a_ : List[Any]=262 , a_ : Any=2048 , a_ : Optional[int]=56 , a_ : Dict=[368, 496] , a_ : List[Any]=16 , a_ : int=1920 , a_ : Tuple=16 , a_ : Optional[int]=[1, 16, 224, 224] , **a_ : str , )-> int:
"""simple docstring"""
super().__init__(**a_ )
SCREAMING_SNAKE_CASE__ : str = num_latents
SCREAMING_SNAKE_CASE__ : Optional[int] = d_latents
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_model
SCREAMING_SNAKE_CASE__ : Any = num_blocks
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_self_attends_per_block
SCREAMING_SNAKE_CASE__ : Tuple = num_self_attention_heads
SCREAMING_SNAKE_CASE__ : int = num_cross_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] = qk_channels
SCREAMING_SNAKE_CASE__ : Tuple = v_channels
SCREAMING_SNAKE_CASE__ : str = cross_attention_shape_for_attention
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self_attention_widening_factor
SCREAMING_SNAKE_CASE__ : str = cross_attention_widening_factor
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict = use_query_residual
# masked language modeling attributes
SCREAMING_SNAKE_CASE__ : Dict = vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] = max_position_embeddings
# image classification attributes
SCREAMING_SNAKE_CASE__ : str = image_size
# flow attributes
SCREAMING_SNAKE_CASE__ : int = train_size
# multimodal autoencoding attributes
SCREAMING_SNAKE_CASE__ : Optional[int] = num_frames
SCREAMING_SNAKE_CASE__ : int = audio_samples_per_frame
SCREAMING_SNAKE_CASE__ : int = samples_per_patch
SCREAMING_SNAKE_CASE__ : List[str] = output_shape
class snake_case ( UpperCamelCase_ ):
@property
def __lowercase( self : str )-> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE__ : Any = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def __lowercase( self : Tuple )-> float:
"""simple docstring"""
return 1e-4
def __lowercase( self : List[str] , a_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , a_ : int = -1 , a_ : int = -1 , a_ : int = -1 , a_ : bool = False , a_ : Optional[TensorType] = None , a_ : int = 3 , a_ : int = 40 , a_ : int = 40 , )-> Mapping[str, Any]:
"""simple docstring"""
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(a_ , a_ ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Union[str, Any] = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Union[str, Any] = preprocessor.num_special_tokens_to_add(a_ )
SCREAMING_SNAKE_CASE__ : str = compute_effective_axis_dimension(
a_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=a_ )
# Generate dummy inputs according to compute batch and sequence
SCREAMING_SNAKE_CASE__ : Dict = [' '.join(['a'] ) * seq_length] * batch_size
SCREAMING_SNAKE_CASE__ : Any = dict(preprocessor(a_ , return_tensors=a_ ) )
SCREAMING_SNAKE_CASE__ : int = inputs.pop('input_ids' )
return inputs
elif isinstance(a_ , a_ ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
SCREAMING_SNAKE_CASE__ : Union[str, Any] = compute_effective_axis_dimension(a_ , fixed_dimension=OnnxConfig.default_fixed_batch )
SCREAMING_SNAKE_CASE__ : List[str] = self._generate_dummy_images(a_ , a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = dict(preprocessor(images=a_ , return_tensors=a_ ) )
SCREAMING_SNAKE_CASE__ : int = inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 636 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Tuple = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"tokenization_lxmert": ["LxmertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = ["LxmertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Dict = [
"LxmertEncoder",
"LxmertForPreTraining",
"LxmertForQuestionAnswering",
"LxmertModel",
"LxmertPreTrainedModel",
"LxmertVisualFeatureEncoder",
"LxmertXLayer",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Any = [
"TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLxmertForPreTraining",
"TFLxmertMainLayer",
"TFLxmertModel",
"TFLxmertPreTrainedModel",
"TFLxmertVisualFeatureEncoder",
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : List[Any] , *a_ : int , **a_ : Any )-> Dict:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : str , **a_ : int )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Union[str, Any] , **a_ : Tuple )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : str , *a_ : List[Any] , **a_ : Dict )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : List[Any] , **a_ : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : str , **a_ : List[Any] )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : str , *a_ : List[str] , **a_ : Dict )-> str:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : Tuple , **a_ : Optional[int] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : Any , **a_ : Tuple )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Any , *a_ : Union[str, Any] , **a_ : Union[str, Any] )-> List[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : int , **a_ : List[Any] )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : List[Any] , **a_ : List[str] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : List[Any] , *a_ : List[str] , **a_ : Dict )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Dict , **a_ : List[Any] )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Tuple , **a_ : int )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Any , *a_ : Any , **a_ : Dict )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Optional[int] , **a_ : Optional[int] )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : List[Any] , **a_ : Dict )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[int] , *a_ : Optional[Any] , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Any , **a_ : List[Any] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Union[str, Any] , *a_ : List[Any] , **a_ : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Optional[Any] , **a_ : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : List[Any] , **a_ : List[Any] )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : List[str] , *a_ : Union[str, Any] , **a_ : str )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Tuple , **a_ : Optional[int] )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : List[str] , **a_ : List[Any] )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Tuple , *a_ : Union[str, Any] , **a_ : Any )-> str:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : List[Any] , **a_ : int )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : str , **a_ : str )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Union[str, Any] , *a_ : Optional[int] , **a_ : Dict )-> List[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Optional[int] , **a_ : Optional[Any] )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : List[Any] , **a_ : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
def _a ( *lowercase__ : Optional[int] , **lowercase__ : List[str] ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : str , **lowercase__ : Optional[int] ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : List[Any] , **lowercase__ : Dict ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : List[Any] , **lowercase__ : List[Any] ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : List[Any] , **lowercase__ : Any ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : List[Any] , **lowercase__ : Dict ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
def _a ( *lowercase__ : List[Any] , **lowercase__ : Union[str, Any] ):
'''simple docstring'''
requires_backends(lowercase__ , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Any , **a_ : List[str] )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Tuple , **a_ : str )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Tuple , **a_ : List[str] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Union[str, Any] , *a_ : Tuple , **a_ : Optional[Any] )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Dict , **a_ : int )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Any , **a_ : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Union[str, Any] , **a_ : List[Any] )-> str:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Tuple , **a_ : Dict )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : List[str] , **a_ : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[Any] , *a_ : int , **a_ : int )-> List[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : int , **a_ : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Optional[int] , **a_ : Union[str, Any] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Optional[Any] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Tuple , **a_ : Dict )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : Any , **a_ : str )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Any , *a_ : str , **a_ : List[Any] )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : int , **a_ : Tuple )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Tuple , **a_ : Dict )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Union[str, Any] , *a_ : Optional[Any] , **a_ : Optional[int] )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Union[str, Any] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Optional[Any] , **a_ : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : str , *a_ : int , **a_ : Optional[Any] )-> Dict:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Optional[Any] , **a_ : Any )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : List[str] , **a_ : Optional[int] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : Any , **a_ : Dict )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Optional[int] , **a_ : List[str] )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : List[str] , **a_ : Tuple )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Tuple , *a_ : Any , **a_ : Optional[Any] )-> Any:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : int , **a_ : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Optional[Any] , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[int] , *a_ : Any , **a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Tuple , **a_ : List[Any] )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Optional[int] , **a_ : Tuple )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[int] , *a_ : Any , **a_ : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Tuple , **a_ : List[str] )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Tuple , **a_ : str )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Tuple , *a_ : List[Any] , **a_ : Union[str, Any] )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : str , **a_ : Any )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : List[Any] , **a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Union[str, Any] , **a_ : int )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Dict , **a_ : List[str] )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Any , **a_ : List[Any] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : Optional[Any] , **a_ : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : str , **a_ : int )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : str , **a_ : Dict )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[Any] , *a_ : Dict , **a_ : List[Any] )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Optional[Any] , **a_ : Tuple )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : Union[str, Any] , **a_ : Optional[Any] )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : str , **a_ : Dict )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : str , **a_ : Tuple )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Any , **a_ : str )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : str , *a_ : Dict , **a_ : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Tuple , **a_ : Dict )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : int , **a_ : Dict )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : str , **a_ : List[str] )-> Any:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : Union[str, Any] , **a_ : List[str] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Tuple , **a_ : List[str] )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Tuple , *a_ : Optional[int] , **a_ : Any )-> Optional[int]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : List[str] , **a_ : Optional[int] )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Union[str, Any] , **a_ : int )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Any , *a_ : int , **a_ : List[str] )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Dict , **a_ : List[Any] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : int , **a_ : Any )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Tuple , *a_ : str , **a_ : Dict )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Dict , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Optional[int] , **a_ : Tuple )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Any , *a_ : Optional[int] , **a_ : int )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Optional[int] , **a_ : Any )-> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : List[str] , **a_ : Optional[Any] )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[Any] , *a_ : List[str] , **a_ : Optional[int] )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Optional[int] , **a_ : List[str] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Dict , **a_ : Tuple )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[int] , *a_ : List[str] , **a_ : List[str] )-> Optional[int]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : str , **a_ : List[Any] )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : int , **a_ : str )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : int , **a_ : int )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : Tuple , **a_ : str )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Any , **a_ : Dict )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[Any] , *a_ : Optional[int] , **a_ : Optional[int] )-> str:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : List[str] , **a_ : Any )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : int , **a_ : Dict )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : List[Any] , *a_ : Any , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : Tuple , **a_ : Tuple )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : List[str] , **a_ : Tuple )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : Dict , **a_ : Optional[Any] )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : str , **a_ : Optional[Any] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : Any , **a_ : Optional[int] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : List[Any] , **a_ : Optional[Any] )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Tuple , **a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Optional[Any] , **a_ : Union[str, Any] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : Dict , **a_ : Any )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Tuple , *a_ : int , **a_ : Tuple )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Union[str, Any] , **a_ : Dict )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Optional[Any] , **a_ : Any )-> Dict:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Optional[Any] , *a_ : Any , **a_ : int )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Union[str, Any] , **a_ : List[Any] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : int , **a_ : int )-> Dict:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Optional[Any] , **a_ : Optional[Any] )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : Any , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Dict , *a_ : int , **a_ : Tuple )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : str , **a_ : Any )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Tuple , **a_ : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : int , *a_ : Dict , **a_ : Optional[Any] )-> Tuple:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Dict , *a_ : Tuple , **a_ : Optional[int] )-> Tuple:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : List[str] , **a_ : List[Any] )-> str:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[int] , *a_ : List[Any] , **a_ : Any )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : str , **a_ : Union[str, Any] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : List[Any] , **a_ : int )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Union[str, Any] , *a_ : str , **a_ : Tuple )-> List[str]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : int , *a_ : Any , **a_ : Union[str, Any] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Optional[int] , *a_ : Any , **a_ : Tuple )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : str , *a_ : List[str] , **a_ : Union[str, Any] )-> List[Any]:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : str , *a_ : Union[str, Any] , **a_ : Optional[Any] )-> Dict:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : Any , *a_ : List[Any] , **a_ : int )-> List[Any]:
"""simple docstring"""
requires_backends(cls , ['torch'] )
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['torch']
def __init__( self : Optional[Any] , *a_ : Union[str, Any] , **a_ : Optional[Any] )-> int:
"""simple docstring"""
requires_backends(self , ['torch'] )
@classmethod
def __lowercase( cls : Union[str, Any] , *a_ : Union[str, Any] , **a_ : List[str] )-> int:
"""simple docstring"""
requires_backends(cls , ['torch'] )
@classmethod
def __lowercase( cls : List[str] , *a_ : int , **a_ : List[str] )-> Any:
"""simple docstring"""
requires_backends(cls , ['torch'] )
| 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {} # {vertex:distance}
def __lt__( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
return self.key < other.key
def __repr__( self : Any )-> Dict:
"""simple docstring"""
return self.id
def __lowercase( self : Optional[Any] , a_ : int )-> List[str]:
"""simple docstring"""
self.neighbors.append(a_ )
def __lowercase( self : int , a_ : int , a_ : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = weight
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowercase__ )
graph[b - 1].add_edge(graph[a - 1] , lowercase__ )
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
for u in graph:
SCREAMING_SNAKE_CASE__ : Dict = math.inf
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = graph[:]
while q:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(lowercase__ )
q.remove(lowercase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : int = u
SCREAMING_SNAKE_CASE__ : Any = u.edges[v.id]
for i in range(1 , len(lowercase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
for u in graph:
SCREAMING_SNAKE_CASE__ : List[str] = math.inf
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = list(lowercase__ )
hq.heapify(lowercase__ )
while h:
SCREAMING_SNAKE_CASE__ : Optional[int] = hq.heappop(lowercase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : List[str] = u
SCREAMING_SNAKE_CASE__ : Dict = u.edges[v.id]
hq.heapify(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | 1 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=UpperCamelCase_ ):
lowercase_ = ['onnx']
def __init__( self : str , *a_ : Tuple , **a_ : Dict )-> Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['onnx'] )
@classmethod
def __lowercase( cls : Any , *a_ : List[Any] , **a_ : int )-> Optional[int]:
"""simple docstring"""
requires_backends(cls , ['onnx'] )
@classmethod
def __lowercase( cls : List[Any] , *a_ : int , **a_ : List[str] )-> List[str]:
"""simple docstring"""
requires_backends(cls , ['onnx'] )
| 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | 1 |
from math import isclose, sqrt
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = point_y / 4 / point_x
SCREAMING_SNAKE_CASE__ : Tuple = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
SCREAMING_SNAKE_CASE__ : List[str] = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
SCREAMING_SNAKE_CASE__ : int = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = outgoing_gradient**2 + 4
SCREAMING_SNAKE_CASE__ : Any = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
SCREAMING_SNAKE_CASE__ : List[str] = (point_y - outgoing_gradient * point_x) ** 2 - 1_00
SCREAMING_SNAKE_CASE__ : Tuple = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
SCREAMING_SNAKE_CASE__ : int = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
SCREAMING_SNAKE_CASE__ : List[Any] = x_minus if isclose(lowercase__ , lowercase__ ) else x_plus
SCREAMING_SNAKE_CASE__ : List[str] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _a ( lowercase__ : float = 1.4 , lowercase__ : float = -9.6 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : float = first_x_coord
SCREAMING_SNAKE_CASE__ : float = first_y_coord
SCREAMING_SNAKE_CASE__ : float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = next_point(lowercase__ , lowercase__ , lowercase__ )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 636 | 1 |
def _a ( lowercase__ : int = 4_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0, 1]
SCREAMING_SNAKE_CASE__ : Any = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
SCREAMING_SNAKE_CASE__ : Any = 0
for j in range(len(lowercase__ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__ : str = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self : Optional[int] , *lowercase__ : int , **lowercase__ : Tuple ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 636 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lowercase__ ) ) )
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
if dataset.ndim != value_array.ndim:
SCREAMING_SNAKE_CASE__ : Dict = (
'Wrong input data\'s dimensions... '
f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
)
raise ValueError(lowercase__ )
try:
if dataset.shape[1] != value_array.shape[1]:
SCREAMING_SNAKE_CASE__ : str = (
'Wrong input data\'s shape... '
f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
)
raise ValueError(lowercase__ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError('Wrong shape' )
if dataset.dtype != value_array.dtype:
SCREAMING_SNAKE_CASE__ : List[Any] = (
'Input data have different datatype... '
f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
)
raise TypeError(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = []
for value in value_array:
SCREAMING_SNAKE_CASE__ : List[Any] = euclidean(lowercase__ , dataset[0] )
SCREAMING_SNAKE_CASE__ : str = dataset[0].tolist()
for dataset_value in dataset[1:]:
SCREAMING_SNAKE_CASE__ : Any = euclidean(lowercase__ , lowercase__ )
if dist > temp_dist:
SCREAMING_SNAKE_CASE__ : List[str] = temp_dist
SCREAMING_SNAKE_CASE__ : List[Any] = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
return np.dot(lowercase__ , lowercase__ ) / (norm(lowercase__ ) * norm(lowercase__ ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(lowercase__ )
or left < -len(lowercase__ )
or right >= len(lowercase__ )
or right < -len(lowercase__ )
):
raise IndexError('list index out of range' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ : int = find_max(lowercase__ , lowercase__ , lowercase__ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ : Tuple = find_max(lowercase__ , mid + 1 , lowercase__ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 636 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'Speech2TextFeatureExtractor'
lowercase_ = 'Speech2TextTokenizer'
def __init__( self : Union[str, Any] , a_ : Optional[int] , a_ : Any )-> List[str]:
"""simple docstring"""
super().__init__(a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = self.feature_extractor
SCREAMING_SNAKE_CASE__ : Dict = False
def __call__( self : Tuple , *a_ : Optional[Any] , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*a_ , **a_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
SCREAMING_SNAKE_CASE__ : Any = kwargs.pop('raw_speech' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop('audio' , a_ )
SCREAMING_SNAKE_CASE__ : int = kwargs.pop('sampling_rate' , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs.pop('text' , a_ )
if len(a_ ) > 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] = args[0]
SCREAMING_SNAKE_CASE__ : List[Any] = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
SCREAMING_SNAKE_CASE__ : int = self.feature_extractor(a_ , *a_ , sampling_rate=a_ , **a_ )
if text is not None:
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer(a_ , **a_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE__ : List[str] = encodings['input_ids']
return inputs
def __lowercase( self : int , *a_ : List[Any] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@contextmanager
def __lowercase( self : Dict )-> List[Any]:
"""simple docstring"""
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[int] = self.tokenizer
yield
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extractor
SCREAMING_SNAKE_CASE__ : Optional[int] = False
| 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Any] , lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное обучение - это здорово, не так ли?',
'de': 'Maschinelles Lernen ist großartig, nicht wahr?',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
SCREAMING_SNAKE_CASE__ : str = {
'wmt16-en-de-dist-12-1': [28.3, 27.52],
'wmt16-en-de-dist-6-1': [27.4, 27.11],
'wmt16-en-de-12-1': [26.9, 25.75],
}
SCREAMING_SNAKE_CASE__ : Dict = f'''{src_lang}-{tgt_lang}'''
SCREAMING_SNAKE_CASE__ : Tuple = f'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.
For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).
All 3 models are available:
* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
## Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).
## Eval results
Here are the BLEU scores:
model | fairseq | transformers
-------|---------|----------
{model_name} | {scores[model_name][0]} | {scores[model_name][1]}
The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
## Data Sources
- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)
### BibTeX entry and citation info
```
@misc{{kasai2020deep,
title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
year={{2020}},
eprint={{2006.10369}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}}
}}
```
'''
model_card_dir.mkdir(parents=lowercase__ , exist_ok=lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.join(lowercase__ , 'README.md' )
print(f'''Generating {path}''' )
with open(lowercase__ , 'w' , encoding='utf-8' ) as f:
f.write(lowercase__ )
# make sure we are under the root of the project
SCREAMING_SNAKE_CASE__ : str = Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE__ : Any = repo_dir / "model_cards"
for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
SCREAMING_SNAKE_CASE__ : Tuple = model_cards_dir / "allenai" / model_name
write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)
| 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[int] , a_ : Dict=7 , a_ : Any=3 , a_ : Any=18 , a_ : int=30 , a_ : int=400 , a_ : List[Any]=None , a_ : int=True , a_ : int=True , a_ : Dict=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'height': 20, 'width': 20}
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : List[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_convert_rgb
SCREAMING_SNAKE_CASE__ : List[str] = [512, 1024, 2048, 4096]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(a_ , stream=a_ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = PixaStructImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.prepare_dummy_image()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE__ : List[Any] = 2048
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(a_ , return_tensors='pt' , max_patches=a_ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
SCREAMING_SNAKE_CASE__ : int = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
SCREAMING_SNAKE_CASE__ : List[Any] = 'Hello'
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processor(
a_ , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : int = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = PixaStructImageProcessingTester(self , num_channels=4 )
SCREAMING_SNAKE_CASE__ : Dict = 3
@property
def __lowercase( self : Any )-> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 636 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\nimport os\n"
SCREAMING_SNAKE_CASE__ : List[Any] = "\ndef foo():\n import os\n return False\n"
SCREAMING_SNAKE_CASE__ : str = "\ndef foo():\n def bar():\n if True:\n import os\n return False\n return bar()\n"
SCREAMING_SNAKE_CASE__ : Any = "\nimport os\n\ntry:\n import bar\nexcept ImportError:\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : int = "\nimport os\n\ndef foo():\n try:\n import bar\n except ImportError:\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : Dict = "\nimport os\n\ntry:\n import bar\nexcept (ImportError, AttributeError):\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : Dict = "\nimport os\n\ntry:\n import bar\nexcept ImportError as e:\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : List[str] = "\nimport os\n\ntry:\n import bar\nexcept:\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : List[str] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : Optional[int] = "\nimport os\n\ntry:\n import bar\n import baz\nexcept ImportError:\n x = 1\n raise ValueError()\n"
SCREAMING_SNAKE_CASE__ : int = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('case' , lowercase__ )
def _a ( lowercase__ : Optional[Any] , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(lowercase__ , 'test_file.py' )
with open(lowercase__ , 'w' ) as _tmp_file:
_tmp_file.write(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = get_imports(lowercase__ )
assert parsed_imports == ["os"]
| 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {} # {vertex:distance}
def __lt__( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
return self.key < other.key
def __repr__( self : Any )-> Dict:
"""simple docstring"""
return self.id
def __lowercase( self : Optional[Any] , a_ : int )-> List[str]:
"""simple docstring"""
self.neighbors.append(a_ )
def __lowercase( self : int , a_ : int , a_ : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = weight
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowercase__ )
graph[b - 1].add_edge(graph[a - 1] , lowercase__ )
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
for u in graph:
SCREAMING_SNAKE_CASE__ : Dict = math.inf
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = graph[:]
while q:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(lowercase__ )
q.remove(lowercase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : int = u
SCREAMING_SNAKE_CASE__ : Any = u.edges[v.id]
for i in range(1 , len(lowercase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
for u in graph:
SCREAMING_SNAKE_CASE__ : List[str] = math.inf
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = list(lowercase__ )
hq.heapify(lowercase__ )
while h:
SCREAMING_SNAKE_CASE__ : Optional[int] = hq.heappop(lowercase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : List[str] = u
SCREAMING_SNAKE_CASE__ : Dict = u.edges[v.id]
hq.heapify(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | 1 |
import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE__ : Tuple = range(3 , int(math.sqrt(lowercase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _a ( lowercase__ : List[str] , lowercase__ : Any=1 , **lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = factor * value
SCREAMING_SNAKE_CASE__ : Dict = value
while not is_prime(lowercase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase__ )
return value
| 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 636 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
class snake_case :
lowercase_ = None
@experimental
def _a ( lowercase__ : Any , lowercase__ : str , lowercase__ : Dict , lowercase__ : Any , lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
return _map_with_joblib(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
def _a ( lowercase__ : Optional[int] , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : int , lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = num_proc if num_proc <= len(lowercase__ ) else len(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = [] # We organize the splits ourselve (contiguous splits)
for index in range(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = len(lowercase__ ) // num_proc
SCREAMING_SNAKE_CASE__ : Any = len(lowercase__ ) % num_proc
SCREAMING_SNAKE_CASE__ : Dict = div * index + min(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(lowercase__ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f'''Error dividing inputs iterable among processes. '''
f'''Total number of objects {len(lowercase__ )}, '''
f'''length: {sum(len(i[1] ) for i in split_kwds )}''' )
logger.info(
f'''Spawning {num_proc} processes for {len(lowercase__ )} objects in slices of {[len(i[1] ) for i in split_kwds]}''' )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = None, None
if not disable_tqdm:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = (RLock(),), tqdm.set_lock
with Pool(lowercase__ , initargs=lowercase__ , initializer=lowercase__ ) as pool:
SCREAMING_SNAKE_CASE__ : Any = pool.map(lowercase__ , lowercase__ )
logger.info(f'''Finished {num_proc} processes''' )
SCREAMING_SNAKE_CASE__ : List[Any] = [obj for proc_res in mapped for obj in proc_res]
logger.info(f'''Unpacked {len(lowercase__ )} objects''' )
return mapped
def _a ( lowercase__ : Dict , lowercase__ : List[str] , lowercase__ : List[Any] , lowercase__ : Any , lowercase__ : Tuple , lowercase__ : Optional[int] , lowercase__ : str ):
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=lowercase__ ):
return joblib.Parallel()(
joblib.delayed(lowercase__ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
SCREAMING_SNAKE_CASE__ : Tuple = None
| 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE__ : int = radians(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = angle_in_radians
SCREAMING_SNAKE_CASE__ : Optional[int] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = -1
for _ in range(lowercase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase__ , lowercase__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 636 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'gpt_neox'
def __init__( self : Any , a_ : Optional[int]=5_0432 , a_ : int=6144 , a_ : List[str]=44 , a_ : int=64 , a_ : str=2_4576 , a_ : Dict="gelu" , a_ : Any=0.25 , a_ : Any=1_0000 , a_ : Tuple=0.0 , a_ : str=0.0 , a_ : int=0.1 , a_ : List[Any]=2048 , a_ : List[Any]=0.02 , a_ : Dict=1e-5 , a_ : Optional[int]=True , a_ : Optional[int]=0 , a_ : Any=2 , a_ : int=False , a_ : Tuple=True , a_ : Dict=None , **a_ : List[Any] , )-> Tuple:
"""simple docstring"""
super().__init__(bos_token_id=a_ , eos_token_id=a_ , **a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Any = rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[int] = rotary_emb_base
SCREAMING_SNAKE_CASE__ : Any = attention_dropout
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout
SCREAMING_SNAKE_CASE__ : Optional[int] = classifier_dropout
SCREAMING_SNAKE_CASE__ : Dict = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : int = use_cache
SCREAMING_SNAKE_CASE__ : Tuple = tie_word_embeddings
SCREAMING_SNAKE_CASE__ : List[str] = use_parallel_residual
SCREAMING_SNAKE_CASE__ : List[Any] = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , a_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'''got {self.rope_scaling}''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rope_scaling.get('type' , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.rope_scaling.get('factor' , a_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
if rope_scaling_factor is None or not isinstance(a_ , a_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
| 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE__ : Tuple = range(3 , int(math.sqrt(lowercase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _a ( lowercase__ : List[str] , lowercase__ : Any=1 , **lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = factor * value
SCREAMING_SNAKE_CASE__ : Dict = value
while not is_prime(lowercase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase__ )
return value
| 636 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'fnet'
def __init__( self : Optional[Any] , a_ : List[str]=3_2000 , a_ : Dict=768 , a_ : int=12 , a_ : List[Any]=3072 , a_ : List[str]="gelu_new" , a_ : List[Any]=0.1 , a_ : str=512 , a_ : str=4 , a_ : List[str]=0.02 , a_ : Optional[Any]=1e-1_2 , a_ : int=False , a_ : Optional[int]=512 , a_ : Optional[int]=3 , a_ : List[str]=1 , a_ : Any=2 , **a_ : Optional[Any] , )-> Any:
"""simple docstring"""
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Any = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_act
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : str = type_vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE__ : List[Any] = use_tpu_fourier_optimizations
SCREAMING_SNAKE_CASE__ : Optional[Any] = tpu_short_seq_length
| 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class snake_case :
def __init__( self : str , a_ : List[str] , a_ : Tuple=13 , a_ : Dict=30 , a_ : Optional[int]=2 , a_ : Tuple=3 , a_ : Dict=True , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[str]=5 , a_ : Any=4 , a_ : Dict=37 , a_ : Dict="gelu" , a_ : int=0.1 , a_ : Optional[Any]=0.1 , a_ : Any=10 , a_ : List[str]=0.02 , a_ : Any=3 , a_ : List[str]=None , a_ : Optional[int]=2 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : int = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
SCREAMING_SNAKE_CASE__ : Optional[int] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_patches + 2
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase( self : List[str] , a_ : List[str] , a_ : Optional[Any] , a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = DeiTModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : List[str] , a_ : List[str] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = DeiTForMaskedImageModeling(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DeiTForMaskedImageModeling(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : int = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = DeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class(a_ )
SCREAMING_SNAKE_CASE__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a_ )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def __lowercase( self : str , a_ : str , a_ : Tuple , a_ : Union[str, Any]=False )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE__ : Tuple = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
model.gradient_checkpointing_enable()
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a_ ),
*get_values(a_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
SCREAMING_SNAKE_CASE__ : int = problem_type['title']
SCREAMING_SNAKE_CASE__ : Tuple = problem_type['num_labels']
SCREAMING_SNAKE_CASE__ : str = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
SCREAMING_SNAKE_CASE__ : Any = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a_ ) as warning_list:
SCREAMING_SNAKE_CASE__ : str = model(**a_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def __lowercase( self : int )-> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(images=a_ , return_tensors='pt' ).to(a_ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**a_ )
# verify the logits
SCREAMING_SNAKE_CASE__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
SCREAMING_SNAKE_CASE__ : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = inputs.pixel_values.to(a_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
| 636 | 1 |
class snake_case :
def __init__( self : Tuple , a_ : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = size
SCREAMING_SNAKE_CASE__ : Dict = [0] * size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [0] * size
@staticmethod
def __lowercase( a_ : int )-> int:
"""simple docstring"""
return index | (index + 1)
@staticmethod
def __lowercase( a_ : int )-> int:
"""simple docstring"""
return (index & (index + 1)) - 1
def __lowercase( self : List[str] , a_ : int , a_ : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = value
while index < self.size:
SCREAMING_SNAKE_CASE__ : Tuple = self.get_prev(a_ ) + 1
if current_left_border == index:
SCREAMING_SNAKE_CASE__ : str = value
else:
SCREAMING_SNAKE_CASE__ : Any = max(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_next(a_ )
def __lowercase( self : int , a_ : int , a_ : int )-> int:
"""simple docstring"""
right -= 1 # Because of right is exclusive
SCREAMING_SNAKE_CASE__ : Dict = 0
while left <= right:
SCREAMING_SNAKE_CASE__ : Tuple = self.get_prev(a_ )
if left <= current_left:
SCREAMING_SNAKE_CASE__ : List[str] = max(a_ , self.tree[right] )
SCREAMING_SNAKE_CASE__ : Optional[int] = current_left
else:
SCREAMING_SNAKE_CASE__ : Any = max(a_ , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class snake_case ( UpperCamelCase_ , UpperCamelCase_ ):
@register_to_config
def __init__( self : Tuple , a_ : int = 128 , a_ : int = 256 , a_ : float = 2000.0 , a_ : int = 768 , a_ : int = 12 , a_ : int = 12 , a_ : int = 64 , a_ : int = 2048 , a_ : float = 0.1 , )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = nn.Sequential(
nn.Linear(a_ , d_model * 4 , bias=a_ ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=a_ ) , nn.SiLU() , )
SCREAMING_SNAKE_CASE__ : Any = nn.Embedding(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Linear(a_ , a_ , bias=a_ )
SCREAMING_SNAKE_CASE__ : int = nn.Dropout(p=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.ModuleList()
for lyr_num in range(a_ ):
# FiLM conditional T5 decoder
SCREAMING_SNAKE_CASE__ : Optional[int] = DecoderLayer(d_model=a_ , d_kv=a_ , num_heads=a_ , d_ff=a_ , dropout_rate=a_ )
self.decoders.append(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = TaLayerNorm(a_ )
SCREAMING_SNAKE_CASE__ : Any = nn.Dropout(p=a_ )
SCREAMING_SNAKE_CASE__ : str = nn.Linear(a_ , a_ , bias=a_ )
def __lowercase( self : Dict , a_ : List[str] , a_ : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : Optional[Any] , a_ : Tuple )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
SCREAMING_SNAKE_CASE__ : Dict = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.conditioning_emb(a_ ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
SCREAMING_SNAKE_CASE__ : Tuple = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.broadcast_to(
torch.arange(a_ , device=decoder_input_tokens.device ) , (batch, seq_length) , )
SCREAMING_SNAKE_CASE__ : str = self.position_encoding(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.continuous_inputs_projection(a_ )
inputs += position_encodings
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dropout(a_ )
# decoder: No padding present.
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [(x, self.encoder_decoder_mask(a_ , a_ )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lyr(
a_ , conditioning_emb=a_ , encoder_hidden_states=a_ , encoder_attention_mask=a_ , )[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.decoder_norm(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.post_dropout(a_ )
SCREAMING_SNAKE_CASE__ : Any = self.spec_out(a_ )
return spec_out
class snake_case ( nn.Module ):
def __init__( self : int , a_ : Any , a_ : int , a_ : Optional[int] , a_ : int , a_ : int , a_ : str=1e-6 )-> Any:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=a_ , d_kv=a_ , num_heads=a_ , dropout_rate=a_ ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=a_ , d_kv=a_ , num_heads=a_ , dropout_rate=a_ , layer_norm_epsilon=a_ , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=a_ , d_ff=a_ , dropout_rate=a_ , layer_norm_epsilon=a_ ) )
def __lowercase( self : Tuple , a_ : Union[str, Any] , a_ : Tuple=None , a_ : int=None , a_ : List[Any]=None , a_ : Any=None , a_ : Any=None , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.layer[0](
a_ , conditioning_emb=a_ , attention_mask=a_ , )
if encoder_hidden_states is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.where(encoder_attention_mask > 0 , 0 , -1e1_0 ).to(
encoder_hidden_states.dtype )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.layer[1](
a_ , key_value_states=a_ , attention_mask=a_ , )
# Apply Film Conditional Feed Forward layer
SCREAMING_SNAKE_CASE__ : List[Any] = self.layer[-1](a_ , a_ )
return (hidden_states,)
class snake_case ( nn.Module ):
def __init__( self : Union[str, Any] , a_ : Tuple , a_ : Dict , a_ : Union[str, Any] , a_ : int )-> str:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TaLayerNorm(a_ )
SCREAMING_SNAKE_CASE__ : Any = TaFiLMLayer(in_features=d_model * 4 , out_features=a_ )
SCREAMING_SNAKE_CASE__ : str = Attention(query_dim=a_ , heads=a_ , dim_head=a_ , out_bias=a_ , scale_qk=a_ )
SCREAMING_SNAKE_CASE__ : Dict = nn.Dropout(a_ )
def __lowercase( self : Optional[Any] , a_ : List[str] , a_ : str=None , a_ : Tuple=None , )-> List[Any]:
"""simple docstring"""
# pre_self_attention_layer_norm
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.layer_norm(a_ )
if conditioning_emb is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.FiLMLayer(a_ , a_ )
# Self-attention block
SCREAMING_SNAKE_CASE__ : str = self.attention(a_ )
SCREAMING_SNAKE_CASE__ : int = hidden_states + self.dropout(a_ )
return hidden_states
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : str , a_ : int , a_ : str , a_ : Union[str, Any] , a_ : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Dict = Attention(query_dim=a_ , heads=a_ , dim_head=a_ , out_bias=a_ , scale_qk=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = TaLayerNorm(a_ , eps=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.Dropout(a_ )
def __lowercase( self : List[Any] , a_ : Dict , a_ : List[str]=None , a_ : Tuple=None , )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.layer_norm(a_ )
SCREAMING_SNAKE_CASE__ : int = self.attention(
a_ , encoder_hidden_states=a_ , attention_mask=attention_mask.squeeze(1 ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_states + self.dropout(a_ )
return layer_output
class snake_case ( nn.Module ):
def __init__( self : List[str] , a_ : str , a_ : List[Any] , a_ : List[Any] , a_ : Any )-> Optional[int]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[Any] = TaDenseGatedActDense(d_model=a_ , d_ff=a_ , dropout_rate=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = TaFiLMLayer(in_features=d_model * 4 , out_features=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = TaLayerNorm(a_ , eps=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.Dropout(a_ )
def __lowercase( self : Tuple , a_ : Dict , a_ : Any=None )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.layer_norm(a_ )
if conditioning_emb is not None:
SCREAMING_SNAKE_CASE__ : Dict = self.film(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.DenseReluDense(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_states + self.dropout(a_ )
return hidden_states
class snake_case ( nn.Module ):
def __init__( self : Optional[Any] , a_ : Tuple , a_ : int , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.Linear(a_ , a_ , bias=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Linear(a_ , a_ , bias=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Linear(a_ , a_ , bias=a_ )
SCREAMING_SNAKE_CASE__ : Any = nn.Dropout(a_ )
SCREAMING_SNAKE_CASE__ : Any = NewGELUActivation()
def __lowercase( self : Optional[Any] , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.act(self.wi_a(a_ ) )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.wi_a(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_gelu * hidden_linear
SCREAMING_SNAKE_CASE__ : List[str] = self.dropout(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.wo(a_ )
return hidden_states
class snake_case ( nn.Module ):
def __init__( self : str , a_ : Union[str, Any] , a_ : Optional[int]=1e-6 )-> Any:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Parameter(torch.ones(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = eps
def __lowercase( self : Union[str, Any] , a_ : int )-> Optional[Any]:
"""simple docstring"""
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
SCREAMING_SNAKE_CASE__ : int = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : str = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
SCREAMING_SNAKE_CASE__ : List[str] = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class snake_case ( nn.Module ):
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_4715 * torch.pow(a_ , 3.0 )) ))
class snake_case ( nn.Module ):
def __init__( self : Union[str, Any] , a_ : Tuple , a_ : Tuple )-> List[str]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Linear(a_ , out_features * 2 , bias=a_ )
def __lowercase( self : int , a_ : List[Any] , a_ : Dict )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.scale_bias(a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = torch.chunk(a_ , 2 , -1 )
SCREAMING_SNAKE_CASE__ : int = x * (1 + scale) + shift
return x
| 636 | import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger("transformers.models.encodec")
SCREAMING_SNAKE_CASE__ : Tuple = {
"quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
"quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
"quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
"quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"encoder.model.0.conv.conv": "encoder.layers.0.conv",
"encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
"encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
"encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
"encoder.model.3.conv.conv": "encoder.layers.3.conv",
"encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
"encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
"encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
"encoder.model.6.conv.conv": "encoder.layers.6.conv",
"encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
"encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
"encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
"encoder.model.9.conv.conv": "encoder.layers.9.conv",
"encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
"encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
"encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
"encoder.model.12.conv.conv": "encoder.layers.12.conv",
"encoder.model.13.lstm": "encoder.layers.13.lstm",
"encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
SCREAMING_SNAKE_CASE__ : str = {
"encoder.model.0.conv.norm": "encoder.layers.0.norm",
"encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
"encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
"encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
"encoder.model.3.conv.norm": "encoder.layers.3.norm",
"encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
"encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
"encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
"encoder.model.6.conv.norm": "encoder.layers.6.norm",
"encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
"encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
"encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
"encoder.model.9.conv.norm": "encoder.layers.9.norm",
"encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
"encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
"encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
"encoder.model.12.conv.norm": "encoder.layers.12.norm",
"encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
SCREAMING_SNAKE_CASE__ : Any = {
"decoder.model.0.conv.conv": "decoder.layers.0.conv",
"decoder.model.1.lstm": "decoder.layers.1.lstm",
"decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
"decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
"decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
"decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
"decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
"decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
"decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
"decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
"decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
"decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
"decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
"decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
"decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
"decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
"decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
"decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
"decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
SCREAMING_SNAKE_CASE__ : str = {
"decoder.model.0.conv.norm": "decoder.layers.0.norm",
"decoder.model.3.convtr.norm": "decoder.layers.3.norm",
"decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
"decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
"decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
"decoder.model.6.convtr.norm": "decoder.layers.6.norm",
"decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
"decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
"decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
"decoder.model.9.convtr.norm": "decoder.layers.9.norm",
"decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
"decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
"decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
"decoder.model.12.convtr.norm": "decoder.layers.12.norm",
"decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
"decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
"decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
"decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
SCREAMING_SNAKE_CASE__ : str = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE__ : List[str] = []
SCREAMING_SNAKE_CASE__ : Dict = []
def _a ( lowercase__ : Union[str, Any] , lowercase__ : int , lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Any] ):
'''simple docstring'''
for attribute in key.split('.' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = getattr(lowercase__ , lowercase__ )
if weight_type is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = getattr(lowercase__ , lowercase__ ).shape
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
SCREAMING_SNAKE_CASE__ : int = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE__ : Optional[int] = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE__ : List[Any] = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE__ : Optional[int] = value
elif weight_type == "running_mean":
SCREAMING_SNAKE_CASE__ : List[Any] = value
elif weight_type == "running_var":
SCREAMING_SNAKE_CASE__ : List[Any] = value
elif weight_type == "num_batches_tracked":
SCREAMING_SNAKE_CASE__ : Dict = value
elif weight_type == "weight_ih_l0":
SCREAMING_SNAKE_CASE__ : str = value
elif weight_type == "weight_hh_l0":
SCREAMING_SNAKE_CASE__ : Dict = value
elif weight_type == "bias_ih_l0":
SCREAMING_SNAKE_CASE__ : Tuple = value
elif weight_type == "bias_hh_l0":
SCREAMING_SNAKE_CASE__ : int = value
elif weight_type == "weight_ih_l1":
SCREAMING_SNAKE_CASE__ : str = value
elif weight_type == "weight_hh_l1":
SCREAMING_SNAKE_CASE__ : Any = value
elif weight_type == "bias_ih_l1":
SCREAMING_SNAKE_CASE__ : Optional[int] = value
elif weight_type == "bias_hh_l1":
SCREAMING_SNAKE_CASE__ : str = value
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = value
logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def _a ( lowercase__ : Tuple , lowercase__ : Tuple ):
'''simple docstring'''
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Optional[Any] , lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = []
if model_name == "encodec_24khz" or "encodec_32khz":
SCREAMING_SNAKE_CASE__ : Tuple = MAPPING_24K
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE__ : str = MAPPING_48K
else:
raise ValueError(f'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(lowercase__ , lowercase__ ):
logger.info(f'''{name} was ignored''' )
continue
SCREAMING_SNAKE_CASE__ : Any = False
for key, mapped_key in MAPPING.items():
if "*" in key:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = key.split('.*.' )
if prefix in name and suffix in name:
SCREAMING_SNAKE_CASE__ : Dict = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
SCREAMING_SNAKE_CASE__ : int = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE__ : str = name.split(lowercase__ )[0].split('.' )[-2]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mapped_key.replace('*' , lowercase__ )
if "weight_g" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = 'weight_g'
elif "weight_v" in name:
SCREAMING_SNAKE_CASE__ : Tuple = 'weight_v'
elif "weight_ih_l0" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = 'weight_ih_l0'
elif "weight_hh_l0" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = 'weight_hh_l0'
elif "bias_ih_l0" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'bias_ih_l0'
elif "bias_hh_l0" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = 'bias_hh_l0'
elif "weight_ih_l1" in name:
SCREAMING_SNAKE_CASE__ : Dict = 'weight_ih_l1'
elif "weight_hh_l1" in name:
SCREAMING_SNAKE_CASE__ : Tuple = 'weight_hh_l1'
elif "bias_ih_l1" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = 'bias_ih_l1'
elif "bias_hh_l1" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'bias_hh_l1'
elif "bias" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'bias'
elif "weight" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = 'weight'
elif "running_mean" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'running_mean'
elif "running_var" in name:
SCREAMING_SNAKE_CASE__ : List[str] = 'running_var'
elif "num_batches_tracked" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'num_batches_tracked'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
set_recursively(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
continue
if not is_used:
unused_weights.append(lowercase__ )
logger.warning(f'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def _a ( lowercase__ : Tuple , lowercase__ : Union[str, Any] , lowercase__ : List[str] , lowercase__ : Optional[Any]=None , lowercase__ : List[Any]=None , ):
'''simple docstring'''
if config_path is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = EncodecConfig.from_pretrained(lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
SCREAMING_SNAKE_CASE__ : Optional[int] = [8, 5, 4, 4]
SCREAMING_SNAKE_CASE__ : List[Any] = [2.2]
SCREAMING_SNAKE_CASE__ : Dict = 64
SCREAMING_SNAKE_CASE__ : Dict = 3_20_00
SCREAMING_SNAKE_CASE__ : Optional[Any] = 20_48
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
elif model_name == "encodec_48khz":
SCREAMING_SNAKE_CASE__ : Optional[Any] = [8, 5, 4, 2]
SCREAMING_SNAKE_CASE__ : Any = [3.0, 6.0, 12.0, 24.0]
SCREAMING_SNAKE_CASE__ : int = 4_80_00
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Tuple = 'time_group_norm'
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : List[str] = 1.0
SCREAMING_SNAKE_CASE__ : Tuple = 0.01
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
SCREAMING_SNAKE_CASE__ : Any = EncodecModel(lowercase__ )
SCREAMING_SNAKE_CASE__ : int = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(lowercase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.load(lowercase__ )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
SCREAMING_SNAKE_CASE__ : str = original_checkpoint['best_state']
recursively_load_weights(lowercase__ , lowercase__ , lowercase__ )
model.save_pretrained(lowercase__ )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(lowercase__ )
model.push_to_hub(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="encodec_24khz",
type=str,
help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE__ : Tuple = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {'0': '0', '1': '1'}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = '', ''
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ )
for i in range(len(lowercase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
SCREAMING_SNAKE_CASE__ : int = lexicon[curr_string]
result += last_match_id
SCREAMING_SNAKE_CASE__ : str = last_match_id + '0'
if math.loga(lowercase__ ).is_integer():
SCREAMING_SNAKE_CASE__ : List[str] = {}
for curr_key in list(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = lexicon.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = new_lex
SCREAMING_SNAKE_CASE__ : Any = last_match_id + '1'
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ''
return result
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 8
try:
with open(lowercase__ , 'wb' ) as opened_file:
SCREAMING_SNAKE_CASE__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase__ ) , lowercase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = data_bits[counter:]
SCREAMING_SNAKE_CASE__ : int = data_bits[counter + 1 :]
return data_bits
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = read_file_binary(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = remove_prefix(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = decompress_data(lowercase__ )
write_file_binary(lowercase__ , lowercase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 636 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
@dataclass
class snake_case ( UpperCamelCase_ ):
lowercase_ = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self : List[Any] , **a_ : Optional[int] )-> List[str]:
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
SCREAMING_SNAKE_CASE__ : Any = deprecated_arg[3:]
SCREAMING_SNAKE_CASE__ : str = not kwargs.pop(a_ )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
SCREAMING_SNAKE_CASE__ : str = kwargs.pop('tpu_name' , self.tpu_name )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('device_idx' , self.device_idx )
SCREAMING_SNAKE_CASE__ : Dict = kwargs.pop('eager_mode' , self.eager_mode )
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs.pop('use_xla' , self.use_xla )
super().__init__(**a_ )
lowercase_ = field(
default=UpperCamelCase_ , metadata={'help': 'Name of TPU'} , )
lowercase_ = field(
default=0 , metadata={'help': 'CPU / GPU device index. Defaults to 0.'} , )
lowercase_ = field(default=UpperCamelCase_ , metadata={'help': 'Benchmark models in eager model.'} )
lowercase_ = field(
default=UpperCamelCase_ , metadata={
'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
} , )
@cached_property
def __lowercase( self : int )-> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
"""simple docstring"""
requires_backends(self , ['tf'] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.tpu:
try:
if self.tpu_name:
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
SCREAMING_SNAKE_CASE__ : Dict = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
SCREAMING_SNAKE_CASE__ : Optional[int] = None
return tpu
@cached_property
def __lowercase( self : Any )-> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
"""simple docstring"""
requires_backends(self , ['tf'] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
SCREAMING_SNAKE_CASE__ : Any = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , 'GPU' )
SCREAMING_SNAKE_CASE__ : Tuple = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , 'GPU' ) # disable GPU
SCREAMING_SNAKE_CASE__ : int = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def __lowercase( self : List[str] )-> bool:
"""simple docstring"""
requires_backends(self , ['tf'] )
return self._setup_tpu is not None
@property
def __lowercase( self : List[Any] )-> "tf.distribute.Strategy":
"""simple docstring"""
requires_backends(self , ['tf'] )
return self._setup_strategy
@property
def __lowercase( self : Any )-> Optional[Any]:
"""simple docstring"""
requires_backends(self , ['tf'] )
return tf.config.list_physical_devices('GPU' )
@property
def __lowercase( self : Optional[Any] )-> int:
"""simple docstring"""
requires_backends(self , ['tf'] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def __lowercase( self : Optional[Any] )-> bool:
"""simple docstring"""
return self.n_gpu > 0
| 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | 1 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "Input must be a string of 8 numbers plus letter"
SCREAMING_SNAKE_CASE__ : Optional[Any] = "TRWAGMYFPDXBNJZSQVHLCKE"
def _a ( lowercase__ : str ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = f'''Expected string as input, found {type(lowercase__ ).__name__}'''
raise TypeError(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = spanish_id.replace('-' , '' ).upper()
if len(lowercase__ ) != 9:
raise ValueError(lowercase__ )
try:
SCREAMING_SNAKE_CASE__ : List[str] = int(spanish_id_clean[0:8] )
SCREAMING_SNAKE_CASE__ : int = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(lowercase__ ) from ex
if letter.isdigit():
raise ValueError(lowercase__ )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def __lowercase( self : Any , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] , a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '</s>'
SCREAMING_SNAKE_CASE__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(a_ ) , 1103 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
SCREAMING_SNAKE_CASE__ : Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
SCREAMING_SNAKE_CASE__ : int = 'To ensure a smooth flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ['This is going to be way too long.' * 150, 'short example']
SCREAMING_SNAKE_CASE__ : int = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def __lowercase( self : Any )-> str:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[int] = PegasusTokenizer(a_ , offset=0 , mask_token_sent=a_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def __lowercase( self : List[str] , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple )-> str:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : str = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
@require_torch
def __lowercase( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ['This is going to be way too long.' * 1000, 'short example']
SCREAMING_SNAKE_CASE__ : Optional[int] = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : str = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 636 | 1 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class snake_case ( unittest.TestCase ):
def __lowercase( self : Union[str, Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
SCREAMING_SNAKE_CASE__ : int = Vector()
def __lowercase( self : Any )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(a_ ) , '(0,0,0,0,0,1)' )
def __lowercase( self : Tuple )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Vector([1, 2, 3, 4] )
self.assertEqual(len(a_ ) , 4 )
def __lowercase( self : Dict )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = Vector([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Vector([1, 2, 3, 4, 5] )
SCREAMING_SNAKE_CASE__ : int = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def __lowercase( self : List[Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def __lowercase( self : Dict )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def __lowercase( self : Union[str, Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE__ : Dict = Vector([2, -1, 4] ) # for test of dot product
SCREAMING_SNAKE_CASE__ : Tuple = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def __lowercase( self : Optional[Any] )-> None:
"""simple docstring"""
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def __lowercase( self : Union[str, Any] )-> None:
"""simple docstring"""
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def __lowercase( self : Optional[Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE__ : str = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , a_ , a_ ) ) , '(3,4,7)' )
def __lowercase( self : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = Vector([1, 0, 0, 0, 0, 0] )
SCREAMING_SNAKE_CASE__ : List[Any] = x.copy()
self.assertEqual(str(a_ ) , str(a_ ) )
def __lowercase( self : List[str] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(a_ ) , '(0,1,0)' )
def __lowercase( self : Tuple )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(a_ ) )
def __lowercase( self : Optional[Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE__ : Dict = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(a_ , a_ ) )
def __lowercase( self : str )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE__ : Dict = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(a_ , a_ ) )
def __lowercase( self : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def __lowercase( self : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
SCREAMING_SNAKE_CASE__ : Tuple = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def __lowercase( self : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(a_ ) )
def __lowercase( self : Optional[Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def __lowercase( self : Tuple )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE__ : Any = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def __lowercase( self : Optional[Any] )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE__ : Dict = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def __lowercase( self : List[str] )-> None:
"""simple docstring"""
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 636 | 1 |
from __future__ import annotations
def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[list[int]] = []
create_all_state(1 , lowercase__ , lowercase__ , [] , lowercase__ )
return result
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : int , lowercase__ : list[int] , lowercase__ : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(lowercase__ , total_number - level + 2 ):
current_list.append(lowercase__ )
create_all_state(i + 1 , lowercase__ , level - 1 , lowercase__ , lowercase__ )
current_list.pop()
def _a ( lowercase__ : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 4
SCREAMING_SNAKE_CASE__ : Dict = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = generate_all_combinations(n, k)
print_all_state(total_list)
| 636 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = 3_84
if "tiny" in model_name:
SCREAMING_SNAKE_CASE__ : List[str] = [3, 3, 9, 3]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
SCREAMING_SNAKE_CASE__ : Dict = [3, 3, 27, 3]
SCREAMING_SNAKE_CASE__ : Dict = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
SCREAMING_SNAKE_CASE__ : Tuple = [3, 3, 27, 3]
SCREAMING_SNAKE_CASE__ : Any = [1_28, 2_56, 5_12, 10_24]
SCREAMING_SNAKE_CASE__ : List[Any] = 5_12
if "large" in model_name:
SCREAMING_SNAKE_CASE__ : List[Any] = [3, 3, 27, 3]
SCREAMING_SNAKE_CASE__ : Any = [1_92, 3_84, 7_68, 15_36]
SCREAMING_SNAKE_CASE__ : List[str] = 7_68
if "xlarge" in model_name:
SCREAMING_SNAKE_CASE__ : Any = [3, 3, 27, 3]
SCREAMING_SNAKE_CASE__ : Dict = [2_56, 5_12, 10_24, 20_48]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 10_24
# set label information
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1_50
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'ade20k-id2label.json'
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Optional[Any] = ConvNextConfig(
depths=lowercase__ , hidden_sizes=lowercase__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = UperNetConfig(
backbone_config=lowercase__ , auxiliary_in_channels=lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ , )
return config
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.stages.{i}.{j}.gamma''', f'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.depthwise_conv.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.norm.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((f'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', f'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((f'''backbone.downsample_layers.{i}.0.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.0.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.weight''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((f'''backbone.downsample_layers.{i}.1.bias''', f'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def _a ( lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = val
def _a ( lowercase__ : int , lowercase__ : List[str] , lowercase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_name_to_url[model_name]
SCREAMING_SNAKE_CASE__ : List[Any] = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : List[Any] = get_upernet_config(lowercase__ )
SCREAMING_SNAKE_CASE__ : int = UperNetForSemanticSegmentation(lowercase__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(lowercase__ )
if "bn" in key:
SCREAMING_SNAKE_CASE__ : int = key.replace('bn' , 'batch_norm' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = val
# rename keys
SCREAMING_SNAKE_CASE__ : List[Any] = create_rename_keys(lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ )
# verify on image
SCREAMING_SNAKE_CASE__ : List[Any] = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
SCREAMING_SNAKE_CASE__ : int = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw ).convert('RGB' )
SCREAMING_SNAKE_CASE__ : List[str] = SegformerImageProcessor()
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(lowercase__ )
if model_name == "upernet-convnext-tiny":
SCREAMING_SNAKE_CASE__ : Any = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , lowercase__ , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(lowercase__ )
if push_to_hub:
print(f'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(f'''openmmlab/{model_name}''' )
processor.push_to_hub(f'''openmmlab/{model_name}''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="upernet-convnext-tiny",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["tiny", "small", "base", "large", "xlarge"]],
help="Name of the ConvNext UperNet model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
SCREAMING_SNAKE_CASE__ : int = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[int] , a_ : Dict=7 , a_ : Any=3 , a_ : Any=18 , a_ : int=30 , a_ : int=400 , a_ : List[Any]=None , a_ : int=True , a_ : int=True , a_ : Dict=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'height': 20, 'width': 20}
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : List[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_convert_rgb
SCREAMING_SNAKE_CASE__ : List[str] = [512, 1024, 2048, 4096]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(a_ , stream=a_ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = PixaStructImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.prepare_dummy_image()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE__ : List[Any] = 2048
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(a_ , return_tensors='pt' , max_patches=a_ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
SCREAMING_SNAKE_CASE__ : int = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
SCREAMING_SNAKE_CASE__ : List[Any] = 'Hello'
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processor(
a_ , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : int = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = PixaStructImageProcessingTester(self , num_channels=4 )
SCREAMING_SNAKE_CASE__ : Dict = 3
@property
def __lowercase( self : Any )-> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | 1 |
import os
def _a ( lowercase__ : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) as input_file:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
[int(lowercase__ ) for element in line.split(',' )]
for line in input_file.readlines()
]
SCREAMING_SNAKE_CASE__ : List[Any] = len(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = len(matrix[0] )
SCREAMING_SNAKE_CASE__ : int = [[-1 for _ in range(lowercase__ )] for _ in range(lowercase__ )]
for i in range(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Dict = matrix[i][0]
for j in range(1 , lowercase__ ):
for i in range(lowercase__ ):
SCREAMING_SNAKE_CASE__ : str = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
SCREAMING_SNAKE_CASE__ : int = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 636 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | 1 |
import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'deta'
lowercase_ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : List[str] , a_ : Tuple=None , a_ : Any=900 , a_ : Tuple=2048 , a_ : Union[str, Any]=6 , a_ : List[str]=2048 , a_ : int=8 , a_ : Tuple=6 , a_ : List[Any]=1024 , a_ : Dict=8 , a_ : Any=0.0 , a_ : Union[str, Any]=True , a_ : List[Any]="relu" , a_ : Optional[Any]=256 , a_ : Any=0.1 , a_ : str=0.0 , a_ : Union[str, Any]=0.0 , a_ : Tuple=0.02 , a_ : Union[str, Any]=1.0 , a_ : Tuple=True , a_ : Dict=False , a_ : int="sine" , a_ : str=5 , a_ : Any=4 , a_ : int=4 , a_ : List[Any]=True , a_ : List[Any]=300 , a_ : Dict=True , a_ : str=True , a_ : Optional[int]=1 , a_ : str=5 , a_ : Tuple=2 , a_ : List[Any]=1 , a_ : Dict=1 , a_ : Any=5 , a_ : Any=2 , a_ : Optional[int]=0.1 , a_ : str=0.25 , **a_ : Optional[int] , )-> List[Any]:
"""simple docstring"""
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
else:
if isinstance(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = backbone_config.pop('model_type' )
SCREAMING_SNAKE_CASE__ : List[Any] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ : Dict = config_class.from_dict(a_ )
SCREAMING_SNAKE_CASE__ : Dict = backbone_config
SCREAMING_SNAKE_CASE__ : Optional[int] = num_queries
SCREAMING_SNAKE_CASE__ : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : str = d_model
SCREAMING_SNAKE_CASE__ : Any = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[int] = encoder_layers
SCREAMING_SNAKE_CASE__ : Dict = encoder_attention_heads
SCREAMING_SNAKE_CASE__ : Any = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : List[str] = decoder_layers
SCREAMING_SNAKE_CASE__ : List[str] = decoder_attention_heads
SCREAMING_SNAKE_CASE__ : Any = dropout
SCREAMING_SNAKE_CASE__ : str = attention_dropout
SCREAMING_SNAKE_CASE__ : Any = activation_dropout
SCREAMING_SNAKE_CASE__ : Any = activation_function
SCREAMING_SNAKE_CASE__ : Dict = init_std
SCREAMING_SNAKE_CASE__ : Any = init_xavier_std
SCREAMING_SNAKE_CASE__ : Optional[int] = encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Optional[Any] = auxiliary_loss
SCREAMING_SNAKE_CASE__ : Dict = position_embedding_type
# deformable attributes
SCREAMING_SNAKE_CASE__ : str = num_feature_levels
SCREAMING_SNAKE_CASE__ : str = encoder_n_points
SCREAMING_SNAKE_CASE__ : Any = decoder_n_points
SCREAMING_SNAKE_CASE__ : Optional[int] = two_stage
SCREAMING_SNAKE_CASE__ : Dict = two_stage_num_proposals
SCREAMING_SNAKE_CASE__ : List[str] = with_box_refine
SCREAMING_SNAKE_CASE__ : Any = assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.' )
# Hungarian matcher
SCREAMING_SNAKE_CASE__ : Optional[Any] = class_cost
SCREAMING_SNAKE_CASE__ : Dict = bbox_cost
SCREAMING_SNAKE_CASE__ : Dict = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ : str = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ : str = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ : Optional[Any] = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ : int = eos_coefficient
SCREAMING_SNAKE_CASE__ : int = focal_alpha
super().__init__(is_encoder_decoder=a_ , **a_ )
@property
def __lowercase( self : Optional[Any] )-> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def __lowercase( self : str )-> int:
"""simple docstring"""
return self.d_model
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__class__.model_type
return output
| 636 | class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 636 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
SCREAMING_SNAKE_CASE__ : List[Any] = s_dict.pop(lowercase__ )
elif "subsample" in key:
SCREAMING_SNAKE_CASE__ : Optional[Any] = s_dict.pop(lowercase__ )
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = emb.weight.shape
SCREAMING_SNAKE_CASE__ : int = nn.Linear(lowercase__ , lowercase__ , bias=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = emb.weight.data
return lin_layer
def _a ( lowercase__ : List[str] , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.load(lowercase__ , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = mam_aaa['args']
SCREAMING_SNAKE_CASE__ : Dict = mam_aaa['model']
SCREAMING_SNAKE_CASE__ : List[str] = state_dict['decoder.output_projection.weight']
remove_ignore_keys_(lowercase__ )
rename_keys(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = state_dict['decoder.embed_tokens.weight'].shape[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = args.share_decoder_input_output_embed
SCREAMING_SNAKE_CASE__ : Optional[int] = [int(lowercase__ ) for i in args.conv_kernel_sizes.split(',' )]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SpeechaTextConfig(
vocab_size=lowercase__ , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , num_conv_layers=len(lowercase__ ) , conv_channels=args.conv_channels , conv_kernel_sizes=lowercase__ , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=lowercase__ , num_beams=5 , max_length=2_00 , use_cache=lowercase__ , decoder_start_token_id=2 , early_stopping=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Any = SpeechaTextForConditionalGeneration(lowercase__ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = model.model.load_state_dict(lowercase__ , strict=lowercase__ )
if len(lowercase__ ) > 0 and not set(lowercase__ ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
f''' but all the following weights are missing {missing}''' )
if tie_embeds:
SCREAMING_SNAKE_CASE__ : Optional[int] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm_head_weights
model.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 636 | from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__ : str = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self : Optional[int] , *lowercase__ : int , **lowercase__ : Tuple ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 636 | 1 |
from __future__ import annotations
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0' )
if daily_interest_rate < 0:
raise ValueError('daily_interest_rate must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return principal * daily_interest_rate * days_between_payments
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ):
'''simple docstring'''
if number_of_compounding_periods <= 0:
raise ValueError('number_of_compounding_periods must be > 0' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('nominal_annual_interest_rate_percentage must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def _a ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ):
'''simple docstring'''
if number_of_years <= 0:
raise ValueError('number_of_years must be > 0' )
if nominal_annual_percentage_rate < 0:
raise ValueError('nominal_annual_percentage_rate must be >= 0' )
if principal <= 0:
raise ValueError('principal must be > 0' )
return compound_interest(
lowercase__ , nominal_annual_percentage_rate / 3_65 , number_of_years * 3_65 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
SCREAMING_SNAKE_CASE__ : Optional[Any] = pytest.mark.integration
SCREAMING_SNAKE_CASE__ : int = {"comet"}
SCREAMING_SNAKE_CASE__ : Optional[int] = importlib.util.find_spec("fairseq") is not None
SCREAMING_SNAKE_CASE__ : List[str] = {"code_eval"}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.name == "nt"
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"bertscore", "frugalscore", "perplexity"}
SCREAMING_SNAKE_CASE__ : Tuple = importlib.util.find_spec("transformers") is not None
def _a ( lowercase__ : Union[str, Any] ):
'''simple docstring'''
@wraps(lowercase__ )
def wrapper(self : Any , lowercase__ : List[Any] ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('"test requires Fairseq"' )
else:
test_case(self , lowercase__ )
return wrapper
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
@wraps(lowercase__ )
def wrapper(self : Optional[Any] , lowercase__ : int ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('"test requires transformers"' )
else:
test_case(self , lowercase__ )
return wrapper
def _a ( lowercase__ : str ):
'''simple docstring'''
@wraps(lowercase__ )
def wrapper(self : Any , lowercase__ : Dict ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('"test not supported on Windows"' )
else:
test_case(self , lowercase__ )
return wrapper
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@local
class snake_case ( parameterized.TestCase ):
lowercase_ = {}
lowercase_ = None
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning' )
def __lowercase( self : List[Any] , a_ : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '[...]'
SCREAMING_SNAKE_CASE__ : List[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , a_ ) ).module_path )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = datasets.load.import_main_class(metric_module.__name__ , dataset=a_ )
# check parameters
SCREAMING_SNAKE_CASE__ : List[str] = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(a_ , metric_module.__name__ ):
with self.use_local_metrics():
try:
SCREAMING_SNAKE_CASE__ : Optional[int] = doctest.testmod(a_ , verbose=a_ , raise_on_error=a_ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def __lowercase( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = '[...]'
SCREAMING_SNAKE_CASE__ : str = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , a_ ) ).module_path )
# run doctest
with self.use_local_metrics():
SCREAMING_SNAKE_CASE__ : List[Any] = doctest.testmod(a_ , verbose=a_ , raise_on_error=a_ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def __lowercase( self : List[Any] , a_ : Optional[Any] , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](a_ ):
yield
else:
yield
@contextmanager
def __lowercase( self : List[str] )-> Tuple:
"""simple docstring"""
def load_local_metric(a_ : Any , *a_ : str , **a_ : Optional[int] ):
return load_metric(os.path.join('metrics' , a_ ) , *a_ , **a_ )
with patch('datasets.load_metric' ) as mock_load_metric:
SCREAMING_SNAKE_CASE__ : Tuple = load_local_metric
yield
@classmethod
def __lowercase( cls : Optional[int] , a_ : List[str] )-> List[str]:
"""simple docstring"""
def wrapper(a_ : str ):
SCREAMING_SNAKE_CASE__ : Optional[int] = contextmanager(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def _a ( lowercase__ : str ):
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('sv' , '' , '' ) # handle pytest cli flags
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Any , a_ : Optional[Any] )-> str:
"""simple docstring"""
assert len(input_dict['input_ids'] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
SCREAMING_SNAKE_CASE__ : int = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def _a ( lowercase__ : Dict ):
'''simple docstring'''
import torch
def bert_cos_score_idf(lowercase__ : Optional[Any] , lowercase__ : Dict , *lowercase__ : Dict , **lowercase__ : List[str] ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase__ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('bert_score.scorer.get_model' ), patch(
'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
SCREAMING_SNAKE_CASE__ : Optional[int] = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('comet' )
def _a ( lowercase__ : int ):
'''simple docstring'''
def load_from_checkpoint(lowercase__ : Optional[int] ):
class snake_case :
def __lowercase( self : Tuple , a_ : List[str] , *a_ : Optional[Any] , **a_ : str )-> Optional[Any]:
"""simple docstring"""
assert len(a_ ) == 2
SCREAMING_SNAKE_CASE__ : Any = [0.19, 0.92]
return scores, sum(a_ ) / len(a_ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('comet.download_model' ) as mock_download_model:
SCREAMING_SNAKE_CASE__ : Tuple = None
with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
SCREAMING_SNAKE_CASE__ : List[str] = load_from_checkpoint
yield
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = load_metric(os.path.join('metrics' , 'seqeval' ) )
SCREAMING_SNAKE_CASE__ : int = 'ERROR'
SCREAMING_SNAKE_CASE__ : str = f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(lowercase__ , match=re.escape(lowercase__ ) ):
metric.compute(predictions=[] , references=[] , scheme=lowercase__ )
| 636 | from __future__ import annotations
def _a ( lowercase__ : list[int | float] , lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
if len(lowercase__ ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(lowercase__ )
or left < -len(lowercase__ )
or right >= len(lowercase__ )
or right < -len(lowercase__ )
):
raise IndexError('list index out of range' )
if left == right:
return nums[left]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (left + right) >> 1 # the middle
SCREAMING_SNAKE_CASE__ : int = find_max(lowercase__ , lowercase__ , lowercase__ ) # find max in range[left, mid]
SCREAMING_SNAKE_CASE__ : Tuple = find_max(lowercase__ , mid + 1 , lowercase__ ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 636 | 1 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( UpperCamelCase_ ):
lowercase_ = (KDPMaDiscreteScheduler,)
lowercase_ = 10
def __lowercase( self : str , **a_ : str )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {
'num_train_timesteps': 1100,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**a_ )
return config
def __lowercase( self : Tuple )-> str:
"""simple docstring"""
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=a_ )
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=a_ , beta_end=a_ )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=a_ )
def __lowercase( self : Optional[int] )-> Tuple:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=a_ )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Any = self.get_scheduler_config(prediction_type='v_prediction' )
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE__ : List[Any] = sample.to(a_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : Optional[int] = scheduler.scale_model_input(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.step(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = output.prev_sample
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.sum(torch.abs(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = torch.mean(torch.abs(a_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.6_9_3_4e-0_7 ) < 1e-2
assert abs(result_mean.item() - 6.1_1_1_2e-1_0 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7 ) < 1e-2
assert abs(result_mean.item() - 0.0002 ) < 1e-3
def __lowercase( self : Any )-> str:
"""simple docstring"""
if torch_device == "mps":
return
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE__ : str = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE__ : Tuple = sample.to(a_ )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : Optional[int] = scheduler.scale_model_input(a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.step(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Tuple = output.prev_sample
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.sum(torch.abs(a_ ) )
SCREAMING_SNAKE_CASE__ : str = torch.mean(torch.abs(a_ ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
def __lowercase( self : int )-> int:
"""simple docstring"""
if torch_device == "mps":
return
SCREAMING_SNAKE_CASE__ : Any = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**a_ )
scheduler.set_timesteps(self.num_inference_steps , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_sample_deter.to(a_ ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.scale_model_input(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Any = model(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Dict = scheduler.step(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = output.prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(a_ ) )
SCREAMING_SNAKE_CASE__ : str = torch.mean(torch.abs(a_ ) )
if str(a_ ).startswith('cpu' ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 20.4125 ) < 1e-2
assert abs(result_mean.item() - 0.0266 ) < 1e-3
| 636 | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def _a ( lowercase__ : Any ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def _a ( lowercase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(lowercase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def _a ( lowercase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [state.process_index]
SCREAMING_SNAKE_CASE__ : Any = gather_object(lowercase__ )
assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = broadcast(lowercase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.is_main_process:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.arange(state.num_processes ).to(state.device )
SCREAMING_SNAKE_CASE__ : Any = pad_across_processes(lowercase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : List[Any] = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = reduce(lowercase__ , 'sum' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
if state.num_processes != 2:
return
SCREAMING_SNAKE_CASE__ : Any = create_tensor(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = reduce(lowercase__ , 'mean' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowercase__ , lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''
def _a ( lowercase__ : int ):
'''simple docstring'''
main()
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = PartialState()
state.print(f'''State: {state}''' )
state.print('testing gather' )
test_gather(lowercase__ )
state.print('testing gather_object' )
test_gather_object(lowercase__ )
state.print('testing broadcast' )
test_broadcast(lowercase__ )
state.print('testing pad_across_processes' )
test_pad_across_processes(lowercase__ )
state.print('testing reduce_sum' )
test_reduce_sum(lowercase__ )
state.print('testing reduce_mean' )
test_reduce_mean(lowercase__ )
if __name__ == "__main__":
main()
| 636 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class snake_case ( UpperCamelCase_ ):
lowercase_ = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
lowercase_ = Features({'image': Image()} )
lowercase_ = Features({'labels': ClassLabel} )
lowercase_ = "image"
lowercase_ = "labels"
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , a_ ):
raise ValueError(F'''Column {self.label_column} is not a ClassLabel.''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = copy.deepcopy(self )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.label_schema.copy()
SCREAMING_SNAKE_CASE__ : Optional[int] = features[self.label_column]
SCREAMING_SNAKE_CASE__ : List[Any] = label_schema
return task_template
@property
def __lowercase( self : Tuple )-> Dict[str, str]:
"""simple docstring"""
return {
self.image_column: "image",
self.label_column: "labels",
}
| 636 | import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
SCREAMING_SNAKE_CASE__ : Any = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[int] , a_ : Dict=7 , a_ : Any=3 , a_ : Any=18 , a_ : int=30 , a_ : int=400 , a_ : List[Any]=None , a_ : int=True , a_ : int=True , a_ : Dict=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = size if size is not None else {'height': 20, 'width': 20}
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Any = num_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : Dict = max_resolution
SCREAMING_SNAKE_CASE__ : List[Any] = size
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_convert_rgb
SCREAMING_SNAKE_CASE__ : List[str] = [512, 1024, 2048, 4096]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(a_ , stream=a_ ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = PixaStructImageProcessingTester(self )
@property
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.prepare_dummy_image()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
SCREAMING_SNAKE_CASE__ : List[Any] = 2048
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(a_ , return_tensors='pt' , max_patches=a_ )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1e-3 , rtol=1e-3 ) )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
SCREAMING_SNAKE_CASE__ : int = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(a_ ):
SCREAMING_SNAKE_CASE__ : Dict = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
SCREAMING_SNAKE_CASE__ : List[Any] = 'Hello'
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Any = image_processor(
a_ , return_tensors='pt' , max_patches=a_ , header_text=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
SCREAMING_SNAKE_CASE__ : str = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : int = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Any = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PixaStructImageProcessor if is_vision_available() else None
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = PixaStructImageProcessingTester(self , num_channels=4 )
SCREAMING_SNAKE_CASE__ : Dict = 3
@property
def __lowercase( self : Any )-> Any:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase( self : Dict )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , 'do_normalize' ) )
self.assertTrue(hasattr(a_ , 'do_convert_rgb' ) )
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
# Initialize image_processor
SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Tuple = image_processor(
a_ , return_tensors='pt' , max_patches=a_ ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 636 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | import heapq as hq
import math
from collections.abc import Iterator
class snake_case :
def __init__( self : str , a_ : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = str(id_ )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {} # {vertex:distance}
def __lt__( self : int , a_ : Tuple )-> Union[str, Any]:
"""simple docstring"""
return self.key < other.key
def __repr__( self : Any )-> Dict:
"""simple docstring"""
return self.id
def __lowercase( self : Optional[Any] , a_ : int )-> List[str]:
"""simple docstring"""
self.neighbors.append(a_ )
def __lowercase( self : int , a_ : int , a_ : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = weight
def _a ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : Tuple , lowercase__ : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , lowercase__ )
graph[b - 1].add_edge(graph[a - 1] , lowercase__ )
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
for u in graph:
SCREAMING_SNAKE_CASE__ : Dict = math.inf
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : int = graph[:]
while q:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(lowercase__ )
q.remove(lowercase__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : int = u
SCREAMING_SNAKE_CASE__ : Any = u.edges[v.id]
for i in range(1 , len(lowercase__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def _a ( lowercase__ : list , lowercase__ : Vertex ):
'''simple docstring'''
for u in graph:
SCREAMING_SNAKE_CASE__ : List[str] = math.inf
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = list(lowercase__ )
hq.heapify(lowercase__ )
while h:
SCREAMING_SNAKE_CASE__ : Optional[int] = hq.heappop(lowercase__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
SCREAMING_SNAKE_CASE__ : List[str] = u
SCREAMING_SNAKE_CASE__ : Dict = u.edges[v.id]
hq.heapify(lowercase__ )
for i in range(1 , len(lowercase__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def _a ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 636 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__ : str = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase__ ) < version.parse('0.17.0' ):
return method
def wrapper(self : Optional[int] , *lowercase__ : int , **lowercase__ : Tuple ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase__ , **lowercase__ )
return wrapper
| 636 | def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def _a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 636 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | from math import factorial, radians
def _a ( lowercase__ : float , lowercase__ : int = 18 , lowercase__ : int = 10 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE__ : int = radians(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = angle_in_radians
SCREAMING_SNAKE_CASE__ : Optional[int] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = -1
for _ in range(lowercase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase__ , lowercase__ )
if __name__ == "__main__":
__import__("doctest").testmod()
| 636 | 1 |
from __future__ import annotations
class snake_case :
def __init__( self : Any , a_ : int )-> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = data
SCREAMING_SNAKE_CASE__ : Node | None = None
SCREAMING_SNAKE_CASE__ : Node | None = None
def _a ( lowercase__ : Node | None ): # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def _a ( lowercase__ : Node | None ):
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def _a ( lowercase__ : Node ):
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def _a ( ): # Main function for testing.
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Node(1 )
SCREAMING_SNAKE_CASE__ : Any = Node(2 )
SCREAMING_SNAKE_CASE__ : str = Node(3 )
SCREAMING_SNAKE_CASE__ : int = Node(4 )
SCREAMING_SNAKE_CASE__ : Optional[int] = Node(5 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Node(6 )
SCREAMING_SNAKE_CASE__ : Dict = Node(7 )
SCREAMING_SNAKE_CASE__ : List[str] = Node(8 )
SCREAMING_SNAKE_CASE__ : int = Node(9 )
print(is_full_binary_tree(lowercase__ ) )
print(depth_of_tree(lowercase__ ) )
print('Tree is: ' )
display(lowercase__ )
if __name__ == "__main__":
main()
| 636 | import math
def _a ( lowercase__ : int ):
'''simple docstring'''
assert isinstance(lowercase__ , lowercase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE__ : Tuple = range(3 , int(math.sqrt(lowercase__ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _a ( lowercase__ : List[str] , lowercase__ : Any=1 , **lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = factor * value
SCREAMING_SNAKE_CASE__ : Dict = value
while not is_prime(lowercase__ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase__ )
return value
| 636 | 1 |
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class snake_case :
def __init__( self : str , a_ : List[str] , a_ : Tuple=13 , a_ : Dict=30 , a_ : Optional[int]=2 , a_ : Tuple=3 , a_ : Dict=True , a_ : int=True , a_ : Optional[Any]=32 , a_ : List[str]=5 , a_ : Any=4 , a_ : Dict=37 , a_ : Dict="gelu" , a_ : int=0.1 , a_ : Optional[Any]=0.1 , a_ : Any=10 , a_ : List[str]=0.02 , a_ : Any=3 , a_ : List[str]=None , a_ : Optional[int]=2 , )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : int = batch_size
SCREAMING_SNAKE_CASE__ : int = image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = patch_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels
SCREAMING_SNAKE_CASE__ : int = is_training
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : List[str] = scope
SCREAMING_SNAKE_CASE__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
SCREAMING_SNAKE_CASE__ : Optional[int] = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_patches + 2
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowercase( self : List[str] , a_ : List[str] , a_ : Optional[Any] , a_ : str )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = DeiTModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : List[str] , a_ : List[str] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = DeiTForMaskedImageModeling(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[int] = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DeiTForMaskedImageModeling(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowercase( self : List[str] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : int = DeiTForImageClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = DeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
pass
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[str] = model_class(a_ )
SCREAMING_SNAKE_CASE__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , a_ )
def __lowercase( self : List[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def __lowercase( self : str , a_ : str , a_ : Tuple , a_ : Union[str, Any]=False )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(a_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
SCREAMING_SNAKE_CASE__ : Tuple = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : List[Any] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = True
for model_class in self.all_model_classes:
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = model_class(a_ )
model.gradient_checkpointing_enable()
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(**a_ ).loss
loss.backward()
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(a_ ),
*get_values(a_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
SCREAMING_SNAKE_CASE__ : int = problem_type['title']
SCREAMING_SNAKE_CASE__ : Tuple = problem_type['num_labels']
SCREAMING_SNAKE_CASE__ : str = model_class(a_ )
model.to(a_ )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(a_ , a_ , return_labels=a_ )
if problem_type["num_labels"] > 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['labels'].unsqueeze(1 ).repeat(1 , problem_type['num_labels'] )
SCREAMING_SNAKE_CASE__ : Any = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=a_ ) as warning_list:
SCREAMING_SNAKE_CASE__ : str = model(**a_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = DeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def __lowercase( self : int )-> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' ).to(
a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] = image_processor(images=a_ , return_tensors='pt' ).to(a_ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(**a_ )
# verify the logits
SCREAMING_SNAKE_CASE__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowercase( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224' , torch_dtype=torch.floataa , device_map='auto' )
SCREAMING_SNAKE_CASE__ : Dict = self.default_image_processor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = inputs.pixel_values.to(a_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] = model(a_ )
| 636 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE__ : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
SCREAMING_SNAKE_CASE__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 636 | import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[Any] , a_ : Dict , a_ : Any=13 , a_ : Any=7 , a_ : Tuple=True , a_ : Tuple=True , a_ : Optional[int]=False , a_ : Dict=True , a_ : Optional[Any]=99 , a_ : Any=32 , a_ : Dict=5 , a_ : Tuple=4 , a_ : List[str]=37 , a_ : Union[str, Any]="gelu" , a_ : Dict=0.1 , a_ : Tuple=0.1 , a_ : List[str]=512 , a_ : List[str]=16 , a_ : List[str]=2 , a_ : Optional[int]=0.02 , a_ : List[str]=3 , a_ : Union[str, Any]=4 , a_ : Optional[Any]=None , )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Dict = batch_size
SCREAMING_SNAKE_CASE__ : Dict = seq_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] = num_labels
SCREAMING_SNAKE_CASE__ : Dict = num_choices
SCREAMING_SNAKE_CASE__ : str = scope
def __lowercase( self : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : Any , a_ : str , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Tuple )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : List[Any] , a_ : Union[str, Any] , a_ : Optional[int] , a_ : Tuple , a_ : Optional[Any] , a_ : int , a_ : Optional[int] , a_ : int , a_ : str , a_ : Optional[Any] , )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BioGptForCausalLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : Optional[int] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Optional[int] , *a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(config=a_ )
model.to(a_ )
model.eval()
# create attention mask
SCREAMING_SNAKE_CASE__ : Any = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.seq_length // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = 0
# first forward pass
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ , attention_mask=a_ ).to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
SCREAMING_SNAKE_CASE__ : str = ids_tensor((1,) , a_ ).item() + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = random_other_next_tokens
# append to next input_ids and attn_mask
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Dict = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=a_ )] , dim=1 , )
# get two different outputs
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , past_key_values=a_ , attention_mask=a_ )['last_hidden_state']
# select random slice
SCREAMING_SNAKE_CASE__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[str] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : str , a_ : List[Any] , a_ : str , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any] , *a_ : List[str] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel(config=a_ ).to(a_ ).eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.ones(input_ids.shape , dtype=torch.long , device=a_ )
# first forward pass
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , use_cache=a_ )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : int = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ )['last_hidden_state']
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , past_key_values=a_ )[
'last_hidden_state'
]
# select random slice
SCREAMING_SNAKE_CASE__ : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Any = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Any , a_ : List[str] , a_ : Optional[int] , a_ : Any , a_ : Tuple , a_ : Any , *a_ : List[Any] , a_ : Union[str, Any]=False )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BioGptForCausalLM(a_ )
model.to(a_ )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ , labels=a_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def __lowercase( self : Union[str, Any] , a_ : List[str] , *a_ : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = BioGptModel(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def __lowercase( self : Dict , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : Any , a_ : str , *a_ : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.num_labels
SCREAMING_SNAKE_CASE__ : str = BioGptForTokenClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ , attention_mask=a_ , token_type_ids=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Any )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
lowercase_ = (BioGptForCausalLM,) if is_torch_available() else ()
lowercase_ = (
{
'feature-extraction': BioGptModel,
'text-classification': BioGptForSequenceClassification,
'text-generation': BioGptForCausalLM,
'token-classification': BioGptForTokenClassification,
'zero-shot': BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : List[str] = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : int )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a_ )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*a_ , gradient_checkpointing=a_ )
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a_ )
def __lowercase( self : Any )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*a_ )
def __lowercase( self : str )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*a_ )
@slow
def __lowercase( self : List[str] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = 'left'
# Define PAD Token = EOS Token = 50256
SCREAMING_SNAKE_CASE__ : Any = tokenizer.eos_token
SCREAMING_SNAKE_CASE__ : Tuple = model.config.eos_token_id
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'Hello, my dog is a little',
'Today, I',
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer(a_ , return_tensors='pt' , padding=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['input_ids'].to(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = model.generate(
input_ids=a_ , attention_mask=inputs['attention_mask'].to(a_ ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = model.generate(input_ids=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids=a_ , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.batch_decode(a_ , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.decode(output_non_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = [
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(a_ , a_ )
self.assertListEqual(a_ , [non_padded_sentence, padded_sentence] )
@slow
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def __lowercase( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : int = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : str = 3
SCREAMING_SNAKE_CASE__ : Any = 'multi_label_classification'
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_dict['input_ids']
SCREAMING_SNAKE_CASE__ : Any = input_ids.ne(1 ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(a_ , attention_mask=a_ , labels=a_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : List[str] = 4_2384
SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : int = torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BioGptTokenizer.from_pretrained('microsoft/biogpt' )
SCREAMING_SNAKE_CASE__ : Dict = BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer('COVID-19 is' , return_tensors='pt' ).to(a_ )
SCREAMING_SNAKE_CASE__ : int = model.generate(
**a_ , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=a_ , )
SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_ids[0] , skip_special_tokens=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(a_ , a_ )
| 636 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE__ : List[Any] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
SCREAMING_SNAKE_CASE__ : int = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
SCREAMING_SNAKE_CASE__ : Any = "▁"
# Segments (not really needed)
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2
SCREAMING_SNAKE_CASE__ : List[str] = 3
SCREAMING_SNAKE_CASE__ : Tuple = 4
class snake_case ( UpperCamelCase_ ):
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = 'left'
lowercase_ = XLNetTokenizer
def __init__( self : Union[str, Any] , a_ : int=None , a_ : Dict=None , a_ : Optional[Any]=False , a_ : Tuple=True , a_ : Dict=False , a_ : str="<s>" , a_ : str="</s>" , a_ : int="<unk>" , a_ : Tuple="<sep>" , a_ : Union[str, Any]="<pad>" , a_ : List[str]="<cls>" , a_ : List[Any]="<mask>" , a_ : List[str]=["<eop>", "<eod>"] , **a_ : int , )-> Optional[Any]:
"""simple docstring"""
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : Any = AddedToken(a_ , lstrip=a_ , rstrip=a_ ) if isinstance(a_ , a_ ) else mask_token
super().__init__(
vocab_file=a_ , tokenizer_file=a_ , do_lower_case=a_ , remove_space=a_ , keep_accents=a_ , bos_token=a_ , eos_token=a_ , unk_token=a_ , sep_token=a_ , pad_token=a_ , cls_token=a_ , mask_token=a_ , additional_special_tokens=a_ , **a_ , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 3
SCREAMING_SNAKE_CASE__ : List[Any] = do_lower_case
SCREAMING_SNAKE_CASE__ : str = remove_space
SCREAMING_SNAKE_CASE__ : Tuple = keep_accents
SCREAMING_SNAKE_CASE__ : str = vocab_file
SCREAMING_SNAKE_CASE__ : Any = False if not self.vocab_file else True
def __lowercase( self : Any , a_ : List[int] , a_ : Optional[List[int]] = None )-> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowercase( self : str , a_ : List[int] , a_ : Optional[List[int]] = None )-> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Dict = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowercase( self : List[Any] , a_ : str , a_ : Optional[str] = None )-> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(a_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
a_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ):
copyfile(self.vocab_file , a_ )
return (out_vocab_file,)
| 636 | import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : Optional[Any] = random.Random()
def _a ( lowercase__ : List[str] , lowercase__ : List[Any]=1.0 , lowercase__ : Optional[int]=None , lowercase__ : List[str]=None ):
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = global_rng
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class snake_case ( unittest.TestCase ):
def __init__( self : List[Any] , a_ : Optional[Any] , a_ : Union[str, Any]=7 , a_ : Any=400 , a_ : List[Any]=2000 , a_ : Tuple=1 , a_ : Optional[int]=0.0 , a_ : Optional[Any]=1_6000 , a_ : str=True , a_ : Union[str, Any]=80 , a_ : Dict=16 , a_ : Tuple=64 , a_ : Any="hann_window" , a_ : Union[str, Any]=80 , a_ : List[Any]=7600 , a_ : Optional[Any]=1e-1_0 , a_ : Dict=True , )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : str = min_seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = max_seq_length
SCREAMING_SNAKE_CASE__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE__ : int = feature_size
SCREAMING_SNAKE_CASE__ : str = padding_value
SCREAMING_SNAKE_CASE__ : Any = sampling_rate
SCREAMING_SNAKE_CASE__ : Optional[int] = do_normalize
SCREAMING_SNAKE_CASE__ : int = num_mel_bins
SCREAMING_SNAKE_CASE__ : int = hop_length
SCREAMING_SNAKE_CASE__ : str = win_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = win_function
SCREAMING_SNAKE_CASE__ : List[str] = fmin
SCREAMING_SNAKE_CASE__ : Dict = fmax
SCREAMING_SNAKE_CASE__ : int = mel_floor
SCREAMING_SNAKE_CASE__ : Tuple = return_attention_mask
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __lowercase( self : List[Any] , a_ : str=False , a_ : List[Any]=False )-> Optional[Any]:
"""simple docstring"""
def _flatten(a_ : int ):
return list(itertools.chain(*a_ ) )
if equal_length:
SCREAMING_SNAKE_CASE__ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Optional[int] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : int = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
def __lowercase( self : Any , a_ : int=False , a_ : Any=False )-> Union[str, Any]:
"""simple docstring"""
if equal_length:
SCREAMING_SNAKE_CASE__ : str = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE__ : List[str] = [np.asarray(a_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = SpeechTaFeatureExtractor
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = SpeechTaFeatureExtractionTester(self )
def __lowercase( self : Any , a_ : Optional[int] )-> List[str]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(a_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(a_ , axis=0 ) - 1 ) < 1e-3 ) )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : List[Any] = feat_extract(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Tuple = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : str = feat_extract(a_ , padding=a_ , max_length=a_ , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : List[Any] = range(800 , 1400 , 200 )
SCREAMING_SNAKE_CASE__ : int = [floats_list((1, x) )[0] for x in lengths]
SCREAMING_SNAKE_CASE__ : int = ['longest', 'max_length', 'do_not_pad']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(a_ , a_ ):
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(a_ , max_length=a_ , padding=a_ )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __lowercase( self : int )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='max_length' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : List[str] = feat_extract(
a_ , truncation=a_ , max_length=1000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
SCREAMING_SNAKE_CASE__ : Optional[int] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : str = feat_extract(
a_ , truncation=a_ , max_length=2000 , padding='longest' , return_tensors='np' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.random.rand(100 ).astype(np.floataa )
SCREAMING_SNAKE_CASE__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE__ : Any = feature_extractor.pad([{'input_values': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor.pad([{'input_values': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
# Tests that all call wrap to encode_plus and batch_encode_plus
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
SCREAMING_SNAKE_CASE__ : Dict = [np.asarray(a_ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(audio_target=a_ , padding=a_ , return_tensors='np' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Tuple = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : int = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_values
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : Optional[Any] = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE__ : List[str] = np.asarray(a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = feature_extractor(a_ , return_tensors='np' ).input_values
SCREAMING_SNAKE_CASE__ : str = feature_extractor(a_ , return_tensors='np' ).input_values
for enc_seq_a, enc_seq_a in zip(a_ , a_ ):
self.assertTrue(np.allclose(a_ , a_ , atol=1e-3 ) )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(a_ ) == len(a_ ) for x, y in zip(a_ , processed_features[input_name] ) ) )
SCREAMING_SNAKE_CASE__ : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type='np' )
SCREAMING_SNAKE_CASE__ : List[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=a_ )
SCREAMING_SNAKE_CASE__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Any = BatchFeature({input_name: speech_inputs} , tensor_type='pt' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __lowercase( self : Tuple )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.feature_extraction_class(**self.feat_extract_dict )
SCREAMING_SNAKE_CASE__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : str = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : List[Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )[input_name]
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='pt' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def __lowercase( self : Any )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Any = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Optional[int] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : Any = feat_extract.pad(a_ , padding='longest' , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , a_ )
def __lowercase( self : str )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.feat_extract_dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.feature_extraction_class(**a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
SCREAMING_SNAKE_CASE__ : Tuple = [len(a_ ) for x in speech_inputs]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = feat_extract.model_input_names[0]
SCREAMING_SNAKE_CASE__ : Dict = BatchFeature({input_name: speech_inputs} )
SCREAMING_SNAKE_CASE__ : str = min(a_ )
SCREAMING_SNAKE_CASE__ : Any = feat_extract.num_mel_bins # hack!
SCREAMING_SNAKE_CASE__ : int = feat_extract.pad(
a_ , padding='max_length' , max_length=a_ , truncation=a_ , return_tensors='np' )
self.assertIn('attention_mask' , a_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __lowercase( self : Optional[int] , a_ : List[str] )-> Any:
"""simple docstring"""
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ : int = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE__ : List[Any] = ds.sort('id' ).select(range(a_ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def __lowercase( self : List[str] )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
SCREAMING_SNAKE_CASE__ : List[str] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , a_ , atol=1e-6 ) )
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE__ : int = SpeechTaFeatureExtractor()
SCREAMING_SNAKE_CASE__ : str = feature_extractor(audio_target=a_ , return_tensors='pt' ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , a_ , atol=1e-4 ) )
| 636 | 1 |
import operator as op
def _a ( lowercase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
SCREAMING_SNAKE_CASE__ : List[Any] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': op.sub,
} # operators & their respective operation
# print table header
print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' )
print('-' * (30 + len(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
SCREAMING_SNAKE_CASE__ : Dict = stack.pop() # pop stack
# output in tabular format
print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(lowercase__ ) , sep=' | ' , )
return int(stack[0] )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Optional[Any] = input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
print("\n\tResult = ", solve(Postfix))
| 636 | import math
import sys
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ''
try:
with open(lowercase__ , 'rb' ) as binary_file:
SCREAMING_SNAKE_CASE__ : Tuple = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE__ : Tuple = f'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = {'0': '0', '1': '1'}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = '', ''
SCREAMING_SNAKE_CASE__ : Tuple = len(lowercase__ )
for i in range(len(lowercase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
SCREAMING_SNAKE_CASE__ : int = lexicon[curr_string]
result += last_match_id
SCREAMING_SNAKE_CASE__ : str = last_match_id + '0'
if math.loga(lowercase__ ).is_integer():
SCREAMING_SNAKE_CASE__ : List[str] = {}
for curr_key in list(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = lexicon.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = new_lex
SCREAMING_SNAKE_CASE__ : Any = last_match_id + '1'
index += 1
SCREAMING_SNAKE_CASE__ : Tuple = ''
return result
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = 8
try:
with open(lowercase__ , 'wb' ) as opened_file:
SCREAMING_SNAKE_CASE__ : Dict = [
to_write[i : i + byte_length]
for i in range(0 , len(lowercase__ ) , lowercase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowercase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
SCREAMING_SNAKE_CASE__ : Optional[int] = data_bits[counter:]
SCREAMING_SNAKE_CASE__ : int = data_bits[counter + 1 :]
return data_bits
def _a ( lowercase__ : str , lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = read_file_binary(lowercase__ )
SCREAMING_SNAKE_CASE__ : Dict = remove_prefix(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = decompress_data(lowercase__ )
write_file_binary(lowercase__ , lowercase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 636 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase_ = (
{
'feature-extraction': TFMobileBertModel,
'fill-mask': TFMobileBertForMaskedLM,
'question-answering': TFMobileBertForQuestionAnswering,
'text-classification': TFMobileBertForSequenceClassification,
'token-classification': TFMobileBertForTokenClassification,
'zero-shot': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase_ = False
lowercase_ = False
def __lowercase( self : List[Any] , a_ : int , a_ : str , a_ : Optional[int]=False )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if model_class in get_values(a_ ):
SCREAMING_SNAKE_CASE__ : List[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : str , a_ : str=13 , a_ : Dict=7 , a_ : Any=True , a_ : List[Any]=True , a_ : Any=True , a_ : List[str]=True , a_ : Optional[Any]=99 , a_ : Optional[int]=32 , a_ : int=32 , a_ : List[str]=2 , a_ : Dict=4 , a_ : List[str]=37 , a_ : Optional[int]="gelu" , a_ : List[str]=0.1 , a_ : str=0.1 , a_ : Any=512 , a_ : Optional[Any]=16 , a_ : Union[str, Any]=2 , a_ : Tuple=0.02 , a_ : str=3 , a_ : Optional[Any]=4 , a_ : Optional[int]=None , )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = parent
SCREAMING_SNAKE_CASE__ : Any = batch_size
SCREAMING_SNAKE_CASE__ : List[Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_input_mask
SCREAMING_SNAKE_CASE__ : List[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] = use_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = intermediate_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_act
SCREAMING_SNAKE_CASE__ : Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE__ : str = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[int] = num_labels
SCREAMING_SNAKE_CASE__ : Tuple = num_choices
SCREAMING_SNAKE_CASE__ : Tuple = scope
SCREAMING_SNAKE_CASE__ : Optional[int] = embedding_size
def __lowercase( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Tuple = MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Optional[int] , a_ : int , a_ : int , a_ : str , a_ : Dict , a_ : str , a_ : Optional[int] , a_ : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFMobileBertModel(config=a_ )
SCREAMING_SNAKE_CASE__ : int = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Tuple = model(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [input_ids, input_mask]
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowercase( self : str , a_ : List[Any] , a_ : Optional[Any] , a_ : Optional[Any] , a_ : Tuple , a_ : int , a_ : Optional[Any] , a_ : List[str] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFMobileBertForMaskedLM(config=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Any = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Tuple , a_ : List[str] , a_ : Dict , a_ : List[str] , a_ : Tuple , a_ : int , a_ : Union[str, Any] , a_ : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFMobileBertForNextSentencePrediction(config=a_ )
SCREAMING_SNAKE_CASE__ : Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Dict = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowercase( self : Union[str, Any] , a_ : Optional[int] , a_ : List[Any] , a_ : Dict , a_ : str , a_ : Optional[int] , a_ : Dict , a_ : List[Any] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = TFMobileBertForPreTraining(config=a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Optional[int] = model(a_ )
self.parent.assertEqual(
result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __lowercase( self : List[Any] , a_ : Any , a_ : Union[str, Any] , a_ : Tuple , a_ : Dict , a_ : Optional[int] , a_ : Optional[int] , a_ : List[Any] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.num_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFMobileBertForSequenceClassification(config=a_ )
SCREAMING_SNAKE_CASE__ : str = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : int = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase( self : List[Any] , a_ : Any , a_ : Dict , a_ : Tuple , a_ : Tuple , a_ : Union[str, Any] , a_ : str , a_ : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.num_choices
SCREAMING_SNAKE_CASE__ : str = TFMobileBertForMultipleChoice(config=a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = tf.tile(tf.expand_dims(a_ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : List[str] = tf.tile(tf.expand_dims(a_ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : List[str] = tf.tile(tf.expand_dims(a_ , 1 ) , (1, self.num_choices, 1) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowercase( self : Dict , a_ : Any , a_ : List[Any] , a_ : Union[str, Any] , a_ : List[Any] , a_ : Tuple , a_ : Tuple , a_ : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.num_labels
SCREAMING_SNAKE_CASE__ : Any = TFMobileBertForTokenClassification(config=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Union[str, Any] , a_ : Tuple , a_ : Tuple , a_ : List[str] , a_ : List[str] , a_ : Union[str, Any] , a_ : Optional[Any] , a_ : Tuple )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFMobileBertForQuestionAnswering(config=a_ )
SCREAMING_SNAKE_CASE__ : Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE__ : str = model(a_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowercase( self : List[str] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : List[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = TFMobileBertModelTest.TFMobileBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*a_ )
def __lowercase( self : str )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*a_ )
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a_ )
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a_ )
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*a_ )
def __lowercase( self : Dict )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*a_ )
def __lowercase( self : int )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a_ )
def __lowercase( self : Any )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*a_ )
@slow
def __lowercase( self : Dict )-> List[str]:
"""simple docstring"""
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = TFMobileBertModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_tf
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Optional[Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : Any = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1, 6, 3_0522]
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : List[str] = tf.constant(
[
[
[-4.591_9547, -9.24_8295, -9.64_5256],
[-6.730_6175, -6.44_0284, -6.605_2837],
[-7.274_3506, -6.784_7915, -6.02_4673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , a_ , atol=1e-4 )
| 636 | def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : List[Any] = set({'(', '[', '{'} )
SCREAMING_SNAKE_CASE__ : Optional[int] = set({')', ']', '}'} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {'{': '}', '[': ']', '(': ')'}
for i in range(len(lowercase__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(lowercase__ ) == 0
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = input('Enter sequence of brackets: ' )
if is_balanced(lowercase__ ):
print(lowercase__ , 'is balanced' )
else:
print(lowercase__ , 'is not balanced' )
if __name__ == "__main__":
main()
| 636 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self : List[str] , a_ : Optional[Any] , a_ : Union[str, Any]=13 , a_ : str=7 , a_ : List[str]=True , a_ : Tuple=True , a_ : str=True , a_ : Optional[Any]=True , a_ : List[str]=99 , a_ : List[str]=32 , a_ : Tuple=5 , a_ : Optional[int]=4 , a_ : List[Any]=37 , a_ : Any="gelu" , a_ : int=0.1 , a_ : str=0.1 , a_ : Optional[Any]=512 , a_ : Dict=16 , a_ : Tuple=2 , a_ : List[Any]=0.02 , a_ : Optional[int]=3 , a_ : List[str]=4 , a_ : List[str]=None , )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = parent
SCREAMING_SNAKE_CASE__ : str = batch_size
SCREAMING_SNAKE_CASE__ : int = seq_length
SCREAMING_SNAKE_CASE__ : str = is_training
SCREAMING_SNAKE_CASE__ : List[str] = use_input_mask
SCREAMING_SNAKE_CASE__ : int = use_token_type_ids
SCREAMING_SNAKE_CASE__ : str = use_labels
SCREAMING_SNAKE_CASE__ : Any = vocab_size
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : int = num_labels
SCREAMING_SNAKE_CASE__ : List[Any] = num_choices
SCREAMING_SNAKE_CASE__ : Dict = scope
def __lowercase( self : int )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : str = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowercase( self : Optional[Any] )-> Any:
"""simple docstring"""
return NystromformerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a_ , initializer_range=self.initializer_range , )
def __lowercase( self : int , a_ : str , a_ : Any , a_ : Dict , a_ : Tuple , a_ : List[str] , a_ : int , a_ : str )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = NystromformerModel(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(a_ , attention_mask=a_ , token_type_ids=a_ )
SCREAMING_SNAKE_CASE__ : str = model(a_ , token_type_ids=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowercase( self : Union[str, Any] , a_ : int , a_ : Optional[int] , a_ : List[Any] , a_ : Union[str, Any] , a_ : Any , a_ : Any , a_ : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = NystromformerForMaskedLM(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : str = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowercase( self : Dict , a_ : int , a_ : List[str] , a_ : Any , a_ : Union[str, Any] , a_ : str , a_ : int , a_ : Optional[int] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = NystromformerForQuestionAnswering(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] = model(
a_ , attention_mask=a_ , token_type_ids=a_ , start_positions=a_ , end_positions=a_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowercase( self : Dict , a_ : int , a_ : Tuple , a_ : int , a_ : Tuple , a_ : Dict , a_ : int , a_ : Optional[int] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.num_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = NystromformerForSequenceClassification(a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : Any = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase( self : List[Any] , a_ : List[Any] , a_ : int , a_ : List[Any] , a_ : int , a_ : Union[str, Any] , a_ : List[str] , a_ : Union[str, Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.num_labels
SCREAMING_SNAKE_CASE__ : int = NystromformerForTokenClassification(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] = model(a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowercase( self : Union[str, Any] , a_ : Any , a_ : Union[str, Any] , a_ : Optional[Any] , a_ : str , a_ : Any , a_ : Dict , a_ : int )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.num_choices
SCREAMING_SNAKE_CASE__ : Dict = NystromformerForMultipleChoice(config=a_ )
model.to(a_ )
model.eval()
SCREAMING_SNAKE_CASE__ : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : int = model(
a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowercase( self : Dict )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase_ = (
{
'feature-extraction': NystromformerModel,
'fill-mask': NystromformerForMaskedLM,
'question-answering': NystromformerForQuestionAnswering,
'text-classification': NystromformerForSequenceClassification,
'token-classification': NystromformerForTokenClassification,
'zero-shot': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = NystromformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[str] = ConfigTester(self , config_class=a_ , hidden_size=37 )
def __lowercase( self : Optional[Any] )-> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowercase( self : str )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[str] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : Tuple = type
self.model_tester.create_and_check_model(*a_ )
def __lowercase( self : List[str] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*a_ )
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*a_ )
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*a_ )
def __lowercase( self : List[str] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*a_ )
def __lowercase( self : List[str] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*a_ )
@slow
def __lowercase( self : str )-> Union[str, Any]:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Dict = NystromformerModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def __lowercase( self : Optional[Any] )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = NystromformerModel.from_pretrained('uw-madison/nystromformer-512' )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(a_ )[0]
SCREAMING_SNAKE_CASE__ : str = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
@slow
def __lowercase( self : List[Any] )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = 'the [MASK] of Belgium is Brussels'
SCREAMING_SNAKE_CASE__ : Optional[Any] = AutoTokenizer.from_pretrained('uw-madison/nystromformer-512' )
SCREAMING_SNAKE_CASE__ : Optional[Any] = NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512' )
SCREAMING_SNAKE_CASE__ : str = tokenizer(a_ , return_tensors='pt' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : str = model(encoding.input_ids ).logits
SCREAMING_SNAKE_CASE__ : Union[str, Any] = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(a_ ) , 'capital' )
| 636 | import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : List[Any] = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : int )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] = PegasusTokenizer(a_ )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/pegasus-large' )
def __lowercase( self : Any , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Union[str, Any] , a_ : List[Any] )-> Optional[int]:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = '</s>'
SCREAMING_SNAKE_CASE__ : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def __lowercase( self : Dict )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '</s>' )
self.assertEqual(vocab_keys[-1] , 'v' )
self.assertEqual(len(a_ ) , 1103 )
def __lowercase( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
' </s> <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : List[str] = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : Any )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
SCREAMING_SNAKE_CASE__ : Any = '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
SCREAMING_SNAKE_CASE__ : int = 'To ensure a smooth flow of bank resolutions.'
SCREAMING_SNAKE_CASE__ : List[Any] = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer([raw_input_str] , return_tensors=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __lowercase( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ['This is going to be way too long.' * 150, 'short example']
SCREAMING_SNAKE_CASE__ : int = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
@slow
def __lowercase( self : Any )-> str:
"""simple docstring"""
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='google/bigbird-pegasus-large-arxiv' , revision='ba85d0851d708441f91440d509690f1ab6353415' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCamelCase_ , unittest.TestCase ):
lowercase_ = PegasusTokenizer
lowercase_ = PegasusTokenizerFast
lowercase_ = True
lowercase_ = True
def __lowercase( self : Any )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[int] = PegasusTokenizer(a_ , offset=0 , mask_token_sent=a_ , mask_token='[MASK]' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
def __lowercase( self : List[str] , **a_ : Optional[Any] )-> PegasusTokenizer:
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **a_ )
def __lowercase( self : Optional[Any] , a_ : Tuple )-> str:
"""simple docstring"""
return ("This is a test", "This is a test")
def __lowercase( self : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple = (
'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
' <pad> <pad> <pad>'
)
SCREAMING_SNAKE_CASE__ : str = rust_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
SCREAMING_SNAKE_CASE__ : str = py_tokenizer([raw_input_str] , return_tensors=a_ , add_special_tokens=a_ ).input_ids[0]
self.assertListEqual(a_ , a_ )
@require_torch
def __lowercase( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ['This is going to be way too long.' * 1000, 'short example']
SCREAMING_SNAKE_CASE__ : Optional[int] = ['not super long but more than 5 tokens', 'tiny']
SCREAMING_SNAKE_CASE__ : str = self._large_tokenizer(a_ , padding=a_ , truncation=a_ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : int = self._large_tokenizer(
text_target=a_ , max_length=5 , padding=a_ , truncation=a_ , return_tensors='pt' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(a_ ) == 2 # input_ids, attention_mask.
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = (
'This is an example string that is used to test the original TF implementation against the HF'
' implementation'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._large_tokenizer(a_ ).input_ids
self.assertListEqual(
a_ , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 636 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _a ( lowercase__ : Dict , lowercase__ : str=10_00 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
SCREAMING_SNAKE_CASE__ : Dict = n - 1
SCREAMING_SNAKE_CASE__ : List[str] = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
SCREAMING_SNAKE_CASE__ : int = 0
while count < prec:
SCREAMING_SNAKE_CASE__ : str = random.randint(2 , n - 1 )
SCREAMING_SNAKE_CASE__ : Dict = bin_exp_mod(lowercase__ , lowercase__ , lowercase__ )
if b != 1:
SCREAMING_SNAKE_CASE__ : List[Any] = True
for _ in range(lowercase__ ):
if b == n - 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
break
SCREAMING_SNAKE_CASE__ : Any = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[Any] = abs(int(input("Enter bound : ").strip()))
print("Here's the list of primes:")
print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 636 | def _a ( lowercase__ : int = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowercase__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 636 | 1 |
from math import ceil
def _a ( lowercase__ : int , lowercase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = list(range(0 , lowercase__ ) )
SCREAMING_SNAKE_CASE__ : List[Any] = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
SCREAMING_SNAKE_CASE__ : Any = []
for i in device_map_blocks:
if device_map_blocks.count(lowercase__ ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(lowercase__ )
# Missing blocks
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [i for i in blocks if i not in device_map_blocks]
SCREAMING_SNAKE_CASE__ : Any = [i for i in device_map_blocks if i not in blocks]
if len(lowercase__ ) != 0:
raise ValueError(
'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
' These attention blocks were specified more than once: ' + str(lowercase__ ) )
if len(lowercase__ ) != 0:
raise ValueError(
'There are attention blocks for this model that are not specified in the device_map. Add these attention '
'blocks to a device on the device_map: ' + str(lowercase__ ) )
if len(lowercase__ ) != 0:
raise ValueError(
'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
+ str(lowercase__ ) )
def _a ( lowercase__ : str , lowercase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = list(range(lowercase__ ) )
SCREAMING_SNAKE_CASE__ : List[Any] = int(ceil(n_layers / len(lowercase__ ) ) )
SCREAMING_SNAKE_CASE__ : Tuple = [layers[i : i + n_blocks] for i in range(0 , lowercase__ , lowercase__ )]
return dict(zip(lowercase__ , lowercase__ ) )
| 636 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
def _a ( lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any]=False , lowercase__ : str=False , lowercase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _a ( lowercase__ : List[str] , lowercase__ : Dict ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
SCREAMING_SNAKE_CASE__ : Dict = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Any = state_dict.pop(f'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Tuple = in_proj_bias[-config.hidden_size :]
def _a ( lowercase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : int , lowercase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = dct.pop(lowercase__ )
SCREAMING_SNAKE_CASE__ : Any = val
@torch.no_grad()
def _a ( lowercase__ : Dict , lowercase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : Tuple = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : str = False
if "vqa" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : str = 31_29
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : int = 'vqa2-id2label.json'
SCREAMING_SNAKE_CASE__ : str = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type='dataset' ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Dict = idalabel
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[str] = ViltForQuestionAnswering(lowercase__ )
elif "nlvr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] = True
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Dict = {0: 'False', 1: 'True'}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in config.idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = 3
SCREAMING_SNAKE_CASE__ : int = ViltForImagesAndTextClassification(lowercase__ )
elif "irtr" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = True
SCREAMING_SNAKE_CASE__ : str = ViltForImageAndTextRetrieval(lowercase__ )
elif "mlm_itm" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Optional[int] = ViltForMaskedLM(lowercase__ )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Any = torch.hub.load_state_dict_from_url(lowercase__ , map_location='cpu' )['state_dict']
SCREAMING_SNAKE_CASE__ : Any = create_rename_keys(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
for src, dest in rename_keys:
rename_key(lowercase__ , lowercase__ , lowercase__ )
read_in_q_k_v(lowercase__ , lowercase__ )
if mlm_model or irtr_model:
SCREAMING_SNAKE_CASE__ : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model.load_state_dict(lowercase__ , strict=lowercase__ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(lowercase__ )
# Define processor
SCREAMING_SNAKE_CASE__ : str = ViltImageProcessor(size=3_84 )
SCREAMING_SNAKE_CASE__ : List[Any] = BertTokenizer.from_pretrained('bert-base-uncased' )
SCREAMING_SNAKE_CASE__ : List[Any] = ViltProcessor(lowercase__ , lowercase__ )
# Forward pass on example inputs (image + text)
if nlvr_model:
SCREAMING_SNAKE_CASE__ : List[str] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Any = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=lowercase__ ).raw )
SCREAMING_SNAKE_CASE__ : Tuple = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
SCREAMING_SNAKE_CASE__ : List[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[str] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : List[Any] = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=lowercase__ ).raw )
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'a bunch of [MASK] laying on a [MASK].'
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'How many cats are there?'
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(lowercase__ , lowercase__ , return_tensors='pt' )
SCREAMING_SNAKE_CASE__ : str = model(**lowercase__ )
# Verify outputs
if mlm_model:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 11, 3_05_22] )
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify masked token prediction equals "cats"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 31_29] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , lowercase__ , atol=1E-4 )
# verify vqa prediction equals "2"
SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size([1, 2] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 636 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class snake_case :
@add_start_docstrings(a_ )
def __call__( self : Optional[int] , a_ : jnp.ndarray , a_ : jnp.ndarray )-> jnp.ndarray:
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class snake_case :
@add_start_docstrings(a_ )
def __call__( self : Any , a_ : jnp.ndarray , a_ : jnp.ndarray )-> jnp.ndarray:
"""simple docstring"""
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class snake_case ( UpperCamelCase_ ):
@add_start_docstrings(a_ )
def __call__( self : Optional[Any] , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int , **a_ : Union[str, Any] )-> jnp.ndarray:
"""simple docstring"""
for processor in self:
SCREAMING_SNAKE_CASE__ : List[str] = inspect.signature(processor.__call__ ).parameters
if len(a_ ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F'''Make sure that all the required parameters: {list(function_args.keys() )} for '''
F'''{processor.__class__} are passed to the logits processor.''' )
SCREAMING_SNAKE_CASE__ : int = processor(a_ , a_ , a_ , **a_ )
else:
SCREAMING_SNAKE_CASE__ : int = processor(a_ , a_ , a_ )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : float )-> str:
"""simple docstring"""
if not isinstance(a_ , a_ ) or not (temperature > 0):
raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = temperature
def __call__( self : Any , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = scores / self.temperature
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : float , a_ : float = -float('Inf' ) , a_ : int = 1 )-> Tuple:
"""simple docstring"""
if not isinstance(a_ , a_ ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}''' )
if not isinstance(a_ , a_ ) or (min_tokens_to_keep < 1):
raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = top_p
SCREAMING_SNAKE_CASE__ : Optional[int] = filter_value
SCREAMING_SNAKE_CASE__ : Optional[Any] = min_tokens_to_keep
def __call__( self : Any , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = lax.top_k(a_ , scores.shape[-1] )
SCREAMING_SNAKE_CASE__ : List[str] = jnp.full_like(a_ , self.filter_value )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.nn.softmax(a_ , axis=-1 ).cumsum(axis=-1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.roll(a_ , 1 )
score_mask |= score_mask.at[:, 0].set(a_ )
# min tokens to keep
SCREAMING_SNAKE_CASE__ : Optional[Any] = score_mask.at[:, : self.min_tokens_to_keep].set(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.where(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Any = jax.lax.sort_key_val(a_ , a_ )[-1]
return next_scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Union[str, Any] , a_ : int , a_ : float = -float('Inf' ) , a_ : int = 1 )-> Optional[Any]:
"""simple docstring"""
if not isinstance(a_ , a_ ) or top_k <= 0:
raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}''' )
SCREAMING_SNAKE_CASE__ : str = max(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Dict = filter_value
def __call__( self : Dict , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = scores.shape
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.full(batch_size * vocab_size , self.filter_value )
SCREAMING_SNAKE_CASE__ : List[Any] = min(self.top_k , scores.shape[-1] ) # Safety check
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = lax.top_k(a_ , a_ )
SCREAMING_SNAKE_CASE__ : Any = jnp.broadcast_to((jnp.arange(a_ ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
SCREAMING_SNAKE_CASE__ : Any = topk_scores.flatten()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = topk_indices.flatten() + shift
SCREAMING_SNAKE_CASE__ : Optional[int] = next_scores_flat.at[topk_indices_flat].set(a_ )
SCREAMING_SNAKE_CASE__ : str = next_scores_flat.reshape(a_ , a_ )
return next_scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : str , a_ : int )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = bos_token_id
def __call__( self : Optional[int] , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = jnp.full(scores.shape , -float('inf' ) )
SCREAMING_SNAKE_CASE__ : int = 1 - jnp.bool_(cur_len - 1 )
SCREAMING_SNAKE_CASE__ : Any = jnp.where(a_ , new_scores.at[:, self.bos_token_id].set(0 ) , a_ )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Any , a_ : int , a_ : int )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = max_length
SCREAMING_SNAKE_CASE__ : Dict = eos_token_id
def __call__( self : Tuple , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.full(scores.shape , -float('inf' ) )
SCREAMING_SNAKE_CASE__ : List[str] = 1 - jnp.bool_(cur_len - self.max_length + 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.where(a_ , new_scores.at[:, self.eos_token_id].set(0 ) , a_ )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Optional[Any] , a_ : int , a_ : int )-> Dict:
"""simple docstring"""
if not isinstance(a_ , a_ ) or min_length < 0:
raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}''' )
if not isinstance(a_ , a_ ) or eos_token_id < 0:
raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}''' )
SCREAMING_SNAKE_CASE__ : Dict = min_length
SCREAMING_SNAKE_CASE__ : Optional[Any] = eos_token_id
def __call__( self : Any , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
# create boolean flag to decide if min length penalty should be applied
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
SCREAMING_SNAKE_CASE__ : Dict = jnp.where(a_ , scores.at[:, self.eos_token_id].set(-float('inf' ) ) , a_ )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : Tuple , a_ : List[str] , a_ : Optional[int] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = list(a_ )
SCREAMING_SNAKE_CASE__ : int = begin_index
def __call__( self : List[Any] , a_ : Dict , a_ : Optional[Any] , a_ : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = 1 - jnp.bool_(cur_len - self.begin_index )
SCREAMING_SNAKE_CASE__ : Optional[int] = jnp.where(a_ , scores.at[:, self.begin_suppress_tokens].set(-float('inf' ) ) , a_ )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[Any] , a_ : list )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = list(a_ )
def __call__( self : List[Any] , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = scores.at[..., self.suppress_tokens].set(-float('inf' ) )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : List[Any] , a_ : Union[str, Any] )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = dict(a_ )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
SCREAMING_SNAKE_CASE__ : int = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
SCREAMING_SNAKE_CASE__ : List[Any] = force_token_array.at[index].set(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.intaa(a_ )
def __call__( self : str , a_ : jnp.ndarray , a_ : jnp.ndarray , a_ : int )-> jnp.ndarray:
"""simple docstring"""
def _force_token(a_ : Any ):
SCREAMING_SNAKE_CASE__ : List[str] = scores.shape[0]
SCREAMING_SNAKE_CASE__ : str = self.force_token_array[generation_idx]
SCREAMING_SNAKE_CASE__ : Tuple = jnp.ones_like(a_ , dtype=scores.dtype ) * -float('inf' )
SCREAMING_SNAKE_CASE__ : List[str] = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lax.dynamic_update_slice(a_ , a_ , (0, current_token) )
return new_scores
SCREAMING_SNAKE_CASE__ : Any = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(a_ ) , lambda: scores , ) , )
return scores
class snake_case ( UpperCamelCase_ ):
def __init__( self : int , a_ : Dict , a_ : Optional[int] , a_ : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = generate_config.eos_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] = generate_config.no_timestamps_token_id
SCREAMING_SNAKE_CASE__ : Optional[int] = generate_config.no_timestamps_token_id + 1
SCREAMING_SNAKE_CASE__ : int = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(a_ , 'max_initial_timestamp_index' ):
SCREAMING_SNAKE_CASE__ : str = generate_config.max_initial_timestamp_index
else:
SCREAMING_SNAKE_CASE__ : List[Any] = model_config.vocab_size
if self.max_initial_timestamp_index is None:
SCREAMING_SNAKE_CASE__ : str = model_config.vocab_size
def __call__( self : Union[str, Any] , a_ : List[str] , a_ : List[Any] , a_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
# suppress <|notimestamps|> which is handled by without_timestamps
SCREAMING_SNAKE_CASE__ : Tuple = scores.at[:, self.no_timestamps_token_id].set(-float('inf' ) )
def handle_pairs(a_ : int , a_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE__ : List[str] = jnp.where((cur_len - self.begin_index) >= 1 , a_ , a_ )
SCREAMING_SNAKE_CASE__ : str = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , a_ , )
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.where((cur_len - self.begin_index) < 2 , a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , a_ , a_ , )
return jnp.where(
a_ , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float('inf' ) ) , scores_k.at[: self.eos_token_id].set(-float('inf' ) ) , ) , a_ , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jax.vmap(a_ )(a_ , a_ )
SCREAMING_SNAKE_CASE__ : int = jnp.where(cur_len == self.begin_index , a_ , a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , a_ , )
SCREAMING_SNAKE_CASE__ : str = self.timestamp_begin + self.max_initial_timestamp_index
SCREAMING_SNAKE_CASE__ : List[str] = jnp.where(
a_ , scores.at[:, last_allowed + 1 :].set(-float('inf' ) ) , a_ , )
# if sum of probability over timestamps is above any other token, sample timestamp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jax.nn.log_softmax(a_ , axis=-1 )
def handle_cumulative_probs(a_ : str , a_ : Any ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float('inf' ) ) , a_ , )
SCREAMING_SNAKE_CASE__ : List[str] = jax.vmap(a_ )(a_ , a_ )
return scores
| 636 | from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class snake_case :
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42 # [batch_size x 3]
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
lowercase_ = 42
def __lowercase( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def __lowercase( self : Dict )-> Tuple:
"""simple docstring"""
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def __lowercase( self : Dict )-> Union[str, Any]:
"""simple docstring"""
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def __lowercase( self : Tuple )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.arange(self.height * self.width )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.stack(
[
pixel_indices % self.width,
torch.div(a_ , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def __lowercase( self : Any )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.shape
SCREAMING_SNAKE_CASE__ : Tuple = int(np.prod(a_ ) )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_coords()
SCREAMING_SNAKE_CASE__ : Dict = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
SCREAMING_SNAKE_CASE__ : Any = self.get_camera_rays(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = rays.view(a_ , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def __lowercase( self : Optional[Any] , a_ : torch.Tensor )-> torch.Tensor:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
SCREAMING_SNAKE_CASE__ : str = coords.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = self.resolution()
SCREAMING_SNAKE_CASE__ : str = self.fov()
SCREAMING_SNAKE_CASE__ : Any = (flat.float() / (res - 1)) * 2 - 1
SCREAMING_SNAKE_CASE__ : Any = fracs * torch.tan(fov / 2 )
SCREAMING_SNAKE_CASE__ : List[str] = fracs.view(a_ , -1 , 2 )
SCREAMING_SNAKE_CASE__ : str = (
self.z.view(a_ , 1 , 3 )
+ self.x.view(a_ , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a_ , 1 , 3 ) * fracs[:, :, 1:]
)
SCREAMING_SNAKE_CASE__ : Tuple = directions / directions.norm(dim=-1 , keepdim=a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a_ , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a_ , *a_ , 2 , 3 )
def __lowercase( self : Optional[int] , a_ : int , a_ : int )-> "DifferentiableProjectiveCamera":
"""simple docstring"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a_ , height=a_ , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( lowercase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : str = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
SCREAMING_SNAKE_CASE__ : Tuple = -z * 4
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.cross(lowercase__ , lowercase__ )
origins.append(lowercase__ )
xs.append(lowercase__ )
ys.append(lowercase__ )
zs.append(lowercase__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(lowercase__ , axis=0 ) ).float() , width=lowercase__ , height=lowercase__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(lowercase__ )) , )
| 636 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class snake_case ( UpperCamelCase_ ):
lowercase_ = ''
lowercase_ = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self : List[Any] , a_ : Optional[DatasetInfo] = None , a_ : Optional[str] = None , **a_ : List[str] , )-> Tuple:
"""simple docstring"""
super().__init__(self , **a_ )
SCREAMING_SNAKE_CASE__ : int = repo_info
SCREAMING_SNAKE_CASE__ : Any = token
SCREAMING_SNAKE_CASE__ : Any = None
def __lowercase( self : List[str] )-> int:
"""simple docstring"""
if self.dir_cache is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'name': hf_file.rfilename,
'size': None,
'type': 'file',
}
self.dir_cache.update(
{
str(a_ ): {'name': str(a_ ), 'size': None, 'type': 'directory'}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def __lowercase( self : Union[str, Any] , a_ : str , a_ : str = "rb" , **a_ : Union[str, Any] , )-> Dict:
"""simple docstring"""
if not isinstance(self.repo_info , a_ ):
raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )
SCREAMING_SNAKE_CASE__ : str = hf_hub_url(self.repo_info.id , a_ , revision=self.repo_info.sha )
return fsspec.open(
a_ , mode=a_ , headers=get_authentication_headers_for_url(a_ , use_auth_token=self.token ) , client_kwargs={'trust_env': True} , ).open()
def __lowercase( self : Dict , a_ : str , **a_ : List[Any] )-> List[str]:
"""simple docstring"""
self._get_dirs()
SCREAMING_SNAKE_CASE__ : Dict = self._strip_protocol(a_ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(a_ )
def __lowercase( self : List[Any] , a_ : str , a_ : int=False , **a_ : Optional[Any] )-> List[str]:
"""simple docstring"""
self._get_dirs()
SCREAMING_SNAKE_CASE__ : Optional[int] = PurePosixPath(path.strip('/' ) )
SCREAMING_SNAKE_CASE__ : List[str] = {}
for p, f in self.dir_cache.items():
SCREAMING_SNAKE_CASE__ : Any = PurePosixPath(p.strip('/' ) )
SCREAMING_SNAKE_CASE__ : str = p.parent
if root == path:
SCREAMING_SNAKE_CASE__ : str = f
SCREAMING_SNAKE_CASE__ : List[str] = list(paths.values() )
if detail:
return out
else:
return sorted(f['name'] for f in out )
| 636 | import requests
SCREAMING_SNAKE_CASE__ : int = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def _a ( lowercase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['articles'] , 1 ):
print(f'''{i}.) {article['title']}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 636 | 1 |
def _a ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : List[str] , lowercase__ : str , lowercase__ : Dict , lowercase__ : Union[str, Any] ):
'''simple docstring'''
if index == r:
for j in range(lowercase__ ):
print(data[j] , end=' ' )
print(' ' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
SCREAMING_SNAKE_CASE__ : int = arr[i]
combination_util(lowercase__ , lowercase__ , lowercase__ , index + 1 , lowercase__ , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def _a ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(lowercase__ , lowercase__ , lowercase__ , 0 , lowercase__ , 0 )
if __name__ == "__main__":
# Driver code to check the function above
SCREAMING_SNAKE_CASE__ : List[Any] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 636 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger()
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
def __lowercase( self : Dict , a_ : Dict , a_ : Tensor , a_ : Tensor )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = len(list(m.modules() ) ) == 1 or isinstance(a_ , nn.Convad ) or isinstance(a_ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(a_ )
def __call__( self : Tuple , a_ : Tensor )-> Any:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(a_ )
[x.remove() for x in self.handles]
return self
@property
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda a_ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class snake_case :
lowercase_ = 42
lowercase_ = 42
lowercase_ = 1
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = field(default_factory=UpperCamelCase_ )
lowercase_ = True
def __call__( self : List[Any] , a_ : Tensor )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = Tracker(self.dest )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : Optional[int] = Tracker(self.src )(a_ ).parametrized
SCREAMING_SNAKE_CASE__ : List[str] = list(filter(lambda a_ : type(a_ ) not in self.src_skip , a_ ) )
SCREAMING_SNAKE_CASE__ : Dict = list(filter(lambda a_ : type(a_ ) not in self.dest_skip , a_ ) )
if len(a_ ) != len(a_ ) and self.raise_if_mismatch:
raise Exception(
F'''Numbers of operations are different. Source module has {len(a_ )} operations while'''
F''' destination module has {len(a_ )}.''' )
for dest_m, src_m in zip(a_ , a_ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
class snake_case ( nn.Module ):
def __init__( self : List[Any] , a_ : nn.Module )-> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F'''Unexpected layer name {k}'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(a_ ) + 1
feature_blocks.append((F'''res{block_index}''', v) )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleDict(a_ )
def __lowercase( self : Tuple , a_ : Tensor )-> Dict:
"""simple docstring"""
return get_trunk_forward_outputs(
a_ , out_feat_keys=a_ , feature_blocks=self._feature_blocks , )
class snake_case ( UpperCamelCase_ ):
def __lowercase( self : Optional[Any] , a_ : str )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : Union[str, Any] , a_ : str )-> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
# default to timm!
if x not in self:
SCREAMING_SNAKE_CASE__ : Any = self.convert_name_to_timm(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = partial(lambda: (timm.create_model(a_ , pretrained=a_ ).eval(), None) )
else:
SCREAMING_SNAKE_CASE__ : List[str] = super().__getitem__(a_ )
return val
class snake_case ( UpperCamelCase_ ):
def __getitem__( self : Any , a_ : str )-> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
SCREAMING_SNAKE_CASE__ : Any = RegNetModel
else:
SCREAMING_SNAKE_CASE__ : Any = RegNetForImageClassification
return val
def _a ( lowercase__ : Any , lowercase__ : Optional[Any] , lowercase__ : List[Tuple[str, str]] ):
'''simple docstring'''
for from_key, to_key in keys:
SCREAMING_SNAKE_CASE__ : Tuple = from_state_dict[from_key].clone()
print(f'''Copied key={from_key} to={to_key}''' )
return to_state_dict
def _a ( lowercase__ : str , lowercase__ : Callable[[], nn.Module] , lowercase__ : Callable[[], nn.Module] , lowercase__ : RegNetConfig , lowercase__ : Path , lowercase__ : bool = True , ):
'''simple docstring'''
print(f'''Converting {name}...''' )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = from_model_func()
SCREAMING_SNAKE_CASE__ : int = our_model_func(lowercase__ ).eval()
SCREAMING_SNAKE_CASE__ : List[Any] = ModuleTransfer(src=lowercase__ , dest=lowercase__ , raise_if_mismatch=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(lowercase__ )
if from_state_dict is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : int = [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
SCREAMING_SNAKE_CASE__ : Optional[Any] = manually_copy_vissl_head(lowercase__ , our_model.state_dict() , lowercase__ )
our_model.load_state_dict(lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = our_model(lowercase__ , output_hidden_states=lowercase__ )
SCREAMING_SNAKE_CASE__ : Tuple = (
our_outputs.logits if isinstance(lowercase__ , lowercase__ ) else our_outputs.last_hidden_state
)
SCREAMING_SNAKE_CASE__ : List[Any] = from_model(lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = from_output[-1] if type(lowercase__ ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = our_outputs.hidden_states[-1]
assert torch.allclose(lowercase__ , lowercase__ ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=lowercase__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2_24 if 'seer' not in name else 3_84
# we can use the convnext one
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=lowercase__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=lowercase__ , )
print(f'''Pushed {name}''' )
def _a ( lowercase__ : Path , lowercase__ : str = None , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = 'imagenet-1k-id2label.json'
SCREAMING_SNAKE_CASE__ : Tuple = 10_00
SCREAMING_SNAKE_CASE__ : Tuple = (1, num_labels)
SCREAMING_SNAKE_CASE__ : str = 'huggingface/label-files'
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_labels
SCREAMING_SNAKE_CASE__ : List[str] = json.load(open(cached_download(hf_hub_url(lowercase__ , lowercase__ , repo_type='dataset' ) ) , 'r' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {int(lowercase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : str = idalabel
SCREAMING_SNAKE_CASE__ : Tuple = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any = partial(lowercase__ , num_labels=lowercase__ , idalabel=lowercase__ , labelaid=lowercase__ )
SCREAMING_SNAKE_CASE__ : List[Any] = {
'regnet-x-002': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='x' ),
'regnet-x-004': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='x' ),
'regnet-x-006': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='x' ),
'regnet-x-008': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='x' ),
'regnet-x-016': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='x' ),
'regnet-x-032': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='x' ),
'regnet-x-040': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='x' ),
'regnet-x-064': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='x' ),
'regnet-x-080': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='x' ),
'regnet-x-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='x' ),
'regnet-x-160': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='x' ),
'regnet-x-320': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='x' ),
# y variant
'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'regnet-y-004': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'regnet-y-006': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'regnet-y-008': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'regnet-y-016': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'regnet-y-032': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'regnet-y-040': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'regnet-y-064': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'regnet-y-080': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'regnet-y-120': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'regnet-y-160': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'regnet-y-320': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
SCREAMING_SNAKE_CASE__ : List[Any] = NameToOurModelFuncMap()
SCREAMING_SNAKE_CASE__ : Dict = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(lowercase__ : str , lowercase__ : Callable[[], nn.Module] ) -> Tuple[nn.Module, Dict]:
SCREAMING_SNAKE_CASE__ : str = torch.hub.load_state_dict_from_url(lowercase__ , model_dir=str(lowercase__ ) , map_location='cpu' )
SCREAMING_SNAKE_CASE__ : Tuple = model_func()
# check if we have a head, if yes add it
SCREAMING_SNAKE_CASE__ : str = files['classy_state_dict']['base_model']['model']
SCREAMING_SNAKE_CASE__ : str = model_state_dict['trunk']
model.load_state_dict(lowercase__ )
return model.eval(), model_state_dict["heads"]
# pretrained
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : int = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
# IN1K finetuned
SCREAMING_SNAKE_CASE__ : List[Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
SCREAMING_SNAKE_CASE__ : Optional[int] = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
SCREAMING_SNAKE_CASE__ : Any = partial(
lowercase__ , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=620.83 , w_m=2.52 ) ) ) , )
if model_name:
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , lowercase__ , lowercase__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
lowercase__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , lowercase__ , lowercase__ , lowercase__ , )
return config, expected_shape
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default=None,
type=str,
help=(
"The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=Path,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
default=True,
type=bool,
required=False,
help="If True, push model and image processor to the hub.",
)
SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 636 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[Any] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class snake_case ( UpperCamelCase_ ):
lowercase_ = 'vivit'
def __init__( self : Optional[int] , a_ : str=224 , a_ : Optional[int]=32 , a_ : Union[str, Any]=[2, 16, 16] , a_ : str=3 , a_ : Dict=768 , a_ : Dict=12 , a_ : Union[str, Any]=12 , a_ : Any=3072 , a_ : Union[str, Any]="gelu_fast" , a_ : List[Any]=0.0 , a_ : Tuple=0.0 , a_ : int=0.02 , a_ : Optional[int]=1e-0_6 , a_ : Dict=True , **a_ : Tuple , )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Optional[Any] = image_size
SCREAMING_SNAKE_CASE__ : Any = num_frames
SCREAMING_SNAKE_CASE__ : Tuple = tubelet_size
SCREAMING_SNAKE_CASE__ : Tuple = num_channels
SCREAMING_SNAKE_CASE__ : Any = qkv_bias
super().__init__(**a_ )
| 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'OwlViTImageProcessor'
lowercase_ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : List[str] , a_ : List[Any]=None , a_ : str=None , **a_ : Any )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , a_ , )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop('feature_extractor' )
SCREAMING_SNAKE_CASE__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(a_ , a_ )
def __call__( self : Any , a_ : Optional[int]=None , a_ : Tuple=None , a_ : List[Any]=None , a_ : Tuple="max_length" , a_ : str="np" , **a_ : Any )-> int:
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'You have to specify at least one text or query image or image. All three cannot be none.' )
if text is not None:
if isinstance(a_ , a_ ) or (isinstance(a_ , a_ ) and not isinstance(text[0] , a_ )):
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )]
elif isinstance(a_ , a_ ) and isinstance(text[0] , a_ ):
SCREAMING_SNAKE_CASE__ : Any = []
# Maximum number of queries across batch
SCREAMING_SNAKE_CASE__ : str = max([len(a_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(a_ ) != max_num_queries:
SCREAMING_SNAKE_CASE__ : Tuple = t + [' '] * (max_num_queries - len(a_ ))
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(a_ , padding=a_ , return_tensors=a_ , **a_ )
encodings.append(a_ )
else:
raise TypeError('Input text should be a string, a list of strings or a nested list of strings' )
if return_tensors == "np":
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
SCREAMING_SNAKE_CASE__ : Union[str, Any] = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ : int = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ : str = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 )
SCREAMING_SNAKE_CASE__ : Dict = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 )
else:
raise ValueError('Target return tensor type could not be returned' )
SCREAMING_SNAKE_CASE__ : Optional[int] = BatchEncoding()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids
SCREAMING_SNAKE_CASE__ : Tuple = attention_mask
if query_images is not None:
SCREAMING_SNAKE_CASE__ : Any = BatchEncoding()
SCREAMING_SNAKE_CASE__ : Dict = self.image_processor(
a_ , return_tensors=a_ , **a_ ).pixel_values
SCREAMING_SNAKE_CASE__ : Dict = query_pixel_values
if images is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor(a_ , return_tensors=a_ , **a_ )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Dict = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**a_ ) , tensor_type=a_ )
def __lowercase( self : str , *a_ : List[str] , **a_ : int )-> List[Any]:
"""simple docstring"""
return self.image_processor.post_process(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : List[str] , **a_ : str )-> Union[str, Any]:
"""simple docstring"""
return self.image_processor.post_process_object_detection(*a_ , **a_ )
def __lowercase( self : Optional[Any] , *a_ : str , **a_ : Dict )-> Optional[int]:
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*a_ , **a_ )
def __lowercase( self : Optional[int] , *a_ : Tuple , **a_ : Tuple )-> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*a_ , **a_ )
def __lowercase( self : Tuple , *a_ : Tuple , **a_ : Tuple )-> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*a_ , **a_ )
@property
def __lowercase( self : Tuple )-> Any:
"""simple docstring"""
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , a_ , )
return self.image_processor_class
@property
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , a_ , )
return self.image_processor
| 636 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.