code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from collections.abc import Sequence
def lowercase_ ( _A : Union[str, Any] = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
lowerCamelCase__ : Optional[Any] = nums[0]
for i in range(1 , len(A_ ) ):
lowerCamelCase__ : Union[str, Any] = nums[i]
lowerCamelCase__ : Optional[Any] = max(A_ , ans + num , A_ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
A : str = int(input("Enter number of elements : ").strip())
A : List[str] = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 184 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase : str = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any = ['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[int] = [
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Tuple = [
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 88 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
__A : Any = set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)
def A_ ( snake_case_ : str ,snake_case_ : int = 1 ,snake_case_ : str = "new" ,snake_case_ : list | None = None ):
'''simple docstring'''
UpperCamelCase : List[str] = wanted_data or []
if invalid_search_terms := ", ".join(sorted(set(_a ) - valid_terms ) ):
UpperCamelCase : int = f'Invalid search term: {invalid_search_terms}'
raise ValueError(_a )
UpperCamelCase : Optional[int] = requests.get(
f'https://reddit.com/r/{subreddit}/{age}.json?limit={limit}' ,headers={"""User-agent""": """A random string"""} ,)
if response.status_code == 4_2_9:
raise requests.HTTPError
UpperCamelCase : List[Any] = response.json()
if not wanted_data:
return {id_: data["data"]["children"][id_] for id_ in range(_a )}
UpperCamelCase : Dict = {}
for id_ in range(_a ):
UpperCamelCase : List[str] = {
item: data["data"]["children"][id_]["data"][item] for item in wanted_data
}
return data_dict
if __name__ == "__main__":
# If you get Error 429, that means you are rate limited.Try after some time
print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase : Optional[int] , lowercase : List[str] , lowercase : bool = True , lowercase : bool = False ):
'''simple docstring'''
_snake_case = scheduler
_snake_case = optimizers if isinstance(_UpperCAmelCase , (list, tuple) ) else [optimizers]
_snake_case = split_batches
_snake_case = step_with_optimizer
_snake_case = GradientState()
def A ( self : Optional[int] , *lowercase : int , **lowercase : str ):
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
_snake_case = AcceleratorState().num_processes
for _ in range(_UpperCAmelCase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
def A ( self : Optional[int] ):
'''simple docstring'''
return self.scheduler.get_last_lr()
def A ( self : List[str] ):
'''simple docstring'''
return self.scheduler.state_dict()
def A ( self : Optional[int] , lowercase : Optional[int] ):
'''simple docstring'''
self.scheduler.load_state_dict(_UpperCAmelCase )
def A ( self : Dict ):
'''simple docstring'''
return self.scheduler.get_lr()
def A ( self : Union[str, Any] , *lowercase : Union[str, Any] , **lowercase : List[str] ):
'''simple docstring'''
return self.scheduler.print_lr(*_UpperCAmelCase , **_UpperCAmelCase ) | 282 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class A__ ( unittest.TestCase ):
def a__ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowercase = tempfile.mkdtemp()
__lowercase = SamImageProcessor()
__lowercase = SamProcessor(_UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : int , **_UpperCAmelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).image_processor
def a__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowercase = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowercase = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
__lowercase = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def a__ ( self : int ) -> Tuple:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = self.prepare_image_inputs()
__lowercase = image_processor(_UpperCAmelCase , return_tensors='np' )
__lowercase = processor(images=_UpperCAmelCase , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_torch
def a__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = [torch.ones((1, 3, 5, 5) )]
__lowercase = [[17_64, 26_46]]
__lowercase = [[6_83, 10_24]]
__lowercase = processor.post_process_masks(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
__lowercase = processor.post_process_masks(
_UpperCAmelCase , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
# should also work with np
__lowercase = [np.ones((1, 3, 5, 5) )]
__lowercase = processor.post_process_masks(_UpperCAmelCase , np.array(_UpperCAmelCase ) , np.array(_UpperCAmelCase ) )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
__lowercase = [[1, 0], [0, 1]]
with self.assertRaises(_UpperCAmelCase ):
__lowercase = processor.post_process_masks(_UpperCAmelCase , np.array(_UpperCAmelCase ) , np.array(_UpperCAmelCase ) )
@require_vision
@require_tf
class A__ ( unittest.TestCase ):
def a__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__lowercase = tempfile.mkdtemp()
__lowercase = SamImageProcessor()
__lowercase = SamProcessor(_UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : str , **_UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).image_processor
def a__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowercase = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowercase = self.get_image_processor(do_normalize=_UpperCAmelCase , padding_value=1.0 )
__lowercase = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=_UpperCAmelCase , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _UpperCAmelCase )
def a__ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = self.prepare_image_inputs()
__lowercase = image_processor(_UpperCAmelCase , return_tensors='np' )
__lowercase = processor(images=_UpperCAmelCase , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
@require_tf
def a__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = [tf.ones((1, 3, 5, 5) )]
__lowercase = [[17_64, 26_46]]
__lowercase = [[6_83, 10_24]]
__lowercase = processor.post_process_masks(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
__lowercase = processor.post_process_masks(
_UpperCAmelCase , tf.convert_to_tensor(_UpperCAmelCase ) , tf.convert_to_tensor(_UpperCAmelCase ) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
# should also work with np
__lowercase = [np.ones((1, 3, 5, 5) )]
__lowercase = processor.post_process_masks(
_UpperCAmelCase , np.array(_UpperCAmelCase ) , np.array(_UpperCAmelCase ) , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
__lowercase = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
__lowercase = processor.post_process_masks(
_UpperCAmelCase , np.array(_UpperCAmelCase ) , np.array(_UpperCAmelCase ) , return_tensors='tf' )
@require_vision
@require_torchvision
class A__ ( unittest.TestCase ):
def a__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = tempfile.mkdtemp()
__lowercase = SamImageProcessor()
__lowercase = SamProcessor(_UpperCAmelCase )
processor.save_pretrained(self.tmpdirname )
def a__ ( self : Dict , **_UpperCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
return AutoProcessor.from_pretrained(self.tmpdirname , **_UpperCAmelCase ).image_processor
def a__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def a__ ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
__lowercase = [Image.fromarray(np.moveaxis(_UpperCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def a__ ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
__lowercase = [tf.convert_to_tensor(_UpperCAmelCase )]
__lowercase = [torch.tensor(_UpperCAmelCase )]
__lowercase = [[17_64, 26_46]]
__lowercase = [[6_83, 10_24]]
__lowercase = processor.post_process_masks(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , return_tensors='tf' )
__lowercase = processor.post_process_masks(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , return_tensors='pt' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def a__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = SamProcessor(image_processor=_UpperCAmelCase )
__lowercase = self.prepare_image_inputs()
__lowercase = image_processor(_UpperCAmelCase , return_tensors='pt' )['pixel_values'].numpy()
__lowercase = processor(images=_UpperCAmelCase , return_tensors='pt' )['pixel_values'].numpy()
__lowercase = image_processor(_UpperCAmelCase , return_tensors='tf' )['pixel_values'].numpy()
__lowercase = processor(images=_UpperCAmelCase , return_tensors='tf' )['pixel_values'].numpy()
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
| 325 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : int=1_3 , UpperCamelCase__ : Optional[Any]=7 , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : int=True , UpperCamelCase__ : str=False , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : List[Any]=9_9 , UpperCamelCase__ : Optional[int]=3_2 , UpperCamelCase__ : str=5 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : Optional[Any]=3_7 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : Dict=0.1 , UpperCamelCase__ : int=5_1_2 , UpperCamelCase__ : str=1_6 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : List[Any]=0.0_2 , UpperCamelCase__ : str=3 , UpperCamelCase__ : Union[str, Any]=4 , UpperCamelCase__ : Tuple=None , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_token_type_ids
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = num_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = None
if self.use_token_type_ids:
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Union[str, Any] ):
"""simple docstring"""
return OpenLlamaConfig(
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=UpperCamelCase__ , initializer_range=self.initializer_range , use_stable_embedding=UpperCamelCase__ , )
def A ( self : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = OpenLlamaModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
UpperCamelCase = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , ):
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = OpenLlamaModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , )
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : str , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str , ):
"""simple docstring"""
UpperCamelCase = OpenLlamaForCausalLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , ):
"""simple docstring"""
UpperCamelCase = True
UpperCamelCase = True
UpperCamelCase = OpenLlamaForCausalLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
# first forward pass
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ , )
UpperCamelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , )['hidden_states'][0]
UpperCamelCase = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , )['hidden_states'][0]
# select random slice
UpperCamelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase = 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(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE = (OpenLlamaForCausalLM,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": OpenLlamaModel,
"""text-classification""": OpenLlamaForSequenceClassification,
"""text-generation""": OpenLlamaForCausalLM,
"""zero-shot""": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def A ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = OpenLlamaModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=3_7 )
def A ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(UpperCamelCase__ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'single_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(UpperCamelCase__ )
UpperCamelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
UpperCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = 3
UpperCamelCase = 'multi_label_classification'
UpperCamelCase = input_dict['input_ids']
UpperCamelCase = input_ids.ne(1 ).to(UpperCamelCase__ )
UpperCamelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
UpperCamelCase = OpenLlamaForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def A ( self : List[str] ):
"""simple docstring"""
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def A ( self : Optional[int] , UpperCamelCase__ : Optional[int] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase = ids_tensor([1, 1_0] , config.vocab_size )
UpperCamelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = OpenLlamaModel(UpperCamelCase__ )
original_model.to(UpperCamelCase__ )
original_model.eval()
UpperCamelCase = original_model(UpperCamelCase__ ).last_hidden_state
UpperCamelCase = original_model(UpperCamelCase__ ).last_hidden_state
set_seed(4_2 ) # Fixed seed at init time so the two models get the same random weights
UpperCamelCase = {'type': scaling_type, 'factor': 1_0.0}
UpperCamelCase = OpenLlamaModel(UpperCamelCase__ )
scaled_model.to(UpperCamelCase__ )
scaled_model.eval()
UpperCamelCase = scaled_model(UpperCamelCase__ ).last_hidden_state
UpperCamelCase = scaled_model(UpperCamelCase__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
| 353 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowerCamelCase ( A__ , A__ , A__ ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')
UpperCamelCase = (
('layer.', 'layer_'),
('word_embeddings.weight', 'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'),
('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'),
('weight', 'kernel'),
)
if not os.path.isdir(A__ ):
os.makedirs(A__ )
UpperCamelCase = model.state_dict()
def to_tf_var_name(A__ ):
for patt, repl in iter(A__ ):
UpperCamelCase = name.replace(A__ , A__ )
return F"""bert/{name}"""
def create_tf_var(A__ , A__ , A__ ):
UpperCamelCase = tf.dtypes.as_dtype(tensor.dtype )
UpperCamelCase = tf.get_variable(dtype=A__ , shape=tensor.shape , name=A__ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(A__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
UpperCamelCase = to_tf_var_name(A__ )
UpperCamelCase = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
UpperCamelCase = torch_tensor.T
UpperCamelCase = create_tf_var(tensor=A__ , name=A__ , session=A__ )
tf.keras.backend.set_value(A__ , A__ )
UpperCamelCase = session.run(A__ )
print(F"""Successfully created {tf_name}: {np.allclose(A__ , A__ )}""" )
UpperCamelCase = tf.train.Saver(tf.trainable_variables() )
saver.save(A__ , os.path.join(A__ , model_name.replace('-' , '_' ) + '.ckpt' ) )
def __lowerCamelCase ( A__=None ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=A__ , required=A__ , help='model name e.g. bert-base-uncased' )
parser.add_argument(
'--cache_dir' , type=A__ , default=A__ , required=A__ , help='Directory containing pytorch model' )
parser.add_argument('--pytorch_model_path' , type=A__ , required=A__ , help='/path/to/<pytorch-model-name>.bin' )
parser.add_argument('--tf_cache_dir' , type=A__ , required=A__ , help='Directory in which to save tensorflow model' )
UpperCamelCase = parser.parse_args(A__ )
UpperCamelCase = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=A__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 249 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 8 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A__(nn.Module ):
"""simple docstring"""
_A : int
_A : int
_A : float = 0.0
_A : int = 1
_A : int = 1
_A : bool = True
_A : bool = False
_A : bool = False
_A : bool = False
_A : jnp.dtype = jnp.floataa
def UpperCamelCase__ ( self ) -> Tuple:
a_ : int = []
a_ : List[Any] = []
for i in range(self.num_layers ):
a_ : Any = self.in_channels if i == 0 else self.out_channels
a_ : List[str] = FlaxResnetBlockaD(
in_channels=_lowercase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_lowercase )
a_ : Dict = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_lowercase )
a_ : List[str] = resnets
a_ : str = attentions
if self.add_downsample:
a_ : Optional[Any] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , _lowercase , _lowercase , _lowercase , _lowercase=True ) -> Optional[int]:
a_ : Optional[Any] = ()
for resnet, attn in zip(self.resnets , self.attentions ):
a_ : Any = resnet(_lowercase , _lowercase , deterministic=_lowercase )
a_ : Any = attn(_lowercase , _lowercase , deterministic=_lowercase )
output_states += (hidden_states,)
if self.add_downsample:
a_ : str = self.downsamplers_a(_lowercase )
output_states += (hidden_states,)
return hidden_states, output_states
class A__(nn.Module ):
"""simple docstring"""
_A : int
_A : int
_A : float = 0.0
_A : int = 1
_A : bool = True
_A : jnp.dtype = jnp.floataa
def UpperCamelCase__ ( self ) -> Dict:
a_ : int = []
for i in range(self.num_layers ):
a_ : List[str] = self.in_channels if i == 0 else self.out_channels
a_ : Optional[Any] = FlaxResnetBlockaD(
in_channels=_lowercase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_lowercase )
a_ : Tuple = resnets
if self.add_downsample:
a_ : List[str] = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , _lowercase , _lowercase , _lowercase=True ) -> int:
a_ : Tuple = ()
for resnet in self.resnets:
a_ : Union[str, Any] = resnet(_lowercase , _lowercase , deterministic=_lowercase )
output_states += (hidden_states,)
if self.add_downsample:
a_ : List[Any] = self.downsamplers_a(_lowercase )
output_states += (hidden_states,)
return hidden_states, output_states
class A__(nn.Module ):
"""simple docstring"""
_A : int
_A : int
_A : int
_A : float = 0.0
_A : int = 1
_A : int = 1
_A : bool = True
_A : bool = False
_A : bool = False
_A : bool = False
_A : jnp.dtype = jnp.floataa
def UpperCamelCase__ ( self ) -> Any:
a_ : Dict = []
a_ : Union[str, Any] = []
for i in range(self.num_layers ):
a_ : Any = self.in_channels if (i == self.num_layers - 1) else self.out_channels
a_ : Optional[int] = self.prev_output_channel if i == 0 else self.out_channels
a_ : Any = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_lowercase )
a_ : Optional[int] = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_lowercase )
a_ : Any = resnets
a_ : Dict = attentions
if self.add_upsample:
a_ : Union[str, Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=True ) -> int:
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
a_ : Optional[Any] = res_hidden_states_tuple[-1]
a_ : Tuple = res_hidden_states_tuple[:-1]
a_ : Tuple = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
a_ : Dict = resnet(_lowercase , _lowercase , deterministic=_lowercase )
a_ : List[str] = attn(_lowercase , _lowercase , deterministic=_lowercase )
if self.add_upsample:
a_ : str = self.upsamplers_a(_lowercase )
return hidden_states
class A__(nn.Module ):
"""simple docstring"""
_A : int
_A : int
_A : int
_A : float = 0.0
_A : int = 1
_A : bool = True
_A : jnp.dtype = jnp.floataa
def UpperCamelCase__ ( self ) -> Any:
a_ : List[str] = []
for i in range(self.num_layers ):
a_ : Dict = self.in_channels if (i == self.num_layers - 1) else self.out_channels
a_ : Optional[int] = self.prev_output_channel if i == 0 else self.out_channels
a_ : Optional[Any] = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_lowercase )
a_ : Optional[int] = resnets
if self.add_upsample:
a_ : Union[str, Any] = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , _lowercase , _lowercase , _lowercase , _lowercase=True ) -> int:
for resnet in self.resnets:
# pop res hidden states
a_ : int = res_hidden_states_tuple[-1]
a_ : List[Any] = res_hidden_states_tuple[:-1]
a_ : Tuple = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
a_ : str = resnet(_lowercase , _lowercase , deterministic=_lowercase )
if self.add_upsample:
a_ : Any = self.upsamplers_a(_lowercase )
return hidden_states
class A__(nn.Module ):
"""simple docstring"""
_A : int
_A : float = 0.0
_A : int = 1
_A : int = 1
_A : bool = False
_A : bool = False
_A : jnp.dtype = jnp.floataa
def UpperCamelCase__ ( self ) -> List[Any]:
# there is always at least one resnet
a_ : Optional[int] = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
a_ : Optional[Any] = []
for _ in range(self.num_layers ):
a_ : List[Any] = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_lowercase )
a_ : Any = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_lowercase )
a_ : Any = resnets
a_ : Tuple = attentions
def __call__( self , _lowercase , _lowercase , _lowercase , _lowercase=True ) -> Dict:
a_ : int = self.resnets[0](_lowercase , _lowercase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
a_ : Dict = attn(_lowercase , _lowercase , deterministic=_lowercase )
a_ : str = resnet(_lowercase , _lowercase , deterministic=_lowercase )
return hidden_states
| 248 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__lowerCamelCase : str = 2048
__lowerCamelCase : List[Any] = 4096
__lowerCamelCase : Optional[int] = 42
__lowerCamelCase : Union[str, Any] = os.environ.pop("""PROCESS_TRAIN""", """false""")
__lowerCamelCase : List[Any] = {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
def choose_first(snake_case_ : str , snake_case_ : List[Any]=False ):
assert isinstance(a__ , a__ )
if len(a__ ) == 1:
snake_case__ : Tuple = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
snake_case__ : int = {k: [a[k]] for k in a}
if len(a["start_token"] ) > 0:
break
return a
snake_case__ : List[Any] = {"id": example["id"]}
snake_case__ : Optional[int] = example["annotations"]
snake_case__ : Union[str, Any] = annotation["yes_no_answer"]
if 0 in yes_no_answer or 1 in yes_no_answer:
snake_case__ : Union[str, Any] = ["yes"] if 1 in yes_no_answer else ["no"]
snake_case__ : Tuple = []
snake_case__ : Any = []
snake_case__ : Union[str, Any] = ["<cls>"]
else:
snake_case__ : Optional[int] = ["short"]
snake_case__ : Union[str, Any] = choose_first(annotation["short_answers"] )
if len(out["start_token"] ) == 0:
# answer will be long if short is not available
snake_case__ : Optional[int] = ["long"]
snake_case__ : Dict = choose_first(annotation["long_answer"] , is_long_answer=a__ )
snake_case__ : Dict = []
answer.update(a__ )
# disregard some samples
if len(answer["start_token"] ) > 1 or answer["start_token"] == answer["end_token"]:
snake_case__ : Union[str, Any] = True
else:
snake_case__ : Tuple = False
snake_case__ : Tuple = ["start_token", "end_token", "start_byte", "end_byte", "text"]
if not all(isinstance(answer[k] , a__ ) for k in cols ):
raise ValueError("Issue in ID" , example["id"] )
return answer
def SCREAMING_SNAKE_CASE ( snake_case_ : Dict , snake_case_ : Union[str, Any]=False ):
snake_case__ : Any = _get_single_answer(a__ )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case__ : Any = example["document"]["tokens"]
snake_case__ : Tuple = []
for i in range(len(doc["token"] ) ):
if not doc["is_html"][i]:
context.append(doc["token"][i] )
return {
"context": " ".join(a__ ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
snake_case__ : Optional[int] = ["start_token", "end_token"]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
snake_case__ : List[str] = example["document"]["tokens"]
snake_case__ : Any = answer["start_token"]
snake_case__ : str = answer["end_token"]
snake_case__ : Optional[int] = []
for i in range(len(doc["token"] ) ):
if not doc["is_html"][i]:
context.append(doc["token"][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
snake_case__ : str = " ".join(context[start_token:end_token] )
# checking above code
if assertion:
snake_case__ : Optional[Any] = doc["is_html"][answer["start_token"] : answer["end_token"]]
snake_case__ : List[str] = doc["token"][answer["start_token"] : answer["end_token"]]
snake_case__ : List[Any] = " ".join([old[i] for i in range(len(a__ ) ) if not is_html[i]] )
if new != old:
print("ID:" , example["id"] )
print("New:" , a__ , end="\n" )
print("Old:" , a__ , end="\n\n" )
return {
"context": " ".join(a__ ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] , snake_case_ : Optional[Any] , snake_case_ : Union[str, Any]=2048 , snake_case_ : Optional[int]=4096 , snake_case_ : Optional[Any]=True ):
snake_case__ : List[Any] = get_context_and_ans(a__ , assertion=a__ )
snake_case__ : Optional[Any] = out["answer"]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
snake_case__ : Tuple = tokenizer(example["question"]["text"] , out["context"] ).input_ids
snake_case__ : List[Any] = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case__ : Dict = []
snake_case__ : Any = []
snake_case__ : Tuple = input_ids[:q_len]
snake_case__ : Tuple = range(a__ , len(a__ ) , max_length - doc_stride )
for i in doc_start_indices:
snake_case__ : Tuple = i + max_length - q_len
snake_case__ : Optional[Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["category"][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(a__ ),
"end_token": [-100] * len(a__ ),
"category": category,
},
}
snake_case__ : int = out["context"].split()
snake_case__ : Optional[int] = splitted_context[answer["end_token"]]
snake_case__ : Tuple = len(
tokenizer(
" ".join(splitted_context[: answer["start_token"]] ) , add_special_tokens=a__ , ).input_ids )
snake_case__ : str = len(
tokenizer(" ".join(splitted_context[: answer["end_token"]] ) , add_special_tokens=a__ ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
snake_case__ : Dict = len(tokenizer(a__ , add_special_tokens=a__ ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
snake_case__ : Tuple = input_ids[answer["start_token"] : answer["end_token"] + 1] # right & left are inclusive
snake_case__ : List[Any] = answer["start_token"]
snake_case__ : Tuple = answer["end_token"]
if assertion:
snake_case__ : int = tokenizer.decode(a__ )
if answer["span"] != new:
print("ISSUE IN TOKENIZATION" )
print("OLD:" , answer["span"] )
print("NEW:" , a__ , end="\n\n" )
if len(a__ ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
snake_case__ : Tuple = input_ids[:q_len]
snake_case__ : List[str] = range(a__ , len(a__ ) , max_length - doc_stride )
snake_case__ : Optional[Any] = []
snake_case__ : List[Any] = []
snake_case__ : int = []
snake_case__ : Dict = [] # null, yes, no, long, short
for i in doc_start_indices:
snake_case__ : Union[str, Any] = i + max_length - q_len
snake_case__ : Dict = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
snake_case__ : List[Any] = start_token - i + q_len
snake_case__ : List[str] = end_token - i + q_len
answers_category.append(answer["category"][0] ) # ["short"] -> "short"
else:
snake_case__ : Optional[int] = -100
snake_case__ : Any = -100
answers_category.append("null" )
snake_case__ : List[Any] = inputs[-1][start_token : end_token + 1]
answers_start_token.append(a__ )
answers_end_token.append(a__ )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("ISSUE in strided for ID:" , example["id"] )
print("New:" , tokenizer.decode(a__ ) )
print("Old:" , tokenizer.decode(a__ ) , end="\n\n" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Optional[Any]=2048 , snake_case_ : Tuple=4096 , snake_case_ : Optional[Any]=False ):
snake_case__ : List[str] = get_strided_contexts_and_ans(
a__ , a__ , doc_stride=a__ , max_length=a__ , assertion=a__ , )
return example
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] , snake_case_ : Optional[int] ):
with jsonlines.open(a__ , "a" ) as writer:
for example in tqdm(a__ , total=len(a__ ) , desc="Saving samples ... " ):
snake_case__ : Dict = example["labels"]
for ids, start, end, cat in zip(
example["input_ids"] , labels["start_token"] , labels["end_token"] , labels["category"] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"input_ids": ids,
"start_token": start,
"end_token": end,
"category": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__lowerCamelCase : str = load_dataset("""natural_questions""")
__lowerCamelCase : Any = BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""")
__lowerCamelCase : int = data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]
__lowerCamelCase : Optional[Any] = {
"""tokenizer""": tokenizer,
"""doc_stride""": DOC_STRIDE,
"""max_length""": MAX_LENGTH,
"""assertion""": False,
}
__lowerCamelCase : Tuple = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__lowerCamelCase : Dict = data.remove_columns(["""annotations""", """document""", """id""", """question"""])
print(data)
np.random.seed(SEED)
__lowerCamelCase : List[Any] = """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
save_to_disk(data, file_name=cache_file_name)
| 371 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = {
"""facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
"""facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
"""facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
"""facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
"""facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
"""facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
"""facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
"""facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "xmod"
def __init__( self : List[str] , __A : List[str]=3_0_5_2_2 , __A : Tuple=7_6_8 , __A : str=1_2 , __A : List[Any]=1_2 , __A : List[str]=3_0_7_2 , __A : List[str]="gelu" , __A : List[Any]=0.1 , __A : Tuple=0.1 , __A : str=5_1_2 , __A : Union[str, Any]=2 , __A : List[Any]=0.0_2 , __A : List[str]=1e-1_2 , __A : Tuple=1 , __A : List[Any]=0 , __A : Optional[Any]=2 , __A : Optional[int]="absolute" , __A : Optional[int]=True , __A : Dict=None , __A : Optional[int]=False , __A : Dict=2 , __A : List[str]=False , __A : Dict=True , __A : Union[str, Any]=True , __A : Tuple=("en_XX",) , __A : Optional[Any]=None , **__A : Tuple , ):
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
snake_case__ : Tuple = vocab_size
snake_case__ : Union[str, Any] = hidden_size
snake_case__ : int = num_hidden_layers
snake_case__ : str = num_attention_heads
snake_case__ : Tuple = hidden_act
snake_case__ : Optional[Any] = intermediate_size
snake_case__ : List[str] = hidden_dropout_prob
snake_case__ : Optional[int] = attention_probs_dropout_prob
snake_case__ : Union[str, Any] = max_position_embeddings
snake_case__ : str = type_vocab_size
snake_case__ : List[str] = initializer_range
snake_case__ : Dict = layer_norm_eps
snake_case__ : str = position_embedding_type
snake_case__ : List[str] = use_cache
snake_case__ : Tuple = classifier_dropout
snake_case__ : Any = pre_norm
snake_case__ : List[str] = adapter_reduction_factor
snake_case__ : List[Any] = adapter_layer_norm
snake_case__ : str = adapter_reuse_layer_norm
snake_case__ : Union[str, Any] = ln_before_adapter
snake_case__ : Tuple = list(__A )
snake_case__ : int = default_language
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
@property
def _lowercase ( self : int ):
if self.task == "multiple-choice":
snake_case__ : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case__ : Any = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 286 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a__( unittest.TestCase , lowerCamelCase__ ):
def lowercase_ ( self : Dict ):
a : Union[str, Any] = load_tool('text-to-speech' )
self.tool.setup()
def lowercase_ ( self : str ):
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
a : Union[str, Any] = self.tool('hey' )
a : str = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] ) , ) )
def lowercase_ ( self : str ):
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
a : Tuple = self.tool('hey' )
a : Union[str, Any] = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] ) , ) ) | 297 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('Relative densities cannot be greater than one' )
else:
a : Union[str, Any] = 1 - (matter_density + radiation_density + dark_energy)
a : Union[str, Any] = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
a : int = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowerCAmelCase: Optional[Any] = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
) | 297 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
RandomResizedCrop,
Resize,
ToTensor,
)
import transformers
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForImageClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__snake_case = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')
__snake_case = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def lowerCAmelCase_ ( __lowerCAmelCase )-> Tuple:
'''simple docstring'''
with open(__lowerCAmelCase , '''rb''' ) as f:
UpperCAmelCase : Any =Image.open(__lowerCAmelCase )
return im.convert('''RGB''' )
@dataclass
class __snake_case :
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={
"""help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__lowerCamelCase : Optional[str] = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the training data."""} )
__lowerCamelCase : Optional[str] = field(default=lowerCamelCase__ , metadata={"""help""": """A folder containing the validation data."""} )
__lowerCamelCase : Optional[float] = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
__lowerCamelCase : Optional[int] = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
__lowerCamelCase : Optional[int] = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
raise ValueError(
'''You must specify either a dataset name from the hub or a train and/or validation directory.''' )
@dataclass
class __snake_case :
__lowerCamelCase : str = field(
default="""google/vit-base-patch16-224-in21k""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCamelCase__ )} , )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__lowerCamelCase : Optional[str] = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
__lowerCamelCase : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
__lowerCamelCase : str = field(default=lowerCamelCase__ , metadata={"""help""": """Name or path of preprocessor config."""} )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
__lowerCamelCase : bool = field(
default=lowerCamelCase__ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowerCAmelCase_ ( __lowerCAmelCase )-> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =torch.stack([example['''pixel_values'''] for example in examples] )
UpperCAmelCase : Optional[int] =torch.tensor([example['''labels'''] for example in examples] )
return {"pixel_values": pixel_values, "labels": labels}
def lowerCAmelCase_ ( )-> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Any =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : str =parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_image_classification''' , __lowerCAmelCase , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCAmelCase : int =training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
UpperCAmelCase : List[Any] =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase : Tuple =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Initialize our dataset and prepare it for the 'image-classification' task.
if data_args.dataset_name is not None:
UpperCAmelCase : str =load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task='''image-classification''' , use_auth_token=True if model_args.use_auth_token else None , )
else:
UpperCAmelCase : List[Any] ={}
if data_args.train_dir is not None:
UpperCAmelCase : Union[str, Any] =os.path.join(data_args.train_dir , '''**''' )
if data_args.validation_dir is not None:
UpperCAmelCase : Any =os.path.join(data_args.validation_dir , '''**''' )
UpperCAmelCase : Any =load_dataset(
'''imagefolder''' , data_files=__lowerCAmelCase , cache_dir=model_args.cache_dir , task='''image-classification''' , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCAmelCase : str =None if '''validation''' in dataset.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __lowerCAmelCase ) and data_args.train_val_split > 0.0:
UpperCAmelCase : List[str] =dataset['''train'''].train_test_split(data_args.train_val_split )
UpperCAmelCase : Optional[int] =split['''train''']
UpperCAmelCase : int =split['''test''']
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
UpperCAmelCase : Dict =dataset['''train'''].features['''labels'''].names
UpperCAmelCase , UpperCAmelCase : List[str] ={}, {}
for i, label in enumerate(__lowerCAmelCase ):
UpperCAmelCase : Optional[Any] =str(__lowerCAmelCase )
UpperCAmelCase : int =label
# Load the accuracy metric from the datasets package
UpperCAmelCase : Tuple =evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids )
UpperCAmelCase : List[str] =AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__lowerCAmelCase ) , labelaid=__lowerCAmelCase , idalabel=__lowerCAmelCase , finetuning_task='''image-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase : Dict =AutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
UpperCAmelCase : Any =AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Define torchvision transforms to be applied to each image.
if "shortest_edge" in image_processor.size:
UpperCAmelCase : int =image_processor.size['''shortest_edge''']
else:
UpperCAmelCase : Tuple =(image_processor.size['''height'''], image_processor.size['''width'''])
UpperCAmelCase : Dict =Normalize(mean=image_processor.image_mean , std=image_processor.image_std )
UpperCAmelCase : Tuple =Compose(
[
RandomResizedCrop(__lowerCAmelCase ),
RandomHorizontalFlip(),
ToTensor(),
normalize,
] )
UpperCAmelCase : Any =Compose(
[
Resize(__lowerCAmelCase ),
CenterCrop(__lowerCAmelCase ),
ToTensor(),
normalize,
] )
def train_transforms(__lowerCAmelCase ):
UpperCAmelCase : Dict =[
_train_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']
]
return example_batch
def val_transforms(__lowerCAmelCase ):
UpperCAmelCase : Any =[_val_transforms(pil_img.convert('''RGB''' ) ) for pil_img in example_batch['''image''']]
return example_batch
if training_args.do_train:
if "train" not in dataset:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
UpperCAmelCase : Dict =(
dataset['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
dataset["train"].set_transform(__lowerCAmelCase )
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
UpperCAmelCase : Optional[Any] =(
dataset['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
dataset["validation"].set_transform(__lowerCAmelCase )
# Initalize our trainer
UpperCAmelCase : Dict =Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=dataset['''train'''] if training_args.do_train else None , eval_dataset=dataset['''validation'''] if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
UpperCAmelCase : Dict =None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase : int =training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase : List[Any] =last_checkpoint
UpperCAmelCase : Any =trainer.train(resume_from_checkpoint=__lowerCAmelCase )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCAmelCase : List[str] =trainer.evaluate()
trainer.log_metrics('''eval''' , __lowerCAmelCase )
trainer.save_metrics('''eval''' , __lowerCAmelCase )
# Write model card and (optionally) push to hub
UpperCAmelCase : List[Any] ={
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''image-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''image-classification''', '''vision'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__lowerCAmelCase )
else:
trainer.create_model_card(**__lowerCAmelCase )
if __name__ == "__main__":
main()
| 78 | import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
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 ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : List[Any] = KandinskyVaaControlnetPipeline
__lowerCamelCase : int = ["""image_embeds""", """negative_image_embeds""", """hint"""]
__lowerCamelCase : Optional[int] = ["""image_embeds""", """negative_image_embeds""", """hint"""]
__lowerCamelCase : Optional[Any] = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__lowerCamelCase : Dict = False
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return 32
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return 32
@property
def UpperCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.time_input_dim
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
return self.time_input_dim * 4
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
return 100
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any ={
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''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,
}
UpperCAmelCase : List[Any] =UNetaDConditionModel(**snake_case__ )
return model
@property
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase : Any =VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.dummy_unet
UpperCAmelCase : Tuple =self.dummy_movq
UpperCAmelCase : Union[str, Any] =DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=snake_case__ , set_alpha_to_one=snake_case__ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=snake_case__ , )
UpperCAmelCase : Tuple ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def UpperCAmelCase__ ( self , snake_case__ , snake_case__=0 ) -> Any:
'''simple docstring'''
UpperCAmelCase : str =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
UpperCAmelCase : Tuple =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case__ )
# create hint
UpperCAmelCase : Tuple =floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case__ ) ).to(snake_case__ )
if str(snake_case__ ).startswith('''mps''' ):
UpperCAmelCase : Optional[int] =torch.manual_seed(snake_case__ )
else:
UpperCAmelCase : int =torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
UpperCAmelCase : List[str] ={
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] ='''cpu'''
UpperCAmelCase : List[Any] =self.get_dummy_components()
UpperCAmelCase : Tuple =self.pipeline_class(**snake_case__ )
UpperCAmelCase : Tuple =pipe.to(snake_case__ )
pipe.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : Optional[int] =pipe(**self.get_dummy_inputs(snake_case__ ) )
UpperCAmelCase : str =output.images
UpperCAmelCase : List[str] =pipe(
**self.get_dummy_inputs(snake_case__ ) , return_dict=snake_case__ , )[0]
UpperCAmelCase : Union[str, Any] =image[0, -3:, -3:, -1]
UpperCAmelCase : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase : Union[str, Any] =np.array(
[0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595] )
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()}'''
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )
UpperCAmelCase : Tuple =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
UpperCAmelCase : int =torch.from_numpy(np.array(snake_case__ ) ).float() / 255.0
UpperCAmelCase : List[str] =hint.permute(2 , 0 , 1 ).unsqueeze(0 )
UpperCAmelCase : Dict =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case__ )
UpperCAmelCase : int =KandinskyVaaControlnetPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
UpperCAmelCase : str =pipeline.to(snake_case__ )
pipeline.set_progress_bar_config(disable=snake_case__ )
UpperCAmelCase : int ='''A robot, 4k photo'''
UpperCAmelCase : int =torch.Generator(device='''cuda''' ).manual_seed(0 )
UpperCAmelCase , UpperCAmelCase : List[str] =pipe_prior(
snake_case__ , generator=snake_case__ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
UpperCAmelCase : List[str] =torch.Generator(device='''cuda''' ).manual_seed(0 )
UpperCAmelCase : Dict =pipeline(
image_embeds=snake_case__ , negative_image_embeds=snake_case__ , hint=snake_case__ , generator=snake_case__ , num_inference_steps=100 , output_type='''np''' , )
UpperCAmelCase : List[Any] =output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(snake_case__ , snake_case__ )
| 78 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCAmelCase__ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int ) -> int:
__lowerCamelCase : Union[str, Any] = []
for part_id in partition_order:
__lowerCamelCase : List[str] = df.where(F'SPARK_PARTITION_ID() = {part_id}' ).collect()
for row_idx, row in enumerate(UpperCAmelCase_ ):
expected_row_ids_and_row_dicts.append((F'{part_id}_{row_idx}', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> Tuple:
__lowerCamelCase : Optional[int] = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : Any = spark.range(1_00 ).repartition(1 )
__lowerCamelCase : str = Spark(UpperCAmelCase_ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> Dict:
__lowerCamelCase : Dict = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : Union[str, Any] = spark.range(10 ).repartition(2 )
__lowerCamelCase : Union[str, Any] = [1, 0]
__lowerCamelCase : Optional[int] = _generate_iterable_examples(UpperCAmelCase_ , UpperCAmelCase_ ) # Reverse the partitions.
__lowerCamelCase : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ , UpperCAmelCase_ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
__lowerCamelCase , __lowerCamelCase : int = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> Dict:
__lowerCamelCase : str = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : int = spark.range(10 ).repartition(1 )
__lowerCamelCase : Optional[int] = SparkExamplesIterable(UpperCAmelCase_ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(UpperCAmelCase_ ):
assert row_id == F'0_{i}'
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> List[Any]:
__lowerCamelCase : int = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : List[Any] = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch('numpy.random.Generator' ) as generator_mock:
__lowerCamelCase : int = lambda UpperCAmelCase_ : x.reverse()
__lowerCamelCase : Dict = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ , [2, 1, 0] )
__lowerCamelCase : str = SparkExamplesIterable(UpperCAmelCase_ ).shuffle_data_sources(UpperCAmelCase_ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(UpperCAmelCase_ ):
__lowerCamelCase , __lowerCamelCase : int = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> str:
__lowerCamelCase : int = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : List[str] = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
__lowerCamelCase : int = SparkExamplesIterable(UpperCAmelCase_ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
__lowerCamelCase : Any = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ , [0, 2] )
for i, (row_id, row_dict) in enumerate(UpperCAmelCase_ ):
__lowerCamelCase , __lowerCamelCase : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
__lowerCamelCase : Union[str, Any] = SparkExamplesIterable(UpperCAmelCase_ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
__lowerCamelCase : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(UpperCAmelCase_ , [1, 3] )
for i, (row_id, row_dict) in enumerate(UpperCAmelCase_ ):
__lowerCamelCase , __lowerCamelCase : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCAmelCase__ ( ) -> Optional[int]:
__lowerCamelCase : Optional[Any] = pyspark.sql.SparkSession.builder.master('local[*]' ).appName('pyspark' ).getOrCreate()
__lowerCamelCase : Optional[Any] = spark.range(1_00 ).repartition(1 )
__lowerCamelCase : Optional[Any] = Spark(UpperCAmelCase_ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 185 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
A__ : Optional[int] = logging.get_logger(__name__)
A__ : List[Any] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
A__ : Tuple = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ) -> Tuple:
for attribute in key.split('.' ):
__lowerCamelCase : List[Any] = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
__lowerCamelCase : Any = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
__lowerCamelCase : Any = hf_pointer.shape
assert hf_shape == value.shape, (
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":
__lowerCamelCase : Tuple = value
elif weight_type == "weight_g":
__lowerCamelCase : Optional[int] = value
elif weight_type == "weight_v":
__lowerCamelCase : str = value
elif weight_type == "bias":
__lowerCamelCase : List[Any] = value
else:
__lowerCamelCase : List[str] = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] ) -> Optional[int]:
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : Dict = fairseq_model.state_dict()
__lowerCamelCase : List[str] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase : str = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__lowerCamelCase : int = True
if "*" in mapped_key:
__lowerCamelCase : Optional[int] = name.split(UpperCAmelCase_ )[0].split('.' )[-2]
__lowerCamelCase : List[str] = mapped_key.replace('*' , UpperCAmelCase_ )
if "weight_g" in name:
__lowerCamelCase : Dict = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase : Any = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
__lowerCamelCase : Any = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase : List[Any] = 'weight'
else:
__lowerCamelCase : str = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F'Unused weights: {unused_weights}' )
def UpperCAmelCase__ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] ) -> Tuple:
__lowerCamelCase : List[str] = full_name.split('conv_layers.' )[-1]
__lowerCamelCase : List[Any] = name.split('.' )
__lowerCamelCase : Any = int(items[0] )
__lowerCamelCase : Optional[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
__lowerCamelCase : Union[str, Any] = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
__lowerCamelCase : Tuple = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
__lowerCamelCase : str = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
__lowerCamelCase : List[Any] = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(UpperCAmelCase_ )
@torch.no_grad()
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any]=None ) -> Optional[int]:
# load the pre-trained checkpoints
__lowerCamelCase : Optional[Any] = torch.load(UpperCAmelCase_ )
__lowerCamelCase : Optional[Any] = WavLMConfigOrig(checkpoint['cfg'] )
__lowerCamelCase : Union[str, Any] = WavLMOrig(UpperCAmelCase_ )
model.load_state_dict(checkpoint['model'] )
model.eval()
if config_path is not None:
__lowerCamelCase : Optional[int] = WavLMConfig.from_pretrained(UpperCAmelCase_ )
else:
__lowerCamelCase : Any = WavLMConfig()
__lowerCamelCase : Optional[int] = WavLMModel(UpperCAmelCase_ )
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ )
hf_wavlm.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
A__ : List[str] = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 185 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
__UpperCamelCase : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCamelCase : Optional[Any] = '\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n >>> pipe.to("cuda")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save("cat.png")\n ```\n'
def A ( _lowercase , _lowercase , _lowercase=8 ):
SCREAMING_SNAKE_CASE : Optional[Any] = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
SCREAMING_SNAKE_CASE : str = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class lowercase__ ( UpperCamelCase_):
def __init__( self : int , UpperCamelCase__ : MultilingualCLIP , UpperCamelCase__ : XLMRobertaTokenizer , UpperCamelCase__ : UNetaDConditionModel , UpperCamelCase__ : Union[DDIMScheduler, DDPMScheduler] , UpperCamelCase__ : VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ , movq=UpperCamelCase__ , )
SCREAMING_SNAKE_CASE : Any = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __A ( self : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : str ):
'''simple docstring'''
if latents is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__ , device=UpperCamelCase__ , dtype=UpperCamelCase__ )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
SCREAMING_SNAKE_CASE : Optional[Any] = latents.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = latents * scheduler.init_noise_sigma
return latents
def __A ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int]=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = len(UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else 1
# get prompt text embeddings
SCREAMING_SNAKE_CASE : Any = self.tokenizer(
UpperCamelCase__ , padding='''max_length''' , truncation=UpperCamelCase__ , max_length=77 , return_attention_mask=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE : Dict = text_inputs.input_ids
SCREAMING_SNAKE_CASE : int = self.tokenizer(UpperCamelCase__ , padding='''longest''' , return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
SCREAMING_SNAKE_CASE : Tuple = text_input_ids.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = text_inputs.attention_mask.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.text_encoder(
input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = prompt_embeds.repeat_interleave(UpperCamelCase__ , dim=0 )
SCREAMING_SNAKE_CASE : List[Any] = text_encoder_hidden_states.repeat_interleave(UpperCamelCase__ , dim=0 )
SCREAMING_SNAKE_CASE : Dict = text_mask.repeat_interleave(UpperCamelCase__ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : List[str]
if negative_prompt is None:
SCREAMING_SNAKE_CASE : Optional[Any] = [''''''] * batch_size
elif type(UpperCamelCase__ ) is not type(UpperCamelCase__ ):
raise TypeError(
f"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__ )} !="""
f""" {type(UpperCamelCase__ )}.""" )
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : int = [negative_prompt]
elif batch_size != len(UpperCamelCase__ ):
raise ValueError(
f"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__ )}, but `prompt`:"""
f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
''' the batch size of `prompt`.''' )
else:
SCREAMING_SNAKE_CASE : int = negative_prompt
SCREAMING_SNAKE_CASE : Dict = self.tokenizer(
UpperCamelCase__ , padding='''max_length''' , max_length=77 , truncation=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE : Optional[Any] = uncond_input.input_ids.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Tuple = uncond_input.attention_mask.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = self.text_encoder(
input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
SCREAMING_SNAKE_CASE : Any = negative_prompt_embeds.shape[1]
SCREAMING_SNAKE_CASE : str = negative_prompt_embeds.repeat(1 , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = negative_prompt_embeds.view(batch_size * num_images_per_prompt , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = uncond_text_encoder_hidden_states.shape[1]
SCREAMING_SNAKE_CASE : Any = uncond_text_encoder_hidden_states.repeat(1 , UpperCamelCase__ , 1 )
SCREAMING_SNAKE_CASE : Optional[int] = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , UpperCamelCase__ , -1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = uncond_text_mask.repeat_interleave(UpperCamelCase__ , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
SCREAMING_SNAKE_CASE : str = torch.cat([negative_prompt_embeds, prompt_embeds] )
SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def __A ( self : Dict , UpperCamelCase__ : str=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
SCREAMING_SNAKE_CASE : List[Any] = torch.device(f"""cuda:{gpu_id}""" )
SCREAMING_SNAKE_CASE : Any = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCamelCase__ , UpperCamelCase__ )
def __A ( self : int , UpperCamelCase__ : Union[str, Any]=0 ):
'''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 : int = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=UpperCamelCase__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
SCREAMING_SNAKE_CASE : Dict = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = cpu_offload_with_hook(UpperCamelCase__ , UpperCamelCase__ , prev_module_hook=UpperCamelCase__ )
if self.safety_checker is not None:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = cpu_offload_with_hook(self.safety_checker , UpperCamelCase__ , prev_module_hook=UpperCamelCase__ )
# We'll offload the last model manually.
SCREAMING_SNAKE_CASE : List[Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __A ( self : Dict ):
'''simple docstring'''
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCamelCase__ , '''_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(UpperCamelCase__ )
def __call__( self : Dict , UpperCamelCase__ : Union[str, List[str]] , UpperCamelCase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase__ : Optional[Union[str, List[str]]] = None , UpperCamelCase__ : int = 512 , UpperCamelCase__ : int = 512 , UpperCamelCase__ : int = 100 , UpperCamelCase__ : float = 4.0 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : Optional[torch.FloatTensor] = None , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , ):
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[Any] = 1
elif isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Optional[Any] = len(UpperCamelCase__ )
else:
raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__ )}""" )
SCREAMING_SNAKE_CASE : List[Any] = self._execution_device
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size * num_images_per_prompt
SCREAMING_SNAKE_CASE : Union[str, Any] = guidance_scale > 1.0
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self._encode_prompt(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Tuple = torch.cat(UpperCamelCase__ , dim=0 )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : str = torch.cat(UpperCamelCase__ , dim=0 )
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE : Optional[Any] = image_embeds.repeat_interleave(UpperCamelCase__ , dim=0 )
SCREAMING_SNAKE_CASE : Any = negative_image_embeds.repeat_interleave(UpperCamelCase__ , dim=0 )
SCREAMING_SNAKE_CASE : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=UpperCamelCase__ )
self.scheduler.set_timesteps(UpperCamelCase__ , device=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : str = self.scheduler.timesteps
SCREAMING_SNAKE_CASE : Optional[int] = self.unet.config.in_channels
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = get_new_h_w(UpperCamelCase__ , UpperCamelCase__ , self.movq_scale_factor )
# create initial latent
SCREAMING_SNAKE_CASE : int = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(UpperCamelCase__ ) ):
# expand the latents if we are doing classifier free guidance
SCREAMING_SNAKE_CASE : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
SCREAMING_SNAKE_CASE : Tuple = {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
SCREAMING_SNAKE_CASE : Optional[Any] = self.unet(
sample=UpperCamelCase__ , timestep=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , added_cond_kwargs=UpperCamelCase__ , return_dict=UpperCamelCase__ , )[0]
if do_classifier_free_guidance:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = noise_pred.split(latents.shape[1] , dim=1 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = noise_pred.chunk(2 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = variance_pred.chunk(2 )
SCREAMING_SNAKE_CASE : str = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
SCREAMING_SNAKE_CASE : Optional[int] = 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 : List[str] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE : Any = self.scheduler.step(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__ , ).prev_sample
# post-processing
SCREAMING_SNAKE_CASE : List[str] = self.movq.decode(UpperCamelCase__ , force_not_quantize=UpperCamelCase__ )['''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 : Any = image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : Tuple = image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : List[str] = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase__ )
| 258 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
__UpperCamelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 258 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
@property
def _a (self ):
torch.manual_seed(0 )
A_ : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
@property
def _a (self ):
torch.manual_seed(0 )
A_ : Optional[Any] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=3 , )
return model
@property
def _a (self ):
torch.manual_seed(0 )
A_ : Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModel(lowercase )
def _a (self ):
A_ : Optional[Any] = self.dummy_uncond_unet
A_ : List[str] = DDIMScheduler()
A_ : List[str] = self.dummy_vq_model
A_ : Dict = LDMPipeline(unet=lowercase , vqvae=lowercase , scheduler=lowercase )
ldm.to(lowercase )
ldm.set_progress_bar_config(disable=lowercase )
A_ : str = torch.manual_seed(0 )
A_ : Union[str, Any] = ldm(generator=lowercase , num_inference_steps=2 , output_type="""numpy""" ).images
A_ : Tuple = torch.manual_seed(0 )
A_ : Tuple = ldm(generator=lowercase , num_inference_steps=2 , output_type="""numpy""" , return_dict=lowercase )[0]
A_ : int = image[0, -3:, -3:, -1]
A_ : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A_ : str = np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72] )
A_ : Union[str, Any] = 1E-2 if torch_device != """mps""" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
A_ : List[str] = LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256""" )
ldm.to(lowercase )
ldm.set_progress_bar_config(disable=lowercase )
A_ : Dict = torch.manual_seed(0 )
A_ : str = ldm(generator=lowercase , num_inference_steps=5 , output_type="""numpy""" ).images
A_ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A_ : List[str] = np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47] )
A_ : List[Any] = 1E-2 if torch_device != """mps""" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance | 206 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=0.2 , lowercase=0.2 ):
A_ : str = bp_numa
A_ : Optional[int] = bp_numa
A_ : Optional[Any] = bp_numa
A_ : str = conva_get[:2]
A_ : Union[str, Any] = conva_get[2]
A_ : Union[str, Any] = size_pa
A_ : List[str] = rate_w
A_ : Dict = rate_t
A_ : Dict = [
np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 )
for i in range(self.conva[1] )
]
A_ : Dict = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
A_ : Tuple = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 )
A_ : List[Any] = -2 * np.random.rand(self.conva[1] ) + 1
A_ : Dict = -2 * np.random.rand(self.num_bpa ) + 1
A_ : Any = -2 * np.random.rand(self.num_bpa ) + 1
def _a (self , lowercase ):
# save model dict with pickle
A_ : Union[str, Any] = {
"""num_bp1""": self.num_bpa,
"""num_bp2""": self.num_bpa,
"""num_bp3""": self.num_bpa,
"""conv1""": self.conva,
"""step_conv1""": self.step_conva,
"""size_pooling1""": self.size_poolinga,
"""rate_weight""": self.rate_weight,
"""rate_thre""": self.rate_thre,
"""w_conv1""": self.w_conva,
"""wkj""": self.wkj,
"""vji""": self.vji,
"""thre_conv1""": self.thre_conva,
"""thre_bp2""": self.thre_bpa,
"""thre_bp3""": self.thre_bpa,
}
with open(lowercase , """wb""" ) as f:
pickle.dump(lowercase , lowercase )
print(F'Model saved： {save_path}' )
@classmethod
def _a (cls , lowercase ):
# read saved model
with open(lowercase , """rb""" ) as f:
A_ : Optional[int] = pickle.load(lowercase ) # noqa: S301
A_ : Optional[int] = model_dic.get("""conv1""" )
conv_get.append(model_dic.get("""step_conv1""" ) )
A_ : Tuple = model_dic.get("""size_pooling1""" )
A_ : Optional[Any] = model_dic.get("""num_bp1""" )
A_ : List[str] = model_dic.get("""num_bp2""" )
A_ : Dict = model_dic.get("""num_bp3""" )
A_ : Tuple = model_dic.get("""rate_weight""" )
A_ : List[Any] = model_dic.get("""rate_thre""" )
# create model instance
A_ : List[str] = CNN(lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase )
# modify model parameter
A_ : int = model_dic.get("""w_conv1""" )
A_ : str = model_dic.get("""wkj""" )
A_ : str = model_dic.get("""vji""" )
A_ : int = model_dic.get("""thre_conv1""" )
A_ : Union[str, Any] = model_dic.get("""thre_bp2""" )
A_ : List[Any] = model_dic.get("""thre_bp3""" )
return conv_ins
def _a (self , lowercase ):
return 1 / (1 + np.exp(-1 * x ))
def _a (self , lowercase ):
return round(lowercase , 3 )
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase ):
# convolution process
A_ : Dict = convs[0]
A_ : Any = convs[1]
A_ : Tuple = np.shape(lowercase )[0]
# get the data slice of original image data, data_focus
A_ : List[str] = []
for i_focus in range(0 , size_data - size_conv + 1 , lowercase ):
for j_focus in range(0 , size_data - size_conv + 1 , lowercase ):
A_ : List[Any] = data[
i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
]
data_focus.append(lowercase )
# calculate the feature map of every single kernel, and saved as list of matrix
A_ : int = []
A_ : List[str] = int((size_data - size_conv) / conv_step + 1 )
for i_map in range(lowercase ):
A_ : List[Any] = []
for i_focus in range(len(lowercase ) ):
A_ : List[str] = (
np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) )
- thre_convs[i_map]
)
featuremap.append(self.sig(lowercase ) )
A_ : Tuple = np.asmatrix(lowercase ).reshape(
lowercase , lowercase )
data_featuremap.append(lowercase )
# expanding the data slice to One dimenssion
A_ : Optional[int] = []
for each_focus in data_focus:
focusa_list.extend(self.Expand_Mat(lowercase ) )
A_ : Dict = np.asarray(lowercase )
return focus_list, data_featuremap
def _a (self , lowercase , lowercase , lowercase="average_pool" ):
# pooling process
A_ : Union[str, Any] = len(featuremaps[0] )
A_ : str = int(size_map / size_pooling )
A_ : List[str] = []
for i_map in range(len(lowercase ) ):
A_ : Any = featuremaps[i_map]
A_ : Any = []
for i_focus in range(0 , lowercase , lowercase ):
for j_focus in range(0 , lowercase , lowercase ):
A_ : Tuple = feature_map[
i_focus : i_focus + size_pooling,
j_focus : j_focus + size_pooling,
]
if pooling_type == "average_pool":
# average pooling
map_pooled.append(np.average(lowercase ) )
elif pooling_type == "max_pooling":
# max pooling
map_pooled.append(np.max(lowercase ) )
A_ : List[str] = np.asmatrix(lowercase ).reshape(lowercase , lowercase )
featuremap_pooled.append(lowercase )
return featuremap_pooled
def _a (self , lowercase ):
# expanding three dimension data to one dimension list
A_ : List[Any] = []
for i in range(len(lowercase ) ):
A_ : Tuple = np.shape(data[i] )
A_ : str = data[i].reshape(1 , shapes[0] * shapes[1] )
A_ : Tuple = data_listed.getA().tolist()[0]
data_expanded.extend(lowercase )
A_ : Optional[Any] = np.asarray(lowercase )
return data_expanded
def _a (self , lowercase ):
# expanding matrix to one dimension list
A_ : str = np.asarray(lowercase )
A_ : Any = np.shape(lowercase )
A_ : int = data_mat.reshape(1 , shapes[0] * shapes[1] )
return data_expanded
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : List[str] = []
A_ : Union[str, Any] = 0
for i_map in range(lowercase ):
A_ : Union[str, Any] = np.ones((size_map, size_map) )
for i in range(0 , lowercase , lowercase ):
for j in range(0 , lowercase , lowercase ):
A_ : str = pd_pool[
i_pool
]
A_ : Optional[Any] = i_pool + 1
A_ : Union[str, Any] = np.multiply(
lowercase , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) )
pd_all.append(lowercase )
return pd_all
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase=bool ):
# model traning
print("""----------------------Start Training-------------------------""" )
print((""" - - Shape: Train_Data """, np.shape(lowercase )) )
print((""" - - Shape: Teach_Data """, np.shape(lowercase )) )
A_ : Optional[Any] = 0
A_ : Dict = []
A_ : List[Any] = 10000
while rp < n_repeat and mse >= error_accuracy:
A_ : List[Any] = 0
print(F'-------------Learning Time {rp}--------------' )
for p in range(len(lowercase ) ):
# print('------------Learning Image: %d--------------'%p)
A_ : Optional[Any] = np.asmatrix(datas_train[p] )
A_ : str = np.asarray(datas_teach[p] )
A_, A_ : Dict = self.convolute(
lowercase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
A_ : Any = self.pooling(lowercase , self.size_poolinga )
A_ : int = np.shape(lowercase )
A_ : Union[str, Any] = self._expand(lowercase )
A_ : Dict = data_bp_input
A_ : int = np.dot(lowercase , self.vji.T ) - self.thre_bpa
A_ : Any = self.sig(lowercase )
A_ : Optional[int] = np.dot(lowercase , self.wkj.T ) - self.thre_bpa
A_ : List[str] = self.sig(lowercase )
# --------------Model Leaning ------------------------
# calculate error and gradient---------------
A_ : Optional[Any] = np.multiply(
(data_teach - bp_outa) , np.multiply(lowercase , (1 - bp_outa) ) )
A_ : Tuple = np.multiply(
np.dot(lowercase , self.wkj ) , np.multiply(lowercase , (1 - bp_outa) ) )
A_ : Union[str, Any] = np.dot(lowercase , self.vji )
A_ : int = pd_i_all / (self.size_poolinga * self.size_poolinga)
A_ : str = pd_conva_pooled.T.getA().tolist()
A_ : List[Any] = self._calculate_gradient_from_pool(
lowercase , lowercase , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , )
# weight and threshold learning process---------
# convolution layer
for k_conv in range(self.conva[1] ):
A_ : int = self._expand_mat(pd_conva_all[k_conv] )
A_ : Any = self.rate_weight * np.dot(lowercase , lowercase )
A_ : Union[str, Any] = self.w_conva[k_conv] + delta_w.reshape(
(self.conva[0], self.conva[0]) )
A_ : Tuple = (
self.thre_conva[k_conv]
- np.sum(pd_conva_all[k_conv] ) * self.rate_thre
)
# all connected layer
A_ : Optional[int] = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
A_ : List[str] = self.vji + pd_j_all.T * bp_outa * self.rate_weight
A_ : List[Any] = self.thre_bpa - pd_k_all * self.rate_thre
A_ : Optional[Any] = self.thre_bpa - pd_j_all * self.rate_thre
# calculate the sum error of all single image
A_ : Optional[int] = np.sum(abs(data_teach - bp_outa ) )
error_count += errors
# print(' ----Teach ',data_teach)
# print(' ----BP_output ',bp_out3)
A_ : List[Any] = rp + 1
A_ : Union[str, Any] = error_count / patterns
all_mse.append(lowercase )
def draw_error():
A_ : str = [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
plt.plot(lowercase , """+-""" )
plt.plot(lowercase , """r--""" )
plt.xlabel("""Learning Times""" )
plt.ylabel("""All_mse""" )
plt.grid(lowercase , alpha=0.5 )
plt.show()
print("""------------------Training Complished---------------------""" )
print((""" - - Training epoch: """, rp, F' - - Mse: {mse:.6f}') )
if draw_e:
draw_error()
return mse
def _a (self , lowercase ):
# model predict
A_ : Tuple = []
print("""-------------------Start Testing-------------------------""" )
print((""" - - Shape: Test_Data """, np.shape(lowercase )) )
for p in range(len(lowercase ) ):
A_ : int = np.asmatrix(datas_test[p] )
A_, A_ : str = self.convolute(
lowercase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
A_ : Dict = self.pooling(lowercase , self.size_poolinga )
A_ : Tuple = self._expand(lowercase )
A_ : int = data_bp_input
A_ : Union[str, Any] = bp_outa * self.vji.T - self.thre_bpa
A_ : List[str] = self.sig(lowercase )
A_ : Optional[Any] = bp_outa * self.wkj.T - self.thre_bpa
A_ : List[Any] = self.sig(lowercase )
produce_out.extend(bp_outa.getA().tolist() )
A_ : Any = [list(map(self.do_round , lowercase ) ) for each in produce_out]
return np.asarray(lowercase )
def _a (self , lowercase ):
# return the data of image after convoluting process so we can check it out
A_ : Optional[Any] = np.asmatrix(lowercase )
A_, A_ : Optional[Any] = self.convolute(
lowercase , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , )
A_ : Union[str, Any] = self.pooling(lowercase , self.size_poolinga )
return data_conveda, data_pooleda
if __name__ == "__main__":
pass | 206 | 1 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(__lowerCamelCase: int , __lowerCamelCase: int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
lowercase_ = update_area_of_max_square(__lowerCamelCase , col + 1 )
lowercase_ = update_area_of_max_square(row + 1 , col + 1 )
lowercase_ = update_area_of_max_square(row + 1 , __lowerCamelCase )
if mat[row][col]:
lowercase_ = 1 + min([right, diagonal, down] )
lowercase_ = max(largest_square_area[0] , __lowerCamelCase )
return sub_problem_sol
else:
return 0
lowercase_ = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square_using_dp_array(
__lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
lowercase_ = update_area_of_max_square_using_dp_array(__lowerCamelCase , col + 1 , __lowerCamelCase )
lowercase_ = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , __lowerCamelCase )
lowercase_ = update_area_of_max_square_using_dp_array(row + 1 , __lowerCamelCase , __lowerCamelCase )
if mat[row][col]:
lowercase_ = 1 + min([right, diagonal, down] )
lowercase_ = max(largest_square_area[0] , __lowerCamelCase )
lowercase_ = sub_problem_sol
return sub_problem_sol
else:
return 0
lowercase_ = [0]
lowercase_ = [[-1] * cols for _ in range(__lowerCamelCase )]
update_area_of_max_square_using_dp_array(0 , 0 , __lowerCamelCase )
return largest_square_area[0]
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: list[list[int]] ):
'''simple docstring'''
lowercase_ = [[0] * (cols + 1) for _ in range(rows + 1 )]
lowercase_ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase_ = dp_array[row][col + 1]
lowercase_ = dp_array[row + 1][col + 1]
lowercase_ = dp_array[row + 1][col]
if mat[row][col] == 1:
lowercase_ = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase_ = max(dp_array[row][col] , __lowerCamelCase )
else:
lowercase_ = 0
return largest_square_area
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: list[list[int]] ):
'''simple docstring'''
lowercase_ = [0] * (cols + 1)
lowercase_ = [0] * (cols + 1)
lowercase_ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
lowercase_ = current_row[col + 1]
lowercase_ = next_row[col + 1]
lowercase_ = next_row[col]
if mat[row][col] == 1:
lowercase_ = 1 + min(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
lowercase_ = max(current_row[col] , __lowerCamelCase )
else:
lowercase_ = 0
lowercase_ = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 297 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
def A__ ( self ) -> int:
'''simple docstring'''
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
return Dataset.from_dict(UpperCAmelCase )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ = self._create_example_records()
lowercase_ = Dataset.from_list(UpperCAmelCase )
self.assertListEqual(dset.column_names , ["col_1", "col_2"] )
for i, r in enumerate(UpperCAmelCase ):
self.assertDictEqual(UpperCAmelCase , example_records[i] )
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = self._create_example_records()
lowercase_ = Dataset.from_list(UpperCAmelCase )
lowercase_ = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def A__ ( self ) -> Any: # checks what happens with missing columns
'''simple docstring'''
lowercase_ = [{"col_1": 1}, {"col_2": "x"}]
lowercase_ = Dataset.from_list(UpperCAmelCase )
self.assertDictEqual(dset[0] , {"col_1": 1} )
self.assertDictEqual(dset[1] , {"col_1": None} ) # NB: first record is used for columns
def A__ ( self ) -> List[Any]: # checks if the type can be inferred from the second record
'''simple docstring'''
lowercase_ = [{"col_1": []}, {"col_1": [1, 2]}]
lowercase_ = Dataset.from_list(UpperCAmelCase )
self.assertEqual(dset.info.features["col_1"] , Sequence(Value("int64" ) ) )
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = Dataset.from_list([] )
self.assertEqual(len(UpperCAmelCase ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 297 | 1 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 30 | """simple docstring"""
from scipy.stats import pearsonr
import datasets
_a : str = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'
_a : List[str] = '\nArgs:\n predictions (`list` of `int`): Predicted class labels, as returned by a model.\n references (`list` of `int`): Ground truth labels.\n return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n Example 1-A simple example using only predictions and references.\n >>> pearsonr_metric = datasets.load_metric("pearsonr")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n >>> print(round(results[\'pearsonr\'], 2))\n -0.74\n\n Example 2-The same as Example 1, but that also returns the `p-value`.\n >>> pearsonr_metric = datasets.load_metric("pearsonr")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n >>> print(sorted(list(results.keys())))\n [\'p-value\', \'pearsonr\']\n >>> print(round(results[\'pearsonr\'], 2))\n -0.74\n >>> print(round(results[\'p-value\'], 2))\n 0.15\n'
_a : List[Any] = '\n@article{2020SciPy-NMeth,\nauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Antonio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\njournal = {Nature Methods},\nyear = {2020},\nvolume = {17},\npages = {261--272},\nadsurl = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
def __A ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""] , )
def __A ( self , a__ , a__ , a__=False ):
if return_pvalue:
_lowerCAmelCase : List[Any] = pearsonr(a__ , a__ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(a__ , a__ )[0] )}
| 44 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCamelCase_( snake_case__: str , snake_case__: str , snake_case__: str , snake_case__: Path , snake_case__: str = None , snake_case__: str = None , snake_case__: str = None , ) -> List[str]:
if config_name_or_path is None:
UpperCAmelCase__ = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
UpperCAmelCase__ = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCAmelCase__ = question_encoder_name_or_path
UpperCAmelCase__ = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
UpperCAmelCase__ = RagConfig.from_pretrained(snake_case__ )
UpperCAmelCase__ = AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase__ = AutoConfig.from_pretrained(snake_case__ )
UpperCAmelCase__ = gen_config
UpperCAmelCase__ = question_encoder_config
UpperCAmelCase__ = model_class.from_pretrained_question_encoder_generator(
snake_case__ , snake_case__ , config=snake_case__ )
rag_model.save_pretrained(snake_case__ )
# Sanity check.
model_class.from_pretrained(snake_case__ )
# Save tokenizers.
UpperCAmelCase__ = AutoTokenizer.from_pretrained(snake_case__ )
gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/' )
UpperCAmelCase__ = AutoTokenizer.from_pretrained(snake_case__ )
question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/' )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''',
choices=['''rag_sequence''', '''rag_token'''],
required=True,
type=str,
help='''RAG model type: rag_sequence, rag_token''',
)
parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
parser.add_argument(
'''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
)
parser.add_argument(
'''--generator_tokenizer_name_or_path''',
type=str,
help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
)
parser.add_argument(
'''--question_encoder_tokenizer_name_or_path''',
type=str,
help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
)
parser.add_argument(
'''--config_name_or_path''',
type=str,
help=(
'''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
''' ``model_type``'''
),
)
_UpperCamelCase = parser.parse_args()
_UpperCamelCase = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 362 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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 (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class lowercase :
'''simple docstring'''
def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = 13
UpperCAmelCase__ = 7
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = 99
UpperCAmelCase__ = 384
UpperCAmelCase__ = 2
UpperCAmelCase__ = 4
UpperCAmelCase__ = 37
UpperCAmelCase__ = 'gelu'
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 512
UpperCAmelCase__ = 16
UpperCAmelCase__ = 2
UpperCAmelCase__ = 0.02
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
UpperCAmelCase__ = 128
UpperCAmelCase__ = 2
UpperCAmelCase__ = 9
UpperCAmelCase__ = 1
UpperCAmelCase__ = None
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
if self.use_token_type_ids:
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = ConvBertConfig(
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 , return_dict=__a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel(config=__a )
UpperCAmelCase__ = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
UpperCAmelCase__ = [input_ids, input_mask]
UpperCAmelCase__ = model(__a )
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertForMaskedLM(config=__a )
UpperCAmelCase__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFConvBertForSequenceClassification(config=__a )
UpperCAmelCase__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = self.num_choices
UpperCAmelCase__ = TFConvBertForMultipleChoice(config=__a )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase__ = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFConvBertForTokenClassification(config=__a )
UpperCAmelCase__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
UpperCAmelCase__ = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ (self , __a , __a , __a , __a , __a , __a , __a ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertForQuestionAnswering(config=__a )
UpperCAmelCase__ = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
UpperCAmelCase__ = 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 UpperCamelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class lowercase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
__SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": TFConvBertModel,
"""fill-mask""": TFConvBertForMaskedLM,
"""question-answering""": TFConvBertForQuestionAnswering,
"""text-classification""": TFConvBertForSequenceClassification,
"""token-classification""": TFConvBertForTokenClassification,
"""zero-shot""": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def UpperCamelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=__a , hidden_size=37 )
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a )
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a )
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
@slow
def UpperCamelCase__ (self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = True
UpperCAmelCase__ = True
if hasattr(__a , 'use_cache' ):
UpperCAmelCase__ = True
UpperCAmelCase__ = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , 'key_length' , __a )
for model_class in self.all_model_classes:
UpperCAmelCase__ = self._prepare_for_class(__a , __a )
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = len(model(__a ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a , saved_model=__a )
UpperCAmelCase__ = os.path.join(__a , 'saved_model' , '1' )
UpperCAmelCase__ = tf.keras.models.load_model(__a )
UpperCAmelCase__ = model(__a )
if self.is_encoder_decoder:
UpperCAmelCase__ = outputs['encoder_hidden_states']
UpperCAmelCase__ = outputs['encoder_attentions']
else:
UpperCAmelCase__ = outputs['hidden_states']
UpperCAmelCase__ = outputs['attentions']
self.assertEqual(len(__a ) , __a )
UpperCAmelCase__ = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__a ) , __a )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
self.assertIsNotNone(__a )
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ = True
UpperCAmelCase__ = getattr(self.model_tester , 'decoder_seq_length' , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
UpperCAmelCase__ = getattr(self.model_tester , 'key_length' , __a )
UpperCAmelCase__ = getattr(self.model_tester , 'key_length' , __a )
def check_decoder_attentions_output(__a ):
UpperCAmelCase__ = len(__a )
self.assertEqual(out_len % 2 , 0 )
UpperCAmelCase__ = outputs.decoder_attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(__a ):
UpperCAmelCase__ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCAmelCase__ = True
UpperCAmelCase__ = False
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = model(self._prepare_for_class(__a , __a ) )
UpperCAmelCase__ = len(__a )
self.assertEqual(config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
if self.is_encoder_decoder:
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(config.output_hidden_states , __a )
check_decoder_attentions_output(__a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
# Check attention is always last and order is fine
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = model_class(__a )
UpperCAmelCase__ = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__a ) )
self.assertEqual(model.config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
@require_tf
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = model(__a )[0]
UpperCAmelCase__ = [1, 6, 768]
self.assertEqual(output.shape , __a )
UpperCAmelCase__ = tf.constant(
[
[
[-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
[0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
[0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1E-4 )
| 335 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class lowerCamelCase :
'''simple docstring'''
def __init__( self: Dict , snake_case: Optional[Any] , snake_case: Tuple=13 , snake_case: Any=32 , snake_case: Union[str, Any]=2 , snake_case: Tuple=3 , snake_case: Union[str, Any]=16 , snake_case: Union[str, Any]=[1, 2, 1] , snake_case: Optional[Any]=[2, 2, 4] , snake_case: str=2 , snake_case: List[str]=2.0 , snake_case: Optional[int]=True , snake_case: Union[str, Any]=0.0 , snake_case: Optional[int]=0.0 , snake_case: Optional[Any]=0.1 , snake_case: List[str]="gelu" , snake_case: Any=False , snake_case: Optional[Any]=True , snake_case: Optional[int]=0.0_2 , snake_case: Any=1E-5 , snake_case: Optional[int]=True , snake_case: int=None , snake_case: Any=True , snake_case: str=10 , snake_case: Optional[Any]=8 , snake_case: Union[str, Any]=["stage1", "stage2", "stage3"] , snake_case: Tuple=[1, 2, 3] , ) -> Dict:
snake_case_ :Dict = parent
snake_case_ :List[Any] = batch_size
snake_case_ :Dict = image_size
snake_case_ :Dict = patch_size
snake_case_ :Tuple = num_channels
snake_case_ :List[Any] = embed_dim
snake_case_ :List[str] = depths
snake_case_ :str = num_heads
snake_case_ :Tuple = window_size
snake_case_ :Tuple = mlp_ratio
snake_case_ :int = qkv_bias
snake_case_ :Tuple = hidden_dropout_prob
snake_case_ :Optional[Any] = attention_probs_dropout_prob
snake_case_ :Dict = drop_path_rate
snake_case_ :Any = hidden_act
snake_case_ :Any = use_absolute_embeddings
snake_case_ :int = patch_norm
snake_case_ :List[Any] = layer_norm_eps
snake_case_ :Tuple = initializer_range
snake_case_ :str = is_training
snake_case_ :int = scope
snake_case_ :Tuple = use_labels
snake_case_ :Tuple = type_sequence_label_size
snake_case_ :str = encoder_stride
snake_case_ :List[Any] = out_features
snake_case_ :str = out_indices
def lowerCAmelCase_ ( self: Tuple ) -> Dict:
snake_case_ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ :str = None
if self.use_labels:
snake_case_ :Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ :Union[str, Any] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self: int ) -> Optional[Any]:
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def lowerCAmelCase_ ( self: List[Any] , snake_case: str , snake_case: int , snake_case: List[str] ) -> Any:
snake_case_ :Dict = MaskFormerSwinModel(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Tuple = model(snake_case )
snake_case_ :Dict = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ :Any = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def lowerCAmelCase_ ( self: Optional[Any] , snake_case: int , snake_case: List[str] , snake_case: Tuple ) -> Union[str, Any]:
snake_case_ :Any = MaskFormerSwinBackbone(config=snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Optional[Any] = model(snake_case )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(snake_case ):
snake_case_ :Optional[Any] = ["""stem"""]
snake_case_ :str = MaskFormerSwinBackbone(config=snake_case )
def lowerCAmelCase_ ( self: List[str] ) -> Optional[Any]:
snake_case_ :Optional[int] = self.prepare_config_and_inputs()
snake_case_, snake_case_, snake_case_ :str = config_and_inputs
snake_case_ :Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : Union[str, Any] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
_A : str = {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
_A : List[str] = False
_A : Any = False
_A : Dict = False
_A : List[Any] = False
_A : Optional[int] = False
def lowerCAmelCase_ ( self: Dict ) -> Any:
snake_case_ :str = MaskFormerSwinModelTester(self )
snake_case_ :Optional[Any] = ConfigTester(self , config_class=snake_case , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def lowerCAmelCase_ ( self: List[str] ) -> Optional[int]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Dict:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase_ ( self: Any ) -> Tuple:
return
def lowerCAmelCase_ ( self: Any ) -> Any:
snake_case_ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> int:
snake_case_ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*snake_case )
@unittest.skip("""Swin does not use inputs_embeds""" )
def lowerCAmelCase_ ( self: str ) -> List[str]:
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
pass
def lowerCAmelCase_ ( self: List[str] ) -> List[Any]:
snake_case_, snake_case_ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ :str = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ :Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def lowerCAmelCase_ ( self: Tuple ) -> Dict:
snake_case_, snake_case_ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ :Optional[int] = model_class(snake_case )
snake_case_ :str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ :str = [*signature.parameters.keys()]
snake_case_ :str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , snake_case )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def lowerCAmelCase_ ( self: List[Any] ) -> List[Any]:
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def lowerCAmelCase_ ( self: Dict ) -> List[Any]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] , snake_case: Union[str, Any] , snake_case: int , snake_case: Any , snake_case: List[str] ) -> str:
snake_case_ :List[str] = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
snake_case_ :List[Any] = model(**self._prepare_for_class(snake_case , snake_case ) )
snake_case_ :Any = outputs.hidden_states
snake_case_ :Optional[int] = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(snake_case ) , snake_case )
# Swin has a different seq_length
snake_case_ :str = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ :int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCAmelCase_ ( self: List[Any] ) -> Optional[int]:
snake_case_, snake_case_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ :Tuple = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ :List[Any] = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , snake_case )
def lowerCAmelCase_ ( self: Optional[Any] ) -> Tuple:
snake_case_, snake_case_ :int = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :List[Any] = 3
snake_case_ :List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ :Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ :Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ :List[str] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ :str = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ :Any = True
self.check_hidden_states_output(snake_case , snake_case , snake_case , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def lowerCAmelCase_ ( self: Union[str, Any] ) -> List[str]:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def lowerCAmelCase_ ( self: List[str] ) -> str:
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def lowerCAmelCase_ ( self: str ) -> List[Any]:
pass
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Optional[Any]:
snake_case_, snake_case_ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(snake_case: str ):
snake_case_ :Optional[int] = 0
return t
def check_equivalence(snake_case: List[Any] , snake_case: Union[str, Any] , snake_case: int , snake_case: Tuple={} ):
with torch.no_grad():
snake_case_ :List[Any] = model(**snake_case , return_dict=snake_case , **snake_case )
snake_case_ :Any = model(**snake_case , return_dict=snake_case , **snake_case ).to_tuple()
def recursive_check(snake_case: List[Any] , snake_case: int ):
if isinstance(snake_case , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(snake_case , snake_case ):
recursive_check(snake_case , snake_case )
elif isinstance(snake_case , snake_case ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(snake_case , snake_case )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(snake_case ) , set_nan_tensor_to_zero(snake_case ) , atol=1E-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
f""" {torch.isnan(snake_case ).any()} and `inf`: {torch.isinf(snake_case )}. Dict has"""
f""" `nan`: {torch.isnan(snake_case ).any()} and `inf`: {torch.isinf(snake_case )}."""
) , )
recursive_check(snake_case , snake_case )
for model_class in self.all_model_classes:
snake_case_ :int = model_class(snake_case )
model.to(snake_case )
model.eval()
snake_case_ :Any = self._prepare_for_class(snake_case , snake_case )
snake_case_ :List[Any] = self._prepare_for_class(snake_case , snake_case )
check_equivalence(snake_case , snake_case , snake_case )
snake_case_ :Tuple = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
snake_case_ :Dict = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
check_equivalence(snake_case , snake_case , snake_case )
snake_case_ :Tuple = self._prepare_for_class(snake_case , snake_case )
snake_case_ :Any = self._prepare_for_class(snake_case , snake_case )
check_equivalence(snake_case , snake_case , snake_case , {"""output_hidden_states""": True} )
snake_case_ :Dict = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
snake_case_ :List[str] = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
check_equivalence(snake_case , snake_case , snake_case , {"""output_hidden_states""": True} )
@require_torch
class lowerCamelCase ( unittest.TestCase , _lowerCAmelCase ):
'''simple docstring'''
_A : int = (MaskFormerSwinBackbone,) if is_torch_available() else ()
_A : Tuple = MaskFormerSwinConfig
def lowerCAmelCase_ ( self: List[str] ) -> Optional[int]:
snake_case_ :Optional[Any] = MaskFormerSwinModelTester(self )
def lowerCAmelCase_ ( self: int ) -> Optional[int]:
snake_case_, snake_case_ :Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ :Tuple = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
snake_case_ :List[str] = backbone_class(snake_case )
backbone.to(snake_case )
backbone.eval()
snake_case_ :List[Any] = backbone(**snake_case )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , snake_case )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
snake_case_ :Union[str, Any] = backbone(**snake_case , output_hidden_states=snake_case )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
snake_case_, snake_case_, snake_case_ :List[Any] = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
snake_case_ :List[Any] = backbone(**snake_case , output_attentions=snake_case )
self.assertIsNotNone(outputs.attentions )
| 66 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase_ ( self ) -> List[str]:
__lowerCamelCase : Any = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
__lowerCamelCase : Tuple = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(SCREAMING_SNAKE_CASE_ )
from datasets import load_dataset
__lowerCamelCase : str = load_dataset('nielsr/rvlcdip-demo' )
__lowerCamelCase : List[Any] = dataset['train'][0]['image'].convert('RGB' )
__lowerCamelCase : str = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors='pt' ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
__lowerCamelCase : str = model(**SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = outputs.logits
__lowerCamelCase : List[Any] = torch.Size((1, 16) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[Any] = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=SCREAMING_SNAKE_CASE_ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 185 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _UpperCamelCase :
'''simple docstring'''
@staticmethod
def __lowerCamelCase ( *_lowerCAmelCase : List[Any] , **_lowerCAmelCase : Any):
'''simple docstring'''
pass
def _A ( _lowerCAmelCase ):
"""simple docstring"""
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCamelCase = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __lowerCamelCase ( self : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
__lowercase =pipeline(
'document-question-answering' , model=_lowerCAmelCase , tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase)
__lowercase =INVOICE_URL
__lowercase =list(zip(*apply_tesseract(load_image(_lowerCAmelCase) , _lowerCAmelCase , '')))
__lowercase ='What is the placebo?'
__lowercase =[
{
'image': load_image(_lowerCAmelCase),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def __lowerCamelCase ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
__lowercase =dqa_pipeline(_lowerCAmelCase , top_k=2)
self.assertEqual(
_lowerCAmelCase , [
[
{'score': ANY(_lowerCAmelCase), 'answer': ANY(_lowerCAmelCase), 'start': ANY(_lowerCAmelCase), 'end': ANY(_lowerCAmelCase)},
{'score': ANY(_lowerCAmelCase), 'answer': ANY(_lowerCAmelCase), 'start': ANY(_lowerCAmelCase), 'end': ANY(_lowerCAmelCase)},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCamelCase ( self : Dict):
'''simple docstring'''
__lowercase =pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2')
__lowercase =INVOICE_URL
__lowercase ='How many cats are there?'
__lowercase =[
{'score': 0.0001, 'answer': 'oy 2312/2019', 'start': 3_8, 'end': 3_9},
{'score': 0.0001, 'answer': 'oy 2312/2019 DUE', 'start': 3_8, 'end': 4_0},
]
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4) , _lowerCAmelCase)
__lowercase =dqa_pipeline({'image': image, 'question': question} , top_k=2)
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4) , _lowerCAmelCase)
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__lowercase ='./tests/fixtures/tests_samples/COCO/000000039769.png'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(_lowerCAmelCase , [])
# We can optionnally pass directly the words and bounding boxes
__lowercase ='./tests/fixtures/tests_samples/COCO/000000039769.png'
__lowercase =[]
__lowercase =[]
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , words=_lowerCAmelCase , boxes=_lowerCAmelCase , top_k=2)
self.assertEqual(_lowerCAmelCase , [])
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCamelCase ( self : Any):
'''simple docstring'''
__lowercase =pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
__lowercase =INVOICE_URL
__lowercase ='What is the invoice number?'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
__lowercase =dqa_pipeline({'image': image, 'question': question} , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
__lowercase =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
[
{'score': 0.9944, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0009, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=5_0 , )
__lowercase =INVOICE_URL
__lowercase ='What is the invoice number?'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
__lowercase =dqa_pipeline({'image': image, 'question': question} , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
__lowercase =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
[
{'score': 0.9974, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
{'score': 0.9948, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_lowerCAmelCase)
__lowercase =pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_lowerCAmelCase , revision='3dc6de3' , )
__lowercase =INVOICE_URL
__lowercase ='What is the invoice number?'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
__lowercase =dqa_pipeline({'image': image, 'question': question} , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
__lowercase =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
[
{'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
]
]
* 2 , )
__lowercase =list(zip(*apply_tesseract(load_image(_lowerCAmelCase) , _lowerCAmelCase , '')))
# This model should also work if `image` is set to None
__lowercase =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.4251, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.0819, 'answer': '1110212019', 'start': 2_3, 'end': 2_3},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__lowercase =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=_lowerCAmelCase)
__lowercase =pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=_lowerCAmelCase , revision='3dc6de3' , max_seq_len=5_0 , )
__lowercase =INVOICE_URL
__lowercase ='What is the invoice number?'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
__lowercase =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
[
{'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
]
]
* 2 , )
__lowercase =list(zip(*apply_tesseract(load_image(_lowerCAmelCase) , _lowerCAmelCase , '')))
# This model should also work if `image` is set to None
__lowercase =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2)
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4) , [
{'score': 0.9999, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
{'score': 0.9998, 'answer': 'us-001', 'start': 1_6, 'end': 1_6},
] , )
@slow
@require_torch
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__lowercase =pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa') , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
__lowercase =INVOICE_URL
__lowercase ='What is the invoice number?'
__lowercase =dqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2)
self.assertEqual(nested_simplify(_lowerCAmelCase , decimals=4) , [{'answer': 'us-001'}])
@require_tf
@unittest.skip('Document question answering not implemented in TF')
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
pass
| 48 |
'''simple docstring'''
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
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {"""vocab_file""": """spiece.model"""}
lowerCamelCase = {
"""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""",
}
}
lowerCamelCase = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
# Segments (not really needed)
lowerCamelCase = 0
lowerCamelCase = 1
lowerCamelCase = 2
lowerCamelCase = 3
lowerCamelCase = 4
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = """left"""
def __init__( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any]=False , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : Any=False , _lowerCAmelCase : Any="<s>" , _lowerCAmelCase : Union[str, Any]="</s>" , _lowerCAmelCase : int="<unk>" , _lowerCAmelCase : Union[str, Any]="<sep>" , _lowerCAmelCase : Union[str, Any]="<pad>" , _lowerCAmelCase : Union[str, Any]="<cls>" , _lowerCAmelCase : List[Any]="<mask>" , _lowerCAmelCase : List[Any]=["<eop>", "<eod>"] , _lowerCAmelCase : Optional[Dict[str, Any]] = None , **_lowerCAmelCase : str , ):
'''simple docstring'''
__lowercase =AddedToken(_lowerCAmelCase , lstrip=_lowerCAmelCase , rstrip=_lowerCAmelCase) if isinstance(_lowerCAmelCase , _lowerCAmelCase) else mask_token
__lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowerCAmelCase , remove_space=_lowerCAmelCase , keep_accents=_lowerCAmelCase , bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , additional_special_tokens=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
__lowercase =3
__lowercase =do_lower_case
__lowercase =remove_space
__lowercase =keep_accents
__lowercase =vocab_file
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(_lowerCAmelCase)
@property
def __lowerCamelCase ( self : str):
'''simple docstring'''
return len(self.sp_model)
def __lowerCamelCase ( self : Any):
'''simple docstring'''
__lowercase ={self.convert_ids_to_tokens(_lowerCAmelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : str):
'''simple docstring'''
__lowercase =self.__dict__.copy()
__lowercase =None
return state
def __setstate__( self : List[Any] , _lowerCAmelCase : List[str]):
'''simple docstring'''
__lowercase =d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
__lowercase ={}
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def __lowerCamelCase ( self : Union[str, Any] , _lowerCAmelCase : Any):
'''simple docstring'''
if self.remove_space:
__lowercase =' '.join(inputs.strip().split())
else:
__lowercase =inputs
__lowercase =outputs.replace('``' , '"').replace('\'\'' , '"')
if not self.keep_accents:
__lowercase =unicodedata.normalize('NFKD' , _lowerCAmelCase)
__lowercase =''.join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase)])
if self.do_lower_case:
__lowercase =outputs.lower()
return outputs
def __lowerCamelCase ( self : List[Any] , _lowerCAmelCase : str):
'''simple docstring'''
__lowercase =self.preprocess_text(_lowerCAmelCase)
__lowercase =self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase)
__lowercase =[]
for piece in pieces:
if len(_lowerCAmelCase) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
__lowercase =self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCAmelCase , ''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
__lowercase =cur_pieces[1:]
else:
__lowercase =cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(_lowerCAmelCase)
else:
new_pieces.append(_lowerCAmelCase)
return new_pieces
def __lowerCamelCase ( self : Any , _lowerCAmelCase : Optional[int]):
'''simple docstring'''
return self.sp_model.PieceToId(_lowerCAmelCase)
def __lowerCamelCase ( self : Tuple , _lowerCAmelCase : List[str]):
'''simple docstring'''
return self.sp_model.IdToPiece(_lowerCAmelCase)
def __lowerCamelCase ( self : Any , _lowerCAmelCase : Tuple):
'''simple docstring'''
__lowercase =''.join(_lowerCAmelCase).replace(_lowerCAmelCase , ' ').strip()
return out_string
def __lowerCamelCase ( self : Optional[Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = None , _lowerCAmelCase : bool = True , **_lowerCAmelCase : List[Any] , ):
'''simple docstring'''
__lowercase =kwargs.pop('use_source_tokenizer' , _lowerCAmelCase)
__lowercase =self.convert_ids_to_tokens(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase)
# 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
__lowercase =[]
__lowercase =[]
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(_lowerCAmelCase))
__lowercase =[]
sub_texts.append(_lowerCAmelCase)
else:
current_sub_text.append(_lowerCAmelCase)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(_lowerCAmelCase))
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
__lowercase =''.join(_lowerCAmelCase)
__lowercase =(
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
__lowercase =self.clean_up_tokenization(_lowerCAmelCase)
return clean_text
else:
return text
def __lowerCamelCase ( self : Tuple , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None):
'''simple docstring'''
__lowercase =[self.sep_token_id]
__lowercase =[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 __lowerCamelCase ( self : Any , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None , _lowerCAmelCase : bool = False):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCAmelCase , token_ids_a=_lowerCAmelCase , already_has_special_tokens=_lowerCAmelCase)
if token_ids_a is not None:
return ([0] * len(_lowerCAmelCase)) + [1] + ([0] * len(_lowerCAmelCase)) + [1, 1]
return ([0] * len(_lowerCAmelCase)) + [1, 1]
def __lowerCamelCase ( self : Tuple , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None):
'''simple docstring'''
__lowercase =[self.sep_token_id]
__lowercase =[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 __lowerCamelCase ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(_lowerCAmelCase):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
return
__lowercase =os.path.join(
_lowerCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(_lowerCAmelCase) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , _lowerCAmelCase)
elif not os.path.isfile(self.vocab_file):
with open(_lowerCAmelCase , 'wb') as fi:
__lowercase =self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase)
return (out_vocab_file,)
| 48 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[str] , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
requires_backends(self , "decord" )
self.check_model_type(A )
def _A ( self : Union[str, Any] , A : int=None , A : int=None , A : Any=None ):
_UpperCAmelCase : int = {}
if frame_sampling_rate is not None:
_UpperCAmelCase : Dict = frame_sampling_rate
if num_frames is not None:
_UpperCAmelCase : Optional[int] = num_frames
_UpperCAmelCase : Dict = {}
if top_k is not None:
_UpperCAmelCase : List[str] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , A : Union[str, List[str]] , **A : List[str] ):
return super().__call__(A , **A )
def _A ( self : List[Any] , A : str , A : List[str]=None , A : Optional[int]=1 ):
if num_frames is None:
_UpperCAmelCase : int = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
_UpperCAmelCase : Optional[Any] = BytesIO(requests.get(A ).content )
_UpperCAmelCase : List[Any] = VideoReader(A )
videoreader.seek(0 )
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : int = num_frames * frame_sampling_rate - 1
_UpperCAmelCase : Union[str, Any] = np.linspace(A , A , num=A , dtype=np.intaa )
_UpperCAmelCase : Dict = videoreader.get_batch(A ).asnumpy()
_UpperCAmelCase : Dict = list(A )
_UpperCAmelCase : int = self.image_processor(A , return_tensors=self.framework )
return model_inputs
def _A ( self : Optional[int] , A : List[str] ):
_UpperCAmelCase : Any = self.model(**A )
return model_outputs
def _A ( self : int , A : List[str] , A : str=5 ):
if top_k > self.model.config.num_labels:
_UpperCAmelCase : Optional[int] = self.model.config.num_labels
if self.framework == "pt":
_UpperCAmelCase : Tuple = model_outputs.logits.softmax(-1 )[0]
_UpperCAmelCase , _UpperCAmelCase : int = probs.topk(A )
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
_UpperCAmelCase : List[str] = scores.tolist()
_UpperCAmelCase : Any = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A , A )]
| 31 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class A_ :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( *lowerCamelCase_ :Optional[int] , **lowerCamelCase_ :Optional[Any] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( snake_case_ : List[str] ) ->str:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCAmelCase = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def UpperCAmelCase__ ( self :Optional[Any] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Tuple , lowerCamelCase_ :int ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =pipeline(
'document-question-answering' , model=lowerCamelCase_ , tokenizer=lowerCamelCase_ , image_processor=lowerCamelCase_ )
lowerCamelCase__ : Tuple =INVOICE_URL
lowerCamelCase__ : Optional[Any] =list(zip(*apply_tesseract(load_image(lowerCamelCase_ ) , lowerCamelCase_ , '' ) ) )
lowerCamelCase__ : Optional[Any] ='What is the placebo?'
lowerCamelCase__ : List[str] =[
{
'image': load_image(lowerCamelCase_ ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def UpperCAmelCase__ ( self :int , lowerCamelCase_ :Tuple , lowerCamelCase_ :Tuple ):
"""simple docstring"""
lowerCamelCase__ : List[str] =dqa_pipeline(lowerCamelCase_ , top_k=2 )
self.assertEqual(
lowerCamelCase_ , [
[
{'score': ANY(lowerCamelCase_ ), 'answer': ANY(lowerCamelCase_ ), 'start': ANY(lowerCamelCase_ ), 'end': ANY(lowerCamelCase_ )},
{'score': ANY(lowerCamelCase_ ), 'answer': ANY(lowerCamelCase_ ), 'start': ANY(lowerCamelCase_ ), 'end': ANY(lowerCamelCase_ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase__ ( self :Any ):
"""simple docstring"""
lowerCamelCase__ : str =pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
lowerCamelCase__ : Any =INVOICE_URL
lowerCamelCase__ : Union[str, Any] ='How many cats are there?'
lowerCamelCase__ : List[Any] =[
{'score': 0.00_01, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
{'score': 0.00_01, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
]
lowerCamelCase__ : Dict =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(nested_simplify(lowerCamelCase_ , decimals=4 ) , lowerCamelCase_ )
lowerCamelCase__ : int =dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(lowerCamelCase_ , decimals=4 ) , lowerCamelCase_ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCamelCase__ : str ='./tests/fixtures/tests_samples/COCO/000000039769.png'
lowerCamelCase__ : Union[str, Any] =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(lowerCamelCase_ , [] )
# We can optionnally pass directly the words and bounding boxes
lowerCamelCase__ : str ='./tests/fixtures/tests_samples/COCO/000000039769.png'
lowerCamelCase__ : Optional[Any] =[]
lowerCamelCase__ : Tuple =[]
lowerCamelCase__ : Tuple =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , words=lowerCamelCase_ , boxes=lowerCamelCase_ , top_k=2 )
self.assertEqual(lowerCamelCase_ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase__ ( self :Any ):
"""simple docstring"""
lowerCamelCase__ : int =pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
lowerCamelCase__ : Dict =INVOICE_URL
lowerCamelCase__ : int ='What is the invoice number?'
lowerCamelCase__ : Union[str, Any] =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCamelCase__ : Optional[int] =dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCamelCase__ : List[str] =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
[
{'score': 0.99_44, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.00_09, 'answer': 'us-001', 'start': 16, 'end': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase__ ( self :List[str] ):
"""simple docstring"""
lowerCamelCase__ : int =pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=50 , )
lowerCamelCase__ : Tuple =INVOICE_URL
lowerCamelCase__ : Any ='What is the invoice number?'
lowerCamelCase__ : Union[str, Any] =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCamelCase__ : List[Any] =dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCamelCase__ : List[Any] =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
[
{'score': 0.99_74, 'answer': '1110212019', 'start': 23, 'end': 23},
{'score': 0.99_48, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase__ ( self :Dict ):
"""simple docstring"""
lowerCamelCase__ : int =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowerCamelCase_ )
lowerCamelCase__ : Optional[Any] =pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowerCamelCase_ , revision='3dc6de3' , )
lowerCamelCase__ : int =INVOICE_URL
lowerCamelCase__ : Tuple ='What is the invoice number?'
lowerCamelCase__ : Optional[Any] =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowerCamelCase__ : Optional[int] =dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
lowerCamelCase__ : Optional[Any] =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
[
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
]
]
* 2 , )
lowerCamelCase__ : Tuple =list(zip(*apply_tesseract(load_image(lowerCamelCase_ ) , lowerCamelCase_ , '' ) ) )
# This model should also work if `image` is set to None
lowerCamelCase__ : Optional[int] =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.42_51, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.08_19, 'answer': '1110212019', 'start': 23, 'end': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase__ ( self :Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] =AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowerCamelCase_ )
lowerCamelCase__ : Any =pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowerCamelCase_ , revision='3dc6de3' , max_seq_len=50 , )
lowerCamelCase__ : Dict =INVOICE_URL
lowerCamelCase__ : Optional[Any] ='What is the invoice number?'
lowerCamelCase__ : Tuple =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
lowerCamelCase__ : Tuple =dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
[
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
]
]
* 2 , )
lowerCamelCase__ : str =list(zip(*apply_tesseract(load_image(lowerCamelCase_ ) , lowerCamelCase_ , '' ) ) )
# This model should also work if `image` is set to None
lowerCamelCase__ : Union[str, Any] =dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowerCamelCase_ , decimals=4 ) , [
{'score': 0.99_99, 'answer': 'us-001', 'start': 16, 'end': 16},
{'score': 0.99_98, 'answer': 'us-001', 'start': 16, 'end': 16},
] , )
@slow
@require_torch
def UpperCAmelCase__ ( self :str ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =pipeline(
'document-question-answering' , model='naver-clova-ix/donut-base-finetuned-docvqa' , tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa' ) , feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa' , )
lowerCamelCase__ : Union[str, Any] =INVOICE_URL
lowerCamelCase__ : Union[str, Any] ='What is the invoice number?'
lowerCamelCase__ : Union[str, Any] =dqa_pipeline(image=lowerCamelCase_ , question=lowerCamelCase_ , top_k=2 )
self.assertEqual(nested_simplify(lowerCamelCase_ , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def UpperCAmelCase__ ( self :str ):
"""simple docstring"""
pass | 126 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
__UpperCAmelCase : Any = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class UpperCAmelCase_ ( _a):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = "time_series_transformer"
__UpperCamelCase : Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = "student_t" , __SCREAMING_SNAKE_CASE = "nll" , __SCREAMING_SNAKE_CASE = 1 , __SCREAMING_SNAKE_CASE = [1, 2, 3, 4, 5, 6, 7] , __SCREAMING_SNAKE_CASE = "mean" , __SCREAMING_SNAKE_CASE = 0 , __SCREAMING_SNAKE_CASE = 0 , __SCREAMING_SNAKE_CASE = 0 , __SCREAMING_SNAKE_CASE = 0 , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = 32 , __SCREAMING_SNAKE_CASE = 32 , __SCREAMING_SNAKE_CASE = 2 , __SCREAMING_SNAKE_CASE = 2 , __SCREAMING_SNAKE_CASE = 2 , __SCREAMING_SNAKE_CASE = 2 , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = "gelu" , __SCREAMING_SNAKE_CASE = 64 , __SCREAMING_SNAKE_CASE = 0.1 , __SCREAMING_SNAKE_CASE = 0.1 , __SCREAMING_SNAKE_CASE = 0.1 , __SCREAMING_SNAKE_CASE = 0.1 , __SCREAMING_SNAKE_CASE = 0.1 , __SCREAMING_SNAKE_CASE = 100 , __SCREAMING_SNAKE_CASE = 0.02 , __SCREAMING_SNAKE_CASE=True , **__SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = prediction_length
UpperCamelCase : Tuple = context_length or prediction_length
UpperCamelCase : Any = distribution_output
UpperCamelCase : int = loss
UpperCamelCase : Dict = input_size
UpperCamelCase : Optional[Any] = num_time_features
UpperCamelCase : Union[str, Any] = lags_sequence
UpperCamelCase : List[Any] = scaling
UpperCamelCase : Optional[Any] = num_dynamic_real_features
UpperCamelCase : int = num_static_real_features
UpperCamelCase : Dict = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(__SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
UpperCamelCase : str = cardinality
else:
UpperCamelCase : Optional[int] = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(__SCREAMING_SNAKE_CASE ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
UpperCamelCase : str = embedding_dimension
else:
UpperCamelCase : str = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
UpperCamelCase : Dict = num_parallel_samples
# Transformer architecture configuration
UpperCamelCase : int = input_size * len(__SCREAMING_SNAKE_CASE ) + self._number_of_features
UpperCamelCase : Tuple = d_model
UpperCamelCase : int = encoder_attention_heads
UpperCamelCase : str = decoder_attention_heads
UpperCamelCase : Optional[int] = encoder_ffn_dim
UpperCamelCase : List[Any] = decoder_ffn_dim
UpperCamelCase : List[str] = encoder_layers
UpperCamelCase : List[Any] = decoder_layers
UpperCamelCase : Dict = dropout
UpperCamelCase : List[Any] = attention_dropout
UpperCamelCase : List[str] = activation_dropout
UpperCamelCase : Dict = encoder_layerdrop
UpperCamelCase : Union[str, Any] = decoder_layerdrop
UpperCamelCase : List[Any] = activation_function
UpperCamelCase : str = init_std
UpperCamelCase : List[str] = use_cache
super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _lowercase ( self ):
"""simple docstring"""
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 315 |
import qiskit
def a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
UpperCamelCase : Any = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if bita == 1:
qc_ha.x(1 )
qc_ha.barrier()
# use cnots to write XOR of the inputs on qubit2
qc_ha.cx(0 , 2 )
qc_ha.cx(1 , 2 )
# use ccx / toffoli gate to write AND of the inputs on qubit3
qc_ha.ccx(0 , 1 , 3 )
qc_ha.barrier()
# extract outputs
qc_ha.measure(2 , 0 ) # extract XOR value
qc_ha.measure(3 , 1 ) # extract AND value
# Execute the circuit on the qasm simulator
UpperCamelCase : Any = qiskit.execute(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , shots=1_0_0_0 )
# Return the histogram data of the results of the experiment
return job.result().get_counts(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
__UpperCAmelCase : int = half_adder(1, 1)
print(f'''Half Adder Output Qubit Counts: {counts}''')
| 315 | 1 |
from random import randint, random
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ = False , lowercase_ = False , lowercase_ = 5 , ) -> list:
"""simple docstring"""
A__ = [[-1] * number_of_cells] # Create a highway without any car
A__ = 0
A__ = max(lowercase_ , 0 )
while i < number_of_cells:
A__ = (
randint(0 , lowercase_ ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
A__ = 0
A__ = highway_now[car_index + 1 :]
for cell in range(len(lowercase_ ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(lowercase_ , -1 )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> list:
"""simple docstring"""
A__ = len(lowercase_ )
# Beforce calculations, the highway is empty
A__ = [-1] * number_of_cells
for car_index in range(lowercase_ ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
A__ = min(highway_now[car_index] + 1 , lowercase_ )
# Number of empty cell before the next car
A__ = get_distance(lowercase_ , lowercase_ ) - 1
# We can't have the car causing an accident
A__ = min(next_highway[car_index] , lowercase_ )
if random() < probability:
# Randomly, a driver will slow down
A__ = max(next_highway[car_index] - 1 , 0 )
return next_highway
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> list:
"""simple docstring"""
A__ = len(highway[0] )
for i in range(lowercase_ ):
A__ = update(highway[i] , lowercase_ , lowercase_ )
A__ = [-1] * number_of_cells
for car_index in range(lowercase_ ):
A__ = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
A__ = (car_index + speed) % number_of_cells
# Commit the change of position
A__ = speed
highway.append(lowercase_ )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
__A = "path-to-your-trained-model"
__A = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
__A = "A photo of sks dog in a bucket"
__A = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
| 148 | 0 |
from __future__ import annotations
import bisect
def __lowerCAmelCase ( a__ , a__ , a__ = 0 , a__ = -1 ) -> int:
if hi < 0:
__a = len(a__ )
while lo < hi:
__a = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
__a = mid + 1
else:
__a = mid
return lo
def __lowerCAmelCase ( a__ , a__ , a__ = 0 , a__ = -1 ) -> int:
if hi < 0:
__a = len(a__ )
while lo < hi:
__a = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
__a = mid + 1
else:
__a = mid
return lo
def __lowerCAmelCase ( a__ , a__ , a__ = 0 , a__ = -1 ) -> None:
sorted_collection.insert(bisect_left(a__ , a__ , a__ , a__ ) , a__ )
def __lowerCAmelCase ( a__ , a__ , a__ = 0 , a__ = -1 ) -> None:
sorted_collection.insert(bisect_right(a__ , a__ , a__ , a__ ) , a__ )
def __lowerCAmelCase ( a__ , a__ ) -> int | None:
__a = 0
__a = len(a__ ) - 1
while left <= right:
__a = left + (right - left) // 2
__a = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
__a = midpoint - 1
else:
__a = midpoint + 1
return None
def __lowerCAmelCase ( a__ , a__ ) -> int | None:
__a = bisect.bisect_left(a__ , a__ )
if index != len(a__ ) and sorted_collection[index] == item:
return index
return None
def __lowerCAmelCase ( a__ , a__ , a__ , a__ ) -> int | None:
if right < left:
return None
__a = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(a__ , a__ , a__ , midpoint - 1 )
else:
return binary_search_by_recursion(a__ , a__ , midpoint + 1 , a__ )
if __name__ == "__main__":
A : List[str] = input('Enter numbers separated by comma:\n').strip()
A : Optional[Any] = sorted(int(item) for item in user_input.split(','))
A : str = int(input('Enter a single number to be found in the list:\n'))
A : str = binary_search(collection, target)
if result is None:
print(F"{target} was not found in {collection}.")
else:
print(F"{target} was found at position {result} in {collection}.") | 355 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class __A( a ):
snake_case_ = 0
snake_case_ = False
snake_case_ = 3.0
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
self.assertDictEqual(MockClass().to_kwargs() , {} )
self.assertDictEqual(MockClass(a=2 ).to_kwargs() , {'''a''': 2} )
self.assertDictEqual(MockClass(a=2 , b=_snake_case ).to_kwargs() , {'''a''': 2, '''b''': True} )
self.assertDictEqual(MockClass(a=2 , c=2.25 ).to_kwargs() , {'''a''': 2, '''c''': 2.25} )
@require_cuda
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = GradScalerKwargs(init_scale=1_024 , growth_factor=2 )
AcceleratorState._reset_state()
__a = Accelerator(mixed_precision='''fp16''' , kwargs_handlers=[scaler_handler] )
print(accelerator.use_fpaa )
__a = accelerator.scaler
# Check the kwargs have been applied
self.assertEqual(scaler._init_scale , 1024.0 )
self.assertEqual(scaler._growth_factor , 2.0 )
# Check the other values are at the default
self.assertEqual(scaler._backoff_factor , 0.5 )
self.assertEqual(scaler._growth_interval , 2_000 )
self.assertEqual(scaler._enabled , _snake_case )
@require_multi_gpu
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = ['''torchrun''', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
execute_subprocess_async(_snake_case , env=os.environ.copy() )
if __name__ == "__main__":
A : List[str] = DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
A : Optional[Any] = Accelerator(kwargs_handlers=[ddp_scaler])
A : int = torch.nn.Linear(1_0_0, 2_0_0)
A : Optional[int] = accelerator.prepare(model)
# Check the values changed in kwargs
A : List[Any] = ''
A : Tuple = model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
if observed_bucket_cap_map != 1_5:
error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
if model.find_unused_parameters is not True:
error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"
# Check the values of the defaults
if model.dim != 0:
error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
if model.broadcast_buffers is not True:
error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
if model.gradient_as_bucket_view is not False:
error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg) | 33 | 0 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
lowercase = "autoformer"
lowercase = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self : List[Any] , snake_case_ : Optional[int] = None , snake_case_ : Optional[int] = None , snake_case_ : str = "student_t" , snake_case_ : str = "nll" , snake_case_ : int = 1 , snake_case_ : List[int] = [1, 2, 3, 4, 5, 6, 7] , snake_case_ : bool = True , snake_case_ : int = 0 , snake_case_ : int = 0 , snake_case_ : int = 0 , snake_case_ : int = 0 , snake_case_ : Optional[List[int]] = None , snake_case_ : Optional[List[int]] = None , snake_case_ : int = 64 , snake_case_ : int = 2 , snake_case_ : int = 2 , snake_case_ : int = 2 , snake_case_ : int = 2 , snake_case_ : int = 32 , snake_case_ : int = 32 , snake_case_ : str = "gelu" , snake_case_ : float = 0.1 , snake_case_ : float = 0.1 , snake_case_ : float = 0.1 , snake_case_ : float = 0.1 , snake_case_ : float = 0.1 , snake_case_ : int = 100 , snake_case_ : float = 0.02 , snake_case_ : bool = True , snake_case_ : Optional[Any]=True , snake_case_ : int = 10 , snake_case_ : int = 25 , snake_case_ : int = 3 , **snake_case_ : Optional[int] , ):
# time series specific configuration
snake_case__ : Optional[Any] = prediction_length
snake_case__ : List[str] = context_length if context_length is not None else prediction_length
snake_case__ : List[str] = distribution_output
snake_case__ : Dict = loss
snake_case__ : List[str] = input_size
snake_case__ : Any = num_time_features
snake_case__ : Any = lags_sequence
snake_case__ : List[Any] = scaling
snake_case__ : List[Any] = num_dynamic_real_features
snake_case__ : Union[str, Any] = num_static_real_features
snake_case__ : List[str] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
snake_case__ : Union[str, Any] = cardinality
else:
snake_case__ : Union[str, Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(snake_case_ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
snake_case__ : Union[str, Any] = embedding_dimension
else:
snake_case__ : List[Any] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
snake_case__ : List[str] = num_parallel_samples
# Transformer architecture configuration
snake_case__ : str = input_size * len(self.lags_sequence ) + self._number_of_features
snake_case__ : Optional[Any] = d_model
snake_case__ : Optional[Any] = encoder_attention_heads
snake_case__ : str = decoder_attention_heads
snake_case__ : Any = encoder_ffn_dim
snake_case__ : int = decoder_ffn_dim
snake_case__ : Tuple = encoder_layers
snake_case__ : Dict = decoder_layers
snake_case__ : List[str] = dropout
snake_case__ : Optional[Any] = attention_dropout
snake_case__ : List[Any] = activation_dropout
snake_case__ : Tuple = encoder_layerdrop
snake_case__ : Tuple = decoder_layerdrop
snake_case__ : int = activation_function
snake_case__ : str = init_std
snake_case__ : str = use_cache
# Autoformer
snake_case__ : Optional[Any] = label_length
snake_case__ : Dict = moving_average
snake_case__ : Tuple = autocorrelation_factor
super().__init__(is_encoder_decoder=snake_case_ , **snake_case_ )
@property
def lowerCamelCase ( self : List[str] ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 35 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__lowercase : List[Any] = 'bart'
__lowercase : Union[str, Any] = True
@st.cache(allow_output_mutation=_SCREAMING_SNAKE_CASE )
def lowerCamelCase ():
if LOAD_DENSE_INDEX:
__a : List[Any] = AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
__a : Dict = AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
__a : Optional[int] = qar_model.eval()
else:
__a , __a : str = (None, None)
if MODEL_TYPE == "bart":
__a : Union[str, Any] = AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
__a : int = AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
__a : Optional[Any] = torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
sas_model.load_state_dict(save_dict['model'] )
__a : str = sas_model.eval()
else:
__a , __a : Tuple = make_qa_sas_model(
model_name='t5-small' , from_file='seq2seq_models/eli5_t5_model_1024_4.pth' , device='cuda:0' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=_SCREAMING_SNAKE_CASE )
def lowerCamelCase ():
if LOAD_DENSE_INDEX:
__a : Optional[Any] = faiss.StandardGpuResources()
__a : Dict = datasets.load_dataset(path='wiki_snippets' , name='wiki40b_en_100_0' )['train']
__a : int = np.memmap(
'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' , dtype='float32' , mode='r' , shape=(wikiaab_passages.num_rows, 128) , )
__a : int = faiss.IndexFlatIP(128 )
__a : Any = faiss.index_cpu_to_gpu(_SCREAMING_SNAKE_CASE , 1 , _SCREAMING_SNAKE_CASE )
wikiaab_gpu_index_flat.add(_SCREAMING_SNAKE_CASE ) # TODO fix for larger GPU
else:
__a , __a : str = (None, None)
__a : Optional[int] = Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_SCREAMING_SNAKE_CASE )
def lowerCamelCase ():
__a : Dict = datasets.load_dataset('eli5' , name='LFQA_reddit' )
__a : Dict = elia['train_eli5']
__a : Optional[int] = np.memmap(
'eli5_questions_reps.dat' , dtype='float32' , mode='r' , shape=(elia_train.num_rows, 128) )
__a : str = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(_SCREAMING_SNAKE_CASE )
return (elia_train, eli5_train_q_index)
__lowercase , __lowercase , __lowercase : Any = load_indexes()
__lowercase , __lowercase , __lowercase , __lowercase : Dict = load_models()
__lowercase , __lowercase : int = load_train_data()
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[str]=10 ):
__a : Optional[int] = embed_questions_for_retrieval([question] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a , __a : Union[str, Any] = eli5_train_q_index.search(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__a : Any = [elia_train[int(_SCREAMING_SNAKE_CASE )] for i in I[0]]
return nn_examples
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : str="wiki40b" , _SCREAMING_SNAKE_CASE : List[str]="dense" , _SCREAMING_SNAKE_CASE : Any=10 ):
if source == "none":
__a , __a : Any = (' <P> '.join(['' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
__a , __a : str = query_qa_dense_index(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
__a , __a : Union[str, Any] = query_es_index(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , index_name='english_wiki40b_snippets_100w' , n_results=_SCREAMING_SNAKE_CASE , )
__a : Dict = [
(res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
]
__a : Any = 'question: {} context: {}'.format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda _SCREAMING_SNAKE_CASE : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _SCREAMING_SNAKE_CASE : None),
} )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Dict=64 , _SCREAMING_SNAKE_CASE : Dict=256 , _SCREAMING_SNAKE_CASE : Any=False , _SCREAMING_SNAKE_CASE : Tuple=2 , _SCREAMING_SNAKE_CASE : Union[str, Any]=0.9_5 , _SCREAMING_SNAKE_CASE : str=0.8 ):
with torch.no_grad():
__a : Union[str, Any] = qa_sas_generate(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , num_answers=1 , num_beams=_SCREAMING_SNAKE_CASE , min_len=_SCREAMING_SNAKE_CASE , max_len=_SCREAMING_SNAKE_CASE , do_sample=_SCREAMING_SNAKE_CASE , temp=_SCREAMING_SNAKE_CASE , top_p=_SCREAMING_SNAKE_CASE , top_k=_SCREAMING_SNAKE_CASE , max_input_length=1_024 , device='cuda:0' , )[0]
return (answer, support_list)
st.title('Long Form Question Answering with ELI5')
# Start sidebar
__lowercase : Optional[Any] = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
__lowercase : str = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__lowercase : str = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)
__lowercase : Dict = [
'Answer the question',
'View the retrieved document only',
'View the most similar ELI5 question and answer',
'Show me everything, please!',
]
__lowercase : Union[str, Any] = st.sidebar.checkbox('Demo options')
if demo_options:
__lowercase : Any = st.sidebar.selectbox(
'',
action_list,
index=3,
)
__lowercase : Tuple = action_list.index(action_st)
__lowercase : Tuple = st.sidebar.selectbox(
'',
['Show full text of passages', 'Show passage section titles'],
index=0,
)
__lowercase : List[Any] = show_type == 'Show full text of passages'
else:
__lowercase : int = 3
__lowercase : str = True
__lowercase : Tuple = st.sidebar.checkbox('Retrieval options')
if retrieval_options:
__lowercase : List[Any] = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n '
st.sidebar.markdown(retriever_info)
__lowercase : Union[str, Any] = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
__lowercase : Union[str, Any] = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
__lowercase : str = 'wiki40b'
__lowercase : List[Any] = 'dense'
__lowercase : Dict = 'beam'
__lowercase : Optional[int] = 2
__lowercase : List[str] = 64
__lowercase : Tuple = 2_56
__lowercase : List[str] = None
__lowercase : Tuple = None
__lowercase : List[Any] = st.sidebar.checkbox('Generation options')
if generate_options:
__lowercase : Optional[Any] = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n '
st.sidebar.markdown(generate_info)
__lowercase : List[Any] = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
__lowercase : Tuple = st.sidebar.slider(
'Minimum generation length', min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
)
__lowercase : int = st.sidebar.slider(
'Maximum generation length', min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
)
if sampled == "beam":
__lowercase : Any = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__lowercase : Dict = st.sidebar.slider(
'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
__lowercase : Union[str, Any] = st.sidebar.slider(
'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
__lowercase : List[str] = None
# start main text
__lowercase : int = [
'<MY QUESTION>',
'How do people make chocolate?',
'Why do we get a fever when we are sick?',
'How can different animals perceive different colors?',
'What is natural language processing?',
'What\'s the best way to treat a sunburn?',
'What exactly are vitamins ?',
'How does nuclear energy provide electricity?',
'What\'s the difference between viruses and bacteria?',
'Why are flutes classified as woodwinds when most of them are made out of metal ?',
'Why do people like drinking coffee even though it tastes so bad?',
'What happens when wine ages? How does it make the wine taste better?',
'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
'How does New Zealand have so many large bird predators?',
]
__lowercase : Optional[int] = st.selectbox(
'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__lowercase : Any = st.text_input('Enter your question here:', '')
else:
__lowercase : Any = question_s
if st.button('Show me!'):
if action in [0, 1, 3]:
if index_type == "mixed":
__lowercase , __lowercase : Optional[int] = make_support(question, source=wiki_source, method='dense', n_results=10)
__lowercase , __lowercase : List[Any] = make_support(question, source=wiki_source, method='sparse', n_results=10)
__lowercase : Optional[int] = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__lowercase : str = support_list[:10]
__lowercase : Optional[int] = '<P> ' + ' <P> '.join([res[-1] for res in support_list])
else:
__lowercase , __lowercase : Optional[Any] = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__lowercase , __lowercase : int = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == 'sampled'),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('### The model generated answer is:')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
for i, res in enumerate(support_list):
__lowercase : str = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
__lowercase : Any = res[1].strip()
if sec_titles == "":
__lowercase : List[str] = '[{}]({})'.format(res[0], wiki_url)
else:
__lowercase : Union[str, Any] = sec_titles.split(' & ')
__lowercase : str = ' & '.join(
['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
)
st.markdown(
'{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
)
if action in [2, 3]:
__lowercase : str = find_nearest_training(question)
__lowercase : Optional[int] = nn_train_list[0]
st.markdown(
'--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
)
__lowercase : Any = [
'{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
if i == 0 or sc > 2
]
st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))
__lowercase : List[Any] = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 27 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __A (_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :Dict = [
'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(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __A (_SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ :List[Any] = emb.weight.shape
lowerCAmelCase__ :Dict = nn.Linear(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :Dict = emb.weight.data
return lin_layer
def __A (_SCREAMING_SNAKE_CASE ) ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :Dict = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )
lowerCAmelCase__ :List[Any] = mam_aaa['args'] or mam_aaa['cfg']['model']
lowerCAmelCase__ :Union[str, Any] = mam_aaa['model']
remove_ignore_keys_(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :int = state_dict['encoder.embed_tokens.weight'].shape[0]
lowerCAmelCase__ :int = MaMaaaConfig(
vocab_size=_SCREAMING_SNAKE_CASE , max_position_embeddings=1024 , 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 , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , )
lowerCAmelCase__ :Union[str, Any] = state_dict['decoder.embed_tokens.weight']
lowerCAmelCase__ :Union[str, Any] = MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE )
model.model.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :str = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
__A = parser.parse_args()
__A = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 254 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class _lowerCAmelCase ( a ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase = "▁" , __UpperCAmelCase = True , __UpperCAmelCase = "<unk>" , __UpperCAmelCase = "</s>" , __UpperCAmelCase = "<pad>" , ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
lowerCAmelCase__ :Optional[int] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowerCAmelCase__ :Any = token_dict['token']
lowerCAmelCase__ :int = Tokenizer(Unigram() )
lowerCAmelCase__ :Tuple = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
lowerCAmelCase__ :Any = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase ),
pre_tokenizers.Digits(individual_digits=__UpperCAmelCase ),
pre_tokenizers.Punctuation(),
] )
lowerCAmelCase__ :List[str] = decoders.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase )
lowerCAmelCase__ :Tuple = TemplateProcessing(
single=F"$A {self.special_tokens['eos']['token']}" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
lowerCAmelCase__ :Optional[int] = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = 8_0_0_0 , __UpperCAmelCase = True , ):
'''simple docstring'''
lowerCAmelCase__ :int = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ :int = [files]
self._tokenizer.train(__UpperCAmelCase , trainer=__UpperCAmelCase )
self.add_unk_id()
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase = 8_0_0_0 , __UpperCAmelCase = True , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
self._tokenizer.train_from_iterator(__UpperCAmelCase , trainer=__UpperCAmelCase )
self.add_unk_id()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = json.loads(self._tokenizer.to_str() )
lowerCAmelCase__ :List[str] = self.special_tokens['unk']['id']
lowerCAmelCase__ :Union[str, Any] = Tokenizer.from_str(json.dumps(__UpperCAmelCase ) )
| 254 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase_ : List[str] = HfApi()
lowerCAmelCase_ : str = {}
# fmt: off
lowerCAmelCase_ : Any = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
-1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
lowerCAmelCase_ : List[Any] = torch.tensor([
-2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
-2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
lowerCAmelCase_ : int = torch.tensor([
-0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
-0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
-0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
lowerCAmelCase_ : List[Any] = torch.tensor([
0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
-0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
-0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
lowerCAmelCase_ : Tuple = torch.tensor([
0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
-0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
-0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
lowerCAmelCase_ : List[str] = torch.tensor([
0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
-0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
-0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
lowerCAmelCase_ : Optional[Any] = torch.tensor([
0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
-0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
-0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
lowerCAmelCase_ : Optional[Any] = torch.tensor([
0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
-0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
-0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
lowerCAmelCase_ : Optional[int] = torch.tensor([
-1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
-2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
lowerCAmelCase_ : List[Any] = torch.tensor([
-1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
-2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
lowerCAmelCase_ : Tuple = torch.tensor([
-1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
-2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
lowerCAmelCase_ : str = torch.tensor([
-2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
-3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
lowerCAmelCase_ : int = torch.tensor([
-2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
-2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
lowerCAmelCase_ : Union[str, Any] = torch.tensor([
-2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
-3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
lowerCAmelCase_ : List[str] = torch.tensor([
-1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
-2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on
lowerCAmelCase_ : Any = api.list_models(filter='diffusers')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowerCAmelCase_ : Union[str, Any] = '/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('CompVis'):
lowerCAmelCase_ : Dict = UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
else:
lowerCAmelCase_ : int = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowerCAmelCase_ : Optional[int] = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowerCAmelCase_ : str = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1e-3
)
print(f"""{mod.modelId} has passed successfully!!!""")
| 63 |
"""simple docstring"""
import math
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_60:
raise ValueError('''In Malus Law, the angle is in the range 0-360 degrees''' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name='malus_law')
| 249 | 0 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class a ( unittest.TestCase ):
def A_ ( self : Optional[int] , lowercase_ : int , lowercase_ : str , lowercase_ : Tuple ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for a, b in zip(lowercase_ , lowercase_ ):
self.assertAlmostEqual(lowercase_ , lowercase_ , delta=lowercase_ )
def A_ ( self : str ):
snake_case_ = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(lowercase_ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def A_ ( self : int ):
snake_case_ = None
ops.enable_eager_execution_internal()
snake_case_ = tf.config.list_physical_devices('''CPU''' )
if len(lowercase_ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
snake_case_ = tf.config.list_logical_devices(device_type='''CPU''' )
snake_case_ = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
snake_case_ = GradientAccumulator()
snake_case_ = tf.Variable([4.0, 3.0] )
snake_case_ ,snake_case_ = create_optimizer(5e-5 , 10 , 5 )
snake_case_ = tf.Variable([0.0, 0.0] , trainable=lowercase_ )
def accumulate_on_replica(lowercase_ : List[str] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(lowercase_ : List[str] , lowercase_ : Optional[Any] ):
with strategy.scope():
snake_case_ = strategy.experimental_local_results(lowercase_ )
local_variables[0].assign(lowercase_ )
local_variables[1].assign(lowercase_ )
strategy.run(lowercase_ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(lowercase_ )
def _check_local_values(lowercase_ : Union[str, Any] , lowercase_ : List[str] ):
snake_case_ = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , lowercase_ , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , lowercase_ , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 72 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 72 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : List[Any] = DanceDiffusionPipeline
__lowercase : Dict = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
__lowercase : Optional[int] = PipelineTesterMixin.required_optional_params - {
'''callback''',
'''latents''',
'''callback_steps''',
'''output_type''',
'''num_images_per_prompt''',
}
__lowercase : str = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
__lowercase : List[Any] = False
__lowercase : str = False
def snake_case_ ( self):
torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(3_2, 3_2, 6_4) , extra_in_channels=1_6 , sample_size=5_1_2 , sample_rate=1_6_0_0_0 , in_channels=2 , out_channels=2 , flip_sin_to_cos=lowerCAmelCase__ , use_timestep_embedding=lowerCAmelCase__ , time_embedding_type="""fourier""" , mid_block_type="""UNetMidBlock1D""" , down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""") , up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""") , )
__SCREAMING_SNAKE_CASE = IPNDMScheduler()
__SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
}
return components
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__=0):
if str(lowerCAmelCase__).startswith("""mps"""):
__SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCAmelCase__)
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = {
"""batch_size""": 1,
"""generator""": generator,
"""num_inference_steps""": 4,
}
return inputs
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = DanceDiffusionPipeline(**lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__)
pipe.set_progress_bar_config(disable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = output.audios
__SCREAMING_SNAKE_CASE = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
__SCREAMING_SNAKE_CASE = np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1E-2
@skip_mps
def snake_case_ ( self):
return super().test_save_load_local()
@skip_mps
def snake_case_ ( self):
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)
@skip_mps
def snake_case_ ( self):
return super().test_save_load_optional_components()
@skip_mps
def snake_case_ ( self):
return super().test_attention_slicing_forward_pass()
def snake_case_ ( self):
super().test_inference_batch_single_identical(expected_max_diff=3E-3)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = torch_device
__SCREAMING_SNAKE_CASE = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""")
__SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__)
pipe.set_progress_bar_config(disable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe(generator=lowerCAmelCase__ , num_inference_steps=1_0_0 , audio_length_in_s=4.0_96)
__SCREAMING_SNAKE_CASE = output.audios
__SCREAMING_SNAKE_CASE = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
__SCREAMING_SNAKE_CASE = np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1E-2
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = torch_device
__SCREAMING_SNAKE_CASE = DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k""" , torch_dtype=torch.floataa)
__SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__)
pipe.set_progress_bar_config(disable=lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = torch.manual_seed(0)
__SCREAMING_SNAKE_CASE = pipe(generator=lowerCAmelCase__ , num_inference_steps=1_0_0 , audio_length_in_s=4.0_96)
__SCREAMING_SNAKE_CASE = output.audios
__SCREAMING_SNAKE_CASE = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
__SCREAMING_SNAKE_CASE = np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41])
assert np.abs(audio_slice.flatten() - expected_slice).max() < 1E-2
| 100 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : Any = {
'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowercase_ : Tuple = """xmod"""
def __init__( self , snake_case_=3_0_5_2_2 , snake_case_=7_6_8 , snake_case_=1_2 , snake_case_=1_2 , snake_case_=3_0_7_2 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_1_2 , snake_case_=2 , snake_case_=0.02 , snake_case_=1E-12 , snake_case_=1 , snake_case_=0 , snake_case_=2 , snake_case_="absolute" , snake_case_=True , snake_case_=None , snake_case_=False , snake_case_=2 , snake_case_=False , snake_case_=True , snake_case_=True , snake_case_=("en_XX",) , snake_case_=None , **snake_case_ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
A_ : Union[str, Any] = vocab_size
A_ : Any = hidden_size
A_ : List[str] = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : int = hidden_act
A_ : Any = intermediate_size
A_ : Any = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : List[Any] = type_vocab_size
A_ : List[str] = initializer_range
A_ : Any = layer_norm_eps
A_ : Optional[Any] = position_embedding_type
A_ : int = use_cache
A_ : Dict = classifier_dropout
A_ : int = pre_norm
A_ : Optional[Any] = adapter_reduction_factor
A_ : List[Any] = adapter_layer_norm
A_ : int = adapter_reuse_layer_norm
A_ : Dict = ln_before_adapter
A_ : List[str] = list(snake_case_ )
A_ : Union[str, Any] = default_language
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@property
def lowerCamelCase_ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
A_ : Dict = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A_ : int = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] ) | 286 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class lowerCamelCase_ ( unittest.TestCase ):
def __init__( self : Union[str, Any] ,__lowerCamelCase : Optional[int] ,__lowerCamelCase : List[Any]=13 ,__lowerCamelCase : Tuple=7 ,__lowerCamelCase : List[str]=True ,__lowerCamelCase : List[Any]=True ,__lowerCamelCase : int=True ,__lowerCamelCase : List[str]=True ,__lowerCamelCase : List[str]=99 ,__lowerCamelCase : Optional[Any]=32 ,__lowerCamelCase : Optional[int]=5 ,__lowerCamelCase : List[Any]=4 ,__lowerCamelCase : int=37 ,__lowerCamelCase : Union[str, Any]="gelu" ,__lowerCamelCase : Optional[int]=0.1 ,__lowerCamelCase : Optional[Any]=0.1 ,__lowerCamelCase : Tuple=5_12 ,__lowerCamelCase : int=16 ,__lowerCamelCase : Dict=2 ,__lowerCamelCase : List[str]=0.02 ,__lowerCamelCase : Union[str, Any]=4 ,):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_attention_mask
a = use_token_type_ids
a = use_labels
a = 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 = type_sequence_label_size
a = initializer_range
a = num_choices
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
a = None
if self.use_attention_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_token_type_ids:
a = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
a = BertConfig(
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=__lowerCamelCase ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = True
a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowerCamelCase_ ( a_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
a = FlaxBertModelTester(self )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
'''simple docstring'''
a = FlaxBertModel.from_pretrained('''bert-base-cased''' )
a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__lowerCamelCase )
| 371 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = 'yolos'
def __init__( self : Union[str, Any] ,__lowerCamelCase : int=7_68 ,__lowerCamelCase : Dict=12 ,__lowerCamelCase : Union[str, Any]=12 ,__lowerCamelCase : List[Any]=30_72 ,__lowerCamelCase : int="gelu" ,__lowerCamelCase : int=0.0 ,__lowerCamelCase : str=0.0 ,__lowerCamelCase : Optional[Any]=0.02 ,__lowerCamelCase : int=1e-12 ,__lowerCamelCase : Any=[5_12, 8_64] ,__lowerCamelCase : Tuple=16 ,__lowerCamelCase : int=3 ,__lowerCamelCase : Tuple=True ,__lowerCamelCase : Optional[int]=1_00 ,__lowerCamelCase : List[Any]=True ,__lowerCamelCase : List[str]=False ,__lowerCamelCase : int=1 ,__lowerCamelCase : List[Any]=5 ,__lowerCamelCase : Optional[int]=2 ,__lowerCamelCase : int=5 ,__lowerCamelCase : str=2 ,__lowerCamelCase : Tuple=0.1 ,**__lowerCamelCase : List[Any] ,):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
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 = initializer_range
a = layer_norm_eps
a = image_size
a = patch_size
a = num_channels
a = qkv_bias
a = num_detection_tokens
a = use_mid_position_embeddings
a = auxiliary_loss
# Hungarian matcher
a = class_cost
a = bbox_cost
a = giou_cost
# Loss coefficients
a = bbox_loss_coefficient
a = giou_loss_coefficient
a = eos_coefficient
class lowerCamelCase_ ( a_ ):
SCREAMING_SNAKE_CASE_ = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
'''simple docstring'''
return 1e-4
@property
def SCREAMING_SNAKE_CASE_ ( self : str ):
'''simple docstring'''
return 12
| 330 | 0 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
snake_case_ = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Any , *lowercase_ :str , **lowercase_ :List[Any] ) -> Union[str, Any]:
super().__init__(*lowercase_ , **lowercase_ )
self.check_model_type(lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Any=None , lowercase_ :Optional[int]=None , lowercase_ :Tuple=None , **lowercase_ :Tuple ) -> Dict:
UpperCAmelCase , UpperCAmelCase = {}, {}
if padding is not None:
UpperCAmelCase = padding
if truncation is not None:
UpperCAmelCase = truncation
if top_k is not None:
UpperCAmelCase = top_k
return preprocess_params, {}, postprocess_params
def __call__( self :List[Any] , lowercase_ :Union["Image.Image", str] , lowercase_ :str = None , **lowercase_ :Union[str, Any] ) -> Union[str, Any]:
if isinstance(lowercase_ , (Image.Image, str) ) and isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = {'image': image, 'question': question}
else:
UpperCAmelCase = image
UpperCAmelCase = super().__call__(lowercase_ , **lowercase_ )
return results
def UpperCAmelCase__ ( self :List[str] , lowercase_ :List[Any] , lowercase_ :int=False , lowercase_ :Optional[int]=False ) -> Union[str, Any]:
UpperCAmelCase = load_image(inputs['image'] )
UpperCAmelCase = self.tokenizer(
inputs['question'] , return_tensors=self.framework , padding=lowercase_ , truncation=lowercase_ )
UpperCAmelCase = self.image_processor(images=lowercase_ , return_tensors=self.framework )
model_inputs.update(lowercase_ )
return model_inputs
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :List[str] ) -> Any:
UpperCAmelCase = self.model(**lowercase_ )
return model_outputs
def UpperCAmelCase__ ( self :Dict , lowercase_ :Tuple , lowercase_ :List[Any]=5 ) -> Union[str, Any]:
if top_k > self.model.config.num_labels:
UpperCAmelCase = self.model.config.num_labels
if self.framework == "pt":
UpperCAmelCase = model_outputs.logits.sigmoid()[0]
UpperCAmelCase , UpperCAmelCase = probs.topk(lowercase_ )
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
UpperCAmelCase = scores.tolist()
UpperCAmelCase = ids.tolist()
return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 78 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Dict , lowercase_ :str = "▁" , lowercase_ :bool = True , lowercase_ :Union[str, AddedToken] = "<unk>" , lowercase_ :Union[str, AddedToken] = "</s>" , lowercase_ :Union[str, AddedToken] = "<pad>" , ) -> str:
UpperCAmelCase = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
UpperCAmelCase = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
UpperCAmelCase = token_dict['token']
UpperCAmelCase = Tokenizer(Unigram() )
UpperCAmelCase = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
UpperCAmelCase = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=lowercase_ , add_prefix_space=lowercase_ ),
pre_tokenizers.Digits(individual_digits=lowercase_ ),
pre_tokenizers.Punctuation(),
] )
UpperCAmelCase = decoders.Metaspace(replacement=lowercase_ , add_prefix_space=lowercase_ )
UpperCAmelCase = TemplateProcessing(
single=f"""$A {self.special_tokens['eos']['token']}""" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
UpperCAmelCase = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Union[str, List[str]] , lowercase_ :int = 80_00 , lowercase_ :bool = True , ) -> Union[str, Any]:
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=lowercase_ , special_tokens=self.special_tokens_list , show_progress=lowercase_ , )
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [files]
self._tokenizer.train(lowercase_ , trainer=lowercase_ )
self.add_unk_id()
def UpperCAmelCase__ ( self :str , lowercase_ :Union[Iterator[str], Iterator[Iterator[str]]] , lowercase_ :int = 80_00 , lowercase_ :bool = True , ) -> Tuple:
UpperCAmelCase = trainers.UnigramTrainer(
vocab_size=lowercase_ , special_tokens=self.special_tokens_list , show_progress=lowercase_ , )
self._tokenizer.train_from_iterator(lowercase_ , trainer=lowercase_ )
self.add_unk_id()
def UpperCAmelCase__ ( self :Union[str, Any] ) -> int:
UpperCAmelCase = json.loads(self._tokenizer.to_str() )
UpperCAmelCase = self.special_tokens['unk']['id']
UpperCAmelCase = Tokenizer.from_str(json.dumps(lowercase_ ) )
| 78 | 1 |
from __future__ import annotations
def UpperCAmelCase ( _lowerCamelCase ): # This function is recursive
A : str = len(_lowerCamelCase )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
A : List[str] = array[0]
A : Dict = False
A : List[Any] = 1
A : list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
A : str = True
A : Tuple = [element for element in array[i:] if element >= array[i]]
A : Union[str, Any] = longest_subsequence(_lowerCamelCase )
if len(_lowerCamelCase ) > len(_lowerCamelCase ):
A : Optional[int] = temp_array
else:
i += 1
A : Optional[Any] = [element for element in array[1:] if element >= pivot]
A : Dict = [pivot, *longest_subsequence(_lowerCamelCase )]
if len(_lowerCamelCase ) > len(_lowerCamelCase ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod() | 256 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class lowerCamelCase_ ( _A ,_A ):
'''simple docstring'''
a__ = "resnet"
a__ = ["basic", "bottleneck"]
def __init__( self : Tuple , __lowerCamelCase : int=3 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Union[str, Any]=[2_56, 5_12, 10_24, 20_48] , __lowerCamelCase : Tuple=[3, 4, 6, 3] , __lowerCamelCase : Optional[Any]="bottleneck" , __lowerCamelCase : Dict="relu" , __lowerCamelCase : Tuple=False , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Tuple=None , **__lowerCamelCase : Tuple , ) -> Optional[Any]:
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
A : Any = num_channels
A : Union[str, Any] = embedding_size
A : Any = hidden_sizes
A : List[str] = depths
A : Union[str, Any] = layer_type
A : Any = hidden_act
A : Any = downsample_in_first_stage
A : Any = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
A , A : int = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class lowerCamelCase_ ( _A ):
'''simple docstring'''
a__ = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> float:
return 1e-3 | 256 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __UpperCAmelCase ( A__ , A__ ):
'''simple docstring'''
@register_to_config
def __init__(self : Optional[Any] , _lowerCAmelCase : int = 768 , ):
super().__init__()
A = nn.Parameter(torch.zeros(1 , _lowerCAmelCase ) )
A = nn.Parameter(torch.ones(1 , _lowerCAmelCase ) )
def A (self : int , _lowerCAmelCase : Optional[Union[str, torch.device]] = None , _lowerCAmelCase : Optional[torch.dtype] = None , ):
A = nn.Parameter(self.mean.to(_lowerCAmelCase ).to(_lowerCAmelCase ) )
A = nn.Parameter(self.std.to(_lowerCAmelCase ).to(_lowerCAmelCase ) )
return self
def A (self : Tuple , _lowerCAmelCase : Union[str, Any] ):
A = (embeds - self.mean) * 1.0 / self.std
return embeds
def A (self : Union[str, Any] , _lowerCAmelCase : Optional[Any] ):
A = (embeds * self.std) + self.mean
return embeds
| 258 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __a ( UpperCAmelCase = "" , ) ->bool:
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def __a ( UpperCAmelCase = "" ) ->bool:
"""simple docstring"""
if len(UpperCAmelCase ) == 0:
return True
A = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
A = {}
for character in lower_case_input_str:
A = character_freq_dict.get(UpperCAmelCase , 0 ) + 1
A = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def __a ( UpperCAmelCase = "" ) ->None:
"""simple docstring"""
print("""\nFor string = """ , UpperCAmelCase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(UpperCAmelCase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
_lowerCamelCase : Any = input(
'Enter string to determine if it can be rearranged as a palindrome or not: '
).strip()
benchmark(check_str)
_lowerCamelCase : Any = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")
| 258 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __a :
_a : Dict = BlenderbotConfig
_a : Dict = {}
_a : Union[str, Any] = 'gelu'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
def UpperCAmelCase__ ( self ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_UpperCAmelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_UpperCAmelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCAmelCase = prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
_UpperCAmelCase = TFBlenderbotModel(config=_SCREAMING_SNAKE_CASE ).get_decoder()
_UpperCAmelCase = inputs_dict['input_ids']
_UpperCAmelCase = input_ids[:1, :]
_UpperCAmelCase = inputs_dict['attention_mask'][:1, :]
_UpperCAmelCase = inputs_dict['head_mask']
_UpperCAmelCase = 1
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_UpperCAmelCase = tf.concat([input_ids, next_tokens] , axis=-1 )
_UpperCAmelCase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_UpperCAmelCase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx]
_UpperCAmelCase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , rtol=1e-3 )
def lowerCAmelCase__ ( a__: Dict , a__: Dict , a__: Any , a__: Any=None , a__: List[Any]=None , a__: Union[str, Any]=None , a__: Tuple=None , a__: Union[str, Any]=None , ) -> Any:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = tf.cast(tf.math.not_equal(a__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_UpperCAmelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_UpperCAmelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_UpperCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_UpperCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __a ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : List[Any] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
_a : List[str] = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
_a : List[str] = (
{
'conversational': TFBlenderbotForConditionalGeneration,
'feature-extraction': TFBlenderbotModel,
'summarization': TFBlenderbotForConditionalGeneration,
'text2text-generation': TFBlenderbotForConditionalGeneration,
'translation': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
_a : Dict = True
_a : int = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = TFBlenderbotModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
@require_tokenizers
@require_tf
class __a ( unittest.TestCase ):
_a : int = ['My friends are cool but they eat too many carbs.']
_a : List[Any] = 'facebook/blenderbot-400M-distill'
@cached_property
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.tokenizer(self.src_text , return_tensors='tf' )
_UpperCAmelCase = self.model.generate(
model_inputs.input_ids , )
_UpperCAmelCase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=_SCREAMING_SNAKE_CASE )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 185 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCAmelCase__ ( a__: Dict , a__: Dict , a__: Any , a__: Optional[int]=None , a__: str=None , a__: List[Any]=None , a__: Optional[int]=None , a__: Union[str, Any]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=a__ )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = self.eos_token_id # Eos Token
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = inputs_dict['input_ids']
_UpperCAmelCase = inputs_dict['attention_mask']
_UpperCAmelCase = inputs_dict['head_mask']
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )['last_hidden_state']
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[
'last_hidden_state'
]
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase = 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(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-2 ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaEncoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : List[Any] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_a : List[str] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_a : int = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_a : str = True
_a : Union[str, Any] = True
_a : Optional[int] = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE )
self.assertEqual(info['missing_keys'] , [] )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if not self.is_encoder_decoder:
_UpperCAmelCase = inputs['input_ids']
del inputs["input_ids"]
else:
_UpperCAmelCase = inputs['input_ids']
_UpperCAmelCase = inputs.get('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.get_input_embeddings()
if not self.is_encoder_decoder:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
model(**_SCREAMING_SNAKE_CASE )[0]
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = input_dict['input_ids']
_UpperCAmelCase = input_ids.ne(1 ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval().to(_SCREAMING_SNAKE_CASE )
if torch_device == "cuda":
model.half()
model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
model.generate(num_beams=4 , do_sample=_SCREAMING_SNAKE_CASE , early_stopping=_SCREAMING_SNAKE_CASE , num_return_sequences=3 )
def lowerCAmelCase__ ( a__: Tuple ) -> Optional[int]:
'''simple docstring'''
return torch.tensor(a__ , dtype=torch.long , device=a__ )
lowerCAmelCase__ :str = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __a ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
# change to intended input
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
_UpperCAmelCase = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
_UpperCAmelCase = model.generate(
input_ids=dct['input_ids'].to(_SCREAMING_SNAKE_CASE ) , attention_mask=dct['attention_mask'].to(_SCREAMING_SNAKE_CASE ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
_UpperCAmelCase = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
_UpperCAmelCase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
assert generated == expected_en
| 185 | 1 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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
lowerCAmelCase: Optional[int] = '▁'
lowerCAmelCase: List[str] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class a__( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = BigBirdTokenizer
lowercase__ = BigBirdTokenizerFast
lowercase__ = True
lowercase__ = True
def lowercase_ ( self : Tuple ):
super().setUp()
a : Tuple = self.tokenizer_class(__snake_case , keep_accents=__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self : Optional[int] ):
a : Dict = '<s>'
a : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) , __snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) , __snake_case )
def lowercase_ ( self : str ):
a : int = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '[MASK]' )
self.assertEqual(len(__snake_case ) , 10_04 )
def lowercase_ ( self : List[str] ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def lowercase_ ( self : Dict ):
if not self.test_rust_tokenizer:
return
a : Optional[Any] = self.get_tokenizer()
a : Dict = self.get_rust_tokenizer()
a : str = 'I was born in 92000, and this is falsé.'
a : Optional[Any] = tokenizer.tokenize(__snake_case )
a : int = rust_tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
a : Tuple = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
a : Tuple = rust_tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
self.assertListEqual(__snake_case , __snake_case )
a : str = self.get_rust_tokenizer()
a : Union[str, Any] = tokenizer.encode(__snake_case )
a : List[Any] = rust_tokenizer.encode(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
def lowercase_ ( self : List[str] ):
a : Union[str, Any] = BigBirdTokenizer(__snake_case , keep_accents=__snake_case )
a : List[Any] = tokenizer.tokenize('This is a test' )
self.assertListEqual(__snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [2_85, 46, 10, 1_70, 3_82] , )
a : Any = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
a : Dict = tokenizer.convert_tokens_to_ids(__snake_case )
self.assertListEqual(
__snake_case , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
a : List[Any] = tokenizer.convert_ids_to_tokens(__snake_case )
self.assertListEqual(
__snake_case , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
@cached_property
def lowercase_ ( self : Union[str, Any] ):
return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
@slow
def lowercase_ ( self : List[Any] ):
a : List[str] = 'Hello World!'
a : Union[str, Any] = [65, 1_85_36, 22_60, 1_01, 66]
self.assertListEqual(__snake_case , self.big_tokenizer.encode(__snake_case ) )
@slow
def lowercase_ ( self : List[Any] ):
a : int = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
# fmt: off
a : int = [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66] # noqa: E231
# fmt: on
self.assertListEqual(__snake_case , self.big_tokenizer.encode(__snake_case ) )
@require_torch
@slow
def lowercase_ ( self : List[Any] ):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
a : Union[str, Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
a : Dict = ' '.join(__snake_case )
a : str = self.big_tokenizer.encode_plus(__snake_case , return_tensors='pt' , return_token_type_ids=__snake_case )
a : Optional[int] = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] , return_tensors='pt' , return_token_type_ids=__snake_case )
a : Optional[Any] = BigBirdConfig(attention_type='original_full' )
a : int = BigBirdModel(__snake_case )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__snake_case )
model(**__snake_case )
@slow
def lowercase_ ( self : Tuple ):
a : Union[str, Any] = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
a : Any = tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )
self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )
@slow
def lowercase_ ( self : List[Any] ):
# fmt: off
a : Optional[Any] = {'input_ids': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 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=__snake_case , model_name='google/bigbird-roberta-base' , revision='215c99f1600e06f83acce68422f2035b2b5c3510' , ) | 297 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: Any = logging.get_logger(__name__)
lowerCAmelCase: Any = {'vocab_file': 'vocab.txt'}
lowerCAmelCase: List[Any] = {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
lowerCAmelCase: str = {
'openbmb/cpm-ant-10b': 1_0_2_4,
}
def lowerCamelCase__ ( _A ):
a : Union[str, Any] = collections.OrderedDict()
with open(_A , 'r' , encoding='utf-8' ) as reader:
a : int = reader.readlines()
for index, token in enumerate(_A ):
a : int = token.rstrip('\n' )
a : List[Any] = index
return vocab
class a__( lowerCamelCase__ ):
def __init__( self : Dict , __snake_case : Any , __snake_case : Dict="<unk>" , __snake_case : str=2_00 ):
a : List[Any] = vocab
a : Any = unk_token
a : List[str] = max_input_chars_per_word
def lowercase_ ( self : Optional[int] , __snake_case : Union[str, Any] ):
a : Optional[Any] = list(__snake_case )
if len(__snake_case ) > self.max_input_chars_per_word:
return [self.unk_token]
a : Any = 0
a : Optional[Any] = []
while start < len(__snake_case ):
a : Optional[int] = len(__snake_case )
a : str = None
while start < end:
a : Optional[Any] = ''.join(chars[start:end] )
if substr in self.vocab:
a : List[str] = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(__snake_case )
a : List[str] = end
return sub_tokens
class a__( lowerCamelCase__ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = False
def __init__( self : Any , __snake_case : str , __snake_case : Tuple="<d>" , __snake_case : List[str]="</d>" , __snake_case : Dict="<s>" , __snake_case : List[Any]="</s>" , __snake_case : int="<pad>" , __snake_case : Any="<unk>" , __snake_case : List[str]="</n>" , __snake_case : int="</_>" , __snake_case : Optional[Any]="left" , **__snake_case : Dict , ):
requires_backends(self , ['jieba'] )
super().__init__(
bod_token=__snake_case , eod_token=__snake_case , bos_token=__snake_case , eos_token=__snake_case , pad_token=__snake_case , unk_token=__snake_case , line_token=__snake_case , space_token=__snake_case , padding_side=__snake_case , **__snake_case , )
a : Union[str, Any] = bod_token
a : Any = eod_token
a : List[str] = load_vocab(__snake_case )
a : Optional[int] = self.encoder[space_token]
a : str = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
a : str = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __snake_case : x[1] ) )
a : Tuple = {v: k for k, v in self.encoder.items()}
a : List[str] = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowercase_ ( self : Optional[int] ):
return self.encoder[self.bod_token]
@property
def lowercase_ ( self : Dict ):
return self.encoder[self.eod_token]
@property
def lowercase_ ( self : Any ):
return self.encoder["\n"]
@property
def lowercase_ ( self : Tuple ):
return len(self.encoder )
def lowercase_ ( self : str ):
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase_ ( self : Union[str, Any] , __snake_case : List[str] ):
a : List[str] = []
for x in jieba.cut(__snake_case , cut_all=__snake_case ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(__snake_case ) )
return output_tokens
def lowercase_ ( self : Union[str, Any] , __snake_case : Optional[Any] , **__snake_case : Optional[Any] ):
a : Optional[int] = [i for i in token_ids if i >= 0]
a : Any = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(__snake_case , **__snake_case )
def lowercase_ ( self : Optional[int] , __snake_case : int ):
return token in self.encoder
def lowercase_ ( self : int , __snake_case : List[str] ):
return "".join(__snake_case )
def lowercase_ ( self : List[str] , __snake_case : Union[str, Any] ):
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def lowercase_ ( self : Tuple , __snake_case : List[str] ):
return self.decoder.get(__snake_case , self.unk_token )
def lowercase_ ( self : Union[str, Any] , __snake_case : str , __snake_case : Optional[str] = None ):
if os.path.isdir(__snake_case ):
a : Optional[int] = os.path.join(
__snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
else:
a : int = (filename_prefix + '-' if filename_prefix else '') + save_directory
a : Any = 0
if " " in self.encoder:
a : Union[str, Any] = self.encoder[' ']
del self.encoder[" "]
if "\n" in self.encoder:
a : Tuple = self.encoder['\n']
del self.encoder["\n"]
a : Dict = collections.OrderedDict(sorted(self.encoder.items() , key=lambda __snake_case : x[1] ) )
with open(__snake_case , 'w' , encoding='utf-8' ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
' Please check that the vocabulary is not corrupted!' )
a : List[Any] = token_index
writer.write(token + '\n' )
index += 1
return (vocab_file,)
def lowercase_ ( self : Union[str, Any] , __snake_case : List[int] , __snake_case : List[int] = None ):
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowercase_ ( self : Dict , __snake_case : List[int] , __snake_case : Optional[List[int]] = None , __snake_case : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__snake_case , token_ids_a=__snake_case , already_has_special_tokens=__snake_case )
if token_ids_a is not None:
return [1] + ([0] * len(__snake_case )) + [1] + ([0] * len(__snake_case ))
return [1] + ([0] * len(__snake_case )) | 297 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
lowercase = None
lowercase = BloomTokenizerFast
lowercase = BloomTokenizerFast
lowercase = True
lowercase = False
lowercase = "tokenizer_file"
lowercase = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def lowerCamelCase ( self : List[Any] ):
super().setUp()
snake_case__ : List[str] = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase ( self : Dict , **snake_case_ : Dict ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **snake_case_ )
def lowerCamelCase ( self : Union[str, Any] ):
snake_case__ : Union[str, Any] = self.get_rust_tokenizer()
snake_case__ : Union[str, Any] = ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
snake_case__ : str = [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]
snake_case__ : int = tokenizer.batch_encode_plus(snake_case_ )["""input_ids"""]
self.assertListEqual(snake_case_ , snake_case_ )
snake_case__ : str = tokenizer.batch_decode(snake_case_ )
self.assertListEqual(snake_case_ , snake_case_ )
def lowerCamelCase ( self : Optional[int] , snake_case_ : str=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case__ : Any = self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
snake_case__ : str = """This is a simple input"""
snake_case__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
snake_case__ : List[str] = ("""This is a simple input""", """This is a pair""")
snake_case__ : str = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
try:
tokenizer_r.encode(snake_case_ , max_length=snake_case_ )
tokenizer_r.encode_plus(snake_case_ , max_length=snake_case_ )
tokenizer_r.batch_encode_plus(snake_case_ , max_length=snake_case_ )
tokenizer_r.encode(snake_case_ , max_length=snake_case_ )
tokenizer_r.batch_encode_plus(snake_case_ , max_length=snake_case_ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
snake_case__ : Union[str, Any] = None # Hotfixing padding = None
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="""max_length""" )
# Simple input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="""max_length""" )
# Simple input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="""max_length""" , )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode , snake_case_ , max_length=snake_case_ , padding="""max_length""" )
# Pair input
self.assertRaises(snake_case_ , tokenizer_r.encode_plus , snake_case_ , max_length=snake_case_ , padding="""max_length""" )
# Pair input
self.assertRaises(
snake_case_ , tokenizer_r.batch_encode_plus , snake_case_ , max_length=snake_case_ , padding="""max_length""" , )
def lowerCamelCase ( self : Optional[Any] ):
snake_case__ : Tuple = self.get_rust_tokenizer()
snake_case__ : Any = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=snake_case_ )
snake_case__ : Dict = next(iter(snake_case_ ) )["""premise"""] # pick up one data
snake_case__ : Tuple = list(sample_data.values() )
snake_case__ : Union[str, Any] = list(map(tokenizer.encode , snake_case_ ) )
snake_case__ : Optional[Any] = [tokenizer.decode(snake_case_ , clean_up_tokenization_spaces=snake_case_ ) for x in output_tokens]
self.assertListEqual(snake_case_ , snake_case_ )
def lowerCamelCase ( self : Tuple ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 362 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
__a = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
from run_translation import main # noqa
set_seed(42)
__a = "sshleifer/student_marian_en_ro_6_1"
__a = "sshleifer/tiny-mbart"
@require_torch
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def lowerCamelCase ( self : Any , snake_case_ : List[str]=False , snake_case_ : Tuple=None , snake_case_ : Dict=True , snake_case_ : Any=True , snake_case_ : Tuple=True , snake_case_ : List[str]=True , ):
snake_case__ : List[Any] = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=snake_case_ , num_train_epochs=1 , distributed=snake_case_ , extra_args_str=snake_case_ , predict_with_generate=snake_case_ , do_train=snake_case_ , do_eval=snake_case_ , do_predict=snake_case_ , )
snake_case__ : int = TrainerState.load_from_json(os.path.join(snake_case_ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
snake_case__ : Tuple = [log for log in logs if """eval_loss""" in log.keys()]
snake_case__ : List[Any] = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
snake_case__ : Dict = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , snake_case_ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def lowerCamelCase ( self : List[Any] ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def lowerCamelCase ( self : int ):
self.run_seqaseq_quick(distributed=snake_case_ )
@require_torch_multi_gpu
def lowerCamelCase ( self : Tuple ):
self.run_seqaseq_quick(distributed=snake_case_ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowerCamelCase ( self : int ):
self.run_seqaseq_quick(distributed=snake_case_ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowerCamelCase ( self : str ):
self.run_seqaseq_quick(distributed=snake_case_ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowerCamelCase ( self : List[str] ):
self.run_seqaseq_quick(distributed=snake_case_ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=snake_case_ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowerCamelCase ( self : str ):
self.run_seqaseq_quick(
distributed=snake_case_ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=snake_case_ )
@require_apex
@require_torch_gpu
def lowerCamelCase ( self : str ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=snake_case_ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=snake_case_ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def lowerCamelCase ( self : Optional[int] , snake_case_ : Union[str, Any] ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
snake_case__ : Any = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
snake_case__ : Optional[int] = experiments[experiment_id]
snake_case__ : Optional[int] = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
snake_case__ : Union[str, Any] = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**snake_case_ , extra_args_str=data["""extra_args_str"""] )
snake_case__ : str = len(re.findall(snake_case_ , cl.err ) )
self.assertEqual(snake_case_ , data["""n_matches"""] )
@slow
def lowerCamelCase ( self : Optional[int] ):
snake_case__ : Tuple = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=snake_case_ , learning_rate=3E-4 , num_train_epochs=10 , distributed=snake_case_ , )
# Check metrics
snake_case__ : Dict = TrainerState.load_from_json(os.path.join(snake_case_ , """trainer_state.json""" ) ).log_history
snake_case__ : List[str] = [log for log in logs if """eval_loss""" in log.keys()]
snake_case__ : List[str] = eval_metrics[0]
snake_case__ : Any = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , snake_case_ )
# test if do_predict saves generations and metrics
snake_case__ : Optional[int] = os.listdir(snake_case_ )
snake_case__ : List[str] = {os.path.basename(snake_case_ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def lowerCamelCase ( self : List[str] ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(snake_case_ : str ) -> Tuple[int, float]:
snake_case__ : Dict = """--skip_memory_metrics 0"""
snake_case__ : Optional[int] = self.run_trainer(
max_len=128 , model_name=snake_case_ , learning_rate=3E-4 , num_train_epochs=1 , optim=snake_case_ , distributed=snake_case_ , extra_args_str=snake_case_ , do_eval=snake_case_ , do_predict=snake_case_ , n_gpus_to_use=1 , )
# Check metrics
snake_case__ : Optional[Any] = TrainerState.load_from_json(Path(snake_case_ , """trainer_state.json""" ) ).log_history
snake_case__ : Optional[int] = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
snake_case__ : Tuple = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
snake_case__ : Optional[int] = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
snake_case__ , snake_case__ , snake_case__ : List[Any] = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
snake_case__ , snake_case__ , snake_case__ : List[Any] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
snake_case__ : Dict = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
snake_case__ : Optional[Any] = gpu_peak_mem_orig + gpu_alloc_mem_orig
snake_case__ : Dict = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
snake_case__ : Tuple = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
snake_case__ : int = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
snake_case_ , snake_case_ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
f" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
f" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
snake_case_ , snake_case_ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
f" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
f" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
snake_case_ , snake_case_ , f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def lowerCamelCase ( self : Dict , snake_case_ : int , snake_case_ : str , snake_case_ : int , snake_case_ : float = 3E-3 , snake_case_ : str = "adafactor" , snake_case_ : bool = False , snake_case_ : str = None , snake_case_ : int = 0 , snake_case_ : bool = True , snake_case_ : bool = True , snake_case_ : bool = True , snake_case_ : bool = True , snake_case_ : int = None , ):
snake_case__ : Optional[Any] = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
snake_case__ : Union[str, Any] = self.get_auto_remove_tmp_dir()
snake_case__ : List[Any] = f"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(snake_case_ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(snake_case_ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
snake_case__ : List[Any] = f"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(snake_case_ )}\n ".split()
snake_case__ : Dict = """
--do_predict
""".split()
snake_case__ : List[Any] = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += f"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
snake_case__ : Any = get_gpu_count()
snake_case__ : Optional[int] = get_torch_dist_unique_port()
snake_case__ : List[str] = f"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
snake_case__ : int = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(snake_case_ , env=self.get_env() )
else:
snake_case__ : str = ["""run_translation.py"""] + args
with patch.object(snake_case_ , """argv""" , snake_case_ ):
main()
return output_dir
| 43 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A ={
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
__A =_LazyModule(__name__, globals()["__file__"], _import_structure)
| 226 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
A__ = end_pointa[0] - end_pointa[0]
A__ = end_pointa[1] - end_pointa[1]
A__ = end_pointa[2] - end_pointa[2]
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : Vectorad ):
A__ = ab[1] * ac[2] - ab[2] * ac[1] # *i
A__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j
A__ = ab[0] * ac[1] - ab[1] * ac[0] # *k
return (x, y, z)
def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : int ):
return tuple(round(UpperCAmelCase_ , UpperCAmelCase_ ) for x in vector ) == (0, 0, 0)
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : int = 10 ):
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ )
return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
| 335 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_SCREAMING_SNAKE_CASE : Optional[Any] = '''sshleifer/bart-tiny-random'''
_SCREAMING_SNAKE_CASE : Any = '''patrickvonplaten/t5-tiny-random'''
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self : Union[str, Any] ) -> List[str]:
return AutoConfig.from_pretrained(__lowerCamelCase )
def lowercase_ ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE__,*SCREAMING_SNAKE_CASE__ = create_student_by_copying_alternating_layers(__lowerCamelCase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def lowercase_ ( self : List[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__,*SCREAMING_SNAKE_CASE__ = create_student_by_copying_alternating_layers(__lowerCamelCase , tempfile.mkdtemp() , e=1 , d=__lowerCamelCase )
def lowercase_ ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE__,*SCREAMING_SNAKE_CASE__ = create_student_by_copying_alternating_layers(__lowerCamelCase , tempfile.mkdtemp() , e=1 , d=__lowerCamelCase )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def lowercase_ ( self : Optional[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__,*SCREAMING_SNAKE_CASE__ = create_student_by_copying_alternating_layers(__lowerCamelCase , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def lowercase_ ( self : Union[str, Any] ) -> Tuple:
with self.assertRaises(__lowerCamelCase ):
create_student_by_copying_alternating_layers(__lowerCamelCase , tempfile.mkdtemp() , e=__lowerCamelCase , d=__lowerCamelCase )
| 218 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
a = ["input_features"]
def __init__( self : Dict , __lowerCamelCase : Tuple=80 , __lowerCamelCase : List[Any]=1_6000 , __lowerCamelCase : Optional[int]=160 , __lowerCamelCase : List[str]=30 , __lowerCamelCase : List[Any]=400 , __lowerCamelCase : Union[str, Any]=0.0 , __lowerCamelCase : str=False , **__lowerCamelCase : List[str] , ) -> Any:
super().__init__(
feature_size=__lowerCamelCase , sampling_rate=__lowerCamelCase , padding_value=__lowerCamelCase , return_attention_mask=__lowerCamelCase , **__lowerCamelCase , )
SCREAMING_SNAKE_CASE__ = n_fft
SCREAMING_SNAKE_CASE__ = hop_length
SCREAMING_SNAKE_CASE__ = chunk_length
SCREAMING_SNAKE_CASE__ = chunk_length * sampling_rate
SCREAMING_SNAKE_CASE__ = self.n_samples // hop_length
SCREAMING_SNAKE_CASE__ = sampling_rate
SCREAMING_SNAKE_CASE__ = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=__lowerCamelCase , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=__lowerCamelCase , norm='''slaney''' , mel_scale='''slaney''' , )
def lowercase_ ( self : int , __lowerCamelCase : np.array ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ = spectrogram(
__lowerCamelCase , window_function(self.n_fft , '''hann''' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel='''log10''' , )
SCREAMING_SNAKE_CASE__ = log_spec[:, :-1]
SCREAMING_SNAKE_CASE__ = np.maximum(__lowerCamelCase , log_spec.max() - 8.0 )
SCREAMING_SNAKE_CASE__ = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def lowercase_ ( __lowerCamelCase : List[np.ndarray] , __lowerCamelCase : List[np.ndarray] , __lowerCamelCase : float = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
SCREAMING_SNAKE_CASE__ = np.array(__lowerCamelCase , np.intaa )
SCREAMING_SNAKE_CASE__ = []
for vector, length in zip(__lowerCamelCase , attention_mask.sum(-1 ) ):
SCREAMING_SNAKE_CASE__ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
SCREAMING_SNAKE_CASE__ = padding_value
normed_input_values.append(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE__ = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self : List[str] , __lowerCamelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , __lowerCamelCase : bool = True , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[Union[str, TensorType]] = None , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[str] = "max_length" , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[bool] = None , **__lowerCamelCase : List[str] , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
f''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
SCREAMING_SNAKE_CASE__ = isinstance(__lowerCamelCase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
SCREAMING_SNAKE_CASE__ = is_batched_numpy or (
isinstance(__lowerCamelCase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE__ = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(__lowerCamelCase , np.ndarray ):
SCREAMING_SNAKE_CASE__ = np.asarray(__lowerCamelCase , dtype=np.floataa )
elif isinstance(__lowerCamelCase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE__ = [np.asarray([raw_speech] ).T]
SCREAMING_SNAKE_CASE__ = BatchFeature({'''input_features''': raw_speech} )
# convert into correct format for padding
SCREAMING_SNAKE_CASE__ = self.pad(
__lowerCamelCase , padding=__lowerCamelCase , max_length=max_length if max_length else self.n_samples , truncation=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
SCREAMING_SNAKE_CASE__ = self.zero_mean_unit_var_norm(
padded_inputs['''input_features'''] , attention_mask=padded_inputs['''attention_mask'''] , padding_value=self.padding_value , )
SCREAMING_SNAKE_CASE__ = np.stack(padded_inputs['''input_features'''] , axis=0 )
# make sure list is in array format
SCREAMING_SNAKE_CASE__ = padded_inputs.get('''input_features''' ).transpose(2 , 0 , 1 )
SCREAMING_SNAKE_CASE__ = [self._np_extract_fbank_features(__lowerCamelCase ) for waveform in input_features[0]]
if isinstance(input_features[0] , __lowerCamelCase ):
SCREAMING_SNAKE_CASE__ = [np.asarray(__lowerCamelCase , dtype=np.floataa ) for feature in input_features]
else:
SCREAMING_SNAKE_CASE__ = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
SCREAMING_SNAKE_CASE__ = padded_inputs['''attention_mask'''][:, :: self.hop_length]
if return_tensors is not None:
SCREAMING_SNAKE_CASE__ = padded_inputs.convert_to_tensors(__lowerCamelCase )
return padded_inputs
def lowercase_ ( self : str ) -> Dict[str, Any]:
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 218 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[Any] = '▁'
SCREAMING_SNAKE_CASE__ : int = {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE__ : List[str] = {
'sentencepiece_model_file': 'sentencepiece.bpe.model',
'vocab_file': 'vocab.txt',
}
SCREAMING_SNAKE_CASE__ : List[str] = {
'vocab_file': {
'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
},
'sentencepiece_model_file': {
'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
},
}
SCREAMING_SNAKE_CASE__ : Any = {
'ernie-m-base': 514,
'ernie-m-large': 514,
}
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
'ernie-m-base': {'do_lower_case': False},
'ernie-m-large': {'do_lower_case': False},
}
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : List[str] = ["input_ids"]
lowerCamelCase_ : Optional[Any] = VOCAB_FILES_NAMES
lowerCamelCase_ : Dict = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ : Optional[Any] = RESOURCE_FILES_NAMES
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=False , UpperCamelCase__="utf8" , UpperCamelCase__="[UNK]" , UpperCamelCase__="[SEP]" , UpperCamelCase__="[PAD]" , UpperCamelCase__="[CLS]" , UpperCamelCase__="[MASK]" , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowerCamelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , vocab_file=UpperCamelCase__ , encoding=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
lowerCamelCase : str = do_lower_case
lowerCamelCase : Any = sentencepiece_model_ckpt
lowerCamelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowerCamelCase : str = self.load_vocab(filepath=UpperCamelCase__ )
else:
lowerCamelCase : Optional[Any] = {self.sp_model.id_to_piece(UpperCamelCase__ ): id for id in range(self.sp_model.get_piece_size() )}
lowerCamelCase : str = {v: k for k, v in self.vocab.items()}
def _lowercase ( self , UpperCamelCase__ ) -> List[str]:
if text is None:
return None
lowerCamelCase : Optional[Any] = self.tokenize(UpperCamelCase__ )
lowerCamelCase , lowerCamelCase : List[str] = "", []
for i, ch in enumerate(UpperCamelCase__ ):
if ch in self.SP_CHAR_MAPPING:
lowerCamelCase : List[Any] = self.SP_CHAR_MAPPING.get(UpperCamelCase__ )
else:
lowerCamelCase : Any = unicodedata.normalize("NFKC" , UpperCamelCase__ )
if self.is_whitespace(UpperCamelCase__ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(UpperCamelCase__ ) )
lowerCamelCase , lowerCamelCase , lowerCamelCase : Dict = normalized_text, [], 0
if self.do_lower_case:
lowerCamelCase : Optional[int] = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowerCamelCase : Optional[Any] = token[1:]
lowerCamelCase : Dict = text[offset:].index(UpperCamelCase__ ) + offset
lowerCamelCase : Union[str, Any] = start + len(UpperCamelCase__ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowerCamelCase : str = end
return token_mapping
@property
def _lowercase ( self ) -> Any:
return len(self.vocab )
def _lowercase ( self ) -> List[str]:
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ) -> Optional[Any]:
lowerCamelCase : Union[str, Any] = self.__dict__.copy()
lowerCamelCase : Dict = None
return state
def __setstate__( self , UpperCamelCase__ ) -> Optional[Any]:
lowerCamelCase : Dict = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCamelCase : List[Any] = {}
lowerCamelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def _lowercase ( self , UpperCamelCase__ ) -> Dict:
return "".join((self.SP_CHAR_MAPPING.get(UpperCamelCase__ , UpperCamelCase__ ) for c in text) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=64 , UpperCamelCase__=0.1 ) -> int:
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowerCamelCase : Tuple = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowerCamelCase : int = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowerCamelCase : str = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowerCamelCase : Dict = self.sp_model.EncodeAsPieces(UpperCamelCase__ )
else:
lowerCamelCase : Optional[int] = self.sp_model.SampleEncodeAsPieces(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Tuple = []
for pi, piece in enumerate(UpperCamelCase__ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(UpperCamelCase__ ) and pi != 0:
new_pieces.append(UpperCamelCase__ )
continue
else:
continue
lowerCamelCase : Dict = 0
for i, chunk in enumerate(UpperCamelCase__ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(UpperCamelCase__ ) or self.is_punct(UpperCamelCase__ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(UpperCamelCase__ )
lowerCamelCase : List[Any] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase : Any = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowerCamelCase : Optional[Any] = i
if len(UpperCamelCase__ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def _lowercase ( self , UpperCamelCase__ ) -> Dict:
lowerCamelCase : Optional[int] = "".join(UpperCamelCase__ ).replace(UpperCamelCase__ , " " ).strip()
return out_string
def _lowercase ( self , UpperCamelCase__ ) -> Any:
lowerCamelCase : Union[str, Any] = self.convert_ids_to_tokens(UpperCamelCase__ )
lowerCamelCase : Tuple = "".join(UpperCamelCase__ ).replace(UpperCamelCase__ , " " ).strip()
return out_string
def _lowercase ( self , UpperCamelCase__ ) -> Tuple:
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def _lowercase ( self , UpperCamelCase__ ) -> int:
return self.reverse_vocab.get(UpperCamelCase__ , self.unk_token )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None ) -> Tuple:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase : List[str] = [self.cls_token_id]
lowerCamelCase : Optional[Any] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None ) -> Union[str, Any]:
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=False ) -> str:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1, 1] + ([0] * len(UpperCamelCase__ )) + [1]
return [1] + ([0] * len(UpperCamelCase__ )) + [1]
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_a is None:
# [CLS] X [SEP]
return (len(UpperCamelCase__ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(UpperCamelCase__ ) + 1) + [1] * (len(UpperCamelCase__ ) + 3)
def _lowercase ( self , UpperCamelCase__ ) -> Union[str, Any]:
if "\u4e00" <= char <= "\u9fff":
return True
return False
def _lowercase ( self , UpperCamelCase__ ) -> Optional[Any]:
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def _lowercase ( self , UpperCamelCase__ ) -> Any:
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def _lowercase ( self , UpperCamelCase__ ) -> int:
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(UpperCamelCase__ ) == 1:
lowerCamelCase : str = unicodedata.category(UpperCamelCase__ )
if cat == "Zs":
return True
return False
def _lowercase ( self , UpperCamelCase__ ) -> Dict:
lowerCamelCase : Any = {}
with io.open(UpperCamelCase__ , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(UpperCamelCase__ ):
lowerCamelCase : Union[str, Any] = line.rstrip("\n" )
lowerCamelCase : Union[str, Any] = int(UpperCamelCase__ )
return token_to_idx
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
lowerCamelCase : Union[str, Any] = 0
if os.path.isdir(UpperCamelCase__ ):
lowerCamelCase : Optional[Any] = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowerCamelCase : str = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda UpperCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowerCamelCase : List[Any] = token_index
writer.write(token + "\n" )
index += 1
lowerCamelCase : Union[str, Any] = os.path.join(UpperCamelCase__ , "sentencepiece.bpe.model" )
with open(UpperCamelCase__ , "wb" ) as fi:
lowerCamelCase : Any = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (vocab_file,)
| 48 |
from __future__ import annotations
import requests
def A ( _SCREAMING_SNAKE_CASE ) -> dict:
lowerCamelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_SCREAMING_SNAKE_CASE ).json()
def A ( _SCREAMING_SNAKE_CASE = 10 ) -> list[dict]:
lowerCamelCase : str = "https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
lowerCamelCase : Any = requests.get(_SCREAMING_SNAKE_CASE ).json()[:max_stories]
return [get_hackernews_story(_SCREAMING_SNAKE_CASE ) for story_id in story_ids]
def A ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
lowerCamelCase : str = hackernews_top_stories(_SCREAMING_SNAKE_CASE )
return "\n".join("* [{title}]({url})".format(**_SCREAMING_SNAKE_CASE ) for story in stories )
if __name__ == "__main__":
print(hackernews_top_stories_as_markdown())
| 48 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def lowercase( UpperCamelCase_ , UpperCamelCase_=False ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = []
# fmt: off
# stem:
rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""") )
rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""") )
rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""") )
# backbone
rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""") )
rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""") )
rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCamelCase = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
# fmt: on
return rename_keys
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ) -> Tuple:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
UpperCamelCase = """"""
else:
UpperCamelCase = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
UpperCamelCase = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase = in_proj_bias[: config.hidden_size]
UpperCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase = in_proj_bias[-config.hidden_size :]
def lowercase( UpperCamelCase_ ) -> Tuple:
'''simple docstring'''
UpperCamelCase = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(UpperCamelCase_ , UpperCamelCase_ )
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
'''simple docstring'''
UpperCamelCase = dct.pop(UpperCamelCase_ )
UpperCamelCase = val
def lowercase( ) -> Dict:
'''simple docstring'''
UpperCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCamelCase = Image.open(requests.get(UpperCamelCase_ , stream=UpperCamelCase_ ).raw )
return im
@torch.no_grad()
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = BitConfig(
global_padding="""same""" , layer_type="""bottleneck""" , depths=(3, 4, 9) , out_features=["""stage3"""] , embedding_dynamic_padding=UpperCamelCase_ , )
UpperCamelCase = ViTHybridConfig(backbone_config=UpperCamelCase_ , image_size=384 , num_labels=1000 )
UpperCamelCase = False
# load original model from timm
UpperCamelCase = timm.create_model(UpperCamelCase_ , pretrained=UpperCamelCase_ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
UpperCamelCase = timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase_ )
UpperCamelCase = create_rename_keys(UpperCamelCase_ , UpperCamelCase_ )
for src, dest in rename_keys:
rename_key(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
read_in_q_k_v(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase = """huggingface/label-files"""
UpperCamelCase = """imagenet-1k-id2label.json"""
UpperCamelCase = json.load(open(hf_hub_download(UpperCamelCase_ , UpperCamelCase_ , repo_type="""dataset""" ) , """r""" ) )
UpperCamelCase = {int(UpperCamelCase_ ): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
UpperCamelCase = ViTHybridModel(UpperCamelCase_ ).eval()
else:
UpperCamelCase = ViTHybridForImageClassification(UpperCamelCase_ ).eval()
model.load_state_dict(UpperCamelCase_ )
# create image processor
UpperCamelCase = create_transform(**resolve_data_config({} , model=UpperCamelCase_ ) )
UpperCamelCase = transform.transforms
UpperCamelCase = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
UpperCamelCase = ViTHybridImageProcessor(
do_resize=UpperCamelCase_ , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=UpperCamelCase_ , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=UpperCamelCase_ , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
UpperCamelCase = prepare_img()
UpperCamelCase = transform(UpperCamelCase_ ).unsqueeze(0 )
UpperCamelCase = processor(UpperCamelCase_ , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(UpperCamelCase_ , UpperCamelCase_ )
# verify logits
with torch.no_grad():
UpperCamelCase = model(UpperCamelCase_ )
UpperCamelCase = outputs.logits
print("""Predicted class:""" , logits.argmax(-1 ).item() )
if base_model:
UpperCamelCase = timm_model.forward_features(UpperCamelCase_ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase_ , outputs.pooler_output , atol=1E-3 )
else:
UpperCamelCase = timm_model(UpperCamelCase_ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase_ , outputs.logits , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(UpperCamelCase_ ).mkdir(exist_ok=UpperCamelCase_ )
print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase_ )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCamelCase_ )
if push_to_hub:
print(f"""Pushing model and processor to the hub {vit_name}""" )
model.push_to_hub(f"""ybelkada/{vit_name}""" )
processor.push_to_hub(f"""ybelkada/{vit_name}""" )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_r50_s16_384""",
type=str,
help="""Name of the hybrid ViT timm 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 to upload the model to the HuggingFace hub."""
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 165 | from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# TODO: upload to AWS
_SCREAMING_SNAKE_CASE = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
),
}
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
__lowerCAmelCase = """retribert"""
def __init__( self : Optional[Any] , lowerCamelCase_ : Any=3_0522 , lowerCamelCase_ : List[Any]=768 , lowerCamelCase_ : List[str]=8 , lowerCamelCase_ : Optional[int]=12 , lowerCamelCase_ : str=3072 , lowerCamelCase_ : List[str]="gelu" , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Dict=512 , lowerCamelCase_ : str=2 , lowerCamelCase_ : int=0.0_2 , lowerCamelCase_ : Any=1E-12 , lowerCamelCase_ : int=True , lowerCamelCase_ : Optional[Any]=128 , lowerCamelCase_ : Optional[Any]=0 , **lowerCamelCase_ : List[str] , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCamelCase_ , **lowerCamelCase_ )
UpperCamelCase = vocab_size
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_act
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = share_encoders
UpperCamelCase = projection_dim
| 165 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''huggingface/time-series-transformer-tourism-monthly''': (
'''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = '''time_series_transformer'''
UpperCAmelCase : Any = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : List[Any] , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : Optional[int] = None , _UpperCAmelCase : str = "student_t" , _UpperCAmelCase : str = "nll" , _UpperCAmelCase : int = 1 , _UpperCAmelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , _UpperCAmelCase : Optional[Union[str, bool]] = "mean" , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : int = 32 , _UpperCAmelCase : int = 32 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : int = 2 , _UpperCAmelCase : bool = True , _UpperCAmelCase : str = "gelu" , _UpperCAmelCase : int = 64 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : int = 100 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : List[Any]=True , **_UpperCAmelCase : Optional[int] , ):
# time series specific configuration
_A = prediction_length
_A = context_length or prediction_length
_A = distribution_output
_A = loss
_A = input_size
_A = num_time_features
_A = lags_sequence
_A = scaling
_A = num_dynamic_real_features
_A = num_static_real_features
_A = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(_UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_A = cardinality
else:
_A = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(_UpperCAmelCase ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_A = embedding_dimension
else:
_A = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_A = num_parallel_samples
# Transformer architecture configuration
_A = input_size * len(_UpperCAmelCase ) + self._number_of_features
_A = d_model
_A = encoder_attention_heads
_A = decoder_attention_heads
_A = encoder_ffn_dim
_A = decoder_ffn_dim
_A = encoder_layers
_A = decoder_layers
_A = dropout
_A = attention_dropout
_A = activation_dropout
_A = encoder_layerdrop
_A = decoder_layerdrop
_A = activation_function
_A = init_std
_A = use_cache
super().__init__(is_encoder_decoder=_UpperCAmelCase , **_UpperCAmelCase )
@property
def lowerCAmelCase_ ( self : Tuple ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 315 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 315 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = ["image_processor", "tokenizer"]
a_ = "ViltImageProcessor"
a_ = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : Optional[int] , __A : Optional[int]=None , __A : Optional[Any]=None , **__A : int ):
snake_case__ : Union[str, Any] = 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 , )
snake_case__ : Tuple = kwargs.pop("feature_extractor" )
snake_case__ : Any = 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 )
snake_case__ : Tuple = self.image_processor
def __call__( self : List[Any] , __A : int , __A : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __A : bool = True , __A : Union[bool, str, PaddingStrategy] = False , __A : Union[bool, str, TruncationStrategy] = None , __A : Optional[int] = None , __A : int = 0 , __A : Optional[int] = None , __A : Optional[bool] = None , __A : Optional[bool] = None , __A : bool = False , __A : bool = False , __A : bool = False , __A : bool = False , __A : bool = True , __A : Optional[Union[str, TensorType]] = None , **__A : List[Any] , ):
snake_case__ : Optional[int] = self.tokenizer(
text=__A , add_special_tokens=__A , padding=__A , truncation=__A , max_length=__A , stride=__A , pad_to_multiple_of=__A , return_token_type_ids=__A , return_attention_mask=__A , return_overflowing_tokens=__A , return_special_tokens_mask=__A , return_offsets_mapping=__A , return_length=__A , verbose=__A , return_tensors=__A , **__A , )
# add pixel_values + pixel_mask
snake_case__ : Optional[Any] = self.image_processor(__A , return_tensors=__A )
encoding.update(__A )
return encoding
def _lowercase ( self : Optional[Any] , *__A : List[str] , **__A : Optional[int] ):
return self.tokenizer.batch_decode(*__A , **__A )
def _lowercase ( self : Dict , *__A : str , **__A : str ):
return self.tokenizer.decode(*__A , **__A )
@property
def _lowercase ( self : str ):
snake_case__ : Optional[Any] = self.tokenizer.model_input_names
snake_case__ : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowercase ( self : List[Any] ):
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 : str ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __A , )
return self.image_processor
| 286 |
__lowerCamelCase : Optional[int] = """Tobias Carryer"""
from time import time
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : List[Any] , __A : List[Any] , __A : Optional[int] , __A : List[str] , __A : Dict=int(time() ) ): # noqa: B008
snake_case__ : List[Any] = multiplier
snake_case__ : Optional[int] = increment
snake_case__ : Optional[int] = modulo
snake_case__ : Union[str, Any] = seed
def _lowercase ( self : str ):
snake_case__ : Union[str, Any] = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
__lowerCamelCase : int = LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
while True:
print(lcg.next_number())
| 286 | 1 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> Dict:
return 1 if input_a == input_a else 0
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 325 |
"""simple docstring"""
def lowercase ( __snake_case : list[int] ):
lowercase_ : List[Any] = len(__snake_case )
for i in range(__snake_case ):
for j in range(i + 1 , __snake_case ):
if numbers[j] < numbers[i]:
lowercase_ , lowercase_ : Optional[int] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
__A : int = input('''Enter numbers separated by a comma:\n''').strip()
__A : Any = [int(item) for item in user_input.split(''',''')]
print(exchange_sort(unsorted))
| 33 | 0 |
'''simple docstring'''
def a_ ( lowerCamelCase : int = 1000 ):
lowerCAmelCase = 2**power
lowerCAmelCase = 0
while n:
lowerCAmelCase , lowerCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 55 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCAmelCase_ :
def __init__( self : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str]=2 , UpperCAmelCase__ : Dict=3 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : int=7 , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : int=True , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Any=9_9 , UpperCAmelCase__ : Any=3_6 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : int=3_7 , UpperCAmelCase__ : Any="gelu" , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Dict=5_1_2 , UpperCAmelCase__ : Optional[Any]=1_6 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : str=6 , UpperCAmelCase__ : List[str]=6 , UpperCAmelCase__ : List[str]=3 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : List[Any]=1_0_0_0 , ) -> int:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = num_channels
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = text_seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_input_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = coordinate_size
lowerCAmelCase = shape_size
lowerCAmelCase = num_labels
lowerCAmelCase = num_choices
lowerCAmelCase = scope
lowerCAmelCase = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
lowerCAmelCase = text_seq_length
lowerCAmelCase = (image_size // patch_size) ** 2 + 1
lowerCAmelCase = self.text_seq_length + self.image_seq_length
def __UpperCAmelCase ( self : str ) -> Dict:
lowerCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.text_seq_length] )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
lowerCAmelCase = LayoutLMvaConfig(
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 , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str ) -> str:
lowerCAmelCase = LayoutLMvaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
# text + image
lowerCAmelCase = model(UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
lowerCAmelCase = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
lowerCAmelCase = model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
lowerCAmelCase = model(UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
lowerCAmelCase = model(pixel_values=UpperCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Optional[int]:
lowerCAmelCase = self.num_labels
lowerCAmelCase = LayoutLMvaForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowerCAmelCase = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Any] ) -> Optional[Any]:
lowerCAmelCase = self.num_labels
lowerCAmelCase = LayoutLMvaForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowerCAmelCase = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict ) -> Optional[Any]:
lowerCAmelCase = LayoutLMvaForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
lowerCAmelCase = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , start_positions=UpperCAmelCase__ , end_positions=UpperCAmelCase__ , )
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 __UpperCAmelCase ( self : Tuple ) -> Any:
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) , (
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase : List[str] = False
lowerCamelCase : Tuple = False
lowerCamelCase : int = False
lowerCamelCase : Optional[int] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase : int = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> str:
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def __UpperCAmelCase ( self : List[Any] ) -> Optional[Any]:
lowerCAmelCase = LayoutLMvaModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=3_7 )
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[int]=False ) -> Optional[int]:
lowerCAmelCase = copy.deepcopy(UpperCAmelCase__ )
if model_class in get_values(UpperCAmelCase__ ):
lowerCAmelCase = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(UpperCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
lowerCAmelCase = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in get_values(UpperCAmelCase__ ):
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=UpperCAmelCase__ , )
return inputs_dict
def __UpperCAmelCase ( self : Tuple ) -> Any:
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : Dict ) -> List[Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __UpperCAmelCase ( self : str ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase = type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def __UpperCAmelCase ( self : Any ) -> Dict:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple ) -> List[str]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
@slow
def __UpperCAmelCase ( self : Any ) -> Any:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = LayoutLMvaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def a_ ( ):
lowerCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
@cached_property
def __UpperCAmelCase ( self : int ) -> str:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__ ) if is_vision_available() else None
@slow
def __UpperCAmelCase ( self : int ) -> Any:
lowerCAmelCase = LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' ).to(UpperCAmelCase__ )
lowerCAmelCase = self.default_image_processor
lowerCAmelCase = prepare_img()
lowerCAmelCase = image_processor(images=UpperCAmelCase__ , return_tensors='pt' ).pixel_values.to(UpperCAmelCase__ )
lowerCAmelCase = torch.tensor([[1, 2]] )
lowerCAmelCase = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
lowerCAmelCase = model(
input_ids=input_ids.to(UpperCAmelCase__ ) , bbox=bbox.to(UpperCAmelCase__ ) , pixel_values=pixel_values.to(UpperCAmelCase__ ) , )
# verify the logits
lowerCAmelCase = torch.Size((1, 1_9_9, 7_6_8) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase__ )
lowerCAmelCase = torch.tensor(
[[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 55 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_UpperCamelCase = logging.getLogger(__name__)
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_0522, type=int)
_UpperCamelCase = parser.parse_args()
logger.info(F'Loading data from {args.data_file}')
with open(args.data_file, '''rb''') as fp:
_UpperCamelCase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
_UpperCamelCase = Counter()
for tk_ids in data:
counter.update(tk_ids)
_UpperCamelCase = [0] * args.vocab_size
for k, v in counter.items():
_UpperCamelCase = v
logger.info(F'Dump to {args.token_counts_dump}')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 254 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_UpperCamelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def lowercase_ ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : tuple , lowerCAmelCase__ : Path , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[int]=False , ):
"""simple docstring"""
output_path.parent.mkdir(parents=lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , use_external_data_format=lowerCAmelCase__ , enable_onnx_checker=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
else:
export(
lowerCAmelCase__ , lowerCAmelCase__ , f=output_path.as_posix() , input_names=lowerCAmelCase__ , output_names=lowerCAmelCase__ , dynamic_axes=lowerCAmelCase__ , do_constant_folding=lowerCAmelCase__ , opset_version=lowerCAmelCase__ , )
@torch.no_grad()
def lowercase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : int , lowerCAmelCase__ : bool = False ):
"""simple docstring"""
__UpperCAmelCase : Tuple = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__UpperCAmelCase : Optional[int] = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
__UpperCAmelCase : Dict = """cpu"""
__UpperCAmelCase : Optional[Any] = StableDiffusionPipeline.from_pretrained(lowerCAmelCase__ , torch_dtype=lowerCAmelCase__ ).to(lowerCAmelCase__ )
__UpperCAmelCase : List[str] = Path(lowerCAmelCase__ )
# TEXT ENCODER
__UpperCAmelCase : Any = pipeline.text_encoder.config.max_position_embeddings
__UpperCAmelCase : str = pipeline.text_encoder.config.hidden_size
__UpperCAmelCase : Optional[Any] = pipeline.tokenizer(
"""A sample prompt""" , padding="""max_length""" , max_length=pipeline.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors="""pt""" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=lowerCAmelCase__ , dtype=torch.intaa )) , output_path=output_path / """text_encoder""" / """model.onnx""" , ordered_input_names=["""input_ids"""] , output_names=["""last_hidden_state""", """pooler_output"""] , dynamic_axes={
"""input_ids""": {0: """batch""", 1: """sequence"""},
} , opset=lowerCAmelCase__ , )
del pipeline.text_encoder
# UNET
__UpperCAmelCase : Optional[int] = pipeline.unet.config.in_channels
__UpperCAmelCase : Tuple = pipeline.unet.config.sample_size
__UpperCAmelCase : Dict = output_path / """unet""" / """model.onnx"""
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
torch.randn(2 ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
torch.randn(2 , lowerCAmelCase__ , lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
False,
) , output_path=lowerCAmelCase__ , ordered_input_names=["""sample""", """timestep""", """encoder_hidden_states""", """return_dict"""] , output_names=["""out_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""timestep""": {0: """batch"""},
"""encoder_hidden_states""": {0: """batch""", 1: """sequence"""},
} , opset=lowerCAmelCase__ , use_external_data_format=lowerCAmelCase__ , )
__UpperCAmelCase : Any = str(unet_path.absolute().as_posix() )
__UpperCAmelCase : int = os.path.dirname(lowerCAmelCase__ )
__UpperCAmelCase : Tuple = onnx.load(lowerCAmelCase__ )
# clean up existing tensor files
shutil.rmtree(lowerCAmelCase__ )
os.mkdir(lowerCAmelCase__ )
# collate external tensor files into one
onnx.save_model(
lowerCAmelCase__ , lowerCAmelCase__ , save_as_external_data=lowerCAmelCase__ , all_tensors_to_one_file=lowerCAmelCase__ , location="""weights.pb""" , convert_attribute=lowerCAmelCase__ , )
del pipeline.unet
# VAE ENCODER
__UpperCAmelCase : Union[str, Any] = pipeline.vae
__UpperCAmelCase : str = vae_encoder.config.in_channels
__UpperCAmelCase : Any = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
__UpperCAmelCase : str = lambda lowerCAmelCase__ , lowerCAmelCase__ : vae_encoder.encode(lowerCAmelCase__ , lowerCAmelCase__ )[0].sample()
onnx_export(
lowerCAmelCase__ , model_args=(
torch.randn(1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
False,
) , output_path=output_path / """vae_encoder""" / """model.onnx""" , ordered_input_names=["""sample""", """return_dict"""] , output_names=["""latent_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=lowerCAmelCase__ , )
# VAE DECODER
__UpperCAmelCase : Optional[Any] = pipeline.vae
__UpperCAmelCase : Optional[int] = vae_decoder.config.latent_channels
__UpperCAmelCase : Dict = vae_decoder.config.out_channels
# forward only through the decoder part
__UpperCAmelCase : List[Any] = vae_encoder.decode
onnx_export(
lowerCAmelCase__ , model_args=(
torch.randn(1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=lowerCAmelCase__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
__UpperCAmelCase : Tuple = pipeline.safety_checker
__UpperCAmelCase : Union[str, Any] = safety_checker.config.vision_config.num_channels
__UpperCAmelCase : Any = safety_checker.config.vision_config.image_size
__UpperCAmelCase : Optional[int] = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
torch.randn(1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ),
) , output_path=output_path / """safety_checker""" / """model.onnx""" , ordered_input_names=["""clip_input""", """images"""] , output_names=["""out_images""", """has_nsfw_concepts"""] , dynamic_axes={
"""clip_input""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""images""": {0: """batch""", 1: """height""", 2: """width""", 3: """channels"""},
} , opset=lowerCAmelCase__ , )
del pipeline.safety_checker
__UpperCAmelCase : Optional[Any] = OnnxRuntimeModel.from_pretrained(output_path / """safety_checker""" )
__UpperCAmelCase : Any = pipeline.feature_extractor
else:
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Any = None
__UpperCAmelCase : Tuple = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_encoder""" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_decoder""" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / """text_encoder""" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / """unet""" ) , scheduler=pipeline.scheduler , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(lowerCAmelCase__ )
print("""ONNX pipeline saved to""" , lowerCAmelCase__ )
del pipeline
del onnx_pipeline
__UpperCAmelCase : Tuple = OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase__ , provider="""CPUExecutionProvider""" )
print("""ONNX pipeline is loadable""" )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=14,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_UpperCamelCase = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 254 | 1 |
"""simple docstring"""
def _lowerCamelCase( a , a ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCamelCase( a , a=0 ):
return sorted(a , key=lambda a : x[column] )
def _lowerCamelCase( a , a , a=float("inf" ) ):
for i in range(points_counts - 1 ):
for j in range(i + 1 , a ):
__a = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
__a = current_dis
return min_dis
def _lowerCamelCase( a , a , a=float("inf" ) ):
for i in range(min(6 , points_counts - 1 ) , a ):
for j in range(max(0 , i - 6 ) , a ):
__a = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
__a = current_dis
return min_dis
def _lowerCamelCase( a , a , a ):
# base case
if points_counts <= 3:
return dis_between_closest_pair(a , a )
# recursion
__a = points_counts // 2
__a = closest_pair_of_points_sqr(
a , points_sorted_on_y[:mid] , a )
__a = closest_pair_of_points_sqr(
a , points_sorted_on_y[mid:] , points_counts - mid )
__a = min(a , a )
__a = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(a )
__a = dis_between_closest_in_strip(
a , len(a ) , a )
return min(a , a )
def _lowerCamelCase( a , a ):
__a = column_based_sort(a , column=0 )
__a = column_based_sort(a , column=1 )
return (
closest_pair_of_points_sqr(
a , a , a )
) ** 0.5
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__:Tuple = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 368 | """simple docstring"""
SCREAMING_SNAKE_CASE__:Any = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
"""O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
"""V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
"""2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
"""8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
""":""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """'""": """.----.""", """\"""": """.-..-.""",
"""?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
"""(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
SCREAMING_SNAKE_CASE__:Any = {value: key for key, value in MORSE_CODE_DICT.items()}
def _lowerCamelCase( a ):
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def _lowerCamelCase( a ):
return "".join(REVERSE_DICT[char] for char in message.split() )
def _lowerCamelCase( ):
__a = "Morse code here!"
print(a )
__a = encrypt(a )
print(a )
__a = decrypt(a )
print(a )
if __name__ == "__main__":
main()
| 268 | 0 |
"""simple docstring"""
import os
import string
import sys
lowerCAmelCase__ = 1 << 8
lowerCAmelCase__ = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARROW_KEY_FLAG,
'''mod_int''': 91,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 50,
'''delete''': 51,
'''pg_up''': 53,
'''pg_down''': 54,
}
lowerCAmelCase__ = KEYMAP['''up''']
lowerCAmelCase__ = KEYMAP['''left''']
if sys.platform == "win32":
lowerCAmelCase__ = []
lowerCAmelCase__ = {
b'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
b'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(10):
lowerCAmelCase__ = ord(str(i))
def snake_case_ ( ):
'''simple docstring'''
if os.name == "nt":
import msvcrt
_lowerCamelCase : str = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(A_ ) == 0:
# Read the keystroke
_lowerCamelCase : Optional[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_lowerCamelCase : Optional[int] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_lowerCamelCase : str = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(A_ )
if ord(A_ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
_lowerCamelCase : str = chr(KEYMAP['''esc'''] )
except KeyError:
_lowerCamelCase : List[Any] = cha[1]
else:
_lowerCamelCase : int = ch.decode(A_ )
else:
_lowerCamelCase : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_lowerCamelCase : Optional[Any] = sys.stdin.fileno()
_lowerCamelCase : List[Any] = termios.tcgetattr(A_ )
try:
tty.setraw(A_ )
_lowerCamelCase : Tuple = sys.stdin.read(1 )
finally:
termios.tcsetattr(A_, termios.TCSADRAIN, A_ )
return ch
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase : Tuple = get_raw_chars()
if ord(A_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(A_ ) == KEYMAP["esc"]:
_lowerCamelCase : Optional[int] = get_raw_chars()
if ord(A_ ) == KEYMAP["mod_int"]:
_lowerCamelCase : Dict = get_raw_chars()
if ord(A_ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(A_ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(A_ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 72 |
"""simple docstring"""
import unittest
import numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : np.ndarray, A_ : np.ndarray, A_ : np.ndarray | None = None, ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = np.shape(A_ )
_lowerCamelCase : List[str] = np.shape(A_ )
_lowerCamelCase : List[str] = np.shape(A_ )
if shape_a[0] != shape_b[0]:
_lowerCamelCase : Tuple = (
'''Expected the same number of rows for A and B. '''
F'''Instead found A of size {shape_a} and B of size {shape_b}'''
)
raise ValueError(A_ )
if shape_b[1] != shape_c[1]:
_lowerCamelCase : Tuple = (
'''Expected the same number of columns for B and C. '''
F'''Instead found B of size {shape_b} and C of size {shape_c}'''
)
raise ValueError(A_ )
_lowerCamelCase : List[str] = pseudo_inv
if a_inv is None:
try:
_lowerCamelCase : Any = np.linalg.inv(A_ )
except np.linalg.LinAlgError:
raise ValueError(
'''Input matrix A is not invertible. Cannot compute Schur complement.''' )
return mat_c - mat_b.T @ a_inv @ mat_b
class __snake_case ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : List[Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_lowerCamelCase : List[str] = np.array([[0, 3], [3, 0], [2, 3]] )
_lowerCamelCase : List[str] = np.array([[2, 1], [6, 3]] )
_lowerCamelCase : List[Any] = schur_complement(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : Dict = np.block([[a, b], [b.T, c]] )
_lowerCamelCase : Tuple = np.linalg.det(__lowerCAmelCase )
_lowerCamelCase : List[str] = np.linalg.det(__lowerCAmelCase )
_lowerCamelCase : Any = np.linalg.det(__lowerCAmelCase )
self.assertAlmostEqual(__lowerCAmelCase , det_a * det_s )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
"""simple docstring"""
_lowerCamelCase : List[Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_lowerCamelCase : Optional[int] = np.array([[0, 3], [3, 0], [2, 3]] )
_lowerCamelCase : int = np.array([[2, 1], [6, 3]] )
with self.assertRaises(__lowerCAmelCase ):
schur_complement(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : str = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
_lowerCamelCase : List[str] = np.array([[0, 3], [3, 0], [2, 3]] )
_lowerCamelCase : Union[str, Any] = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(__lowerCAmelCase ):
schur_complement(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 72 | 1 |
'''simple docstring'''
from __future__ import annotations
_SCREAMING_SNAKE_CASE = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_SCREAMING_SNAKE_CASE = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __lowerCamelCase ( __lowerCAmelCase : list[float] ) -> list[float]:
snake_case = []
snake_case = len(__lowerCAmelCase )
for i in range(__lowerCAmelCase ):
snake_case = -1
for j in range(i + 1 , __lowerCAmelCase ):
if arr[i] < arr[j]:
snake_case = arr[j]
break
result.append(__lowerCAmelCase )
return result
def __lowerCamelCase ( __lowerCAmelCase : list[float] ) -> list[float]:
snake_case = []
for i, outer in enumerate(__lowerCAmelCase ):
snake_case = -1
for inner in arr[i + 1 :]:
if outer < inner:
snake_case = inner
break
result.append(__lowerCAmelCase )
return result
def __lowerCamelCase ( __lowerCAmelCase : list[float] ) -> list[float]:
snake_case = len(__lowerCAmelCase )
snake_case = []
snake_case = [-1] * arr_size
for index in reversed(range(__lowerCAmelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
snake_case = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
_SCREAMING_SNAKE_CASE = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| 3 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
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 _lowerCAmelCase ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = KandinskyVaaControlnetImgaImgPipeline
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = ["image_embeds", "negative_image_embeds", "image", "hint"]
snake_case_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
snake_case_ = False
@property
def lowerCAmelCase ( self : Dict )-> str:
return 32
@property
def lowerCAmelCase ( self : int )-> List[str]:
return 32
@property
def lowerCAmelCase ( self : List[Any] )-> str:
return self.time_input_dim
@property
def lowerCAmelCase ( self : Optional[Any] )-> Any:
return self.time_input_dim * 4
@property
def lowerCAmelCase ( self : str )-> Union[str, Any]:
return 1_00
@property
def lowerCAmelCase ( self : Tuple )-> Optional[Any]:
torch.manual_seed(0 )
snake_case = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""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,
}
snake_case = UNetaDConditionModel(**__snake_case )
return model
@property
def lowerCAmelCase ( self : List[Any] )-> str:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def lowerCAmelCase ( self : str )-> List[str]:
torch.manual_seed(0 )
snake_case = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase ( self : int )-> Dict:
snake_case = self.dummy_unet
snake_case = self.dummy_movq
snake_case = {
"""num_train_timesteps""": 10_00,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_00_85,
"""beta_end""": 0.0_12,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
snake_case = DDIMScheduler(**__snake_case )
snake_case = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCAmelCase ( self : Union[str, Any] , __snake_case : str , __snake_case : Tuple=0 )-> List[Any]:
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__snake_case )
# create init_image
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
snake_case = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create hint
snake_case = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
if str(__snake_case ).startswith("""mps""" ):
snake_case = torch.manual_seed(__snake_case )
else:
snake_case = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
snake_case = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def lowerCAmelCase ( self : Dict )-> Optional[int]:
snake_case = """cpu"""
snake_case = self.get_dummy_components()
snake_case = self.pipeline_class(**__snake_case )
snake_case = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
snake_case = pipe(**self.get_dummy_inputs(__snake_case ) )
snake_case = output.images
snake_case = pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
snake_case = image[0, -3:, -3:, -1]
snake_case = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case = np.array(
[0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36] )
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()}'''
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : List[str] )-> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : List[Any] )-> Optional[int]:
snake_case = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
snake_case = init_image.resize((5_12, 5_12) )
snake_case = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
snake_case = torch.from_numpy(np.array(__snake_case ) ).float() / 2_55.0
snake_case = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
snake_case = """A robot, 4k photo"""
snake_case = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
snake_case = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
snake_case = pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
snake_case = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case , snake_case = pipe_prior(
__snake_case , image=__snake_case , strength=0.85 , generator=__snake_case , negative_prompt="""""" , ).to_tuple()
snake_case = pipeline(
image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , hint=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=5_12 , width=5_12 , strength=0.5 , output_type="""np""" , )
snake_case = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 3 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Optional[int] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class A ( lowerCAmelCase__ ):
'''simple docstring'''
A__ = '''mctct'''
def __init__(self : Optional[Any] , _UpperCAmelCase : List[str]=8065 , _UpperCAmelCase : str=1536 , _UpperCAmelCase : Dict=36 , _UpperCAmelCase : str=6144 , _UpperCAmelCase : Union[str, Any]=4 , _UpperCAmelCase : Any=384 , _UpperCAmelCase : List[Any]=920 , _UpperCAmelCase : Optional[Any]=1E-5 , _UpperCAmelCase : Optional[int]=0.3 , _UpperCAmelCase : Any="relu" , _UpperCAmelCase : List[Any]=0.02 , _UpperCAmelCase : Optional[int]=0.3 , _UpperCAmelCase : Dict=0.3 , _UpperCAmelCase : int=1 , _UpperCAmelCase : Optional[int]=0 , _UpperCAmelCase : List[str]=2 , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : Tuple=0.3 , _UpperCAmelCase : Tuple=1 , _UpperCAmelCase : Dict=(7,) , _UpperCAmelCase : Dict=(3,) , _UpperCAmelCase : Dict=80 , _UpperCAmelCase : int=1 , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : List[Any]="sum" , _UpperCAmelCase : Union[str, Any]=False , **_UpperCAmelCase : Tuple , ) -> List[Any]:
"""simple docstring"""
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = num_attention_heads
lowercase__ = attention_head_dim
lowercase__ = max_position_embeddings
lowercase__ = layer_norm_eps
lowercase__ = layerdrop
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = pad_token_id
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = conv_glu_dim
lowercase__ = conv_dropout
lowercase__ = num_conv_layers
lowercase__ = input_feat_per_channel
lowercase__ = input_channels
lowercase__ = conv_channels
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# prevents config testing fail with exporting to json
lowercase__ = list(__UpperCAmelCase )
lowercase__ = list(__UpperCAmelCase )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
f'''but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, '''
f'''`config.num_conv_layers = {self.num_conv_layers}`.''' )
| 305 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ = logging.get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase=None , __UpperCAmelCase=None ):
'''simple docstring'''
if not conversation_id:
__lowerCamelCase = uuid.uuida()
if past_user_inputs is None:
__lowerCamelCase = []
if generated_responses is None:
__lowerCamelCase = []
__lowerCamelCase = conversation_id
__lowerCamelCase = past_user_inputs
__lowerCamelCase = generated_responses
__lowerCamelCase = text
def __eq__( self , __UpperCAmelCase ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase = False ):
'''simple docstring'''
if self.new_user_input:
if overwrite:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
F"""with: \"{text}\".""" )
__lowerCamelCase = text
else:
logger.warning(
F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input""" )
else:
__lowerCamelCase = text
def lowerCamelCase ( self ):
'''simple docstring'''
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
self.generated_responses.append(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
'''simple docstring'''
__lowerCamelCase = F"""Conversation id: {self.uuid} \n"""
for is_user, text in self.iter_texts():
__lowerCamelCase = '''user''' if is_user else '''bot'''
output += F"""{name} >> {text} \n"""
return output
@add_end_docstrings(
lowerCAmelCase__ , r"""
min_length_for_response (`int`, *optional*, defaults to 32):
The minimum length (in number of tokens) for a response.
minimum_tokens (`int`, *optional*, defaults to 10):
The minimum length of tokens to leave for a response.
""" , )
class __lowerCAmelCase ( lowerCAmelCase__ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
if self.tokenizer.pad_token_id is None:
__lowerCamelCase = self.tokenizer.eos_token
def lowerCamelCase ( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = {}
__lowerCamelCase = {}
__lowerCamelCase = {}
if min_length_for_response is not None:
__lowerCamelCase = min_length_for_response
if minimum_tokens is not None:
__lowerCamelCase = minimum_tokens
if "max_length" in generate_kwargs:
__lowerCamelCase = generate_kwargs['''max_length''']
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
__lowerCamelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(__UpperCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , __UpperCAmelCase , __UpperCAmelCase=0 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = super().__call__(__UpperCAmelCase , num_workers=__UpperCAmelCase , **__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and len(__UpperCAmelCase ) == 1:
return outputs[0]
return outputs
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=32 ):
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''ConversationalPipeline, expects Conversation as inputs''' )
if conversation.new_user_input is None:
raise ValueError(
F"""Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. """
'''Add user inputs with the conversation\'s `add_user_input` method''' )
if hasattr(self.tokenizer , '''_build_conversation_input_ids''' ):
__lowerCamelCase = self.tokenizer._build_conversation_input_ids(__UpperCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
__lowerCamelCase = self._legacy_parse_and_tokenize(__UpperCAmelCase )
if self.framework == "pt":
__lowerCamelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
__lowerCamelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=10 , **__UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = generate_kwargs.get('''max_length''' , self.model.config.max_length )
__lowerCamelCase = model_inputs['''input_ids'''].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})""" )
__lowerCamelCase = max_length - minimum_tokens
__lowerCamelCase = model_inputs['''input_ids'''][:, -trim:]
if "attention_mask" in model_inputs:
__lowerCamelCase = model_inputs['''attention_mask'''][:, -trim:]
__lowerCamelCase = model_inputs.pop('''conversation''' )
__lowerCamelCase = max_length
__lowerCamelCase = self.model.generate(**__UpperCAmelCase , **__UpperCAmelCase )
if self.model.config.is_encoder_decoder:
__lowerCamelCase = 1
else:
__lowerCamelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase=True ):
'''simple docstring'''
__lowerCamelCase = model_outputs['''output_ids''']
__lowerCamelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=__UpperCAmelCase , clean_up_tokenization_spaces=__UpperCAmelCase , )
__lowerCamelCase = model_outputs['''conversation''']
conversation.mark_processed()
conversation.append_response(__UpperCAmelCase )
return conversation
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = self.tokenizer.eos_token_id
__lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > self.tokenizer.model_max_length:
__lowerCamelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 330 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Optional[int] = logging.get_logger(__name__)
def _lowerCamelCase ( lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str]=False ):
"""simple docstring"""
UpperCAmelCase_ : Dict = OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('head' ):
UpperCAmelCase_ : Any = 'segformer.encoder.' + key
if key.startswith('backbone' ):
UpperCAmelCase_ : List[Any] = key.replace('backbone' , 'segformer.encoder' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
UpperCAmelCase_ : Union[str, Any] = key[key.find('patch_embed' ) + len('patch_embed' )]
UpperCAmelCase_ : Optional[int] = key.replace(F'''patch_embed{idx}''' , F'''patch_embeddings.{int(_UpperCAmelCase )-1}''' )
if "norm" in key:
UpperCAmelCase_ : List[str] = key.replace('norm' , 'layer_norm' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
UpperCAmelCase_ : Tuple = key[key.find('segformer.encoder.layer_norm' ) + len('segformer.encoder.layer_norm' )]
UpperCAmelCase_ : Union[str, Any] = key.replace(F'''layer_norm{idx}''' , F'''layer_norm.{int(_UpperCAmelCase )-1}''' )
if "layer_norm1" in key:
UpperCAmelCase_ : str = key.replace('layer_norm1' , 'layer_norm_1' )
if "layer_norm2" in key:
UpperCAmelCase_ : int = key.replace('layer_norm2' , 'layer_norm_2' )
if "block" in key:
# replace for example block1 by block.0
UpperCAmelCase_ : Dict = key[key.find('block' ) + len('block' )]
UpperCAmelCase_ : List[str] = key.replace(F'''block{idx}''' , F'''block.{int(_UpperCAmelCase )-1}''' )
if "attn.q" in key:
UpperCAmelCase_ : Dict = key.replace('attn.q' , 'attention.self.query' )
if "attn.proj" in key:
UpperCAmelCase_ : List[str] = key.replace('attn.proj' , 'attention.output.dense' )
if "attn" in key:
UpperCAmelCase_ : int = key.replace('attn' , 'attention.self' )
if "fc1" in key:
UpperCAmelCase_ : Optional[Any] = key.replace('fc1' , 'dense1' )
if "fc2" in key:
UpperCAmelCase_ : int = key.replace('fc2' , 'dense2' )
if "linear_pred" in key:
UpperCAmelCase_ : Optional[Any] = key.replace('linear_pred' , 'classifier' )
if "linear_fuse" in key:
UpperCAmelCase_ : Any = key.replace('linear_fuse.conv' , 'linear_fuse' )
UpperCAmelCase_ : List[str] = key.replace('linear_fuse.bn' , 'batch_norm' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
UpperCAmelCase_ : int = key[key.find('linear_c' ) + len('linear_c' )]
UpperCAmelCase_ : List[str] = key.replace(F'''linear_c{idx}''' , F'''linear_c.{int(_UpperCAmelCase )-1}''' )
if key.startswith('head' ):
UpperCAmelCase_ : Any = key.replace('head' , 'classifier' )
UpperCAmelCase_ : List[str] = value
return new_state_dict
def _lowerCamelCase ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : List[str] ):
"""simple docstring"""
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
UpperCAmelCase_ : List[Any] = state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight''' )
UpperCAmelCase_ : int = state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
UpperCAmelCase_ : int = kv_weight[
: config.hidden_sizes[i], :
]
UpperCAmelCase_ : List[str] = kv_bias[: config.hidden_sizes[i]]
UpperCAmelCase_ : Union[str, Any] = kv_weight[
config.hidden_sizes[i] :, :
]
UpperCAmelCase_ : Optional[Any] = kv_bias[
config.hidden_sizes[i] :
]
def _lowerCamelCase ( ):
"""simple docstring"""
UpperCAmelCase_ : int = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCAmelCase_ : Optional[int] = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return image
@torch.no_grad()
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = SegformerConfig()
UpperCAmelCase_ : Optional[Any] = False
# set attributes based on model_name
UpperCAmelCase_ : int = 'huggingface/label-files'
if "segformer" in model_name:
UpperCAmelCase_ : Tuple = model_name[len('segformer.' ) : len('segformer.' ) + 2]
if "ade" in model_name:
UpperCAmelCase_ : Optional[Any] = 150
UpperCAmelCase_ : Optional[int] = 'ade20k-id2label.json'
UpperCAmelCase_ : Optional[Any] = (1, 150, 128, 128)
elif "city" in model_name:
UpperCAmelCase_ : Any = 19
UpperCAmelCase_ : List[Any] = 'cityscapes-id2label.json'
UpperCAmelCase_ : Tuple = (1, 19, 128, 128)
else:
raise ValueError(F'''Model {model_name} not supported''' )
elif "mit" in model_name:
UpperCAmelCase_ : Union[str, Any] = True
UpperCAmelCase_ : int = model_name[4:6]
UpperCAmelCase_ : Union[str, Any] = 1000
UpperCAmelCase_ : str = 'imagenet-1k-id2label.json'
UpperCAmelCase_ : Any = (1, 1000)
else:
raise ValueError(F'''Model {model_name} not supported''' )
# set config attributes
UpperCAmelCase_ : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) , 'r' ) )
UpperCAmelCase_ : Optional[int] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase_ : Dict = idalabel
UpperCAmelCase_ : List[Any] = {v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
UpperCAmelCase_ : List[Any] = [64, 128, 320, 512]
UpperCAmelCase_ : str = 256
elif size == "b2":
UpperCAmelCase_ : int = [64, 128, 320, 512]
UpperCAmelCase_ : str = 768
UpperCAmelCase_ : Optional[int] = [3, 4, 6, 3]
elif size == "b3":
UpperCAmelCase_ : Any = [64, 128, 320, 512]
UpperCAmelCase_ : int = 768
UpperCAmelCase_ : Tuple = [3, 4, 18, 3]
elif size == "b4":
UpperCAmelCase_ : Any = [64, 128, 320, 512]
UpperCAmelCase_ : Union[str, Any] = 768
UpperCAmelCase_ : Union[str, Any] = [3, 8, 27, 3]
elif size == "b5":
UpperCAmelCase_ : Any = [64, 128, 320, 512]
UpperCAmelCase_ : int = 768
UpperCAmelCase_ : List[str] = [3, 6, 40, 3]
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor (only resize + normalize)
UpperCAmelCase_ : Optional[Any] = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=_UpperCAmelCase , align=_UpperCAmelCase , do_random_crop=_UpperCAmelCase )
# prepare image
UpperCAmelCase_ : Dict = prepare_img()
UpperCAmelCase_ : List[str] = image_processor(images=_UpperCAmelCase , return_tensors='pt' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
if encoder_only:
UpperCAmelCase_ : List[Any] = torch.load(_UpperCAmelCase , map_location=torch.device('cpu' ) )
else:
UpperCAmelCase_ : Optional[Any] = torch.load(_UpperCAmelCase , map_location=torch.device('cpu' ) )['state_dict']
# rename keys
UpperCAmelCase_ : Optional[int] = rename_keys(_UpperCAmelCase , encoder_only=_UpperCAmelCase )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(_UpperCAmelCase , _UpperCAmelCase )
# create HuggingFace model and load state dict
if encoder_only:
UpperCAmelCase_ : Dict = False
UpperCAmelCase_ : Union[str, Any] = SegformerForImageClassification(_UpperCAmelCase )
else:
UpperCAmelCase_ : Dict = SegformerForSemanticSegmentation(_UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
model.eval()
# forward pass
UpperCAmelCase_ : List[str] = model(_UpperCAmelCase )
UpperCAmelCase_ : Tuple = outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
UpperCAmelCase_ : Tuple = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
UpperCAmelCase_ : str = torch.tensor(
[
[[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
[[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
[[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
UpperCAmelCase_ : Optional[Any] = torch.tensor(
[
[[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
[[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
[[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
UpperCAmelCase_ : Union[str, Any] = torch.tensor(
[
[[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
[[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
[[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
UpperCAmelCase_ : Dict = torch.tensor(
[
[[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
[[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
[[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
UpperCAmelCase_ : Union[str, Any] = torch.tensor(
[
[[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
[[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
[[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
UpperCAmelCase_ : List[str] = torch.tensor(
[
[[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
[[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
[[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
UpperCAmelCase_ : int = torch.tensor(
[
[[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
[[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
[[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
UpperCAmelCase_ : int = torch.tensor(
[
[
[-1.1_372e01, -1.2_787e01, -1.3_477e01],
[-1.2_536e01, -1.4_194e01, -1.4_409e01],
[-1.3_217e01, -1.4_888e01, -1.5_327e01],
],
[
[-1.4_791e01, -1.7_122e01, -1.8_277e01],
[-1.7_163e01, -1.9_192e01, -1.9_533e01],
[-1.7_897e01, -1.9_991e01, -2.0_315e01],
],
[
[7.6_723e-01, 4.1_921e-01, -7.7_878e-02],
[4.7_772e-01, 9.5_557e-03, -2.8_082e-01],
[3.6_032e-01, -2.4_826e-01, -5.1_168e-01],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
UpperCAmelCase_ : Optional[int] = torch.tensor(
[
[[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
[[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
[[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
UpperCAmelCase_ : Tuple = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
UpperCAmelCase_ : Optional[Any] = torch.tensor(
[
[[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
[[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
[[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
UpperCAmelCase_ : Union[str, Any] = torch.tensor(
[
[[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
[[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
[[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
UpperCAmelCase_ : Optional[int] = torch.tensor(
[
[[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
[[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
[[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
UpperCAmelCase_ : int = torch.tensor(
[
[[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
[[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
[[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
] )
else:
UpperCAmelCase_ : Any = logits.argmax(-1 ).item()
print('Predicted class:' , model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3] , _UpperCAmelCase , atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''segformer.b0.512x512.ade.160k''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
snake_case__ : Optional[int] = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 350 | '''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ = "cpu" , snake_case_ = "openai/clip-vit-large-patch14" ):
'''simple docstring'''
UpperCAmelCase_ : Any = device
UpperCAmelCase_ : Tuple = CLIPTokenizerFast.from_pretrained(snake_case_ )
UpperCAmelCase_ : Optional[Any] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase_ : Union[str, Any] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
UpperCAmelCase_ : Tuple = torchvision.transforms.Normalize(self.image_mean , self.image_std )
UpperCAmelCase_ : Optional[Any] = torchvision.transforms.Resize(2_2_4 )
UpperCAmelCase_ : Any = torchvision.transforms.CenterCrop(2_2_4 )
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = self.resize(snake_case_ )
UpperCAmelCase_ : Tuple = self.center_crop(snake_case_ )
UpperCAmelCase_ : Optional[Any] = self.normalize(snake_case_ )
return images
def __call__( self , snake_case_=None , snake_case_=None , **snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : str = self.tokenizer(text=snake_case_ , **snake_case_ )
UpperCAmelCase_ : Optional[Any] = self.preprocess_img(snake_case_ )
UpperCAmelCase_ : Optional[int] = {key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class __SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case_=1_0 , snake_case_=0.01 , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=False , snake_case_=True , snake_case_="image" , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False , ):
'''simple docstring'''
super().__init__()
UpperCAmelCase_ : List[str] = None
UpperCAmelCase_ : Dict = device if device else get_device()
if vqgan:
UpperCAmelCase_ : Any = vqgan
else:
UpperCAmelCase_ : Dict = load_vqgan(self.device , conf_path=snake_case_ , ckpt_path=snake_case_ )
self.vqgan.eval()
if clip:
UpperCAmelCase_ : List[str] = clip
else:
UpperCAmelCase_ : List[Any] = CLIPModel.from_pretrained('openai/clip-vit-base-patch32' )
self.clip.to(self.device )
UpperCAmelCase_ : Tuple = ProcessorGradientFlow(device=self.device )
UpperCAmelCase_ : Dict = iterations
UpperCAmelCase_ : Dict = lr
UpperCAmelCase_ : str = log
UpperCAmelCase_ : Tuple = make_grid
UpperCAmelCase_ : Union[str, Any] = return_val
UpperCAmelCase_ : List[Any] = quantize
UpperCAmelCase_ : int = self.vqgan.decoder.z_shape
def _UpperCamelCase ( self , snake_case_=None , snake_case_=None , snake_case_=5 , snake_case_=True ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = []
if output_path is None:
UpperCAmelCase_ : List[str] = './animation.gif'
if input_path is None:
UpperCAmelCase_ : List[str] = self.save_path
UpperCAmelCase_ : List[str] = sorted(glob(input_path + '/*' ) )
if not len(snake_case_ ):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)' )
if len(snake_case_ ) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)' )
UpperCAmelCase_ : Tuple = total_duration / len(snake_case_ )
UpperCAmelCase_ : str = [frame_duration] * len(snake_case_ )
if extend_frames:
UpperCAmelCase_ : List[str] = 1.5
UpperCAmelCase_ : Any = 3
for file_name in paths:
if file_name.endswith('.png' ):
images.append(imageio.imread(snake_case_ ) )
imageio.mimsave(snake_case_ , snake_case_ , duration=snake_case_ )
print(F'''gif saved to {output_path}''' )
def _UpperCamelCase ( self , snake_case_=None , snake_case_=None ):
'''simple docstring'''
if not (path or img):
raise ValueError('Input either path or tensor' )
if img is not None:
raise NotImplementedError
UpperCAmelCase_ : Optional[Any] = preprocess(Image.open(snake_case_ ) , target_image_size=2_5_6 ).to(self.device )
UpperCAmelCase_ : Dict = preprocess_vqgan(snake_case_ )
UpperCAmelCase_ , *UpperCAmelCase_ : Tuple = self.vqgan.encode(snake_case_ )
return z
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = self.latent.detach().requires_grad_()
UpperCAmelCase_ : List[Any] = base_latent + transform_vector
if self.quantize:
UpperCAmelCase_ , *UpperCAmelCase_ : Tuple = self.vqgan.quantize(snake_case_ )
else:
UpperCAmelCase_ : Optional[int] = trans_latent
return self.vqgan.decode(snake_case_ )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_=None ):
'''simple docstring'''
UpperCAmelCase_ : int = self.clip_preprocessor(text=snake_case_ , images=snake_case_ , return_tensors='pt' , padding=snake_case_ )
UpperCAmelCase_ : Any = self.clip(**snake_case_ )
UpperCAmelCase_ : Dict = clip_outputs.logits_per_image
if weights is not None:
UpperCAmelCase_ : Union[str, Any] = similarity_logits * weights
return similarity_logits.sum()
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = self._get_clip_similarity(pos_prompts['prompts'] , snake_case_ , weights=(1 / pos_prompts['weights']) )
if neg_prompts:
UpperCAmelCase_ : List[Any] = self._get_clip_similarity(neg_prompts['prompts'] , snake_case_ , weights=neg_prompts['weights'] )
else:
UpperCAmelCase_ : Union[str, Any] = torch.tensor([1] , device=self.device )
UpperCAmelCase_ : Dict = -torch.log(snake_case_ ) + torch.log(snake_case_ )
return loss
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = torch.randn_like(self.latent , requires_grad=snake_case_ , device=self.device )
UpperCAmelCase_ : int = torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
UpperCAmelCase_ : Dict = self._add_vector(snake_case_ )
UpperCAmelCase_ : List[Any] = loop_post_process(snake_case_ )
UpperCAmelCase_ : Union[str, Any] = self._get_CLIP_loss(snake_case_ , snake_case_ , snake_case_ )
print('CLIP loss' , snake_case_ )
if self.log:
wandb.log({'CLIP Loss': clip_loss} )
clip_loss.backward(retain_graph=snake_case_ )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
wandb.init(reinit=snake_case_ , project='face-editor' )
wandb.config.update({'Positive Prompts': positive_prompts} )
wandb.config.update({'Negative Prompts': negative_prompts} )
wandb.config.update({'lr': self.lr, 'iterations': self.iterations} )
if image_path:
UpperCAmelCase_ : str = Image.open(snake_case_ )
UpperCAmelCase_ : str = image.resize((2_5_6, 2_5_6) )
wandb.log('Original Image' , wandb.Image(snake_case_ ) )
def _UpperCamelCase ( self , snake_case_ ):
'''simple docstring'''
if not prompts:
return []
UpperCAmelCase_ : int = []
UpperCAmelCase_ : Optional[int] = []
if isinstance(snake_case_ , snake_case_ ):
UpperCAmelCase_ : Union[str, Any] = [prompt.strip() for prompt in prompts.split('|' )]
for prompt in prompts:
if isinstance(snake_case_ , (tuple, list) ):
UpperCAmelCase_ : Tuple = prompt[0]
UpperCAmelCase_ : Optional[Any] = float(prompt[1] )
elif ":" in prompt:
UpperCAmelCase_ , UpperCAmelCase_ : int = prompt.split(':' )
UpperCAmelCase_ : List[str] = float(snake_case_ )
else:
UpperCAmelCase_ : Optional[int] = prompt
UpperCAmelCase_ : List[str] = 1.0
processed_prompts.append(snake_case_ )
weights.append(snake_case_ )
return {
"prompts": processed_prompts,
"weights": torch.tensor(snake_case_ , device=self.device ),
}
def _UpperCamelCase ( self , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=True , snake_case_=False , snake_case_=True , snake_case_=True , snake_case_=None , ):
'''simple docstring'''
if image_path:
UpperCAmelCase_ : List[Any] = self._get_latent(snake_case_ )
else:
UpperCAmelCase_ : Any = torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(snake_case_ , snake_case_ , snake_case_ )
assert pos_prompts, "You must provide at least one positive prompt."
UpperCAmelCase_ : Optional[int] = self.process_prompts(snake_case_ )
UpperCAmelCase_ : int = self.process_prompts(snake_case_ )
if save_final and save_path is None:
UpperCAmelCase_ : Union[str, Any] = os.path.join('./outputs/' , '_'.join(pos_prompts['prompts'] ) )
if not os.path.exists(snake_case_ ):
os.makedirs(snake_case_ )
else:
UpperCAmelCase_ : Any = save_path + '_' + get_timestamp()
os.makedirs(snake_case_ )
UpperCAmelCase_ : List[Any] = save_path
UpperCAmelCase_ : Dict = self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('Original Image' )
show_pil(custom_to_pil(snake_case_ ) )
UpperCAmelCase_ : Optional[int] = loop_post_process(snake_case_ )
for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case_ , snake_case_ , snake_case_ ) ):
if show_intermediate:
show_pil(snake_case_ )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}.png''' ) )
if self.log:
wandb.log({'Image': wandb.Image(snake_case_ )} )
if show_final:
show_pil(snake_case_ )
if save_final:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}_final.png''' ) )
| 274 | 0 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
UpperCAmelCase = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
UpperCAmelCase = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase = re.compile(r"""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase = re.compile(r"""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
UpperCAmelCase = re.compile(r"""^\s*try:""")
# Catches a line with else:
UpperCAmelCase = re.compile(r"""^\s*else:""")
def lowercase ( a__ : Optional[Any] ) -> int:
if _re_test_backend.search(a__ ) is None:
return None
_UpperCamelCase = [b[0] for b in _re_backend.findall(a__ )]
backends.sort()
return "_and_".join(a__ )
def lowercase ( a__ : Dict ) -> Dict:
with open(a__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = 0
while line_index < len(a__ ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(a__ ):
return None
# First grab the objects without a specific backend in _import_structure
_UpperCamelCase = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
_UpperCamelCase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(a__ ):
_UpperCamelCase = _re_one_line_import_struct.search(a__ ).groups()[0]
_UpperCamelCase = re.findall(R'''\[([^\]]+)\]''' , a__ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
_UpperCamelCase = _re_import_struct_key_value.search(a__ )
if single_line_import_search is not None:
_UpperCamelCase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(a__ ) > 0]
objects.extend(a__ )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
_UpperCamelCase = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
_UpperCamelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCamelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
_UpperCamelCase = lines[line_index]
if _re_import_struct_add_one.search(a__ ) is not None:
objects.append(_re_import_struct_add_one.search(a__ ).groups()[0] )
elif _re_import_struct_add_many.search(a__ ) is not None:
_UpperCamelCase = _re_import_struct_add_many.search(a__ ).groups()[0].split(''', ''' )
_UpperCamelCase = [obj[1:-1] for obj in imports if len(a__ ) > 0]
objects.extend(a__ )
elif _re_between_brackets.search(a__ ) is not None:
_UpperCamelCase = _re_between_brackets.search(a__ ).groups()[0].split(''', ''' )
_UpperCamelCase = [obj[1:-1] for obj in imports if len(a__ ) > 0]
objects.extend(a__ )
elif _re_quote_object.search(a__ ) is not None:
objects.append(_re_quote_object.search(a__ ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
_UpperCamelCase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_UpperCamelCase = []
while (
line_index < len(a__ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
_UpperCamelCase = lines[line_index]
_UpperCamelCase = _re_import.search(a__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
_UpperCamelCase = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(a__ ):
# If the line is an if is_backend_available, we grab all objects associated.
_UpperCamelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_UpperCamelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
_UpperCamelCase = lines[line_index]
_UpperCamelCase = _re_import.search(a__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 12 ):
objects.append(line[12:-2] )
line_index += 1
_UpperCamelCase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowercase ( a__ : Dict , a__ : List[Any] ) -> Dict:
def find_duplicates(a__ : Any ):
return [k for k, v in collections.Counter(a__ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_UpperCamelCase = []
for key in import_dict_objects.keys():
_UpperCamelCase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_UpperCamelCase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_UpperCamelCase = '''base imports''' if key == '''none''' else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def lowercase ( ) -> List[str]:
_UpperCamelCase = []
for root, _, files in os.walk(a__ ):
if "__init__.py" in files:
_UpperCamelCase = os.path.join(a__ , '''__init__.py''' )
_UpperCamelCase = parse_init(a__ )
if objects is not None:
_UpperCamelCase = analyze_results(*a__ )
if len(a__ ) > 0:
_UpperCamelCase = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('''\n'''.join(a__ ) )
if len(a__ ) > 0:
raise ValueError('''\n\n'''.join(a__ ) )
def lowercase ( ) -> Any:
_UpperCamelCase = []
for path, directories, files in os.walk(a__ ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(a__ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(a__ ) / folder).glob('''*.py''' ) ) ) == 0:
continue
_UpperCamelCase = str((Path(a__ ) / folder).relative_to(a__ ) )
_UpperCamelCase = short_path.replace(os.path.sep , '''.''' )
submodules.append(a__ )
for fname in files:
if fname == "__init__.py":
continue
_UpperCamelCase = str((Path(a__ ) / fname).relative_to(a__ ) )
_UpperCamelCase = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(a__ )
return submodules
UpperCAmelCase = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
"""models.esm.openfold_utils""",
]
def lowercase ( ) -> str:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
_UpperCamelCase = direct_transformers_import(a__ )
_UpperCamelCase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(a__ , '''__init__.py''' ) , '''r''' ) as f:
_UpperCamelCase = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , a__ ) ) )
_UpperCamelCase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(a__ ) > 0:
_UpperCamelCase = '''\n'''.join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
F'''{list_of_modules}\n'''
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 256 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_UpperCamelCase = arr[mi::-1] + arr[mi + 1 : len(a__ )]
# Reverse whole list
_UpperCamelCase = arr[cur - 1 :: -1] + arr[cur : len(a__ )]
cur -= 1
return arr
if __name__ == "__main__":
UpperCAmelCase = input("""Enter numbers separated by a comma:\n""").strip()
UpperCAmelCase = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 256 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class a_ ( lowerCamelCase , unittest.TestCase ):
lowercase = DiTPipeline
lowercase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowercase = PipelineTesterMixin.required_optional_params - {
"""latents""",
"""num_images_per_prompt""",
"""callback""",
"""callback_steps""",
}
lowercase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowercase = False
def A__ ( self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase_ , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase_ , )
UpperCamelCase = AutoencoderKL()
UpperCamelCase = DDIMScheduler()
UpperCamelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ) -> Dict:
"""simple docstring"""
if str(lowerCamelCase_ ).startswith("""mps""" ):
UpperCamelCase = torch.manual_seed(lowerCamelCase_ )
else:
UpperCamelCase = torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
UpperCamelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = """cpu"""
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = self.pipeline_class(**lowerCamelCase_ )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
UpperCamelCase = self.get_dummy_inputs(lowerCamelCase_ )
UpperCamelCase = pipe(**lowerCamelCase_ ).images
UpperCamelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
UpperCamelCase = np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7] )
UpperCamelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase_ , 1e-3 )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase_ , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class a_ ( unittest.TestCase ):
def A__ ( self ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
UpperCamelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
UpperCamelCase = pipe.get_label_ids(lowerCamelCase_ )
UpperCamelCase = pipe(lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase_ , lowerCamelCase_ ):
UpperCamelCase = load_numpy(
F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
UpperCamelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
UpperCamelCase = ["""vase""", """umbrella"""]
UpperCamelCase = pipe.get_label_ids(lowerCamelCase_ )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = pipe(lowerCamelCase_ , generator=lowerCamelCase_ , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase_ , lowerCamelCase_ ):
UpperCamelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F"/dit/{word}_512.npy" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 355 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
SCREAMING_SNAKE_CASE__ = 'CompVis/stable-diffusion-v1-1'
SCREAMING_SNAKE_CASE__ = 'CompVis/stable-diffusion-v1-2'
SCREAMING_SNAKE_CASE__ = 'CompVis/stable-diffusion-v1-3'
SCREAMING_SNAKE_CASE__ = 'CompVis/stable-diffusion-v1-4'
class a_ ( lowerCamelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , ) -> int:
"""simple docstring"""
super()._init_()
UpperCamelCase = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE )
UpperCamelCase = StableDiffusionPipeline(
vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=_SCREAMING_SNAKE_CASE , requires_safety_checker=_SCREAMING_SNAKE_CASE , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def A__ ( self ) -> Dict[str, Any]:
"""simple docstring"""
return {k: getattr(self , _SCREAMING_SNAKE_CASE ) for k in self.config.keys() if not k.startswith("""_""" )}
def A__ ( self , _SCREAMING_SNAKE_CASE = "auto" ) -> Any:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCamelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
self.enable_attention_slicing(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
"""simple docstring"""
return self.pipea(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ) -> str:
"""simple docstring"""
return self.pipea(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ) -> Optional[Any]:
"""simple docstring"""
return self.pipea(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
return self.pipea(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 512 , _SCREAMING_SNAKE_CASE = 50 , _SCREAMING_SNAKE_CASE = 7.5 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 1 , **_SCREAMING_SNAKE_CASE , ) -> int:
"""simple docstring"""
UpperCamelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
self.to(_SCREAMING_SNAKE_CASE )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"`height` and `width` must be divisible by 8 but are {height} and {width}." )
# Get first result from Stable Diffusion Checkpoint v1.1
UpperCamelCase = self.textaimg_sda_a(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.2
UpperCamelCase = self.textaimg_sda_a(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.3
UpperCamelCase = self.textaimg_sda_a(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# Get first result from Stable Diffusion Checkpoint v1.4
UpperCamelCase = self.textaimg_sda_a(
prompt=_SCREAMING_SNAKE_CASE , height=_SCREAMING_SNAKE_CASE , width=_SCREAMING_SNAKE_CASE , num_inference_steps=_SCREAMING_SNAKE_CASE , guidance_scale=_SCREAMING_SNAKE_CASE , negative_prompt=_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE , eta=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , latents=_SCREAMING_SNAKE_CASE , output_type=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , callback=_SCREAMING_SNAKE_CASE , callback_steps=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 183 | 0 |
'''simple docstring'''
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:
A__ : Dict = None
A__ : List[Any] = logging.get_logger(__name__)
A__ : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
A__ : Union[str, 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""",
},
}
A__ : Any = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
A__ : Dict = """▁"""
# Segments (not really needed)
A__ : List[str] = 0
A__ : List[Any] = 1
A__ : Union[str, Any] = 2
A__ : List[Any] = 3
A__ : str = 4
class UpperCAmelCase_ (_UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase : str = VOCAB_FILES_NAMES
lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : List[Any] = 'left'
lowerCamelCase : Optional[Any] = XLNetTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<sep>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<cls>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"] , **SCREAMING_SNAKE_CASE_ , ) -> Union[str, Any]:
# Mask token behave like a normal word, i.e. include the space before it
__lowerCamelCase : str = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
vocab_file=SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , do_lower_case=SCREAMING_SNAKE_CASE_ , remove_space=SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
__lowerCamelCase : Union[str, Any] = 3
__lowerCamelCase : int = do_lower_case
__lowerCamelCase : Optional[Any] = remove_space
__lowerCamelCase : int = keep_accents
__lowerCamelCase : Any = vocab_file
__lowerCamelCase : Any = False if not self.vocab_file else True
def lowercase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
__lowerCamelCase : Optional[Any] = [self.sep_token_id]
__lowerCamelCase : Any = [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 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
__lowerCamelCase : int = [self.sep_token_id]
__lowerCamelCase : 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 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
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(SCREAMING_SNAKE_CASE_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__lowerCamelCase : Tuple = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 185 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
A__ : str = get_logger(__name__)
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
__lowerCamelCase : List[str] = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('__' ):
setattr(self , SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
__lowerCamelCase : Optional[int] = module._original_module if isinstance(SCREAMING_SNAKE_CASE_ , _PatchedModuleObj ) else module
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : Tuple = []
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Union[str, Any]:
__lowerCamelCase : Optional[int] = obj
__lowerCamelCase : List[Any] = target
__lowerCamelCase : Union[str, Any] = new
__lowerCamelCase : Union[str, Any] = target.split('.' )[0]
__lowerCamelCase : Dict = {}
__lowerCamelCase : Dict = attrs or []
def __enter__( self ) -> Optional[int]:
*__lowerCamelCase , __lowerCamelCase : int = self.target.split('.' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
try:
__lowerCamelCase : Optional[int] = import_module('.'.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
__lowerCamelCase : List[Any] = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(SCREAMING_SNAKE_CASE_ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
__lowerCamelCase : Optional[Any] = obj_attr
# patch at top level
setattr(self.obj , SCREAMING_SNAKE_CASE_ , _PatchedModuleObj(SCREAMING_SNAKE_CASE_ , attrs=self.attrs ) )
__lowerCamelCase : str = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , attrs=self.attrs ) )
__lowerCamelCase : List[Any] = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# finally set the target attribute
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
__lowerCamelCase : Union[str, Any] = getattr(import_module('.'.join(SCREAMING_SNAKE_CASE_ ) ) , SCREAMING_SNAKE_CASE_ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , SCREAMING_SNAKE_CASE_ ) is attr_value:
__lowerCamelCase : Optional[int] = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
__lowerCamelCase : List[Any] = globals()['__builtins__'][target_attr]
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.new )
else:
raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self , *SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
for attr in list(self.original ):
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.original.pop(SCREAMING_SNAKE_CASE_ ) )
def lowercase_ ( self ) -> Optional[int]:
self.__enter__()
self._active_patches.append(self )
def lowercase_ ( self ) -> str:
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 185 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
__UpperCAmelCase =["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a__ ( UpperCAmelCase__ ):
def __init__( self : Dict , a : List[str] , a : Optional[int] , a : List[Any]=None , a : Tuple=1 ):
"""simple docstring"""
__lowerCamelCase = tokenizer
__lowerCamelCase = dataset
__lowerCamelCase = len(a ) if n_tasks is None else n_tasks
__lowerCamelCase = n_copies
def __iter__( self : int ):
"""simple docstring"""
__lowerCamelCase = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
__lowerCamelCase = self.tokenizer(a , padding=a , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a__ ( UpperCAmelCase__ ):
def __init__( self : Dict , a : str , a : int , a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = start_length
__lowerCamelCase = eof_strings
__lowerCamelCase = tokenizer
def __call__( self : Optional[int] , a : Optional[int] , a : Any , **a : str ):
"""simple docstring"""
__lowerCamelCase = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
__lowerCamelCase = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(a )
def __lowerCAmelCase ( UpperCamelCase__ ) -> Any:
__lowerCamelCase = re.split('''(%s)''' % '''|'''.join(UpperCamelCase__ ) , UpperCamelCase__ )
# last string should be ""
return "".join(string_list[:-2] )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=20 , **UpperCamelCase__ ) -> List[Any]:
__lowerCamelCase = defaultdict(UpperCamelCase__ ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(UpperCamelCase__ ) ):
with torch.no_grad():
__lowerCamelCase = batch['''ids'''].shape[-1]
__lowerCamelCase = accelerator.unwrap_model(UpperCamelCase__ ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=UpperCamelCase__ , **UpperCamelCase__ )
# each task is generated batch_size times
__lowerCamelCase = batch['''task_id'''].repeat(UpperCamelCase__ )
__lowerCamelCase = accelerator.pad_across_processes(
UpperCamelCase__ , dim=1 , pad_index=tokenizer.pad_token_id )
__lowerCamelCase , __lowerCamelCase = accelerator.gather((generated_tokens, generated_tasks) )
__lowerCamelCase = generated_tokens.cpu().numpy()
__lowerCamelCase = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(UpperCamelCase__ , UpperCamelCase__ ):
gen_token_dict[task].append(UpperCamelCase__ )
__lowerCamelCase = [[] for _ in range(UpperCamelCase__ )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__lowerCamelCase = tokenizer.decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
code_gens[task].append(remove_last_block(UpperCamelCase__ ) )
return code_gens
def __lowerCAmelCase ( ) -> Optional[Any]:
# Setup configuration
__lowerCamelCase = HfArgumentParser(UpperCamelCase__ )
__lowerCamelCase = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__lowerCamelCase = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__lowerCamelCase = '''false'''
if args.num_workers is None:
__lowerCamelCase = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__lowerCamelCase = Accelerator()
set_seed(args.seed , device_specific=UpperCamelCase__ )
# Load model and tokenizer
__lowerCamelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
__lowerCamelCase = tokenizer.eos_token
__lowerCamelCase = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__lowerCamelCase = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , UpperCamelCase__ , UpperCamelCase__ )] ),
}
# Load evaluation dataset and metric
__lowerCamelCase = load_dataset('''openai_humaneval''' )
__lowerCamelCase = load_metric('''code_eval''' )
__lowerCamelCase = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
__lowerCamelCase = args.n_samples // args.batch_size
__lowerCamelCase = TokenizedDataset(UpperCamelCase__ , human_eval['''test'''] , n_copies=UpperCamelCase__ , n_tasks=UpperCamelCase__ )
# do not confuse args.batch_size, which is actually the num_return_sequences
__lowerCamelCase = DataLoader(UpperCamelCase__ , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__lowerCamelCase = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
__lowerCamelCase , __lowerCamelCase = accelerator.prepare(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = complete_code(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , n_tasks=UpperCamelCase__ , batch_size=args.batch_size , **UpperCamelCase__ , )
if accelerator.is_main_process:
__lowerCamelCase = []
for task in tqdm(range(UpperCamelCase__ ) ):
__lowerCamelCase = human_eval['''test'''][task]['''test''']
__lowerCamelCase = f"""check({human_eval['test'][task]['entry_point']})"""
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
__lowerCamelCase , __lowerCamelCase = code_eval_metric.compute(
references=UpperCamelCase__ , predictions=UpperCamelCase__ , num_workers=args.num_workers )
print(f"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(UpperCamelCase__ , UpperCamelCase__ )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 364 | '''simple docstring'''
from __future__ import annotations
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> list[list[int]]:
__lowerCamelCase = []
create_all_state(1 , UpperCamelCase__ , UpperCamelCase__ , [] , UpperCamelCase__ )
return result
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> None:
if level == 0:
total_list.append(current_list[:] )
return
for i in range(UpperCamelCase__ , total_number - level + 2 ):
current_list.append(UpperCamelCase__ )
create_all_state(i + 1 , UpperCamelCase__ , level - 1 , UpperCamelCase__ , UpperCamelCase__ )
current_list.pop()
def __lowerCAmelCase ( UpperCamelCase__ ) -> None:
for i in total_list:
print(*UpperCamelCase__ )
if __name__ == "__main__":
__UpperCAmelCase =4
__UpperCAmelCase =2
__UpperCAmelCase =generate_all_combinations(n, k)
print_all_state(total_list)
| 237 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
__A = logging.getLogger()
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: List[Any] =argparse.ArgumentParser()
parser.add_argument("-f" )
lowerCamelCase__: Any =parser.parse_args()
return args.f
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] ={}
lowerCamelCase__: str =os.path.join(__a , "all_results.json" )
if os.path.exists(__a ):
with open(__a , "r" ) as f:
lowerCamelCase__: Optional[Any] =json.load(__a )
else:
raise ValueError(F"""can't find {path}""" )
return results
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
__A = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class _SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
'''simple docstring'''
@classmethod
def SCREAMING_SNAKE_CASE_ (cls : str) ->int:
'''simple docstring'''
lowerCamelCase__: List[Any] =tempfile.mkdtemp()
lowerCamelCase__: List[Any] =os.path.join(cls.tmpdir , "default_config.yml")
write_basic_config(save_location=cls.configPath)
lowerCamelCase__: int =['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ (cls : List[Any]) ->Union[str, Any]:
'''simple docstring'''
shutil.rmtree(cls.tmpdir)
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: List[str] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: Any =F"""
{self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--seed=42
--checkpointing_steps epoch
--with_tracking
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16")
run_command(self._launch_args + testargs)
lowerCamelCase__: Optional[Any] =get_results(__lowercase)
self.assertGreaterEqual(result["eval_accuracy"] , 0.75)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "glue_no_trainer")))
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->int:
'''simple docstring'''
lowerCamelCase__: Any =self.get_auto_remove_tmp_dir()
lowerCamelCase__: List[str] =F"""
{self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--block_size 128
--per_device_train_batch_size 5
--per_device_eval_batch_size 5
--num_train_epochs 2
--output_dir {tmp_dir}
--checkpointing_steps epoch
--with_tracking
""".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs)
lowerCamelCase__: Dict =get_results(__lowercase)
self.assertLess(result["perplexity"] , 100)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "clm_no_trainer")))
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Any:
'''simple docstring'''
lowerCamelCase__: List[Any] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: List[Any] =F"""
{self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--num_train_epochs=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Union[str, Any] =get_results(__lowercase)
self.assertLess(result["perplexity"] , 42)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "mlm_no_trainer")))
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =7 if get_gpu_count() > 1 else 2
lowerCamelCase__: List[Any] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: List[Any] =F"""
{self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Optional[Any] =get_results(__lowercase)
self.assertGreaterEqual(result["eval_accuracy"] , 0.75)
self.assertLess(result["train_loss"] , 0.5)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "ner_no_trainer")))
@unittest.skip(reason="Fix me @muellerzr")
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: int =F"""
{self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--seed=42
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Any =get_results(__lowercase)
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result["eval_f1"] , 28)
self.assertGreaterEqual(result["eval_exact"] , 28)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "qa_no_trainer")))
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: int =self.get_auto_remove_tmp_dir()
lowerCamelCase__: int =F"""
{self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/swag/sample.json
--validation_file tests/fixtures/tests_samples/swag/sample.json
--output_dir {tmp_dir}
--max_train_steps=20
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Dict =get_results(__lowercase)
self.assertGreaterEqual(result["eval_accuracy"] , 0.8)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "swag_no_trainer")))
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->str:
'''simple docstring'''
lowerCamelCase__: str =self.get_auto_remove_tmp_dir()
lowerCamelCase__: Dict =F"""
{self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Tuple =get_results(__lowercase)
self.assertGreaterEqual(result["eval_rouge1"] , 10)
self.assertGreaterEqual(result["eval_rouge2"] , 2)
self.assertGreaterEqual(result["eval_rougeL"] , 7)
self.assertGreaterEqual(result["eval_rougeLsum"] , 7)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "summarization_no_trainer")))
@slow
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Dict) ->str:
'''simple docstring'''
lowerCamelCase__: Optional[int] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: Dict =F"""
{self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
--model_name_or_path sshleifer/student_marian_en_ro_6_1
--source_lang en
--target_lang ro
--train_file tests/fixtures/tests_samples/wmt16/sample.json
--validation_file tests/fixtures/tests_samples/wmt16/sample.json
--output_dir {tmp_dir}
--max_train_steps=50
--num_warmup_steps=8
--num_beams=6
--learning_rate=3e-3
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--source_lang en_XX
--target_lang ro_RO
--checkpointing_steps epoch
--with_tracking
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: Optional[int] =get_results(__lowercase)
self.assertGreaterEqual(result["eval_bleu"] , 30)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "epoch_0")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "translation_no_trainer")))
@slow
def SCREAMING_SNAKE_CASE_ (self : Any) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Tuple =logging.StreamHandler(sys.stdout)
logger.addHandler(__lowercase)
lowerCamelCase__: int =self.get_auto_remove_tmp_dir()
lowerCamelCase__: Any =F"""
{self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
--dataset_name huggingface/semantic-segmentation-test-sample
--output_dir {tmp_dir}
--max_train_steps=10
--num_warmup_steps=2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--checkpointing_steps epoch
""".split()
run_command(self._launch_args + testargs)
lowerCamelCase__: List[str] =get_results(__lowercase)
self.assertGreaterEqual(result["eval_overall_accuracy"] , 0.10)
@mock.patch.dict(os.environ , {"WANDB_MODE": "offline"})
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =self.get_auto_remove_tmp_dir()
lowerCamelCase__: Optional[Any] =F"""
{self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
--model_name_or_path google/vit-base-patch16-224-in21k
--dataset_name hf-internal-testing/cats_vs_dogs_sample
--learning_rate 1e-4
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--max_train_steps 2
--train_val_split 0.1
--seed 42
--output_dir {tmp_dir}
--with_tracking
--checkpointing_steps 1
""".split()
if is_cuda_and_apex_available():
testargs.append("--fp16")
run_command(self._launch_args + testargs)
lowerCamelCase__: Optional[Any] =get_results(__lowercase)
# The base model scores a 25%
self.assertGreaterEqual(result["eval_accuracy"] , 0.6)
self.assertTrue(os.path.exists(os.path.join(__lowercase , "step_1")))
self.assertTrue(os.path.exists(os.path.join(__lowercase , "image_classification_no_trainer")))
| 10 | import random
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = a[left_index]
__UpperCamelCase :Any = left_index + 1
for j in range(left_index + 1 , SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
__UpperCamelCase , __UpperCamelCase :str = a[i], a[j]
i += 1
__UpperCamelCase , __UpperCamelCase :Optional[int] = a[i - 1], a[left_index]
return i - 1
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if left < right:
__UpperCamelCase :int = random.randint(SCREAMING_SNAKE_CASE , right - 1 )
__UpperCamelCase , __UpperCamelCase :List[str] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
__UpperCamelCase :Dict = partition(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
quick_sort_random(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # recursive quicksort to the left of the pivot point
quick_sort_random(
SCREAMING_SNAKE_CASE , pivot_index + 1 , SCREAMING_SNAKE_CASE ) # recursive quicksort to the right of the pivot point
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :Tuple = input('''Enter numbers separated by a comma:\n''' ).strip()
__UpperCamelCase :Union[str, Any] = [int(SCREAMING_SNAKE_CASE ) for item in user_input.split(''',''' )]
quick_sort_random(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) )
print(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 43 | 0 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) 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
lowerCAmelCase : List[str] = range(3 , int(math.sqrt(_snake_case ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _snake_case ( _snake_case : List[Any] , _snake_case : int=1 , **_snake_case : Any ):
lowerCAmelCase : Optional[Any] = factor * value
lowerCAmelCase : Union[str, Any] = value
while not is_prime(_snake_case ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_snake_case )
return value
| 314 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : List[str] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
snake_case__ : str = {
'''vocab_file''': {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
'''bert-base-multilingual-uncased''': (
'''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
),
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
),
'''bert-base-cased-finetuned-mrpc''': (
'''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
),
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
'''bert-base-german-dbmdz-uncased''': (
'''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
),
'''wietsedv/bert-base-dutch-cased''': (
'''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
'''bert-base-multilingual-uncased''': (
'''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
),
'''bert-base-multilingual-cased''': (
'''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
),
'''bert-base-cased-finetuned-mrpc''': (
'''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
),
'''bert-base-german-dbmdz-cased''': (
'''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
),
'''bert-base-german-dbmdz-uncased''': (
'''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
),
'''wietsedv/bert-base-dutch-cased''': (
'''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
),
},
}
snake_case__ : Union[str, Any] = {
'''bert-base-uncased''': 512,
'''bert-large-uncased''': 512,
'''bert-base-cased''': 512,
'''bert-large-cased''': 512,
'''bert-base-multilingual-uncased''': 512,
'''bert-base-multilingual-cased''': 512,
'''bert-base-chinese''': 512,
'''bert-base-german-cased''': 512,
'''bert-large-uncased-whole-word-masking''': 512,
'''bert-large-cased-whole-word-masking''': 512,
'''bert-large-uncased-whole-word-masking-finetuned-squad''': 512,
'''bert-large-cased-whole-word-masking-finetuned-squad''': 512,
'''bert-base-cased-finetuned-mrpc''': 512,
'''bert-base-german-dbmdz-cased''': 512,
'''bert-base-german-dbmdz-uncased''': 512,
'''TurkuNLP/bert-base-finnish-cased-v1''': 512,
'''TurkuNLP/bert-base-finnish-uncased-v1''': 512,
'''wietsedv/bert-base-dutch-cased''': 512,
}
snake_case__ : Optional[Any] = {
'''bert-base-uncased''': {'''do_lower_case''': True},
'''bert-large-uncased''': {'''do_lower_case''': True},
'''bert-base-cased''': {'''do_lower_case''': False},
'''bert-large-cased''': {'''do_lower_case''': False},
'''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
'''bert-base-multilingual-cased''': {'''do_lower_case''': False},
'''bert-base-chinese''': {'''do_lower_case''': False},
'''bert-base-german-cased''': {'''do_lower_case''': False},
'''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
'''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
'''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
'''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
'''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
'''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
'''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
'''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
'''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
'''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}
class snake_case_( a__ ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = BertTokenizer
def __init__( self : int , UpperCamelCase_ : Union[str, Any]=None , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : str=True , UpperCamelCase_ : Dict="[UNK]" , UpperCamelCase_ : Any="[SEP]" , UpperCamelCase_ : Any="[PAD]" , UpperCamelCase_ : Tuple="[CLS]" , UpperCamelCase_ : List[Any]="[MASK]" , UpperCamelCase_ : Optional[Any]=True , UpperCamelCase_ : Tuple=None , **UpperCamelCase_ : Optional[int] , ):
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
lowerCAmelCase : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , UpperCamelCase_ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCamelCase_ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCamelCase_ ) != tokenize_chinese_chars
):
lowerCAmelCase : Optional[int] = getattr(UpperCamelCase_ , normalizer_state.pop('''type''' ) )
lowerCAmelCase : Tuple = do_lower_case
lowerCAmelCase : Union[str, Any] = strip_accents
lowerCAmelCase : Tuple = tokenize_chinese_chars
lowerCAmelCase : str = normalizer_class(**UpperCamelCase_ )
lowerCAmelCase : Optional[int] = do_lower_case
def lowerCamelCase__ ( self : List[str] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=None ):
lowerCAmelCase : List[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None ):
lowerCAmelCase : Optional[Any] = [self.sep_token_id]
lowerCAmelCase : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase__ ( self : Dict , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None ):
lowerCAmelCase : str = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
| 314 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Dict = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'sew'
def __init__( self , __snake_case=32 , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case=2 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.0 , __snake_case=0.1 , __snake_case=0.1 , __snake_case=0.02 , __snake_case=1e-5 , __snake_case="group" , __snake_case="gelu" , __snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __snake_case=False , __snake_case=128 , __snake_case=16 , __snake_case=True , __snake_case=0.05 , __snake_case=10 , __snake_case=2 , __snake_case=0.0 , __snake_case=10 , __snake_case=0 , __snake_case="mean" , __snake_case=False , __snake_case=False , __snake_case=256 , __snake_case=0 , __snake_case=1 , __snake_case=2 , **__snake_case , ) -> Tuple:
'''simple docstring'''
super().__init__(**__snake_case , pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case )
__a =hidden_size
__a =feat_extract_norm
__a =feat_extract_activation
__a =list(__snake_case )
__a =list(__snake_case )
__a =list(__snake_case )
__a =conv_bias
__a =num_conv_pos_embeddings
__a =num_conv_pos_embedding_groups
__a =len(self.conv_dim )
__a =num_hidden_layers
__a =intermediate_size
__a =squeeze_factor
__a =hidden_act
__a =num_attention_heads
__a =hidden_dropout
__a =attention_dropout
__a =activation_dropout
__a =feat_proj_dropout
__a =final_dropout
__a =layerdrop
__a =layer_norm_eps
__a =initializer_range
__a =vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
f'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
f'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__a =apply_spec_augment
__a =mask_time_prob
__a =mask_time_length
__a =mask_time_min_masks
__a =mask_feature_prob
__a =mask_feature_length
__a =mask_feature_min_masks
# ctc loss
__a =ctc_loss_reduction
__a =ctc_zero_infinity
# sequence classification
__a =use_weighted_layer_sum
__a =classifier_proj_size
@property
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 218 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_lowerCAmelCase : Union[str, Any] = 16
_lowerCAmelCase : List[str] = 32
def UpperCamelCase_( _snake_case : Accelerator , _snake_case : int = 16 ):
"""simple docstring"""
__a =AutoTokenizer.from_pretrained('bert-base-cased' )
__a =load_dataset('glue' , 'mrpc' )
def tokenize_function(_snake_case : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
__a =tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_snake_case , max_length=_snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__a =datasets.map(
_snake_case , batched=_snake_case , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__a =tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_snake_case : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__a =128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__a =16
elif accelerator.mixed_precision != "no":
__a =8
else:
__a =None
return tokenizer.pad(
_snake_case , padding='longest' , max_length=_snake_case , pad_to_multiple_of=_snake_case , return_tensors='pt' , )
# Instantiate dataloaders.
__a =DataLoader(
tokenized_datasets['train'] , shuffle=_snake_case , collate_fn=_snake_case , batch_size=_snake_case )
__a =DataLoader(
tokenized_datasets['validation'] , shuffle=_snake_case , collate_fn=_snake_case , batch_size=_snake_case )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_lowerCAmelCase : List[Any] = mocked_dataloaders # noqa: F811
def UpperCamelCase_( _snake_case : Tuple , _snake_case : Union[str, Any] ):
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS' , _snake_case ) == "1":
__a =2
# New Code #
__a =int(args.gradient_accumulation_steps )
__a =int(args.local_sgd_steps )
# Initialize accelerator
__a =Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_snake_case )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a =config['lr']
__a =int(config['num_epochs'] )
__a =int(config['seed'] )
__a =int(config['batch_size'] )
__a =evaluate.load('glue' , 'mrpc' )
set_seed(_snake_case )
__a , __a =get_dataloaders(_snake_case , _snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a =AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=_snake_case )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__a =model.to(accelerator.device )
# Instantiate optimizer
__a =AdamW(params=model.parameters() , lr=_snake_case )
# Instantiate scheduler
__a =get_linear_schedule_with_warmup(
optimizer=_snake_case , num_warmup_steps=100 , num_training_steps=(len(_snake_case ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__a , __a , __a , __a , __a =accelerator.prepare(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
# Now we train the model
for epoch in range(_snake_case ):
model.train()
with LocalSGD(
accelerator=_snake_case , model=_snake_case , local_sgd_steps=_snake_case , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_snake_case ):
__a =model(**_snake_case )
__a =output.loss
accelerator.backward(_snake_case )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__a =model(**_snake_case )
__a =outputs.logits.argmax(dim=-1 )
__a , __a =accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=_snake_case , references=_snake_case , )
__a =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , _snake_case )
def UpperCamelCase_( ):
"""simple docstring"""
__a =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=_snake_case , default=_snake_case , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
# New Code #
parser.add_argument(
'--gradient_accumulation_steps' , type=_snake_case , default=1 , help='The number of minibatches to be ran before gradients are accumulated.' , )
parser.add_argument(
'--local_sgd_steps' , type=_snake_case , default=8 , help='Number of local SGD steps or None to disable local SGD' )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
__a =parser.parse_args()
__a ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(_snake_case , _snake_case )
if __name__ == "__main__":
main()
| 218 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[Any]=False ) -> Any:
UpperCamelCase__ : Dict = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
('''module.cls_token''', '''vit.embeddings.cls_token'''),
('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''module.norm.weight''', '''layernorm.weight'''),
('''module.norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCamelCase__ : str = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def lowerCAmelCase_ ( __UpperCAmelCase: Optional[int] , __UpperCAmelCase: int , __UpperCAmelCase: Any=False ) -> Tuple:
for i in range(config.num_hidden_layers ):
if base_model:
UpperCamelCase__ : Optional[int] = ''''''
else:
UpperCamelCase__ : List[Any] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase__ : int = state_dict.pop(f"module.blocks.{i}.attn.qkv.weight" )
UpperCamelCase__ : Union[str, Any] = state_dict.pop(f"module.blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : Tuple = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase__ : List[str] = in_proj_bias[: config.hidden_size]
UpperCamelCase__ : str = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase__ : Any = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase__ : Optional[int] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __UpperCAmelCase: Any ) -> Optional[Any]:
UpperCamelCase__ : Any = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def lowerCAmelCase_ ( __UpperCAmelCase: Dict ) -> str:
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
UpperCamelCase__ : Dict = [
'''module.fc.fc1.weight''',
'''module.fc.fc1.bias''',
'''module.fc.bn1.weight''',
'''module.fc.bn1.bias''',
'''module.fc.bn1.running_mean''',
'''module.fc.bn1.running_var''',
'''module.fc.bn1.num_batches_tracked''',
'''module.fc.fc2.weight''',
'''module.fc.fc2.bias''',
'''module.fc.bn2.weight''',
'''module.fc.bn2.bias''',
'''module.fc.bn2.running_mean''',
'''module.fc.bn2.running_var''',
'''module.fc.bn2.num_batches_tracked''',
'''module.fc.fc3.weight''',
'''module.fc.fc3.bias''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def lowerCAmelCase_ ( __UpperCAmelCase: Optional[Any] , __UpperCAmelCase: Dict , __UpperCAmelCase: Optional[int] ) -> Tuple:
UpperCamelCase__ : List[Any] = dct.pop(__UpperCAmelCase )
UpperCamelCase__ : Optional[int] = val
def lowerCAmelCase_ ( __UpperCAmelCase: Tuple , __UpperCAmelCase: Optional[Any] ) -> Optional[Any]:
UpperCamelCase__ : Optional[Any] = ViTMSNConfig()
UpperCamelCase__ : int = 1000
UpperCamelCase__ : Optional[int] = '''datasets/huggingface/label-files'''
UpperCamelCase__ : str = '''imagenet-1k-id2label.json'''
UpperCamelCase__ : List[Any] = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase ) , '''r''' ) )
UpperCamelCase__ : Tuple = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ : List[str] = idalabel
UpperCamelCase__ : Optional[int] = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
UpperCamelCase__ : int = 384
UpperCamelCase__ : Dict = 1536
UpperCamelCase__ : str = 6
elif "l16" in checkpoint_url:
UpperCamelCase__ : int = 1024
UpperCamelCase__ : Optional[int] = 4096
UpperCamelCase__ : int = 24
UpperCamelCase__ : Tuple = 16
UpperCamelCase__ : Dict = 0.1
elif "b4" in checkpoint_url:
UpperCamelCase__ : str = 4
elif "l7" in checkpoint_url:
UpperCamelCase__ : Optional[Any] = 7
UpperCamelCase__ : List[str] = 1024
UpperCamelCase__ : Any = 4096
UpperCamelCase__ : Union[str, Any] = 24
UpperCamelCase__ : Any = 16
UpperCamelCase__ : Any = 0.1
UpperCamelCase__ : Any = ViTMSNModel(__UpperCAmelCase )
UpperCamelCase__ : Optional[Any] = torch.hub.load_state_dict_from_url(__UpperCAmelCase , map_location='''cpu''' )['''target_encoder''']
UpperCamelCase__ : Union[str, Any] = ViTImageProcessor(size=config.image_size )
remove_projection_head(__UpperCAmelCase )
UpperCamelCase__ : List[str] = create_rename_keys(__UpperCAmelCase , base_model=__UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_q_k_v(__UpperCAmelCase , __UpperCAmelCase , base_model=__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
UpperCamelCase__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ : Union[str, Any] = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
UpperCamelCase__ : Optional[Any] = ViTImageProcessor(
size=config.image_size , image_mean=__UpperCAmelCase , image_std=__UpperCAmelCase )
UpperCamelCase__ : str = image_processor(images=__UpperCAmelCase , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
UpperCamelCase__ : Optional[int] = model(**__UpperCAmelCase )
UpperCamelCase__ : Any = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
UpperCamelCase__ : Tuple = torch.tensor([[-1.0915, -1.4876, -1.1809]] )
elif "b16" in checkpoint_url:
UpperCamelCase__ : Optional[Any] = torch.tensor([[14.2889, -18.9045, 11.7281]] )
elif "l16" in checkpoint_url:
UpperCamelCase__ : Tuple = torch.tensor([[41.5028, -22.8681, 45.6475]] )
elif "b4" in checkpoint_url:
UpperCamelCase__ : Optional[Any] = torch.tensor([[-4.3868, 5.2932, -0.4137]] )
else:
UpperCamelCase__ : List[Any] = torch.tensor([[-0.1792, -0.6465, 2.4263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , __UpperCAmelCase , atol=1e-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
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.'
)
UpperCAmelCase_ = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 369 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
# For consistency across different places the DisjunctiveConstraint is called,
# dc.token_ids is a list of integers. It is also initialized only by integers.
UpperCamelCase__ : Optional[int] = [[1, 2, 4], [1, 2, 3, 4]]
UpperCamelCase__ : str = DisjunctiveConstraint(__magic_name__ )
self.assertTrue(isinstance(dc.token_ids, __magic_name__ ) )
with self.assertRaises(__magic_name__ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(__magic_name__ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def UpperCamelCase__ ( self ) -> Tuple:
"""simple docstring"""
# We can't have constraints that are complete subsets of another. This leads to a preverse
# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
UpperCamelCase__ : str = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(__magic_name__ ):
DisjunctiveConstraint(__magic_name__ ) # fails here
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase__ : str = [[1, 2, 3], [1, 2, 4]]
UpperCamelCase__ : Union[str, Any] = DisjunctiveConstraint(__magic_name__ )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Tuple = dc.update(1 )
UpperCamelCase__ : Any = stepped is True and completed is False and reset is False
self.assertTrue(__magic_name__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Union[str, Any] = dc.update(2 )
UpperCamelCase__ : Dict = stepped is True and completed is False and reset is False
self.assertTrue(__magic_name__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : List[Any] = dc.update(3 )
UpperCamelCase__ : Dict = stepped is True and completed is True and reset is False
self.assertTrue(__magic_name__ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Tuple = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
UpperCamelCase__ : Union[str, Any] = DisjunctiveConstraint(__magic_name__ )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Tuple = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Optional[Any] = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Union[str, Any] = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : int = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Optional[Any] = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Any = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : str = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 247 | 0 |
"""simple docstring"""
def A ( snake_case__ = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ = int(snake_case__ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
while i * i <= n:
while n % i == 0:
SCREAMING_SNAKE_CASE__ = i
n //= i
i += 1
if n > 1:
SCREAMING_SNAKE_CASE__ = n
return int(snake_case__ )
if __name__ == "__main__":
print(F'{solution() = }')
| 165 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[Any] = {
"facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Dict = 'detr'
lowerCamelCase__ : Union[str, Any] = ['past_key_values']
lowerCamelCase__ : Tuple = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Optional[int] , __UpperCAmelCase : Any=True , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : Optional[int]=3 , __UpperCAmelCase : int=1_0_0 , __UpperCAmelCase : Optional[Any]=6 , __UpperCAmelCase : List[str]=2_0_4_8 , __UpperCAmelCase : List[str]=8 , __UpperCAmelCase : Optional[int]=6 , __UpperCAmelCase : Dict=2_0_4_8 , __UpperCAmelCase : List[str]=8 , __UpperCAmelCase : Union[str, Any]=0.0 , __UpperCAmelCase : Dict=0.0 , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : Any="relu" , __UpperCAmelCase : Dict=2_5_6 , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : int=0.0 , __UpperCAmelCase : str=0.0 , __UpperCAmelCase : Optional[int]=0.02 , __UpperCAmelCase : Optional[int]=1.0 , __UpperCAmelCase : Dict=False , __UpperCAmelCase : str="sine" , __UpperCAmelCase : str="resnet50" , __UpperCAmelCase : List[Any]=True , __UpperCAmelCase : int=False , __UpperCAmelCase : Tuple=1 , __UpperCAmelCase : Optional[Any]=5 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : Optional[Any]=1 , __UpperCAmelCase : Union[str, Any]=1 , __UpperCAmelCase : Union[str, Any]=5 , __UpperCAmelCase : Any=2 , __UpperCAmelCase : List[str]=0.1 , **__UpperCAmelCase : Dict , ) -> Optional[int]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ = config_class.from_dict(__UpperCAmelCase )
# set timm attributes to None
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None, None, None
SCREAMING_SNAKE_CASE__ = use_timm_backbone
SCREAMING_SNAKE_CASE__ = backbone_config
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = num_queries
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = encoder_attention_heads
SCREAMING_SNAKE_CASE__ = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ = decoder_layers
SCREAMING_SNAKE_CASE__ = decoder_attention_heads
SCREAMING_SNAKE_CASE__ = dropout
SCREAMING_SNAKE_CASE__ = attention_dropout
SCREAMING_SNAKE_CASE__ = activation_dropout
SCREAMING_SNAKE_CASE__ = activation_function
SCREAMING_SNAKE_CASE__ = init_std
SCREAMING_SNAKE_CASE__ = init_xavier_std
SCREAMING_SNAKE_CASE__ = encoder_layerdrop
SCREAMING_SNAKE_CASE__ = decoder_layerdrop
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = auxiliary_loss
SCREAMING_SNAKE_CASE__ = position_embedding_type
SCREAMING_SNAKE_CASE__ = backbone
SCREAMING_SNAKE_CASE__ = use_pretrained_backbone
SCREAMING_SNAKE_CASE__ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE__ = class_cost
SCREAMING_SNAKE_CASE__ = bbox_cost
SCREAMING_SNAKE_CASE__ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return self.d_model
@classmethod
def SCREAMING_SNAKE_CASE ( cls : str , __UpperCAmelCase : PretrainedConfig , **__UpperCAmelCase : Dict ) -> List[Any]:
return cls(backbone_config=__UpperCAmelCase , **__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict[str, any]:
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
SCREAMING_SNAKE_CASE__ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ = self.__class__.model_type
return output
class lowerCamelCase (A__ ):
lowerCamelCase__ : Union[str, Any] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE ( self : str ) -> float:
return 1e-5
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return 1_2
| 165 | 1 |
def a( A : List[Any] ) -> Optional[int]:
"""simple docstring"""
a = len(A )
while cur > 1:
# Find the maximum number in arr
a = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
a = arr[mi::-1] + arr[mi + 1 : len(A )]
# Reverse whole list
a = arr[cur - 1 :: -1] + arr[cur : len(A )]
cur -= 1
return arr
if __name__ == "__main__":
_lowercase: Union[str, Any] = input("Enter numbers separated by a comma:\n").strip()
_lowercase: Dict = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 71 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a( A : List[str] , A : int=0.999 , A : Union[str, Any]="cosine" , ) -> Optional[int]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(A : Optional[Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
a = []
for i in range(A ):
a = i / num_diffusion_timesteps
a = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A ) / alpha_bar_fn(A ) , A ) )
return torch.tensor(A , dtype=torch.floataa )
class _lowercase ( lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
__A = [e.name for e in KarrasDiffusionSchedulers]
__A = 2
@register_to_config
def __init__(self , lowerCamelCase_ = 1000 , lowerCamelCase_ = 0.0_0085 , lowerCamelCase_ = 0.012 , lowerCamelCase_ = "linear" , lowerCamelCase_ = None , lowerCamelCase_ = "epsilon" , lowerCamelCase_ = "linspace" , lowerCamelCase_ = 0 , ):
"""simple docstring"""
if trained_betas is not None:
a = torch.tensor(lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
a = torch.linspace(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
a = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowerCamelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
a = betas_for_alpha_bar(lowerCamelCase_ )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
a = 1.0 - self.betas
a = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_=None ):
"""simple docstring"""
if schedule_timesteps is None:
a = self.timesteps
a = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
a = 1 if len(lowerCamelCase_ ) > 1 else 0
else:
a = timestep.cpu().item() if torch.is_tensor(lowerCamelCase_ ) else timestep
a = self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , ):
"""simple docstring"""
a = self.index_for_timestep(lowerCamelCase_ )
if self.state_in_first_order:
a = self.sigmas[step_index]
else:
a = self.sigmas_interpol[step_index]
a = sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None , ):
"""simple docstring"""
a = num_inference_steps
a = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
a = np.linspace(0 , num_train_timesteps - 1 , lowerCamelCase_ , dtype=lowerCamelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
a = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
a = (np.arange(0 , lowerCamelCase_ ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
a = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
a = (np.arange(lowerCamelCase_ , 0 , -step_ratio )).round().copy().astype(lowerCamelCase_ )
timesteps -= 1
else:
raise ValueError(
F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
a = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
a = torch.from_numpy(np.log(lowerCamelCase_ ) ).to(lowerCamelCase_ )
a = np.interp(lowerCamelCase_ , np.arange(0 , len(lowerCamelCase_ ) ) , lowerCamelCase_ )
a = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
a = torch.from_numpy(lowerCamelCase_ ).to(device=lowerCamelCase_ )
# interpolate sigmas
a = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
a = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
a = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(lowerCamelCase_ ).startswith("mps" ):
# mps does not support float64
a = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ , dtype=torch.floataa )
else:
a = torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )
# interpolate timesteps
a = self.sigma_to_t(lowerCamelCase_ ).to(lowerCamelCase_ , dtype=timesteps.dtype )
a = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
a = torch.cat([timesteps[:1], interleaved_timesteps] )
a = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
a = defaultdict(lowerCamelCase_ )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = sigma.log()
# get distribution
a = log_sigma - self.log_sigmas[:, None]
# get sigmas range
a = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
a = low_idx + 1
a = self.log_sigmas[low_idx]
a = self.log_sigmas[high_idx]
# interpolate sigmas
a = (low - log_sigma) / (low - high)
a = w.clamp(0 , 1 )
# transform interpolation to time range
a = (1 - w) * low_idx + w * high_idx
a = t.view(sigma.shape )
return t
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return self.sample is None
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = True , ):
"""simple docstring"""
a = self.index_for_timestep(lowerCamelCase_ )
# advance index counter by 1
a = timestep.cpu().item() if torch.is_tensor(lowerCamelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
a = self.sigmas[step_index]
a = self.sigmas_interpol[step_index + 1]
a = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
a = self.sigmas[step_index - 1]
a = self.sigmas_interpol[step_index]
a = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
a = 0
a = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
a = sigma_hat if self.state_in_first_order else sigma_interpol
a = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
a = sigma_hat if self.state_in_first_order else sigma_interpol
a = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("prediction_type not implemented yet: sample" )
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
a = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
a = sigma_interpol - sigma_hat
# store for 2nd order step
a = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
a = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
a = sigma_next - sigma_hat
a = self.sample
a = None
a = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCamelCase_ )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ):
"""simple docstring"""
a = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_ ):
# mps does not support float64
a = self.timesteps.to(original_samples.device , dtype=torch.floataa )
a = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
a = self.timesteps.to(original_samples.device )
a = timesteps.to(original_samples.device )
a = [self.index_for_timestep(lowerCamelCase_ , lowerCamelCase_ ) for t in timesteps]
a = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
a = sigma.unsqueeze(-1 )
a = original_samples + noise * sigma
return noisy_samples
def __len__(self ):
"""simple docstring"""
return self.config.num_train_timesteps
| 71 | 1 |
"""simple docstring"""
lowerCamelCase_ : int = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
assert type(_UpperCAmelCase ) in (int, float) and decimal == int(_UpperCAmelCase )
A_ : Any = int(_UpperCAmelCase )
A_ : str = ''
A_ : List[Any] = False
if decimal < 0:
A_ : Optional[Any] = True
decimal *= -1
while decimal > 0:
A_ , A_ : List[Any] = divmod(_UpperCAmelCase , 16 )
A_ : Optional[int] = values[remainder] + hexadecimal
A_ : Optional[int] = '0x' + hexadecimal
if negative:
A_ : List[str] = '-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
lowerCamelCase_ : Any = re.compile(r'\s+')
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(_UpperCAmelCase , '' , example['content'] ).encode('utf-8' ) ).hexdigest()}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = [len(_UpperCAmelCase ) for line in example['content'].splitlines()]
return {"line_mean": np.mean(_UpperCAmelCase ), "line_max": max(_UpperCAmelCase )}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = np.mean([c.isalnum() for c in example['content']] )
return {"alpha_frac": alpha_frac}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example['hash'] )
return True
else:
return False
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=5 ):
"""simple docstring"""
A_ : Optional[int] = ['auto-generated', 'autogenerated', 'automatically generated']
A_ : List[str] = example['content'].splitlines()
for _, line in zip(range(_UpperCAmelCase ) , _UpperCAmelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=5 , _UpperCAmelCase=0.05 ):
"""simple docstring"""
A_ : Any = ['unit tests', 'test file', 'configuration file']
A_ : Dict = example['content'].splitlines()
A_ : List[Any] = 0
A_ : str = 0
# first test
for _, line in zip(range(_UpperCAmelCase ) , _UpperCAmelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
A_ : Tuple = example['content'].count('\n' )
A_ : Tuple = int(coeff * nlines )
for line in lines:
count_config += line.lower().count('config' )
count_test += line.lower().count('test' )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = ['def ', 'class ', 'for ', 'while ']
A_ : Tuple = example['content'].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=4 ):
"""simple docstring"""
A_ : Union[str, Any] = example['content'].splitlines()
A_ : Any = 0
for line in lines:
counter += line.lower().count('=' )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = tokenizer(example['content'] , truncation=_UpperCAmelCase )['input_ids']
A_ : Dict = len(example['content'] ) / len(_UpperCAmelCase )
return {"ratio": ratio}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = {}
results.update(get_hash(_UpperCAmelCase ) )
results.update(line_stats(_UpperCAmelCase ) )
results.update(alpha_stats(_UpperCAmelCase ) )
results.update(char_token_ratio(_UpperCAmelCase ) )
results.update(is_autogenerated(_UpperCAmelCase ) )
results.update(is_config_or_test(_UpperCAmelCase ) )
results.update(has_no_keywords(_UpperCAmelCase ) )
results.update(has_few_assignments(_UpperCAmelCase ) )
return results
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if not check_uniques(_UpperCAmelCase , _UpperCAmelCase ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
with open(_UpperCAmelCase , 'rb' ) as f_in:
with gzip.open(str(_UpperCAmelCase ) + '.gz' , 'wb' , compresslevel=6 ) as f_out:
shutil.copyfileobj(_UpperCAmelCase , _UpperCAmelCase )
os.unlink(_UpperCAmelCase )
# Settings
lowerCamelCase_ : Optional[int] = HfArgumentParser(PreprocessingArguments)
lowerCamelCase_ : Optional[Any] = parser.parse_args()
if args.num_workers is None:
lowerCamelCase_ : int = multiprocessing.cpu_count()
lowerCamelCase_ : Tuple = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
lowerCamelCase_ : Tuple = time.time()
lowerCamelCase_ : Tuple = load_dataset(args.dataset_name, split='train')
print(F"Time to load dataset: {time.time()-t_start:.2f}")
# Run preprocessing
lowerCamelCase_ : List[str] = time.time()
lowerCamelCase_ : Optional[int] = ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")
# Deduplicate hashes
lowerCamelCase_ : int = set(ds.unique('hash'))
lowerCamelCase_ : Union[str, Any] = len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")
# Deduplicate data and apply heuristics
lowerCamelCase_ : Optional[int] = time.time()
lowerCamelCase_ : Tuple = ds.filter(filter, fn_kwargs={'uniques': uniques, 'args': args})
print(F"Time to filter dataset: {time.time()-t_start:.2f}")
print(F"Size of filtered dataset: {len(ds_filter)}")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
lowerCamelCase_ : Union[str, Any] = time.time()
lowerCamelCase_ , lowerCamelCase_ : str = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"Time to deduplicate dataset: {time.time()-t_start:.2f}")
print(F"Size of deduplicate dataset: {len(ds_filter)}")
# Save data in batches of samples_per_file
lowerCamelCase_ : Tuple = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / 'duplicate_clusters.json', 'w') as f:
json.dump(duplicate_clusters, f)
lowerCamelCase_ : Optional[Any] = output_dir / 'data'
data_dir.mkdir(exist_ok=True)
lowerCamelCase_ : List[str] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
lowerCamelCase_ : Optional[int] = str(data_dir / F"file-{file_number+1:012}.json")
lowerCamelCase_ : List[str] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"Time to save dataset: {time.time()-t_start:.2f}") | 286 | 1 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = """▁"""
__lowerCamelCase = {"""vocab_file""": """prophetnet.tokenizer"""}
__lowerCamelCase = {
"""vocab_file""": {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
),
}
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def UpperCamelCase ( __lowerCamelCase : Dict ):
snake_case : Dict = collections.OrderedDict()
with open(__lowerCamelCase , "r" , encoding="utf-8" ) as reader:
snake_case : Any = reader.readlines()
for index, token in enumerate(__lowerCamelCase ):
snake_case : List[Any] = token.rstrip("\n" )
snake_case : int = index
return vocab
class UpperCAmelCase ( A_ ):
A__ : Tuple = VOCAB_FILES_NAMES
A__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : int = ["input_ids", "attention_mask"]
def __init__(self : Any , snake_case__ : Dict , snake_case__ : List[Any]="[SEP]" , snake_case__ : Optional[int]="[SEP]" , snake_case__ : Union[str, Any]="[SEP]" , snake_case__ : List[Any]="[UNK]" , snake_case__ : List[str]="[PAD]" , snake_case__ : List[str]="[CLS]" , snake_case__ : List[Any]="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : List[str] , ) -> None:
'''simple docstring'''
snake_case : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , sep_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
snake_case : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case__ ) )
snake_case : Dict = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
snake_case : List[Any] = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}
for i in range(10 ):
snake_case : Dict = f"""[unused{i}]"""
snake_case : List[str] = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
snake_case : Dict = 12
snake_case : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(snake_case__ )
def __getstate__(self : str ) -> Union[str, Any]:
'''simple docstring'''
snake_case : str = self.__dict__.copy()
snake_case : Tuple = None
return state
def __setstate__(self : str , snake_case__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Union[str, Any] = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
snake_case : Dict = {}
snake_case : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return ([0] * len(snake_case__ )) + [1]
return ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case : List[str] = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> int:
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset
def _SCREAMING_SNAKE_CASE (self : int ) -> Any:
'''simple docstring'''
snake_case : List[str] = {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : str ) -> str:
'''simple docstring'''
return self.sp_model.encode(snake_case__ , out_type=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case : Optional[Any] = self.sp_model.PieceToId(snake_case__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Optional[int] ) -> int:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : Dict ) -> List[Any]:
'''simple docstring'''
snake_case : Dict = "".join(snake_case__ ).replace(snake_case__ , " " ).strip()
return out_string
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : str , snake_case__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case : Dict = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case__ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case__ , "wb" ) as fi:
snake_case : Tuple = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (out_vocab_file,)
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
snake_case : str = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 358 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCamelCase = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
__lowerCamelCase = json.load(f)
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE (self : Dict , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
return FSMTTokenizer.from_pretrained(snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Optional[Any] , snake_case__ : str ) -> List[str]:
'''simple docstring'''
snake_case : List[Any] = FSMTForConditionalGeneration.from_pretrained(snake_case__ ).to(snake_case__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Tuple , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
snake_case : Optional[int] = f"""facebook/wmt19-{pair}"""
snake_case : Optional[Any] = self.get_tokenizer(snake_case__ )
snake_case : Dict = self.get_model(snake_case__ )
snake_case : List[Any] = bleu_data[pair]["src"]
snake_case : int = bleu_data[pair]["tgt"]
snake_case : Union[str, Any] = tokenizer(snake_case__ , return_tensors="pt" , truncation=snake_case__ , padding="longest" ).to(snake_case__ )
snake_case : str = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
snake_case : Optional[int] = tokenizer.batch_decode(
snake_case__ , skip_special_tokens=snake_case__ , clean_up_tokenization_spaces=snake_case__ )
snake_case : Optional[int] = calculate_bleu(snake_case__ , snake_case__ )
print(snake_case__ )
self.assertGreaterEqual(scores["bleu"] , snake_case__ )
| 10 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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 numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case :
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase=13 , UpperCamelCase=7 , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=99 , UpperCamelCase=32 , UpperCamelCase=2 , UpperCamelCase=4 , UpperCamelCase=37 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=512 , UpperCamelCase=16 , UpperCamelCase=2 , UpperCamelCase=0.02 , UpperCamelCase=3 , UpperCamelCase=4 , UpperCamelCase=None , UpperCamelCase=0 , ):
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = seq_length
lowerCamelCase_ = is_training
lowerCamelCase_ = use_input_mask
lowerCamelCase_ = use_token_type_ids
lowerCamelCase_ = use_labels
lowerCamelCase_ = vocab_size
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = max_position_embeddings
lowerCamelCase_ = type_vocab_size
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = num_labels
lowerCamelCase_ = num_choices
lowerCamelCase_ = scope
lowerCamelCase_ = projection_dim
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase_ = None
if self.use_token_type_ids:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = BertConfig(
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=UpperCamelCase , initializer_range=self.initializer_range , )
lowerCamelCase_ = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFDPRContextEncoder(config=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase , token_type_ids=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFDPRQuestionEncoder(config=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase , token_type_ids=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = TFDPRReader(config=UpperCamelCase )
lowerCamelCase_ = model(UpperCamelCase , attention_mask=UpperCamelCase )
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) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,(
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {"input_ids": input_ids}
return config, inputs_dict
@require_tf
class snake_case ( lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_lowerCamelCase = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFDPRModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=UpperCamelCase , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*UpperCamelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFDPRContextEncoder.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFDPRContextEncoder.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFDPRQuestionEncoder.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFDPRReader.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
@require_tf
class snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base" )
lowerCamelCase_ = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]] ) # [CLS] hello, is my dog cute? [SEP]
lowerCamelCase_ = model(UpperCamelCase )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
lowerCamelCase_ = tf.constant(
[
[
0.03_236_253,
0.12_753_335,
0.16_818_509,
0.00_279_786,
0.3_896_933,
0.24_264_945,
0.2_178_971,
-0.02_335_227,
-0.08_481_959,
-0.14_324_117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 55 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( lowercase , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = BlenderbotSmallTokenizer
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
lowerCamelCase_ = dict(zip(UpperCamelCase , range(len(UpperCamelCase ) ) ) )
lowerCamelCase_ = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
lowerCamelCase_ = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase ) )
def snake_case ( self , **UpperCamelCase ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = "adapt act apte"
lowerCamelCase_ = "adapt act apte"
return input_text, output_text
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase_ = "adapt act apte"
lowerCamelCase_ = ["adapt", "act", "ap@@", "te"]
lowerCamelCase_ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
lowerCamelCase_ = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , UpperCamelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
assert tok("sam" ).input_ids == [1384]
lowerCamelCase_ = "I am a small frog."
lowerCamelCase_ = tok([src_text] , padding=UpperCamelCase , truncation=UpperCamelCase )["input_ids"]
lowerCamelCase_ = tok.batch_decode(UpperCamelCase , skip_special_tokens=UpperCamelCase , clean_up_tokenization_spaces=UpperCamelCase )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
lowerCamelCase_ = "I am a small frog ."
lowerCamelCase_ = "."
lowerCamelCase_ = tok(UpperCamelCase )["input_ids"]
lowerCamelCase_ = tok(UpperCamelCase )["input_ids"]
assert encoded[-1] == encoded_dot[0]
| 55 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: Tuple = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: Optional[int] = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: List[Any] = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: Any = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: Any = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: List[Any] = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
_lowercase: List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 71 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( lowerCAmelCase, unittest.TestCase ):
"""simple docstring"""
__A = LxmertTokenizer
__A = LxmertTokenizerFast
__A = True
__A = True
def UpperCamelCase_ (self ):
"""simple docstring"""
super().setUp()
a = [
"[UNK]",
"[CLS]",
"[SEP]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
a = 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 UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = "UNwant\u00E9d,running"
a = "unwanted, running"
return input_text, output_text
def UpperCamelCase_ (self ):
"""simple docstring"""
a = self.tokenizer_class(self.vocab_file )
a = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(lowerCamelCase_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , [7, 4, 5, 10, 8, 9] )
def UpperCamelCase_ (self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
a = self.get_tokenizer()
a = self.get_rust_tokenizer()
a = "I was born in 92000, and this is falsé."
a = tokenizer.tokenize(lowerCamelCase_ )
a = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
a = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
a = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
a = self.get_rust_tokenizer()
a = tokenizer.encode(lowerCamelCase_ )
a = rust_tokenizer.encode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
| 71 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowerCAmelCase_ ( __A ):
UpperCAmelCase__ : Dict = "Wav2Vec2FeatureExtractor"
UpperCAmelCase__ : Optional[int] = "AutoTokenizer"
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> str:
super().__init__(lowerCAmelCase_, lowerCAmelCase_ )
UpperCamelCase : List[Any] = self.feature_extractor
UpperCamelCase : int = False
@classmethod
def snake_case_ ( cls, SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> Tuple:
try:
return super().from_pretrained(lowerCAmelCase_, **lowerCAmelCase_ )
except OSError:
warnings.warn(
F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ', lowerCAmelCase_, )
UpperCamelCase : List[str] = WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_, **lowerCAmelCase_ )
UpperCamelCase : str = WavaVecaCTCTokenizer.from_pretrained(lowerCAmelCase_, **lowerCAmelCase_ )
return cls(feature_extractor=lowerCAmelCase_, tokenizer=lowerCAmelCase_ )
def __call__( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> List[Any]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*lowerCAmelCase_, **lowerCAmelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
UpperCamelCase : Union[str, Any] = kwargs.pop('raw_speech' )
else:
UpperCamelCase : List[str] = kwargs.pop('audio', lowerCAmelCase_ )
UpperCamelCase : Optional[int] = kwargs.pop('sampling_rate', lowerCAmelCase_ )
UpperCamelCase : Union[str, Any] = kwargs.pop('text', lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Optional[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:
UpperCamelCase : Dict = self.feature_extractor(lowerCAmelCase_, *lowerCAmelCase_, sampling_rate=lowerCAmelCase_, **lowerCAmelCase_ )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(lowerCAmelCase_, **lowerCAmelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
UpperCamelCase : Tuple = encodings["input_ids"]
return inputs
def snake_case_ ( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> Dict:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*lowerCAmelCase_, **lowerCAmelCase_ )
UpperCamelCase : Union[str, Any] = kwargs.pop('input_features', lowerCAmelCase_ )
UpperCamelCase : List[str] = kwargs.pop('labels', lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : List[str] = args[1:]
if input_features is not None:
UpperCamelCase : List[Any] = self.feature_extractor.pad(lowerCAmelCase_, *lowerCAmelCase_, **lowerCAmelCase_ )
if labels is not None:
UpperCamelCase : Any = self.tokenizer.pad(lowerCAmelCase_, **lowerCAmelCase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
UpperCamelCase : List[str] = labels["input_ids"]
return input_features
def snake_case_ ( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> int:
return self.tokenizer.batch_decode(*lowerCAmelCase_, **lowerCAmelCase_ )
def snake_case_ ( self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> int:
return self.tokenizer.decode(*lowerCAmelCase_, **lowerCAmelCase_ )
@contextmanager
def snake_case_ ( self ) -> int:
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.' )
UpperCamelCase : str = True
UpperCamelCase : Optional[int] = self.tokenizer
yield
UpperCamelCase : str = self.feature_extractor
UpperCamelCase : str = False
| 119 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase_ (__A ):
def __init__( self : str , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[Any]=13 , lowerCAmelCase_ : Tuple=7 , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : List[str]=99 , lowerCAmelCase_ : int=32 , lowerCAmelCase_ : List[str]=5 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : str=37 , lowerCAmelCase_ : List[Any]="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : List[Any]=512 , lowerCAmelCase_ : Optional[int]=16 , lowerCAmelCase_ : Union[str, Any]=2 , lowerCAmelCase_ : List[str]=0.0_2 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : Union[str, Any]="None" , lowerCAmelCase_ : List[Any]=3 , lowerCAmelCase_ : Optional[int]=4 , lowerCAmelCase_ : int=None , ) -> Dict:
UpperCAmelCase_ : Dict = parent
UpperCAmelCase_ : Union[str, Any] = batch_size
UpperCAmelCase_ : Optional[Any] = seq_length
UpperCAmelCase_ : List[Any] = is_training
UpperCAmelCase_ : Optional[int] = use_input_mask
UpperCAmelCase_ : int = use_token_type_ids
UpperCAmelCase_ : Any = use_labels
UpperCAmelCase_ : Optional[int] = vocab_size
UpperCAmelCase_ : Any = hidden_size
UpperCAmelCase_ : Dict = num_hidden_layers
UpperCAmelCase_ : List[Any] = num_attention_heads
UpperCAmelCase_ : List[Any] = intermediate_size
UpperCAmelCase_ : int = hidden_act
UpperCAmelCase_ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase_ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase_ : Any = max_position_embeddings
UpperCAmelCase_ : Union[str, Any] = type_vocab_size
UpperCAmelCase_ : Union[str, Any] = type_sequence_label_size
UpperCAmelCase_ : Tuple = initializer_range
UpperCAmelCase_ : int = num_labels
UpperCAmelCase_ : Optional[Any] = num_choices
UpperCAmelCase_ : List[str] = relative_attention
UpperCAmelCase_ : List[Any] = position_biased_input
UpperCAmelCase_ : Dict = pos_att_type
UpperCAmelCase_ : Optional[Any] = scope
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Dict:
UpperCAmelCase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ : Tuple = None
if self.use_input_mask:
UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
UpperCAmelCase_ : Optional[Any] = None
if self.use_token_type_ids:
UpperCAmelCase_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase_ : Optional[Any] = None
UpperCAmelCase_ : List[str] = None
UpperCAmelCase_ : Union[str, Any] = None
if self.use_labels:
UpperCAmelCase_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase_ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase_ : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = self.get_config()
UpperCAmelCase_ : int = 300
return config
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : int ) -> List[Any]:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[Any] ) -> List[Any]:
UpperCAmelCase_ : Optional[Any] = DebertaModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
UpperCAmelCase_ : Optional[Any] = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )[0]
UpperCAmelCase_ : Optional[int] = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )[0]
UpperCAmelCase_ : Optional[Any] = model(lowerCAmelCase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] ) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = DebertaForMaskedLM(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
UpperCAmelCase_ : List[Any] = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : int , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase_ : Any = self.num_labels
UpperCAmelCase_ : List[Any] = DebertaForSequenceClassification(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
UpperCAmelCase_ : Optional[Any] = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple ) -> str:
UpperCAmelCase_ : Optional[int] = self.num_labels
UpperCAmelCase_ : Optional[int] = DebertaForTokenClassification(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
UpperCAmelCase_ : Optional[Any] = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : str ) -> List[Any]:
UpperCAmelCase_ : Dict = DebertaForQuestionAnswering(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
UpperCAmelCase_ : Any = model(
lowerCAmelCase_ , attention_mask=lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , start_positions=lowerCAmelCase_ , end_positions=lowerCAmelCase_ , )
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 _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase_ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) : Tuple = config_and_inputs
UpperCAmelCase_ : List[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ (__A , __A , unittest.TestCase ):
__magic_name__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
__magic_name__ = (
{
'''feature-extraction''': DebertaModel,
'''fill-mask''': DebertaForMaskedLM,
'''question-answering''': DebertaForQuestionAnswering,
'''text-classification''': DebertaForSequenceClassification,
'''token-classification''': DebertaForTokenClassification,
'''zero-shot''': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__ = True
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
UpperCAmelCase_ : int = DebertaModelTester(self )
UpperCAmelCase_ : Any = ConfigTester(self , config_class=lowerCAmelCase_ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
UpperCAmelCase_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[int]:
UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : Optional[int] = DebertaModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase_ (unittest.TestCase ):
@unittest.skip(reason="Model not available yet" )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
pass
@slow
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
UpperCAmelCase_ : Optional[int] = DebertaModel.from_pretrained("microsoft/deberta-base" )
UpperCAmelCase_ : List[Any] = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
UpperCAmelCase_ : Tuple = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase_ : Optional[Any] = model(lowerCAmelCase_ , attention_mask=lowerCAmelCase_ )[0]
# compare the actual values for a slice.
UpperCAmelCase_ : Tuple = torch.tensor(
[[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowerCAmelCase_ , atol=1e-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 268 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
_A = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
_A , _A = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
_A = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
_A = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
_A = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([f"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 167 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCAmelCase_ = UNetaDModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''), up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''), )
return model
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.dummy_uncond_unet
lowerCAmelCase_ = KarrasVeScheduler()
lowerCAmelCase_ = KarrasVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=2, generator=UpperCamelCase__, output_type='''numpy''' ).images
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=2, generator=UpperCamelCase__, output_type='''numpy''', return_dict=UpperCamelCase__ )[0]
lowerCAmelCase_ = image[0, -3:, -3:, -1]
lowerCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class A ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = '''google/ncsnpp-celebahq-256'''
lowerCAmelCase_ = UNetaDModel.from_pretrained(UpperCamelCase__ )
lowerCAmelCase_ = KarrasVeScheduler()
lowerCAmelCase_ = KarrasVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCAmelCase_ = torch.manual_seed(0 )
lowerCAmelCase_ = pipe(num_inference_steps=20, generator=UpperCamelCase__, output_type='''numpy''' ).images
lowerCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCAmelCase_ = np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 167 | 1 |
'''simple docstring'''
from __future__ import annotations
lowercase : Union[str, Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowercase : Optional[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : int = []
A : Optional[int] = len(snake_case__ )
for i in range(snake_case__ ):
A : float = -1
for j in range(i + 1 , snake_case__ ):
if arr[i] < arr[j]:
A : Dict = arr[j]
break
result.append(snake_case__ )
return result
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : Any = []
for i, outer in enumerate(snake_case__ ):
A : float = -1
for inner in arr[i + 1 :]:
if outer < inner:
A : List[str] = inner
break
result.append(snake_case__ )
return result
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : int = len(snake_case__ )
A : list[float] = []
A : list[float] = [-1] * arr_size
for index in reversed(range(snake_case__ ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
A : Dict = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
lowercase : Any = (
'from __main__ import arr, next_greatest_element_slow, '
'next_greatest_element_fast, next_greatest_element'
)
print(
'next_greatest_element_slow():',
timeit('next_greatest_element_slow(arr)', setup=setup),
)
print(
'next_greatest_element_fast():',
timeit('next_greatest_element_fast(arr)', setup=setup),
)
print(
' next_greatest_element():',
timeit('next_greatest_element(arr)', setup=setup),
)
| 3 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowercase : Union[str, Any] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'output.dense',
'attention.self_mask': 'attn_mask',
'mlp.fc1': 'intermediate.dense',
'mlp.fc2': 'output.dense',
'norm1': 'layernorm_before',
'norm2': 'layernorm_after',
'bn0': 'batch_norm',
}
lowercase : Tuple = AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')
def lowerCAmelCase_ ( snake_case__ , snake_case__=False ):
'''simple docstring'''
A, A : Tuple = create_model(
'''HTSAT-tiny''' , '''roberta''' , snake_case__ , precision='''fp32''' , device='''cuda:0''' if torch.cuda.is_available() else '''cpu''' , enable_fusion=snake_case__ , fusion_type='''aff_2d''' if enable_fusion else None , )
return model, model_cfg
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : Dict = {}
A : str = R'''.*sequential.(\d+).*'''
A : Union[str, Any] = R'''.*_projection.(\d+).*'''
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
A : Any = key.replace(snake_case__ , snake_case__ )
if re.match(snake_case__ , snake_case__ ):
# replace sequential layers with list
A : Any = re.match(snake_case__ , snake_case__ ).group(1 )
A : List[str] = key.replace(F'sequential.{sequential_layer}.' , F'layers.{int(snake_case__ )//3}.linear.' )
elif re.match(snake_case__ , snake_case__ ):
A : Union[str, Any] = int(re.match(snake_case__ , snake_case__ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
A : str = 1 if projecton_layer == 0 else 2
A : Optional[Any] = key.replace(F'_projection.{projecton_layer}.' , F'_projection.linear{transformers_projection_layer}.' )
if "audio" and "qkv" in key:
# split qkv into query key and value
A : int = value
A : List[Any] = mixed_qkv.size(0 ) // 3
A : Union[str, Any] = mixed_qkv[:qkv_dim]
A : Optional[int] = mixed_qkv[qkv_dim : qkv_dim * 2]
A : Optional[int] = mixed_qkv[qkv_dim * 2 :]
A : Tuple = query_layer
A : Union[str, Any] = key_layer
A : Optional[int] = value_layer
else:
A : Dict = value
return model_state_dict
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=False ):
'''simple docstring'''
A, A : int = init_clap(snake_case__ , enable_fusion=snake_case__ )
clap_model.eval()
A : str = clap_model.state_dict()
A : Union[str, Any] = rename_state_dict(snake_case__ )
A : Tuple = ClapConfig()
A : str = enable_fusion
A : str = ClapModel(snake_case__ )
# ignore the spectrogram embedding layer
model.load_state_dict(snake_case__ , strict=snake_case__ )
model.save_pretrained(snake_case__ )
transformers_config.save_pretrained(snake_case__ )
if __name__ == "__main__":
lowercase : List[str] = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
lowercase : Tuple = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 3 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class UpperCAmelCase_ :
def __init__( self, __a, __a=2, __a=True, __a=False, __a=10, __a=3, __a=32 * 8, __a=32 * 8, __a=4, __a=64, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = parent
_lowerCAmelCase : str = batch_size
_lowerCAmelCase : List[str] = is_training
_lowerCAmelCase : str = use_auxiliary_loss
_lowerCAmelCase : Optional[Any] = num_queries
_lowerCAmelCase : int = num_channels
_lowerCAmelCase : Optional[Any] = min_size
_lowerCAmelCase : int = max_size
_lowerCAmelCase : str = num_labels
_lowerCAmelCase : int = hidden_dim
_lowerCAmelCase : Dict = hidden_dim
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
__a)
_lowerCAmelCase : Tuple = torch.ones([self.batch_size, self.min_size, self.max_size], device=__a)
_lowerCAmelCase : Tuple = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=__a) > 0.5
).float()
_lowerCAmelCase : Dict = (torch.rand((self.batch_size, self.num_labels), device=__a) > 0.5).long()
_lowerCAmelCase : Tuple = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = MaskaFormerConfig(
hidden_size=self.hidden_dim, )
_lowerCAmelCase : List[Any] = self.num_queries
_lowerCAmelCase : Any = self.num_labels
_lowerCAmelCase : int = [1, 1, 1, 1]
_lowerCAmelCase : Any = self.num_channels
_lowerCAmelCase : List[Any] = 64
_lowerCAmelCase : Optional[Any] = 128
_lowerCAmelCase : Union[str, Any] = self.hidden_dim
_lowerCAmelCase : int = self.hidden_dim
_lowerCAmelCase : List[str] = self.hidden_dim
return config
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Any = self.prepare_config_and_inputs()
_lowerCAmelCase : Optional[Any] = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = output.encoder_hidden_states
_lowerCAmelCase : Dict = output.pixel_decoder_hidden_states
_lowerCAmelCase : Optional[Any] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__a), len(config.backbone_config.depths))
self.parent.assertTrue(len(__a), len(config.backbone_config.depths))
self.parent.assertTrue(len(__a), config.decoder_layers)
def snake_case__ ( self, __a, __a, __a, __a=False):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = MaskaFormerModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(pixel_values=__a, pixel_mask=__a)
_lowerCAmelCase : Optional[Any] = model(__a, output_hidden_states=__a)
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_dim), )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(output.encoder_last_hidden_state is not None)
if output_hidden_states:
self.check_output_hidden_state(__a, __a)
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = MaskaFormerForUniversalSegmentation(config=__a)
model.to(__a)
model.eval()
def comm_check_on_output(__a):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.encoder_last_hidden_state is not None)
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape, (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4), )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(pixel_values=__a, pixel_mask=__a)
_lowerCAmelCase : List[str] = model(__a)
comm_check_on_output(__a)
_lowerCAmelCase : Union[str, Any] = model(
pixel_values=__a, pixel_mask=__a, mask_labels=__a, class_labels=__a)
comm_check_on_output(__a)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape, torch.Size([1]))
@require_torch
class UpperCAmelCase_ ( a , a , unittest.TestCase):
lowerCamelCase__ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowerCamelCase__ = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = MaskaFormerModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, has_text_modality=__a)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__a, **__a, output_hidden_states=__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__a)
@unittest.skip(reason="Mask2Former does not use inputs_embeds")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former is not a generative model")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip(reason="Mask2Former does not use token embeddings")
def snake_case__ ( self):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`")
def snake_case__ ( self):
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : List[Any] = model_class(__a)
_lowerCAmelCase : Optional[int] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase : List[str] = [*signature.parameters.keys()]
_lowerCAmelCase : Union[str, Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1], __a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
_lowerCAmelCase : Tuple = MaskaFormerModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = (self.model_tester.min_size,) * 2
_lowerCAmelCase : Dict = {
"pixel_values": torch.randn((2, 3, *size), device=__a),
"mask_labels": torch.randn((2, 10, *size), device=__a),
"class_labels": torch.zeros(2, 10, device=__a).long(),
}
_lowerCAmelCase : Any = self.model_tester.get_config()
_lowerCAmelCase : Optional[int] = MaskaFormerForUniversalSegmentation(__a).to(__a)
_lowerCAmelCase : Optional[int] = model(**__a)
self.assertTrue(outputs.loss is not None)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__a, **__a, output_hidden_states=__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : List[Any] = model_class(__a).to(__a)
_lowerCAmelCase : Any = model(**__a, output_attentions=__a)
self.assertTrue(outputs.attentions is not None)
def snake_case__ ( self):
'''simple docstring'''
if not self.model_tester.is_training:
return
_lowerCAmelCase : Union[str, Any] = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase : List[str] = model_class(__a)
model.to(__a)
model.train()
_lowerCAmelCase : Any = model(__a, mask_labels=__a, class_labels=__a).loss
loss.backward()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.all_model_classes[1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : List[Any] = True
_lowerCAmelCase : int = model_class(__a).to(__a)
model.train()
_lowerCAmelCase : Optional[int] = model(__a, mask_labels=__a, class_labels=__a)
_lowerCAmelCase : Optional[int] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_lowerCAmelCase : Dict = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
_lowerCAmelCase : str = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_lowerCAmelCase : Dict = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__a)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
_snake_case = 1e-4
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def snake_case__ ( self):
'''simple docstring'''
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def snake_case__ ( self):
'''simple docstring'''
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = MaskaFormerModel.from_pretrained(self.model_checkpoints).to(__a)
_lowerCAmelCase : Optional[int] = self.default_image_processor
_lowerCAmelCase : int = prepare_img()
_lowerCAmelCase : List[str] = image_processor(__a, return_tensors="pt").to(__a)
_lowerCAmelCase : Optional[Any] = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(__a, (1, 3, 384, 384))
with torch.no_grad():
_lowerCAmelCase : int = model(**__a)
_lowerCAmelCase : Union[str, Any] = torch.tensor(
[[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]).to(__a)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3], __a, atol=__a))
_lowerCAmelCase : Any = torch.tensor(
[[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]).to(__a)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], __a, atol=__a))
_lowerCAmelCase : str = torch.tensor(
[[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]).to(__a)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3], __a, atol=__a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(__a).eval()
_lowerCAmelCase : Tuple = self.default_image_processor
_lowerCAmelCase : Union[str, Any] = prepare_img()
_lowerCAmelCase : Any = image_processor(__a, return_tensors="pt").to(__a)
_lowerCAmelCase : int = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(__a, (1, 3, 384, 384))
with torch.no_grad():
_lowerCAmelCase : Tuple = model(**__a)
# masks_queries_logits
_lowerCAmelCase : int = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape, (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
_lowerCAmelCase : Dict = [
[-8.7_839, -9.0_056, -8.8_121],
[-7.4_104, -7.0_313, -6.5_401],
[-6.6_105, -6.3_427, -6.4_675],
]
_lowerCAmelCase : List[Any] = torch.tensor(__a).to(__a)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], __a, atol=__a))
# class_queries_logits
_lowerCAmelCase : List[Any] = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape, (1, model.config.num_queries, model.config.num_labels + 1))
_lowerCAmelCase : Optional[Any] = torch.tensor(
[
[1.8_324, -8.0_835, -4.1_922],
[0.8_450, -9.0_050, -3.6_053],
[0.3_045, -7.7_293, -3.0_275],
]).to(__a)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], __a, atol=__a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(__a).eval()
_lowerCAmelCase : Any = self.default_image_processor
_lowerCAmelCase : Dict = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))], segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)], return_tensors="pt", )
_lowerCAmelCase : Dict = inputs["pixel_values"].to(__a)
_lowerCAmelCase : List[Any] = [el.to(__a) for el in inputs["mask_labels"]]
_lowerCAmelCase : str = [el.to(__a) for el in inputs["class_labels"]]
with torch.no_grad():
_lowerCAmelCase : Dict = model(**__a)
self.assertTrue(outputs.loss is not None)
| 300 |
_snake_case = 8.3144598
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
_snake_case = 300
_snake_case = 28
_snake_case = rms_speed_of_molecule(temperature, molar_mass)
print(f'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')
| 300 | 1 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __lowerCamelCase ( __a :Union[str, Any] ) -> Any:
"""simple docstring"""
if hor == 1_2_8:
A__ = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
A__ = (3_2, 1_2_8, 2_5_6)
A__ = ("""UpResnetBlock1D""", """UpResnetBlock1D""")
elif hor == 3_2:
A__ = ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
A__ = (3_2, 6_4, 1_2_8, 2_5_6)
A__ = ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
A__ = torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch' )
A__ = model.state_dict()
A__ = {
"""down_block_types""": down_block_types,
"""block_out_channels""": block_out_channels,
"""up_block_types""": up_block_types,
"""layers_per_block""": 1,
"""use_timestep_embedding""": True,
"""out_block_type""": """OutConv1DBlock""",
"""norm_num_groups""": 8,
"""downsample_each_block""": False,
"""in_channels""": 1_4,
"""out_channels""": 1_4,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""sample_size""": 6_5_5_3_6,
"""mid_block_type""": """MidResTemporalBlock1D""",
"""act_fn""": """mish""",
}
A__ = UNetaDModel(**__a )
print(F'length of state dict: {len(state_dict.keys() )}' )
print(F'length of value function dict: {len(hf_value_function.state_dict().keys() )}' )
A__ = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A__ = state_dict.pop(__a )
hf_value_function.load_state_dict(__a )
torch.save(hf_value_function.state_dict() , F'hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin' )
with open(F'hub/hopper-medium-v2/unet/hor{hor}/config.json' , """w""" ) as f:
json.dump(__a , __a )
def __lowerCamelCase ( ) -> List[str]:
"""simple docstring"""
A__ = {
"""in_channels""": 1_4,
"""down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
"""up_block_types""": (),
"""out_block_type""": """ValueFunction""",
"""mid_block_type""": """ValueFunctionMidBlock1D""",
"""block_out_channels""": (3_2, 6_4, 1_2_8, 2_5_6),
"""layers_per_block""": 1,
"""downsample_each_block""": True,
"""sample_size""": 6_5_5_3_6,
"""out_channels""": 1_4,
"""extra_in_channels""": 0,
"""time_embedding_type""": """positional""",
"""use_timestep_embedding""": True,
"""flip_sin_to_cos""": False,
"""freq_shift""": 1,
"""norm_num_groups""": 8,
"""act_fn""": """mish""",
}
A__ = torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
A__ = model
A__ = UNetaDModel(**__a )
print(F'length of state dict: {len(state_dict.keys() )}' )
print(F'length of value function dict: {len(hf_value_function.state_dict().keys() )}' )
A__ = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
A__ = state_dict.pop(__a )
hf_value_function.load_state_dict(__a )
torch.save(hf_value_function.state_dict() , """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin""" )
with open("""hub/hopper-medium-v2/value_function/config.json""" , """w""" ) as f:
json.dump(__a , __a )
if __name__ == "__main__":
unet(3_2)
# unet(128)
value_function()
| 274 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def __lowerCamelCase ( __a :List[str] , __a :List[Any] , __a :Union[str, Any] , __a :List[Any] ) -> Dict:
"""simple docstring"""
A__ = multiprocessing.Manager()
A__ = manager.list()
A__ = multiprocessing.Process(target=__a , args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def __lowerCamelCase ( __a :Optional[Any] , __a :Any , __a :List[Any] ) -> Union[str, Any]:
"""simple docstring"""
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
A__ = shutil.rmtree
A__ = os.rmdir
A__ = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
A__ = {}
with swallow_io():
with time_limit(__a ):
exec(__a , __a )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(F'failed: {e}' )
# Needed for cleaning up.
A__ = rmtree
A__ = rmdir
A__ = chdir
@contextlib.contextmanager
def __lowerCamelCase ( __a :List[str] ) -> Dict:
"""simple docstring"""
def signal_handler(__a :List[Any] , __a :Optional[Any] ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL , __a )
signal.signal(signal.SIGALRM , __a )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A__ = WriteOnlyStringIO()
with contextlib.redirect_stdout(__a ):
with contextlib.redirect_stderr(__a ):
with redirect_stdin(__a ):
yield
@contextlib.contextmanager
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
with tempfile.TemporaryDirectory() as dirname:
with chdir(__a ):
yield dirname
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (io.StringIO ):
'''simple docstring'''
def a_ ( self : Any , *__lowerCAmelCase : List[str] , **__lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
raise OSError
def a_ ( self : Optional[Any] , *__lowerCAmelCase : Any , **__lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
raise OSError
def a_ ( self : Optional[Any] , *__lowerCAmelCase : Any , **__lowerCAmelCase : Any ) -> int:
"""simple docstring"""
raise OSError
def a_ ( self : str , *__lowerCAmelCase : Any , **__lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return False
class A (contextlib._RedirectStream ): # type: ignore
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = '''stdin'''
@contextlib.contextmanager
def __lowerCamelCase ( __a :Union[str, Any] ) -> List[str]:
"""simple docstring"""
if root == ".":
yield
return
A__ = os.getcwd()
os.chdir(__a )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(__a )
def __lowerCamelCase ( __a :Union[str, Any]=None ) -> Dict:
"""simple docstring"""
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS , (maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA , (maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK , (maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
A__ = None
A__ = None
import os
A__ = """1"""
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
import shutil
A__ = None
A__ = None
A__ = None
import subprocess
A__ = None # type: ignore
A__ = None
import sys
A__ = None
A__ = None
A__ = None
A__ = None
A__ = None
| 274 | 1 |
"""simple docstring"""
__magic_name__ = {
"Pillow": "Pillow<10.0.0",
"accelerate": "accelerate>=0.20.3",
"av": "av==9.2.0",
"beautifulsoup4": "beautifulsoup4",
"black": "black~=23.1",
"codecarbon": "codecarbon==1.2.0",
"cookiecutter": "cookiecutter==1.7.3",
"dataclasses": "dataclasses",
"datasets": "datasets!=2.5.0",
"decord": "decord==0.6.0",
"deepspeed": "deepspeed>=0.9.3",
"diffusers": "diffusers",
"dill": "dill<0.3.5",
"evaluate": "evaluate>=0.2.0",
"fairscale": "fairscale>0.3",
"faiss-cpu": "faiss-cpu",
"fastapi": "fastapi",
"filelock": "filelock",
"flax": "flax>=0.4.1,<=0.7.0",
"ftfy": "ftfy",
"fugashi": "fugashi>=1.0",
"GitPython": "GitPython<3.1.19",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
"importlib_metadata": "importlib_metadata",
"ipadic": "ipadic>=1.0.0,<2.0",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
"jaxlib": "jaxlib>=0.1.65,<=0.4.13",
"jieba": "jieba",
"kenlm": "kenlm",
"keras-nlp": "keras-nlp>=0.3.1",
"librosa": "librosa",
"nltk": "nltk",
"natten": "natten>=0.14.6",
"numpy": "numpy>=1.17",
"onnxconverter-common": "onnxconverter-common",
"onnxruntime-tools": "onnxruntime-tools>=1.4.2",
"onnxruntime": "onnxruntime>=1.4.0",
"opencv-python": "opencv-python",
"optuna": "optuna",
"optax": "optax>=0.0.8,<=0.1.4",
"packaging": "packaging>=20.0",
"parameterized": "parameterized",
"phonemizer": "phonemizer",
"protobuf": "protobuf",
"psutil": "psutil",
"pyyaml": "pyyaml>=5.1",
"pydantic": "pydantic<2",
"pytest": "pytest>=7.2.0",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"python": "python>=3.8.0",
"ray[tune]": "ray[tune]",
"regex": "regex!=2019.12.17",
"requests": "requests",
"rhoknp": "rhoknp>=1.1.0,<1.3.1",
"rjieba": "rjieba",
"rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff": "ruff>=0.0.241,<=0.0.259",
"sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
"sacremoses": "sacremoses",
"safetensors": "safetensors>=0.3.1",
"sagemaker": "sagemaker>=2.31.0",
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"starlette": "starlette",
"sudachipy": "sudachipy>=0.6.6",
"sudachidict_core": "sudachidict_core>=20220729",
"tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
"tensorflow": "tensorflow>=2.6,<2.14",
"tensorflow-text": "tensorflow-text<2.14",
"tf2onnx": "tf2onnx",
"timeout-decorator": "timeout-decorator",
"timm": "timm",
"tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
"torch": "torch>=1.9,!=1.12.0",
"torchaudio": "torchaudio",
"torchvision": "torchvision",
"pyctcdecode": "pyctcdecode>=0.4.0",
"tqdm": "tqdm>=4.27",
"unidic": "unidic>=1.0.2",
"unidic_lite": "unidic_lite>=1.0.7",
"urllib3": "urllib3<2.0.0",
"uvicorn": "uvicorn",
}
| 255 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None , **UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = [x.strip() for x in open(UpperCamelCase_ ).readlines()]
__SCREAMING_SNAKE_CASE = [x.strip() for x in open(UpperCamelCase_ ).readlines()][: len(UpperCamelCase_ )]
__SCREAMING_SNAKE_CASE = calculate_rouge(UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ )
if save_path is not None:
save_json(UpperCamelCase_ , UpperCamelCase_ , indent=UpperCamelCase_ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 255 | 1 |
'''simple docstring'''
import itertools
import math
def __lowerCAmelCase ( UpperCamelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(UpperCamelCase__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowerCAmelCase ( ) -> List[Any]:
__lowerCamelCase = 2
while True:
if is_prime(UpperCamelCase__ ):
yield num
num += 1
def __lowerCAmelCase ( UpperCamelCase__ = 1_00_01 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , UpperCamelCase__ ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 67 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCamelCase__ ( _lowerCamelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase__ ( _lowerCamelCase : int ) -> list[int]:
lowerCamelCase_ = str(_lowerCamelCase )
lowerCamelCase_ = [n]
for i in range(1 , len(_lowerCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def lowerCamelCase__ ( _lowerCamelCase : int ) -> bool:
if len(str(_lowerCamelCase ) ) > 3:
if not is_prime(int(str(_lowerCamelCase )[-3:] ) ) or not is_prime(int(str(_lowerCamelCase )[:3] ) ):
return False
return True
def lowerCamelCase__ ( _lowerCamelCase : int = 11 ) -> list[int]:
lowerCamelCase_ = []
lowerCamelCase_ = 13
while len(_lowerCamelCase ) != count:
if validate(_lowerCamelCase ):
lowerCamelCase_ = list_truncated_nums(_lowerCamelCase )
if all(is_prime(_lowerCamelCase ) for i in list_nums ):
list_truncated_primes.append(_lowerCamelCase )
num += 2
return list_truncated_primes
def lowerCamelCase__ ( ) -> int:
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(F'''{sum(compute_truncated_primes(11)) = }''')
| 183 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase__ ( snake_case__, unittest.TestCase ):
_UpperCAmelCase :Dict = XLMTokenizer
_UpperCAmelCase :Dict = False
def UpperCAmelCase__ ( self : Optional[int] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ : List[Any] =[
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
lowerCamelCase_ : Tuple =dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
lowerCamelCase_ : Optional[int] =["l o 123", "lo w 1456", "e r</w> 1789", ""]
lowerCamelCase_ : Union[str, Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase_ : Union[str, Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(snake_case__ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(snake_case__ ) )
def UpperCAmelCase__ ( self : str , snake_case__ : Union[str, Any] ):
lowerCamelCase_ : Optional[Any] ="lower newer"
lowerCamelCase_ : Any ="lower newer"
return input_text, output_text
def UpperCAmelCase__ ( self : str ):
lowerCamelCase_ : List[Any] =XLMTokenizer(self.vocab_file , self.merges_file )
lowerCamelCase_ : Tuple ="lower"
lowerCamelCase_ : List[str] =["low", "er</w>"]
lowerCamelCase_ : Any =tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
lowerCamelCase_ : Dict =tokens + ["<unk>"]
lowerCamelCase_ : Optional[Any] =[14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , snake_case__ )
@slow
def UpperCAmelCase__ ( self : Dict ):
lowerCamelCase_ : Any =XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )
lowerCamelCase_ : Optional[int] =tokenizer.encode("sequence builders" , add_special_tokens=snake_case__ )
lowerCamelCase_ : str =tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case__ )
lowerCamelCase_ : Optional[Any] =tokenizer.build_inputs_with_special_tokens(snake_case__ )
lowerCamelCase_ : Any =tokenizer.build_inputs_with_special_tokens(snake_case__ , snake_case__ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 209 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[str] = [
'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
'LiltForQuestionAnswering',
'LiltForSequenceClassification',
'LiltForTokenClassification',
'LiltModel',
'LiltPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 209 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( UpperCamelCase__ ):
UpperCAmelCase__ = ["""image_processor""", """tokenizer"""]
UpperCAmelCase__ = """CLIPImageProcessor"""
UpperCAmelCase__ = ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
def __init__( self : Any , UpperCAmelCase : Any=None , UpperCAmelCase : Dict=None , **UpperCAmelCase : Dict ) -> Optional[int]:
lowerCamelCase__ : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase_ , )
lowerCamelCase__ : List[Any] = kwargs.pop('feature_extractor' )
lowerCamelCase__ : Dict = 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__(lowercase_ , lowercase_ )
def __call__( self : Optional[int] , UpperCAmelCase : str=None , UpperCAmelCase : Any=None , UpperCAmelCase : Dict=None , **UpperCAmelCase : Optional[Any] ) -> Tuple:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
lowerCamelCase__ : Union[str, Any] = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if images is not None:
lowerCamelCase__ : List[str] = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if text is not None and images is not None:
lowerCamelCase__ : List[str] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ )
def A_ ( self : Tuple , *UpperCAmelCase : str , **UpperCAmelCase : Optional[int] ) -> Any:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def A_ ( self : Dict , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : Optional[Any] ) -> Tuple:
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
def A_ ( self : Any ) -> List[Any]:
lowerCamelCase__ : Union[str, Any] = self.tokenizer.model_input_names
lowerCamelCase__ : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 50 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase :
def __init__( self :Optional[int] , lowercase_ :int )-> None:
A__ = order
# a_{0} ... a_{k}
A__ = [1.0] + [0.0] * order
# b_{0} ... b_{k}
A__ = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
A__ = [0.0] * self.order
# y[n-1] ... y[n-k]
A__ = [0.0] * self.order
def UpperCAmelCase_ ( self :List[str] , lowercase_ :list[float] , lowercase_ :list[float] )-> None:
if len(lowercase_ ) < self.order:
A__ = [1.0, *a_coeffs]
if len(lowercase_ ) != self.order + 1:
A__ = (
F"Expected a_coeffs to have {self.order + 1} elements "
F"for {self.order}-order filter, got {len(lowercase_ )}"
)
raise ValueError(lowercase_ )
if len(lowercase_ ) != self.order + 1:
A__ = (
F"Expected b_coeffs to have {self.order + 1} elements "
F"for {self.order}-order filter, got {len(lowercase_ )}"
)
raise ValueError(lowercase_ )
A__ = a_coeffs
A__ = b_coeffs
def UpperCAmelCase_ ( self :Optional[int] , lowercase_ :float )-> float:
A__ = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
A__ = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
A__ = self.input_history[:-1]
A__ = self.output_history[:-1]
A__ = sample
A__ = result
return result
| 237 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str ) -> Optional[Any]:
lowercase_ : List[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowercase_ : Optional[int] = ''
lowercase_ : List[str] = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__a ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
lowercase_ : Optional[int] = 0, 0
# length[i] shows the length of palindromic substring with center i
lowercase_ : Optional[Any] = [1 for i in range(len(__a ) )]
# for each character in new_string find corresponding palindromic string
lowercase_ : Dict = 0
for j in range(len(__a ) ):
lowercase_ : Dict = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__a )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
lowercase_ : Optional[int] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
lowercase_ : str = j - k + 1 # noqa: E741
lowercase_ : Any = j + k - 1
# update max_length and start position
if max_length < length[j]:
lowercase_ : Union[str, Any] = length[j]
lowercase_ : List[str] = j
# create that string
lowercase_ : Tuple = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 355 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["EncodecFeatureExtractor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : Union[str, Any] = [
"ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
"EncodecModel",
"EncodecPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_lowercase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 21 | 0 |
import math
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
assert isinstance(_A , _A ) 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__ = range(3 , int(math.sqrt(_A ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def UpperCAmelCase_ ( _A , _A=1 , **_A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = factor * value
SCREAMING_SNAKE_CASE__ = value
while not is_prime(_A ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **_A )
return value
| 314 |
def UpperCAmelCase_ ( _A = 1_00_00_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = set(range(3 , _A , 2 ) )
primes.add(2 )
for p in range(3 , _A , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , _A , _A ) ) )
SCREAMING_SNAKE_CASE__ = [float(_A ) for n in range(limit + 1 )]
for p in primes:
for n in range(_A , limit + 1 , _A ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F"{solution() = }")
| 314 | 1 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: List[Any] , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Optional[Any] , __lowerCamelCase: int=None , __lowerCamelCase: Union[str, Any]=None ):
'''simple docstring'''
if "." in tensor_name:
lowercase_ = tensor_name.split("." )
for split in splits[:-1]:
lowercase_ = getattr(__lowerCamelCase , __lowerCamelCase )
if new_module is None:
raise ValueError(F'{module} has no attribute {split}.' )
lowercase_ = new_module
lowercase_ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(F'{module} does not have a parameter or a buffer named {tensor_name}.' )
lowercase_ = tensor_name in module._buffers
lowercase_ = getattr(__lowerCamelCase , __lowerCamelCase )
if old_value.device == torch.device("meta" ) and device not in ["meta", torch.device("meta" )] and value is None:
raise ValueError(F'{tensor_name} is on the meta device, we need a `value` to put in on {device}.' )
lowercase_ = False
lowercase_ = False
if is_buffer or not is_bitsandbytes_available():
lowercase_ = False
lowercase_ = False
else:
lowercase_ = hasattr(bnb.nn , "Params4bit" ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
lowercase_ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
lowercase_ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
lowercase_ = old_value.to(__lowerCamelCase )
elif isinstance(__lowerCamelCase , torch.Tensor ):
lowercase_ = value.to("cpu" )
if value.dtype == torch.inta:
lowercase_ = version.parse(importlib.metadata.version("bitsandbytes" ) ) > version.parse(
"0.37.2" )
if not is_abit_serializable:
raise ValueError(
"Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
"Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`." )
else:
lowercase_ = torch.tensor(__lowerCamelCase , device="cpu" )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , __lowerCamelCase ) and fpaa_statistics is None:
lowercase_ = new_value.T
lowercase_ = old_value.__dict__
if is_abit:
lowercase_ = bnb.nn.IntaParams(__lowerCamelCase , requires_grad=__lowerCamelCase , **__lowerCamelCase ).to(__lowerCamelCase )
elif is_abit:
lowercase_ = bnb.nn.Paramsabit(__lowerCamelCase , requires_grad=__lowerCamelCase , **__lowerCamelCase ).to(__lowerCamelCase )
lowercase_ = new_value
if fpaa_statistics is not None:
setattr(module.weight , "SCB" , fpaa_statistics.to(__lowerCamelCase ) )
else:
if value is None:
lowercase_ = old_value.to(__lowerCamelCase )
elif isinstance(__lowerCamelCase , torch.Tensor ):
lowercase_ = value.to(__lowerCamelCase )
else:
lowercase_ = torch.tensor(__lowerCamelCase , device=__lowerCamelCase )
if is_buffer:
lowercase_ = new_value
else:
lowercase_ = nn.Parameter(__lowerCamelCase , requires_grad=old_value.requires_grad )
lowercase_ = new_value
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Dict=None , __lowerCamelCase: Any=None , __lowerCamelCase: Union[str, Any]=None , __lowerCamelCase: Optional[int]=False ):
'''simple docstring'''
for name, module in model.named_children():
if current_key_name is None:
lowercase_ = []
current_key_name.append(__lowerCamelCase )
if (isinstance(__lowerCamelCase , nn.Linear ) or isinstance(__lowerCamelCase , __lowerCamelCase )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in ".".join(__lowerCamelCase ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase_ , lowercase_ = module.weight.shape
else:
lowercase_ = module.in_features
lowercase_ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
lowercase_ = bnb.nn.LinearabitLt(
__lowerCamelCase , __lowerCamelCase , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
lowercase_ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
lowercase_ = bnb.nn.Linearabit(
__lowerCamelCase , __lowerCamelCase , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
lowercase_ = True
# Store the module class in case we need to transpose the weight later
lowercase_ = type(__lowerCamelCase )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(__lowerCamelCase )
if len(list(module.children() ) ) > 0:
lowercase_ , lowercase_ = _replace_with_bnb_linear(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , has_been_replaced=__lowerCamelCase , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: List[str] , __lowerCamelCase: int=None , __lowerCamelCase: Optional[Any]=None , __lowerCamelCase: Optional[Any]=None ):
'''simple docstring'''
lowercase_ = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
lowercase_ , lowercase_ = _replace_with_bnb_linear(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not has_been_replaced:
logger.warning(
"You are loading your model in 8bit or 4bit but no linear modules were found in your model."
" Please double check your model architecture, or submit an issue on github if you think this is"
" a bug." )
return model
def SCREAMING_SNAKE_CASE_ ( *__lowerCamelCase: Dict , **__lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
warnings.warn(
"`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead" , __lowerCamelCase , )
return replace_with_bnb_linear(*__lowerCamelCase , **__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( *__lowerCamelCase: str , **__lowerCamelCase: str ):
'''simple docstring'''
warnings.warn(
"`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead" , __lowerCamelCase , )
return set_module_quantized_tensor_to_device(*__lowerCamelCase , **__lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: List[Any] ):
'''simple docstring'''
lowercase_ = deepcopy(__lowerCamelCase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
lowercase_ = find_tied_parameters(__lowerCamelCase )
# For compatibility with Accelerate < 0.18
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowercase_ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowercase_ = sum(__lowerCamelCase , [] )
lowercase_ = len(__lowerCamelCase ) > 0
# Check if it is a base model
lowercase_ = not hasattr(__lowerCamelCase , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowercase_ = list(model.named_children() )
lowercase_ = [list_modules[-1][0]]
# add last module together with tied weights
lowercase_ = set(__lowerCamelCase ) - set(__lowerCamelCase )
lowercase_ = list(set(__lowerCamelCase ) ) + list(__lowerCamelCase )
# remove ".weight" from the keys
lowercase_ = [".weight", ".bias"]
lowercase_ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowercase_ = name.replace(__lowerCamelCase , "" )
filtered_module_names.append(__lowerCamelCase )
return filtered_module_names
| 358 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=7 , UpperCAmelCase=6 , UpperCAmelCase=17 , UpperCAmelCase=23 , UpperCAmelCase=11 , UpperCAmelCase=True , ) -> Tuple:
'''simple docstring'''
lowercase_ = parent
lowercase_ = batch_size
lowercase_ = seq_length
lowercase_ = act_dim
lowercase_ = state_dim
lowercase_ = hidden_size
lowercase_ = max_length
lowercase_ = is_training
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase_ = floats_tensor((self.batch_size, self.seq_length, 1) )
lowercase_ = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1000 )
lowercase_ = random_attention_mask((self.batch_size, self.seq_length) )
lowercase_ = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def A__ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) -> Optional[int]:
'''simple docstring'''
lowercase_ = DecisionTransformerModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowercase_ = model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = self.prepare_config_and_inputs()
(
(
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) , (
lowercase_
) ,
) = config_and_inputs
lowercase_ = {
"states": states,
"actions": actions,
"rewards": rewards,
"returns_to_go": returns_to_go,
"timesteps": timesteps,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = (DecisionTransformerModel,) if is_torch_available() else ()
lowerCAmelCase__ = ()
lowerCAmelCase__ = {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
lowerCAmelCase__ = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = DecisionTransformerModelTester(self )
lowercase_ = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=37 )
def A__ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
@slow
def A__ ( self ) -> Tuple:
'''simple docstring'''
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ = DecisionTransformerModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase_ , lowercase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ = model_class(UpperCAmelCase )
lowercase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ = [*signature.parameters.keys()]
lowercase_ = [
"states",
"actions",
"rewards",
"returns_to_go",
"timesteps",
"attention_mask",
]
self.assertListEqual(arg_names[: len(UpperCAmelCase )] , UpperCAmelCase )
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = 2 # number of steps of autoregressive prediction we will perform
lowercase_ = 10 # defined by the RL environment, may be normalized
lowercase_ = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
lowercase_ = model.to(UpperCAmelCase )
lowercase_ = model.config
torch.manual_seed(0 )
lowercase_ = torch.randn(1 , 1 , config.state_dim ).to(device=UpperCAmelCase , dtype=torch.floataa ) # env.reset()
lowercase_ = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] , device=UpperCAmelCase )
lowercase_ = torch.tensor(UpperCAmelCase , device=UpperCAmelCase , dtype=torch.floataa ).reshape(1 , 1 , 1 )
lowercase_ = state
lowercase_ = torch.zeros(1 , 0 , config.act_dim , device=UpperCAmelCase , dtype=torch.floataa )
lowercase_ = torch.zeros(1 , 0 , device=UpperCAmelCase , dtype=torch.floataa )
lowercase_ = torch.tensor(0 , device=UpperCAmelCase , dtype=torch.long ).reshape(1 , 1 )
for step in range(UpperCAmelCase ):
lowercase_ = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=UpperCAmelCase )] , dim=1 )
lowercase_ = torch.cat([rewards, torch.zeros(1 , 1 , device=UpperCAmelCase )] , dim=1 )
lowercase_ = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
lowercase_ , lowercase_ , lowercase_ = model(
states=UpperCAmelCase , actions=UpperCAmelCase , rewards=UpperCAmelCase , returns_to_go=UpperCAmelCase , timesteps=UpperCAmelCase , attention_mask=UpperCAmelCase , return_dict=UpperCAmelCase , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
lowercase_ , lowercase_ , lowercase_ , lowercase_ = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=UpperCAmelCase , dtype=torch.floataa ),
1.0,
False,
{},
)
lowercase_ = action_pred[0, -1]
lowercase_ = torch.cat([states, state] , dim=1 )
lowercase_ = returns_to_go[0, -1] - reward
lowercase_ = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
lowercase_ = torch.cat(
[timesteps, torch.ones((1, 1) , device=UpperCAmelCase , dtype=torch.long ) * (step + 1)] , dim=1 )
| 297 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Dict = dataset
lowerCamelCase : str = process
lowerCamelCase : Optional[int] = params
def __len__( self ):
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , __A ):
"""simple docstring"""
lowerCamelCase : Dict = self.dataset[i]
lowerCamelCase : Optional[int] = self.process(snake_case_ , **self.params )
return processed
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A , __A , __A=None ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = loader
lowerCamelCase : Dict = infer
lowerCamelCase : List[Any] = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
lowerCamelCase : List[Any] = None
lowerCamelCase : Optional[int] = loader_batch_size
# Internal bookkeeping
lowerCamelCase : Any = None
lowerCamelCase : Optional[int] = None
def __len__( self ):
"""simple docstring"""
return len(self.loader )
def __iter__( self ):
"""simple docstring"""
lowerCamelCase : Dict = iter(self.loader )
return self
def _snake_case ( self ):
"""simple docstring"""
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
lowerCamelCase : str = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
lowerCamelCase : Dict = {}
for k, element in self._loader_batch_data.items():
if isinstance(snake_case_ , snake_case_ ):
# Convert ModelOutput to tuple first
lowerCamelCase : List[Any] = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
lowerCamelCase : List[str] = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
lowerCamelCase : Dict = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_ , snake_case_ ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
lowerCamelCase : str = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
lowerCamelCase : List[str] = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
lowerCamelCase : Union[str, Any] = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowerCamelCase : str = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
lowerCamelCase : int = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
lowerCamelCase : Tuple = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
lowerCamelCase : Dict = self._loader_batch_data.__class__(snake_case_ )
self._loader_batch_index += 1
return result
def _snake_case ( self ):
"""simple docstring"""
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
lowerCamelCase : List[Any] = next(self.iterator )
lowerCamelCase : str = self.infer(snake_case_ , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(snake_case_ , torch.Tensor ):
lowerCamelCase : Optional[int] = processed
else:
lowerCamelCase : Union[str, Any] = list(processed.keys() )[0]
lowerCamelCase : Any = processed[key]
if isinstance(snake_case_ , snake_case_ ):
lowerCamelCase : Tuple = len(snake_case_ )
else:
lowerCamelCase : Dict = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowerCamelCase : Optional[int] = observed_batch_size
# Setting internal index to unwrap the batch
lowerCamelCase : str = processed
lowerCamelCase : Dict = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A , __A , __A=None ):
"""simple docstring"""
super().__init__(snake_case_ , snake_case_ , snake_case_ )
def __iter__( self ):
"""simple docstring"""
lowerCamelCase : str = iter(self.loader )
lowerCamelCase : Optional[int] = None
return self
def _snake_case ( self ):
"""simple docstring"""
if self.subiterator is None:
lowerCamelCase : Tuple = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
lowerCamelCase : Dict = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
lowerCamelCase : int = self.infer(next(self.iterator ) , **self.params )
lowerCamelCase : int = next(self.subiterator )
return processed
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __iter__( self ):
"""simple docstring"""
lowerCamelCase : int = iter(self.loader )
return self
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = False
lowerCamelCase : List[Any] = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
lowerCamelCase : Optional[int] = self.loader_batch_item()
lowerCamelCase : List[str] = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
while not is_last:
lowerCamelCase : Optional[Any] = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(snake_case_ , torch.Tensor ):
lowerCamelCase : Tuple = processed
else:
lowerCamelCase : Optional[Any] = list(processed.keys() )[0]
lowerCamelCase : Any = processed[key]
if isinstance(snake_case_ , snake_case_ ):
lowerCamelCase : List[str] = len(snake_case_ )
else:
lowerCamelCase : List[Any] = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
lowerCamelCase : Union[str, Any] = observed_batch_size
lowerCamelCase : Tuple = processed
lowerCamelCase : Optional[int] = 0
while self._loader_batch_index < self.loader_batch_size:
lowerCamelCase : Any = self.loader_batch_item()
lowerCamelCase : Optional[int] = item.pop("is_last" )
accumulator.append(snake_case_ )
if is_last:
return accumulator
else:
lowerCamelCase : Any = processed
lowerCamelCase : Dict = item.pop("is_last" )
accumulator.append(snake_case_ )
return accumulator
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A ):
"""simple docstring"""
lowerCamelCase : List[str] = dataset
lowerCamelCase : Any = key
def __len__( self ):
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , __A ):
"""simple docstring"""
return self.dataset[i][self.key]
class UpperCAmelCase_ ( A_ ):
'''simple docstring'''
def __init__( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : List[str] = dataset
lowerCamelCase : Optional[int] = keya
lowerCamelCase : int = keya
def __len__( self ):
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , __A ):
"""simple docstring"""
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 283 |
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( ) -> Tuple:
from torch.utils.cpp_extension import load
A__ = Path(lowercase_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
A__ = [
root / filename
for filename in [
"vision.cpp",
os.path.join("cpu" , "ms_deform_attn_cpu.cpp" ),
os.path.join("cuda" , "ms_deform_attn_cuda.cu" ),
]
]
load(
"MultiScaleDeformableAttention" , lowercase_ , with_cuda=lowercase_ , extra_include_paths=[str(lowercase_ )] , extra_cflags=["-DWITH_CUDA=1"] , extra_cuda_cflags=[
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 247 | 0 |
import unittest
from transformers import XLMConfig, 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 (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : str , __A : List[Any] , __A : List[str]=1_3 , __A : Dict=7 , __A : Dict=True , __A : Dict=True , __A : int=True , __A : Any=True , __A : Optional[Any]=True , __A : int=False , __A : Dict=False , __A : Dict=False , __A : Dict=2 , __A : Dict=9_9 , __A : Any=0 , __A : Optional[Any]=3_2 , __A : List[str]=5 , __A : str=4 , __A : int=0.1 , __A : int=0.1 , __A : str=5_1_2 , __A : Tuple=2 , __A : Tuple=0.0_2 , __A : Union[str, Any]=2 , __A : Optional[int]=4 , __A : List[Any]="last" , __A : List[Any]=True , __A : Dict=None , __A : str=0 , ):
snake_case__ : int = parent
snake_case__ : str = batch_size
snake_case__ : int = seq_length
snake_case__ : Tuple = is_training
snake_case__ : Any = use_input_lengths
snake_case__ : Optional[Any] = use_token_type_ids
snake_case__ : Optional[int] = use_labels
snake_case__ : int = gelu_activation
snake_case__ : Tuple = sinusoidal_embeddings
snake_case__ : Dict = causal
snake_case__ : Optional[Any] = asm
snake_case__ : Any = n_langs
snake_case__ : Any = vocab_size
snake_case__ : Union[str, Any] = n_special
snake_case__ : Any = hidden_size
snake_case__ : Optional[Any] = num_hidden_layers
snake_case__ : List[Any] = num_attention_heads
snake_case__ : Optional[int] = hidden_dropout_prob
snake_case__ : Any = attention_probs_dropout_prob
snake_case__ : List[str] = max_position_embeddings
snake_case__ : Union[str, Any] = type_sequence_label_size
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Union[str, Any] = num_labels
snake_case__ : List[Any] = num_choices
snake_case__ : Optional[int] = summary_type
snake_case__ : Optional[int] = use_proj
snake_case__ : Optional[int] = scope
snake_case__ : int = bos_token_id
def _lowercase ( self : Optional[int] ):
snake_case__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ : str = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : Union[str, Any] = None
if self.use_input_lengths:
snake_case__ : List[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
snake_case__ : List[Any] = None
if self.use_token_type_ids:
snake_case__ : int = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
snake_case__ : int = None
snake_case__ : Dict = None
snake_case__ : Union[str, Any] = None
if self.use_labels:
snake_case__ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case__ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case__ : Union[str, Any] = ids_tensor([self.batch_size] , 2 ).float()
snake_case__ : str = ids_tensor([self.batch_size] , self.num_choices )
snake_case__ : Optional[Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def _lowercase ( self : List[Any] ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def _lowercase ( self : str , __A : str , __A : Optional[Any] , __A : Any , __A : Any , __A : int , __A : Tuple , __A : int , __A : Optional[Any] , __A : Tuple , ):
snake_case__ : Optional[int] = XLMModel(config=__A )
model.to(__A )
model.eval()
snake_case__ : List[str] = model(__A , lengths=__A , langs=__A )
snake_case__ : Optional[int] = model(__A , langs=__A )
snake_case__ : int = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : str , __A : Dict , __A : Optional[int] , __A : int , __A : Optional[int] , __A : Optional[Any] , __A : List[Any] , __A : str , __A : Any , __A : Tuple , ):
snake_case__ : List[str] = XLMWithLMHeadModel(__A )
model.to(__A )
model.eval()
snake_case__ : List[str] = model(__A , token_type_ids=__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : int , __A : Optional[Any] , __A : Any , __A : List[str] , __A : Union[str, Any] , __A : str , __A : str , __A : int , __A : Dict , __A : Union[str, Any] , ):
snake_case__ : Union[str, Any] = XLMForQuestionAnsweringSimple(__A )
model.to(__A )
model.eval()
snake_case__ : Any = model(__A )
snake_case__ : int = model(__A , start_positions=__A , end_positions=__A )
snake_case__ : int = outputs
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 : Optional[int] , __A : List[Any] , __A : int , __A : str , __A : str , __A : List[str] , __A : Optional[Any] , __A : Tuple , __A : List[Any] , __A : Dict , ):
snake_case__ : Optional[Any] = XLMForQuestionAnswering(__A )
model.to(__A )
model.eval()
snake_case__ : Dict = model(__A )
snake_case__ : Tuple = model(
__A , start_positions=__A , end_positions=__A , cls_index=__A , is_impossible=__A , p_mask=__A , )
snake_case__ : List[str] = model(
__A , start_positions=__A , end_positions=__A , cls_index=__A , is_impossible=__A , )
((snake_case__), ) : Union[str, Any] = result_with_labels.to_tuple()
snake_case__ : Union[str, Any] = model(__A , start_positions=__A , end_positions=__A )
((snake_case__), ) : List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def _lowercase ( self : int , __A : List[Any] , __A : Optional[Any] , __A : List[Any] , __A : Dict , __A : Tuple , __A : List[str] , __A : Tuple , __A : Any , __A : Optional[int] , ):
snake_case__ : Union[str, Any] = XLMForSequenceClassification(__A )
model.to(__A )
model.eval()
snake_case__ : List[Any] = model(__A )
snake_case__ : Any = model(__A , labels=__A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowercase ( self : Optional[Any] , __A : Any , __A : Optional[Any] , __A : Union[str, Any] , __A : Dict , __A : Any , __A : List[Any] , __A : Dict , __A : List[str] , __A : str , ):
snake_case__ : int = self.num_labels
snake_case__ : Any = XLMForTokenClassification(__A )
model.to(__A )
model.eval()
snake_case__ : str = model(__A , attention_mask=__A , labels=__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self : Dict , __A : Optional[Any] , __A : Optional[int] , __A : Dict , __A : List[str] , __A : Dict , __A : Union[str, Any] , __A : Any , __A : str , __A : List[str] , ):
snake_case__ : str = self.num_choices
snake_case__ : Optional[Any] = XLMForMultipleChoice(config=__A )
model.to(__A )
model.eval()
snake_case__ : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ : str = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case__ : Tuple = 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 : Union[str, Any] ):
snake_case__ : Any = self.prepare_config_and_inputs()
(
(
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
), (
snake_case__
),
) : List[Any] = config_and_inputs
snake_case__ : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
"""simple docstring"""
a_ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
a_ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
a_ = (
{
"feature-extraction": XLMModel,
"fill-mask": XLMWithLMHeadModel,
"question-answering": XLMForQuestionAnsweringSimple,
"text-classification": XLMForSequenceClassification,
"text-generation": XLMWithLMHeadModel,
"token-classification": XLMForTokenClassification,
"zero-shot": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowercase ( self : List[Any] , __A : str , __A : Optional[int] , __A : Dict , __A : str , __A : List[Any] ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def _lowercase ( self : Any , __A : Dict , __A : Any , __A : Tuple=False ):
snake_case__ : List[Any] = super()._prepare_for_class(__A , __A , return_labels=__A )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
snake_case__ : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
snake_case__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__A )
return inputs_dict
def _lowercase ( self : Any ):
snake_case__ : Optional[int] = XLMModelTester(self )
snake_case__ : Optional[Any] = ConfigTester(self , config_class=__A , emb_dim=3_7 )
def _lowercase ( self : List[str] ):
self.config_tester.run_common_tests()
def _lowercase ( self : Union[str, Any] ):
snake_case__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__A )
def _lowercase ( self : Union[str, Any] ):
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__A )
def _lowercase ( self : Tuple ):
snake_case__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__A )
def _lowercase ( self : Tuple ):
snake_case__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__A )
def _lowercase ( self : int ):
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__A )
def _lowercase ( self : Dict ):
snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__A )
def _lowercase ( self : Any ):
snake_case__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__A )
def _lowercase ( self : Union[str, Any] , __A : Any , __A : List[str] , __A : str , __A : str , __A : str , __A : Tuple=False , __A : Optional[int]=1 ):
self.assertIsInstance(__A , __A )
self.assertListEqual(
[isinstance(__A , __A ) for iter_attentions in attentions] , [True] * len(__A ) )
self.assertEqual(len(__A ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__A ):
# adds PAD dummy token
snake_case__ : List[str] = min_length + idx + 1
snake_case__ : Optional[int] = min_length + idx + 1
snake_case__ : Tuple = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__A ) )
def _lowercase ( self : Tuple , __A : Any , __A : List[str] , __A : Any , __A : Tuple , __A : Optional[Any] , __A : str=False , __A : int=1 ):
self.assertIsInstance(__A , __A )
self.assertListEqual(
[isinstance(__A , __A ) for iter_hidden_states in hidden_states] , [True] * len(__A ) , )
self.assertEqual(len(__A ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__A ):
# adds PAD dummy token
snake_case__ : Union[str, Any] = min_length + idx + 1
snake_case__ : Tuple = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__A ) , )
pass
@slow
def _lowercase ( self : int ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : List[str] = XLMModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def _lowercase ( self : List[Any] ):
snake_case__ : Optional[int] = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(__A )
snake_case__ : Union[str, Any] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=__A ) # the president
snake_case__ : int = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
snake_case__ : Tuple = model.generate(__A , do_sample=__A )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __A )
| 286 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( snake_case_ : bool , snake_case_ : bool ):
def run_func(snake_case_ : str ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : str , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : List[Any] , **snake_case_ : List[Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`." )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def SCREAMING_SNAKE_CASE ( snake_case_ : int , snake_case_ : int , snake_case_ : int ):
snake_case__ : Dict = random.Random()
snake_case__ : List[Any] = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = 42
a_ = 42
a_ = "TensorFlow"
@property
def _lowercase ( self : List[str] ):
return tf.__version__
def _lowercase ( self : List[str] , __A : str , __A : int , __A : int ):
# initialize GPU on separate process
snake_case__ : str = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
snake_case__ : Dict = self._prepare_inference_func(__A , __A , __A )
return self._measure_speed(_inference )
def _lowercase ( self : Tuple , __A : str , __A : int , __A : int ):
snake_case__ : Optional[int] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
snake_case__ : Any = self._prepare_train_func(__A , __A , __A )
return self._measure_speed(_train )
def _lowercase ( self : List[Any] , __A : str , __A : int , __A : int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __A )
snake_case__ : List[str] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
snake_case__ : Optional[Any] = self._prepare_inference_func(__A , __A , __A )
return self._measure_memory(_inference )
def _lowercase ( self : str , __A : str , __A : int , __A : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __A )
snake_case__ : List[Any] = self.args.strategy
if strategy is None:
raise ValueError("A device strategy has to be initialized before using TensorFlow." )
snake_case__ : int = self._prepare_train_func(__A , __A , __A )
return self._measure_memory(_train )
def _lowercase ( self : Union[str, Any] , __A : str , __A : int , __A : int ):
snake_case__ : int = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
snake_case__ : Tuple = (
hasattr(__A , "architectures" )
and isinstance(config.architectures , __A )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
snake_case__ : Dict = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
snake_case__ : Union[str, Any] = __import__("transformers" , fromlist=[model_class] )
snake_case__ : Any = getattr(__A , __A )
snake_case__ : Dict = model_cls(__A )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
snake_case__ : Dict = TF_MODEL_MAPPING[config.__class__](__A )
# encoder-decoder has vocab size saved differently
snake_case__ : Optional[int] = config.vocab_size if hasattr(__A , "vocab_size" ) else config.encoder.vocab_size
snake_case__ : List[Any] = random_input_ids(__A , __A , __A )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__A , decoder_input_ids=__A , training=__A )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__A , training=__A )
snake_case__ : Optional[int] = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _lowercase ( self : List[str] , __A : str , __A : int , __A : int ):
snake_case__ : Optional[Any] = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`." )
if self.args.fpaa:
raise NotImplementedError("Mixed precision is currently not supported." )
snake_case__ : Any = (
hasattr(__A , "architectures" )
and isinstance(config.architectures , __A )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
snake_case__ : Dict = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
snake_case__ : List[Any] = __import__("transformers" , fromlist=[model_class] )
snake_case__ : Optional[int] = getattr(__A , __A )
snake_case__ : str = model_cls(__A )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
" set `--only_pretrain_model` or `args.only_pretrain_model=True`." )
else:
snake_case__ : Union[str, Any] = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__A )
# encoder-decoder has vocab size saved differently
snake_case__ : Union[str, Any] = config.vocab_size if hasattr(__A , "vocab_size" ) else config.encoder.vocab_size
snake_case__ : List[str] = random_input_ids(__A , __A , __A )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
snake_case__ : str = model(__A , decoder_input_ids=__A , labels=__A , training=__A )[0]
snake_case__ : Dict = tf.gradients(__A , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
snake_case__ : Optional[Any] = model(__A , labels=__A , training=__A )[0]
snake_case__ : Dict = tf.gradients(__A , model.trainable_variables )
return gradients
snake_case__ : int = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _lowercase ( self : int , __A : List[Any] ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("Do inference on TPU. Running model 5 times to stabilize compilation" )
timeit.repeat(__A , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
snake_case__ : Optional[Any] = timeit.repeat(
__A , repeat=self.args.repeat , number=1_0 , )
return min(__A ) / 1_0.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def _lowercase ( self : str , __A : Callable[[], None] ):
logger.info(
"Note that TensorFlow allocates more memory than "
"it might need to speed up computation. "
"The memory reported here corresponds to the memory "
"reported by `nvidia-smi`, which can vary depending "
"on total available memory on the GPU that is used." )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
" consumption line by line." )
snake_case__ : Optional[int] = start_memory_tracing("transformers" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
" with `args.memory=False`" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"py3nvml not installed, we won't log GPU memory usage. "
"Install py3nvml (pip install py3nvml) to log information about GPU." )
snake_case__ : List[str] = "N/A"
else:
logger.info(
"Measuring total GPU usage on GPU device. Make sure to not have additional processes"
" running on the same GPU." )
# init nvml
nvml.nvmlInit()
func()
snake_case__ : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
snake_case__ : Optional[Any] = nvml.nvmlDeviceGetMemoryInfo(__A )
snake_case__ : Optional[int] = meminfo.used
snake_case__ : Any = Memory(__A )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
" TensorFlow." )
snake_case__ : int = None
else:
snake_case__ : Any = measure_peak_memory_cpu(__A )
snake_case__ : Tuple = Memory(__A ) if isinstance(__A , __A ) else memory_bytes
if self.args.trace_memory_line_by_line:
snake_case__ : Optional[int] = stop_memory_tracing(__A )
if memory is None:
snake_case__ : Dict = summary.total
else:
snake_case__ : List[str] = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 286 | 1 |
from __future__ import annotations
from fractions import Fraction
def A ( a_ ,a_ ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( a_ ) -> list[str]:
__UpperCamelCase : Dict =[]
__UpperCamelCase : Union[str, Any] =11
__UpperCamelCase : List[str] =int('1' + '0' * digit_len )
for num in range(a_ ,a_ ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(a_ ,a_ ):
solutions.append(F'{num}/{den}' )
den += 1
num += 1
__UpperCamelCase : Any =10
return solutions
def A ( a_ = 2 ) -> int:
__UpperCamelCase : Optional[Any] =1.0
for fraction in fraction_list(a_ ):
__UpperCamelCase : int =Fraction(a_ )
result *= frac.denominator / frac.numerator
return int(a_ )
if __name__ == "__main__":
print(solution())
| 71 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] ="""vit_msn"""
def __init__( self , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3072 , lowerCamelCase__="gelu" , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , lowerCamelCase__=0.02 , lowerCamelCase__=1E-06 , lowerCamelCase__=224 , lowerCamelCase__=16 , lowerCamelCase__=3 , lowerCamelCase__=True , **lowerCamelCase__ , ):
"""simple docstring"""
super().__init__(**lowerCamelCase__ )
__UpperCamelCase : int =hidden_size
__UpperCamelCase : List[Any] =num_hidden_layers
__UpperCamelCase : Union[str, Any] =num_attention_heads
__UpperCamelCase : List[str] =intermediate_size
__UpperCamelCase : Union[str, Any] =hidden_act
__UpperCamelCase : str =hidden_dropout_prob
__UpperCamelCase : Union[str, Any] =attention_probs_dropout_prob
__UpperCamelCase : Union[str, Any] =initializer_range
__UpperCamelCase : Tuple =layer_norm_eps
__UpperCamelCase : Optional[Any] =image_size
__UpperCamelCase : Optional[int] =patch_size
__UpperCamelCase : Any =num_channels
__UpperCamelCase : str =qkv_bias
| 71 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
__UpperCAmelCase : Optional[int] = random.Random()
def a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any=1.0 , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Optional[int]=None ):
"""simple docstring"""
if rng is None:
UpperCamelCase : Optional[Any] = global_rng
UpperCamelCase : Tuple = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCAmelCase_ ( unittest.TestCase):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=7 , __SCREAMING_SNAKE_CASE=400 , __SCREAMING_SNAKE_CASE=2_000 , __SCREAMING_SNAKE_CASE=10 , __SCREAMING_SNAKE_CASE=160 , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=0.0 , __SCREAMING_SNAKE_CASE=4_000 , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True , ):
"""simple docstring"""
UpperCamelCase : Any = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : int = min_seq_length
UpperCamelCase : Tuple = max_seq_length
UpperCamelCase : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase : Optional[Any] = padding_value
UpperCamelCase : int = sampling_rate
UpperCamelCase : str = return_attention_mask
UpperCamelCase : List[Any] = do_normalize
UpperCamelCase : List[Any] = feature_size
UpperCamelCase : Optional[Any] = chunk_length
UpperCamelCase : int = hop_length
def _lowercase ( self ):
"""simple docstring"""
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _lowercase ( self , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
def _flatten(__SCREAMING_SNAKE_CASE ):
return list(itertools.chain(*__SCREAMING_SNAKE_CASE ) )
if equal_length:
UpperCamelCase : List[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase : Any = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase : Dict = [np.asarray(__SCREAMING_SNAKE_CASE ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase_ ( _a, unittest.TestCase):
'''simple docstring'''
__UpperCamelCase : str = WhisperFeatureExtractor if is_speech_available() else None
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Dict = WhisperFeatureExtractionTester(self )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase : Any = feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE )[0]
check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE )
UpperCamelCase : List[str] = self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE )
UpperCamelCase : Dict = feat_extract_first.to_dict()
UpperCamelCase : Optional[Any] = feat_extract_second.to_dict()
UpperCamelCase : str = feat_extract_first.mel_filters
UpperCamelCase : List[str] = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase : Any = os.path.join(__SCREAMING_SNAKE_CASE , '''feat_extract.json''' )
feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE )
UpperCamelCase : Tuple = self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE )
UpperCamelCase : Union[str, Any] = feat_extract_first.to_dict()
UpperCamelCase : Dict = feat_extract_second.to_dict()
UpperCamelCase : Optional[Any] = feat_extract_first.mel_filters
UpperCamelCase : Tuple = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : int = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
UpperCamelCase : Any = [np.asarray(__SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase : Optional[Any] = feature_extractor(__SCREAMING_SNAKE_CASE , padding='''max_length''' , return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
UpperCamelCase : Optional[int] = feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
UpperCamelCase : Optional[int] = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# Test batched
UpperCamelCase : List[str] = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
UpperCamelCase : Optional[int] = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase : List[Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase : List[Any] = np.asarray(__SCREAMING_SNAKE_CASE )
UpperCamelCase : Dict = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
UpperCamelCase : Any = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# Test truncation required
UpperCamelCase : Tuple = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
UpperCamelCase : Union[str, Any] = [np.asarray(__SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
UpperCamelCase : Tuple = [x[: feature_extractor.n_samples] for x in speech_inputs]
UpperCamelCase : Optional[int] = [np.asarray(__SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs_truncated]
UpperCamelCase : Any = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
UpperCamelCase : Tuple = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
def _lowercase ( self ):
"""simple docstring"""
import torch
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Optional[Any] = np.random.rand(100 , 32 ).astype(np.floataa )
UpperCamelCase : Optional[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase : Dict = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCamelCase : Any = feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _lowercase ( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase : str = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
UpperCamelCase : int = ds.sort('''id''' ).select(range(__SCREAMING_SNAKE_CASE ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
UpperCamelCase : Tuple = self._load_datasamples(1 )
UpperCamelCase : List[str] = WhisperFeatureExtractor()
UpperCamelCase : Union[str, Any] = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Any = self._load_datasamples(1 )[0]
UpperCamelCase : Dict = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
UpperCamelCase : List[Any] = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__SCREAMING_SNAKE_CASE )[0]
self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE ) - 1 ) < 1e-3 ) )
| 315 |
import math
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) 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
UpperCamelCase : Union[str, Any] = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple=1 , **SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
UpperCamelCase : Tuple = factor * value
UpperCamelCase : Optional[int] = value
while not is_prime(SCREAMING_SNAKE_CASE_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **SCREAMING_SNAKE_CASE_ )
return value
| 315 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ) -> None:
"""simple docstring"""
snake_case_ : List[Any] = len(__a )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['''. ''' * i + '''Q ''' + '''. ''' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(__a ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , __a , __a , )
def lowerCamelCase_ ( _UpperCamelCase ) -> None:
"""simple docstring"""
snake_case_ : list[list[str]] = []
depth_first_search([] , [] , [] , __a , __a )
# Print all the boards
for board in boards:
for column in board:
print(__a )
print('''''' )
print(len(__a ) , '''solutions were found.''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 279 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__A = logging.get_logger(__name__)
__A = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
__A = {
"vocab_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
),
},
}
__A = {
"yjernite/retribert-base-uncased": 512,
}
__A = {
"yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = VOCAB_FILES_NAMES
lowercase_ = PRETRAINED_VOCAB_FILES_MAP
lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ = PRETRAINED_INIT_CONFIGURATION
lowercase_ = RetriBertTokenizer
lowercase_ = ["input_ids", "attention_mask"]
def __init__(self : int , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Optional[Any]=True , UpperCAmelCase_ : Union[str, Any]="[UNK]" , UpperCAmelCase_ : Any="[SEP]" , UpperCAmelCase_ : List[str]="[PAD]" , UpperCAmelCase_ : Optional[Any]="[CLS]" , UpperCAmelCase_ : Optional[Any]="[MASK]" , UpperCAmelCase_ : Dict=True , UpperCAmelCase_ : str=None , **UpperCAmelCase_ : str , ) ->List[Any]:
'''simple docstring'''
super().__init__(
UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , tokenize_chinese_chars=UpperCAmelCase_ , strip_accents=UpperCAmelCase_ , **UpperCAmelCase_ , )
lowerCamelCase__: List[Any] =json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase" , UpperCAmelCase_) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase_) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase_) != tokenize_chinese_chars
):
lowerCamelCase__: Dict =getattr(UpperCAmelCase_ , normalizer_state.pop("type"))
lowerCamelCase__: int =do_lower_case
lowerCamelCase__: int =strip_accents
lowerCamelCase__: List[str] =tokenize_chinese_chars
lowerCamelCase__: Tuple =normalizer_class(**UpperCAmelCase_)
lowerCamelCase__: Any =do_lower_case
def SCREAMING_SNAKE_CASE_ (self : List[str] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any]=None) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def SCREAMING_SNAKE_CASE_ (self : Dict , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None) ->List[int]:
'''simple docstring'''
lowerCamelCase__: Tuple =[self.sep_token_id]
lowerCamelCase__: Optional[int] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None) ->Tuple[str]:
'''simple docstring'''
lowerCamelCase__: Tuple =self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_)
return tuple(UpperCAmelCase_)
| 10 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE_ (*UpperCAmelCase_ : Any , **UpperCAmelCase_ : Any) ->Dict:
'''simple docstring'''
pass
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
__A = (
"https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"
)
@is_pipeline_test
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
lowercase_ = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any]) ->Optional[Any]:
'''simple docstring'''
lowerCamelCase__: Optional[int] =pipeline(
"document-question-answering" , model=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_)
lowerCamelCase__: List[Any] =INVOICE_URL
lowerCamelCase__: str =list(zip(*apply_tesseract(load_image(UpperCAmelCase_) , UpperCAmelCase_ , "")))
lowerCamelCase__: Optional[int] ="What is the placebo?"
lowerCamelCase__: List[str] =[
{
"image": load_image(UpperCAmelCase_),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def SCREAMING_SNAKE_CASE_ (self : Tuple , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int) ->Dict:
'''simple docstring'''
lowerCamelCase__: Any =dqa_pipeline(UpperCAmelCase_ , top_k=2)
self.assertEqual(
UpperCAmelCase_ , [
[
{"score": ANY(UpperCAmelCase_), "answer": ANY(UpperCAmelCase_), "start": ANY(UpperCAmelCase_), "end": ANY(UpperCAmelCase_)},
{"score": ANY(UpperCAmelCase_), "answer": ANY(UpperCAmelCase_), "start": ANY(UpperCAmelCase_), "end": ANY(UpperCAmelCase_)},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: List[Any] =pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2")
lowerCamelCase__: List[Any] =INVOICE_URL
lowerCamelCase__: Any ="How many cats are there?"
lowerCamelCase__: Tuple =[
{"score": 0.0001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
lowerCamelCase__: Optional[Any] =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4) , UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =dqa_pipeline({"image": image, "question": question} , top_k=2)
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4) , UpperCAmelCase_)
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCamelCase__: Optional[int] ="./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCamelCase__: str =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(UpperCAmelCase_ , [])
# We can optionnally pass directly the words and bounding boxes
lowerCamelCase__: List[Any] ="./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCamelCase__: Dict =[]
lowerCamelCase__: Union[str, Any] =[]
lowerCamelCase__: str =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , words=UpperCAmelCase_ , boxes=UpperCAmelCase_ , top_k=2)
self.assertEqual(UpperCAmelCase_ , [])
@slow
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Any:
'''simple docstring'''
lowerCamelCase__: Any =pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
lowerCamelCase__: List[str] =INVOICE_URL
lowerCamelCase__: Tuple ="What is the invoice number?"
lowerCamelCase__: Any =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase__: List[str] =dqa_pipeline({"image": image, "question": question} , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase__: Optional[Any] =dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
[
{"score": 0.9944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Union[str, Any] =pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
lowerCamelCase__: Tuple =INVOICE_URL
lowerCamelCase__: List[Any] ="What is the invoice number?"
lowerCamelCase__: List[str] =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase__: int =dqa_pipeline({"image": image, "question": question} , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase__: List[str] =dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
[
{"score": 0.9974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Dict:
'''simple docstring'''
lowerCamelCase__: str =AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=UpperCAmelCase_)
lowerCamelCase__: Union[str, Any] =pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=UpperCAmelCase_ , revision="3dc6de3" , )
lowerCamelCase__: Any =INVOICE_URL
lowerCamelCase__: Optional[int] ="What is the invoice number?"
lowerCamelCase__: Any =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowerCamelCase__: int =dqa_pipeline({"image": image, "question": question} , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowerCamelCase__: List[str] =dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
[
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
lowerCamelCase__: Any =list(zip(*apply_tesseract(load_image(UpperCAmelCase_) , UpperCAmelCase_ , "")))
# This model should also work if `image` is set to None
lowerCamelCase__: List[Any] =dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.4251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def SCREAMING_SNAKE_CASE_ (self : int) ->Any:
'''simple docstring'''
lowerCamelCase__: List[Any] =AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=UpperCAmelCase_)
lowerCamelCase__: Optional[Any] =pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=UpperCAmelCase_ , revision="3dc6de3" , max_seq_len=50 , )
lowerCamelCase__: Optional[int] =INVOICE_URL
lowerCamelCase__: List[Any] ="What is the invoice number?"
lowerCamelCase__: List[str] =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase__: Dict =dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
[
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
lowerCamelCase__: Any =list(zip(*apply_tesseract(load_image(UpperCAmelCase_) , UpperCAmelCase_ , "")))
# This model should also work if `image` is set to None
lowerCamelCase__: List[str] =dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2)
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4) , [
{"score": 0.9999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Dict =pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa") , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
lowerCamelCase__: Tuple =INVOICE_URL
lowerCamelCase__: Optional[Any] ="What is the invoice number?"
lowerCamelCase__: Optional[Any] =dqa_pipeline(image=UpperCAmelCase_ , question=UpperCAmelCase_ , top_k=2)
self.assertEqual(nested_simplify(UpperCAmelCase_ , decimals=4) , [{"answer": "us-001"}])
@require_tf
@unittest.skip("Document question answering not implemented in TF")
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->List[str]:
'''simple docstring'''
pass
| 273 |
from __future__ import annotations
def lowerCAmelCase_ ( __a , __a ) -> List[Any]:
"""simple docstring"""
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(__a ):
print(F"""{i}\t\t{d}""" )
def lowerCAmelCase_ ( __a , __a , __a ) -> Tuple:
"""simple docstring"""
for j in range(__a ):
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: str =(graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( __a , __a , __a , __a ) -> list[float]:
"""simple docstring"""
lowerCamelCase__: List[str] =[float("inf" )] * vertex_count
lowerCamelCase__: List[str] =0.0
for _ in range(vertex_count - 1 ):
for j in range(__a ):
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Union[str, Any] =(graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
lowerCamelCase__: int =distance[u] + w
lowerCamelCase__: Tuple =check_negative_cycle(__a , __a , __a )
if negative_cycle_exists:
raise Exception("Negative cycle found" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
__A = int(input("Enter number of vertices: ").strip())
__A = int(input("Enter number of edges: ").strip())
__A = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
__A , __A , __A = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
__A = {"src": src, "dst": dest, "weight": weight}
__A = int(input("\nEnter shortest path source:").strip())
__A = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 273 | 1 |
def A ( a_ ) -> float:
return 10 - x * x
def A ( a_ ,a_ ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(a_ ) * equation(a_ ) >= 0:
raise ValueError('Wrong space!' )
__UpperCamelCase : Optional[Any] =a
while (b - a) >= 0.01:
# Find middle point
__UpperCamelCase : int =(a + b) / 2
# Check if middle point is root
if equation(a_ ) == 0.0:
break
# Decide the side to repeat the steps
if equation(a_ ) * equation(a_ ) < 0:
__UpperCamelCase : List[Any] =c
else:
__UpperCamelCase : Tuple =c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 71 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_max_square(a_ ,col + 1 )
__UpperCamelCase : List[str] =update_area_of_max_square(row + 1 ,col + 1 )
__UpperCamelCase : List[Any] =update_area_of_max_square(row + 1 ,a_ )
if mat[row][col]:
__UpperCamelCase : Optional[Any] =1 + min([right, diagonal, down] )
__UpperCamelCase : Dict =max(largest_square_area[0] ,a_ )
return sub_problem_sol
else:
return 0
__UpperCamelCase : Union[str, Any] =[0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square_using_dp_array(
a_ ,a_ ,a_ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__UpperCamelCase : Tuple =update_area_of_max_square_using_dp_array(a_ ,col + 1 ,a_ )
__UpperCamelCase : Optional[int] =update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,a_ )
__UpperCamelCase : Any =update_area_of_max_square_using_dp_array(row + 1 ,a_ ,a_ )
if mat[row][col]:
__UpperCamelCase : Optional[Any] =1 + min([right, diagonal, down] )
__UpperCamelCase : str =max(largest_square_area[0] ,a_ )
__UpperCamelCase : Any =sub_problem_sol
return sub_problem_sol
else:
return 0
__UpperCamelCase : Tuple =[0]
__UpperCamelCase : List[Any] =[[-1] * cols for _ in range(a_ )]
update_area_of_max_square_using_dp_array(0 ,0 ,a_ )
return largest_square_area[0]
def A ( a_ ,a_ ,a_ ) -> int:
__UpperCamelCase : Dict =[[0] * (cols + 1) for _ in range(rows + 1 )]
__UpperCamelCase : int =0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
__UpperCamelCase : Optional[Any] =dp_array[row][col + 1]
__UpperCamelCase : int =dp_array[row + 1][col + 1]
__UpperCamelCase : Tuple =dp_array[row + 1][col]
if mat[row][col] == 1:
__UpperCamelCase : Tuple =1 + min(a_ ,a_ ,a_ )
__UpperCamelCase : Any =max(dp_array[row][col] ,a_ )
else:
__UpperCamelCase : Dict =0
return largest_square_area
def A ( a_ ,a_ ,a_ ) -> int:
__UpperCamelCase : Any =[0] * (cols + 1)
__UpperCamelCase : List[Any] =[0] * (cols + 1)
__UpperCamelCase : Tuple =0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
__UpperCamelCase : Any =current_row[col + 1]
__UpperCamelCase : Optional[Any] =next_row[col + 1]
__UpperCamelCase : Union[str, Any] =next_row[col]
if mat[row][col] == 1:
__UpperCamelCase : Any =1 + min(a_ ,a_ ,a_ )
__UpperCamelCase : Optional[int] =max(current_row[col] ,a_ )
else:
__UpperCamelCase : List[str] =0
__UpperCamelCase : Optional[Any] =current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 71 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
_lowercase = logging.get_logger(__name__)
# General docstring
_lowercase = """RegNetConfig"""
# Base docstring
_lowercase = """facebook/regnet-y-040"""
_lowercase = [1, 1088, 7, 7]
# Image classification docstring
_lowercase = """facebook/regnet-y-040"""
_lowercase = """tabby, tabby cat"""
_lowercase = [
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase , _lowercase = 3 , _lowercase = 1 , _lowercase = 1 , _lowercase = "relu" , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Convad(
_lowercase , _lowercase , kernel_size=_lowercase , stride=_lowercase , padding=kernel_size // 2 , groups=_lowercase , bias=_lowercase , )
_lowerCAmelCase = nn.BatchNormad(_lowercase )
_lowerCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.convolution(_lowercase )
_lowerCAmelCase = self.normalization(_lowercase )
_lowerCAmelCase = self.activation(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
_lowerCAmelCase = config.num_channels
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
_lowerCAmelCase = self.embedder(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase , _lowercase = 2 ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Convad(_lowercase , _lowercase , kernel_size=1 , stride=_lowercase , bias=_lowercase )
_lowerCAmelCase = nn.BatchNormad(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.convolution(_lowercase )
_lowerCAmelCase = self.normalization(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
_lowerCAmelCase = nn.Sequential(
nn.Convad(_lowercase , _lowercase , kernel_size=1 ) , nn.ReLU() , nn.Convad(_lowercase , _lowercase , kernel_size=1 ) , nn.Sigmoid() , )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.pooler(_lowercase )
_lowerCAmelCase = self.attention(_lowercase )
_lowerCAmelCase = hidden_state * attention
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase = 1 ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = in_channels != out_channels or stride != 1
_lowerCAmelCase = max(1 , out_channels // config.groups_width )
_lowerCAmelCase = (
RegNetShortCut(_lowercase , _lowercase , stride=_lowercase ) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase = nn.Sequential(
RegNetConvLayer(_lowercase , _lowercase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_lowercase , _lowercase , stride=_lowercase , groups=_lowercase , activation=config.hidden_act ) , RegNetConvLayer(_lowercase , _lowercase , kernel_size=1 , activation=_lowercase ) , )
_lowerCAmelCase = ACTaFN[config.hidden_act]
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = hidden_state
_lowerCAmelCase = self.layer(_lowercase )
_lowerCAmelCase = self.shortcut(_lowercase )
hidden_state += residual
_lowerCAmelCase = self.activation(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase = 1 ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = in_channels != out_channels or stride != 1
_lowerCAmelCase = max(1 , out_channels // config.groups_width )
_lowerCAmelCase = (
RegNetShortCut(_lowercase , _lowercase , stride=_lowercase ) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase = nn.Sequential(
RegNetConvLayer(_lowercase , _lowercase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(_lowercase , _lowercase , stride=_lowercase , groups=_lowercase , activation=config.hidden_act ) , RegNetSELayer(_lowercase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(_lowercase , _lowercase , kernel_size=1 , activation=_lowercase ) , )
_lowerCAmelCase = ACTaFN[config.hidden_act]
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = hidden_state
_lowerCAmelCase = self.layer(_lowercase )
_lowerCAmelCase = self.shortcut(_lowercase )
hidden_state += residual
_lowerCAmelCase = self.activation(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase = 2 , _lowercase = 2 , ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = RegNetXLayer if config.layer_type == """x""" else RegNetYLayer
_lowerCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
_lowercase , _lowercase , _lowercase , stride=_lowercase , ) , *[layer(_lowercase , _lowercase , _lowercase ) for _ in range(depth - 1 )] , )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.layers(_lowercase )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
_lowercase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(_lowercase , config.depths[1:] ):
self.stages.append(RegNetStage(_lowercase , _lowercase , _lowercase , depth=_lowercase ) )
def _lowercase ( self , _lowercase , _lowercase = False , _lowercase = True ):
"""simple docstring"""
_lowerCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_lowerCAmelCase = hidden_states + (hidden_state,)
_lowerCAmelCase = stage_module(_lowercase )
if output_hidden_states:
_lowerCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=_lowercase , hidden_states=_lowercase )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[Any] = RegNetConfig
_lowercase : Union[str, Any] = '''regnet'''
_lowercase : Tuple = '''pixel_values'''
_lowercase : Any = True
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if isinstance(_lowercase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(_lowercase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def _lowercase ( self , _lowercase , _lowercase=False ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = value
_lowercase = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_lowercase = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , _SCREAMING_SNAKE_CASE , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super().__init__(_lowercase )
_lowerCAmelCase = config
_lowerCAmelCase = RegNetEmbeddings(_lowercase )
_lowerCAmelCase = RegNetEncoder(_lowercase )
_lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def _lowercase ( self , _lowercase , _lowercase = None , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase = self.embedder(_lowercase )
_lowerCAmelCase = self.encoder(
_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase )
_lowerCAmelCase = encoder_outputs[0]
_lowerCAmelCase = self.pooler(_lowercase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_lowercase , pooler_output=_lowercase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , _SCREAMING_SNAKE_CASE , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super().__init__(_lowercase )
_lowerCAmelCase = config.num_labels
_lowerCAmelCase = RegNetModel(_lowercase )
# classification head
_lowerCAmelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def _lowercase ( self , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase = self.regnet(_lowercase , output_hidden_states=_lowercase , return_dict=_lowercase )
_lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1]
_lowerCAmelCase = self.classifier(_lowercase )
_lowerCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_lowerCAmelCase = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_lowerCAmelCase = """single_label_classification"""
else:
_lowerCAmelCase = """multi_label_classification"""
if self.config.problem_type == "regression":
_lowerCAmelCase = MSELoss()
if self.num_labels == 1:
_lowerCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_lowerCAmelCase = loss_fct(_lowercase , _lowercase )
elif self.config.problem_type == "single_label_classification":
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_lowerCAmelCase = BCEWithLogitsLoss()
_lowerCAmelCase = loss_fct(_lowercase , _lowercase )
if not return_dict:
_lowerCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=_lowercase , logits=_lowercase , hidden_states=outputs.hidden_states )
| 229 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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 (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=2 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = 13
_lowerCAmelCase = 7
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = 99
_lowerCAmelCase = 384
_lowerCAmelCase = 2
_lowerCAmelCase = 4
_lowerCAmelCase = 37
_lowerCAmelCase = """gelu"""
_lowerCAmelCase = 0.1
_lowerCAmelCase = 0.1
_lowerCAmelCase = 512
_lowerCAmelCase = 16
_lowerCAmelCase = 2
_lowerCAmelCase = 0.02
_lowerCAmelCase = 3
_lowerCAmelCase = 4
_lowerCAmelCase = 128
_lowerCAmelCase = 2
_lowerCAmelCase = 9
_lowerCAmelCase = 1
_lowerCAmelCase = None
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = ConvBertConfig(
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 , return_dict=_lowercase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel(config=_lowercase )
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_lowerCAmelCase = [input_ids, input_mask]
_lowerCAmelCase = model(_lowercase )
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertForMaskedLM(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFConvBertForSequenceClassification(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = TFConvBertForMultipleChoice(config=_lowercase )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFConvBertForTokenClassification(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertForQuestionAnswering(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
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 ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : Union[str, Any] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowercase : str = (
{
'''feature-extraction''': TFConvBertModel,
'''fill-mask''': TFConvBertForMaskedLM,
'''question-answering''': TFConvBertForQuestionAnswering,
'''text-classification''': TFConvBertForSequenceClassification,
'''token-classification''': TFConvBertForTokenClassification,
'''zero-shot''': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowercase : Optional[Any] = False
_lowercase : Dict = False
_lowercase : Any = False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = True
_lowerCAmelCase = True
if hasattr(_lowercase , """use_cache""" ):
_lowerCAmelCase = True
_lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
for model_class in self.all_model_classes:
_lowerCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = len(model(_lowercase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowercase , saved_model=_lowercase )
_lowerCAmelCase = os.path.join(_lowercase , """saved_model""" , """1""" )
_lowerCAmelCase = tf.keras.models.load_model(_lowercase )
_lowerCAmelCase = model(_lowercase )
if self.is_encoder_decoder:
_lowerCAmelCase = outputs["""encoder_hidden_states"""]
_lowerCAmelCase = outputs["""encoder_attentions"""]
else:
_lowerCAmelCase = outputs["""hidden_states"""]
_lowerCAmelCase = outputs["""attentions"""]
self.assertEqual(len(_lowercase ) , _lowercase )
_lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowercase ) , _lowercase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = True
_lowerCAmelCase = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
def check_decoder_attentions_output(_lowercase ):
_lowerCAmelCase = len(_lowercase )
self.assertEqual(out_len % 2 , 0 )
_lowerCAmelCase = outputs.decoder_attentions
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_lowercase ):
_lowerCAmelCase = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
_lowerCAmelCase = len(_lowercase )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
if self.is_encoder_decoder:
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_decoder_attentions_output(_lowercase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
# Check attention is always last and order is fine
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowercase ) )
self.assertEqual(model.config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
_lowerCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
_lowerCAmelCase = model(_lowercase )[0]
_lowerCAmelCase = [1, 6, 768]
self.assertEqual(output.shape , _lowercase )
_lowerCAmelCase = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _lowercase , atol=1e-4 )
| 229 | 1 |
"""simple docstring"""
from manim import *
class lowercase ( __UpperCAmelCase):
def a_ ( self : int ):
"""simple docstring"""
A_ : List[str] = Rectangle(height=0.5 , width=0.5 )
A_ : List[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
A_ : Tuple = [mem.copy() for i in range(6 )]
A_ : Optional[int] = [mem.copy() for i in range(6 )]
A_ : Optional[int] = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
A_ : Optional[int] = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
A_ : List[str] = VGroup(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
A_ : Dict = Text('''CPU''' , font_size=24 )
A_ : List[str] = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowerCamelCase )
A_ : Optional[int] = [mem.copy() for i in range(1 )]
A_ : int = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
A_ : List[str] = Text('''GPU''' , font_size=24 )
A_ : List[str] = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
gpu.align_to(_lowerCamelCase , _lowerCamelCase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowerCamelCase )
A_ : List[Any] = [mem.copy() for i in range(6 )]
A_ : List[str] = VGroup(*_lowerCamelCase ).arrange(_lowerCamelCase , buff=0 )
A_ : Any = Text('''Model''' , font_size=24 )
A_ : Optional[int] = Group(_lowerCamelCase , _lowerCamelCase ).arrange(_lowerCamelCase , buff=0.5 , aligned_edge=_lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowerCamelCase , run_time=1 ) , Create(_lowerCamelCase , run_time=1 ) , Create(_lowerCamelCase , run_time=1 ) , )
A_ : List[str] = MarkupText(
F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""" , font_size=24 , )
A_ : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
A_ : Dict = MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowerCamelCase , run_time=2.5 ) , Write(_lowerCamelCase ) , Write(_lowerCamelCase ) )
self.add(_lowerCamelCase )
A_ : str = []
A_ : Any = []
A_ : Tuple = []
for i, rect in enumerate(_lowerCamelCase ):
A_ : str = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowerCamelCase , opacity=0.7 )
cpu_target.move_to(_lowerCamelCase )
cpu_target.generate_target()
A_ : List[str] = 0.46 / 4
A_ : List[Any] = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowerCamelCase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowerCamelCase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowerCamelCase , buff=0.0 )
cpu_targs.append(_lowerCamelCase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowerCamelCase ) )
second_animations.append(MoveToTarget(_lowerCamelCase , run_time=1.5 ) )
self.play(*_lowerCamelCase )
self.play(*_lowerCamelCase )
self.wait()
| 167 |
"""simple docstring"""
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
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : List[Any] = """layoutlmv3"""
def __init__( self : Optional[int] , _lowerCamelCase : str=5_02_65 , _lowerCamelCase : Any=7_68 , _lowerCamelCase : int=12 , _lowerCamelCase : str=12 , _lowerCamelCase : int=30_72 , _lowerCamelCase : List[Any]="gelu" , _lowerCamelCase : Tuple=0.1 , _lowerCamelCase : str=0.1 , _lowerCamelCase : Any=5_12 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Dict=0.02 , _lowerCamelCase : Optional[Any]=1E-5 , _lowerCamelCase : Union[str, Any]=1 , _lowerCamelCase : Any=0 , _lowerCamelCase : int=2 , _lowerCamelCase : Union[str, Any]=10_24 , _lowerCamelCase : Dict=1_28 , _lowerCamelCase : int=1_28 , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : int=32 , _lowerCamelCase : int=1_28 , _lowerCamelCase : Tuple=64 , _lowerCamelCase : List[Any]=2_56 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : List[str]=True , _lowerCamelCase : Tuple=2_24 , _lowerCamelCase : List[Any]=3 , _lowerCamelCase : Dict=16 , _lowerCamelCase : Any=None , **_lowerCamelCase : List[str] , ):
"""simple docstring"""
super().__init__(
vocab_size=_lowerCamelCase , hidden_size=_lowerCamelCase , num_hidden_layers=_lowerCamelCase , num_attention_heads=_lowerCamelCase , intermediate_size=_lowerCamelCase , hidden_act=_lowerCamelCase , hidden_dropout_prob=_lowerCamelCase , attention_probs_dropout_prob=_lowerCamelCase , max_position_embeddings=_lowerCamelCase , type_vocab_size=_lowerCamelCase , initializer_range=_lowerCamelCase , layer_norm_eps=_lowerCamelCase , pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
A_ : List[Any] = max_ad_position_embeddings
A_ : List[str] = coordinate_size
A_ : Tuple = shape_size
A_ : Optional[Any] = has_relative_attention_bias
A_ : Any = rel_pos_bins
A_ : str = max_rel_pos
A_ : Optional[int] = has_spatial_attention_bias
A_ : int = rel_ad_pos_bins
A_ : Tuple = max_rel_ad_pos
A_ : int = text_embed
A_ : List[Any] = visual_embed
A_ : str = input_size
A_ : Dict = num_channels
A_ : Optional[int] = patch_size
A_ : Dict = classifier_dropout
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : Optional[Any] = version.parse("""1.12""")
@property
def a_ ( self : Tuple ):
"""simple docstring"""
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 a_ ( self : int ):
"""simple docstring"""
return 1E-5
@property
def a_ ( self : Optional[int] ):
"""simple docstring"""
return 12
def a_ ( self : Optional[int] , _lowerCamelCase : "ProcessorMixin" , _lowerCamelCase : int = -1 , _lowerCamelCase : int = -1 , _lowerCamelCase : bool = False , _lowerCamelCase : Optional["TensorType"] = None , _lowerCamelCase : int = 3 , _lowerCamelCase : int = 40 , _lowerCamelCase : int = 40 , ):
"""simple docstring"""
setattr(processor.image_processor , '''apply_ocr''' , _lowerCamelCase )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
A_ : Tuple = compute_effective_axis_dimension(
_lowerCamelCase , 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
A_ : Tuple = processor.tokenizer.num_special_tokens_to_add(_lowerCamelCase )
A_ : List[Any] = compute_effective_axis_dimension(
_lowerCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowerCamelCase )
# Generate dummy inputs according to compute batch and sequence
A_ : int = [[''' '''.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
A_ : Optional[int] = [[[48, 84, 73, 1_28]]] * 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)
A_ : str = self._generate_dummy_images(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = dict(
processor(
_lowerCamelCase , text=_lowerCamelCase , boxes=_lowerCamelCase , return_tensors=_lowerCamelCase , ) )
return inputs
| 167 | 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
__snake_case = logging.get_logger(__name__)
@dataclass
class __snake_case ( a_ ):
__lowerCamelCase : str = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
UpperCAmelCase : List[Any] =deprecated_arg[3:]
UpperCAmelCase : str =not kwargs.pop(lowercase_ )
logger.warning(
f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
f''' {positive_arg}={kwargs[positive_arg]}''' )
UpperCAmelCase : str =kwargs.pop('''tpu_name''' , self.tpu_name )
UpperCAmelCase : Union[str, Any] =kwargs.pop('''device_idx''' , self.device_idx )
UpperCAmelCase : int =kwargs.pop('''eager_mode''' , self.eager_mode )
UpperCAmelCase : Tuple =kwargs.pop('''use_xla''' , self.use_xla )
super().__init__(**lowercase_ )
__lowerCamelCase : Any = field(
default=a_ , metadata={"""help""": """Name of TPU"""} , )
__lowerCamelCase : Optional[int] = field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
__lowerCamelCase : List[Any] = field(default=a_ , metadata={"""help""": """Benchmark models in eager model."""} )
__lowerCamelCase : List[str] = field(
default=a_ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
requires_backends(self , ['''tf'''] )
UpperCAmelCase : Optional[int] =None
if self.tpu:
try:
if self.tpu_name:
UpperCAmelCase : str =tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
UpperCAmelCase : Tuple =tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
UpperCAmelCase : Optional[int] =None
return tpu
@cached_property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''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 )
UpperCAmelCase : Dict =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''' )
UpperCAmelCase : int =tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , '''GPU''' ) # disable GPU
UpperCAmelCase : Any =tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}''' )
return strategy
@property
def UpperCAmelCase__ ( self ) -> Any:
'''simple docstring'''
requires_backends(self , ['''tf'''] )
return self._setup_tpu is not None
@property
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
requires_backends(self , ['''tf'''] )
return self._setup_strategy
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''tf'''] )
return tf.config.list_physical_devices('''GPU''' )
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
requires_backends(self , ['''tf'''] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def UpperCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.n_gpu > 0
| 358 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = """efficientnet"""
def __init__( self , snake_case__ = 3 , snake_case__ = 600 , snake_case__ = 2.0 , snake_case__ = 3.1 , snake_case__ = 8 , snake_case__ = [3, 3, 5, 3, 5, 5, 3] , snake_case__ = [32, 16, 24, 40, 80, 112, 192] , snake_case__ = [16, 24, 40, 80, 112, 192, 320] , snake_case__ = [] , snake_case__ = [1, 2, 2, 2, 1, 2, 1] , snake_case__ = [1, 2, 2, 3, 3, 4, 1] , snake_case__ = [1, 6, 6, 6, 6, 6, 6] , snake_case__ = 0.25 , snake_case__ = "swish" , snake_case__ = 2560 , snake_case__ = "mean" , snake_case__ = 0.02 , snake_case__ = 0.001 , snake_case__ = 0.99 , snake_case__ = 0.5 , snake_case__ = 0.2 , **snake_case__ , ) -> int:
'''simple docstring'''
super().__init__(**snake_case__ )
UpperCAmelCase : Tuple =num_channels
UpperCAmelCase : Any =image_size
UpperCAmelCase : Optional[int] =width_coefficient
UpperCAmelCase : Union[str, Any] =depth_coefficient
UpperCAmelCase : List[Any] =depth_divisor
UpperCAmelCase : List[str] =kernel_sizes
UpperCAmelCase : Any =in_channels
UpperCAmelCase : str =out_channels
UpperCAmelCase : Optional[int] =depthwise_padding
UpperCAmelCase : str =strides
UpperCAmelCase : Tuple =num_block_repeats
UpperCAmelCase : Union[str, Any] =expand_ratios
UpperCAmelCase : Dict =squeeze_expansion_ratio
UpperCAmelCase : Union[str, Any] =hidden_act
UpperCAmelCase : int =hidden_dim
UpperCAmelCase : Optional[int] =pooling_type
UpperCAmelCase : Union[str, Any] =initializer_range
UpperCAmelCase : List[str] =batch_norm_eps
UpperCAmelCase : List[str] =batch_norm_momentum
UpperCAmelCase : Tuple =dropout_rate
UpperCAmelCase : Tuple =drop_connect_rate
UpperCAmelCase : int =sum(snake_case__ ) * 4
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[Any] = version.parse("""1.11""" )
@property
def UpperCAmelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def UpperCAmelCase__ ( self ) -> float:
'''simple docstring'''
return 1e-5
| 78 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowerCAmelCase : Dict = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = [
'''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TapasForMaskedLM''',
'''TapasForQuestionAnswering''',
'''TapasForSequenceClassification''',
'''TapasModel''',
'''TapasPreTrainedModel''',
'''load_tf_weights_in_tapas''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Union[str, Any] = [
'''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFTapasForMaskedLM''',
'''TFTapasForQuestionAnswering''',
'''TFTapasForSequenceClassification''',
'''TFTapasModel''',
'''TFTapasPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 300 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __snake_case ( _lowerCAmelCase : List[str] , _lowerCAmelCase : List[Any]="shi-labs/oneformer_demo" ) -> int:
with open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) as f:
A_ : Optional[int] = json.load(_lowerCAmelCase )
A_ : Union[str, Any] = {}
A_ : Tuple = []
A_ : Optional[Any] = []
for key, info in class_info.items():
A_ : Tuple = info["name"]
class_names.append(info["name"] )
if info["isthing"]:
thing_ids.append(int(_lowerCAmelCase ) )
A_ : Optional[Any] = thing_ids
A_ : int = class_names
return metadata
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self :List[Any] , snake_case :List[str] , snake_case :int=7 , snake_case :Optional[int]=3 , snake_case :Union[str, Any]=30 , snake_case :Tuple=400 , snake_case :List[Any]=None , snake_case :Optional[Any]=True , snake_case :Tuple=True , snake_case :Dict=[0.5, 0.5, 0.5] , snake_case :Any=[0.5, 0.5, 0.5] , snake_case :Optional[int]=10 , snake_case :Tuple=False , snake_case :Optional[int]=255 , snake_case :Optional[Any]="shi-labs/oneformer_demo" , snake_case :Optional[Any]="ade20k_panoptic.json" , snake_case :Optional[int]=10 , ):
'''simple docstring'''
A_ : Tuple = parent
A_ : List[str] = batch_size
A_ : Optional[int] = num_channels
A_ : Tuple = min_resolution
A_ : List[Any] = max_resolution
A_ : Union[str, Any] = do_resize
A_ : Any = {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
A_ : Tuple = do_normalize
A_ : List[str] = image_mean
A_ : List[Any] = image_std
A_ : Union[str, Any] = class_info_file
A_ : List[Any] = prepare_metadata(snake_case , snake_case )
A_ : Tuple = num_text
A_ : str = repo_path
# for the post_process_functions
A_ : Any = 2
A_ : int = 10
A_ : Optional[int] = 10
A_ : Tuple = 3
A_ : Tuple = 4
A_ : str = num_labels
A_ : int = do_reduce_labels
A_ : List[Any] = ignore_index
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def SCREAMING_SNAKE_CASE ( self :List[Any] , snake_case :Any , snake_case :Any=False ):
'''simple docstring'''
if not batched:
A_ : List[str] = image_inputs[0]
if isinstance(snake_case , Image.Image ):
A_ , A_ : Dict = image.size
else:
A_ , A_ : Tuple = image.shape[1], image.shape[2]
if w < h:
A_ : str = int(self.size["shortest_edge"] * h / w )
A_ : Any = self.size["shortest_edge"]
elif w > h:
A_ : Optional[int] = self.size["shortest_edge"]
A_ : List[str] = int(self.size["shortest_edge"] * w / h )
else:
A_ : List[str] = self.size["shortest_edge"]
A_ : Optional[Any] = self.size["shortest_edge"]
else:
A_ : Tuple = []
for image in image_inputs:
A_ , A_ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A_ : Tuple = max(snake_case , key=lambda snake_case : item[0] )[0]
A_ : Union[str, Any] = max(snake_case , key=lambda snake_case : item[1] )[1]
return expected_height, expected_width
def SCREAMING_SNAKE_CASE ( self :Tuple ):
'''simple docstring'''
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __magic_name__ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__UpperCamelCase = image_processing_class
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
A_ : Union[str, Any] = OneFormerImageProcessorTester(self )
@property
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
return self.image_processing_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case , "image_mean" ) )
self.assertTrue(hasattr(snake_case , "image_std" ) )
self.assertTrue(hasattr(snake_case , "do_normalize" ) )
self.assertTrue(hasattr(snake_case , "do_resize" ) )
self.assertTrue(hasattr(snake_case , "size" ) )
self.assertTrue(hasattr(snake_case , "ignore_index" ) )
self.assertTrue(hasattr(snake_case , "class_info_file" ) )
self.assertTrue(hasattr(snake_case , "num_text" ) )
self.assertTrue(hasattr(snake_case , "repo_path" ) )
self.assertTrue(hasattr(snake_case , "metadata" ) )
self.assertTrue(hasattr(snake_case , "do_reduce_labels" ) )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self :int ):
'''simple docstring'''
A_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A_ : Optional[Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
A_ : str = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
A_ , A_ : str = self.image_processing_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ , A_ : Optional[Any] = self.image_processing_tester.get_expected_values(snake_case , batched=snake_case )
A_ : List[str] = image_processor(
snake_case , ["semantic"] * len(snake_case ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A_ : List[str] = prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case , numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , np.ndarray )
# Test not batched input
A_ : List[str] = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
A_ , A_ : List[str] = self.image_processing_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ , A_ : int = self.image_processing_tester.get_expected_values(snake_case , batched=snake_case )
A_ : Optional[Any] = image_processor(
snake_case , ["semantic"] * len(snake_case ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A_ : List[str] = prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case , torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , torch.Tensor )
# Test not batched input
A_ : Any = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
A_ , A_ : Tuple = self.image_processing_tester.get_expected_values(snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
A_ , A_ : Tuple = self.image_processing_tester.get_expected_values(snake_case , batched=snake_case )
A_ : Any = image_processor(
snake_case , ["semantic"] * len(snake_case ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] , snake_case :Dict=False , snake_case :str=False , snake_case :Dict="np" ):
'''simple docstring'''
A_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
A_ : Tuple = self.image_processing_tester.num_labels
A_ : str = None
A_ : Tuple = None
A_ : Tuple = prepare_image_inputs(self.image_processing_tester , equal_resolution=snake_case )
if with_segmentation_maps:
A_ : List[str] = num_labels
if is_instance_map:
A_ : List[str] = list(range(snake_case ) ) * 2
A_ : int = dict(enumerate(snake_case ) )
A_ : List[str] = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
A_ : int = [Image.fromarray(snake_case ) for annotation in annotations]
A_ : List[str] = image_processor(
snake_case , ["semantic"] * len(snake_case ) , snake_case , return_tensors="pt" , instance_id_to_semantic_id=snake_case , pad_and_return_pixel_mask=snake_case , )
return inputs
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
def common(snake_case :Dict=False , snake_case :Optional[int]=None ):
A_ : Tuple = self.comm_get_image_processor_inputs(
with_segmentation_maps=snake_case , is_instance_map=snake_case , segmentation_type=snake_case )
A_ : Optional[Any] = inputs["mask_labels"]
A_ : List[Any] = inputs["class_labels"]
A_ : Optional[Any] = inputs["pixel_values"]
A_ : int = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(snake_case , snake_case , snake_case ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(snake_case ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=snake_case )
common(is_instance_map=snake_case , segmentation_type="pil" )
common(is_instance_map=snake_case , segmentation_type="pil" )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
A_ : Any = np.zeros((20, 50) )
A_ : List[str] = 1
A_ : int = 1
A_ : Optional[Any] = 1
A_ : Any = binary_mask_to_rle(snake_case )
self.assertEqual(len(snake_case ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def SCREAMING_SNAKE_CASE ( self :Optional[int] ):
'''simple docstring'''
A_ : Union[str, Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
A_ : Any = self.image_processing_tester.get_fake_oneformer_outputs()
A_ : int = fature_extractor.post_process_semantic_segmentation(snake_case )
self.assertEqual(len(snake_case ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
A_ : Optional[int] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
A_ : List[Any] = fature_extractor.post_process_semantic_segmentation(snake_case , target_sizes=snake_case )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
A_ : List[str] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
A_ : str = self.image_processing_tester.get_fake_oneformer_outputs()
A_ : Optional[Any] = image_processor.post_process_instance_segmentation(snake_case , threshold=0 )
self.assertTrue(len(snake_case ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , snake_case )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
A_ : Tuple = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
A_ : List[Any] = self.image_processing_tester.get_fake_oneformer_outputs()
A_ : Optional[Any] = image_processor.post_process_panoptic_segmentation(snake_case , threshold=0 )
self.assertTrue(len(snake_case ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , snake_case )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 300 | 1 |
def UpperCamelCase ( __lowercase : int = 10_00 ):
'''simple docstring'''
A_ , A_ : Optional[int] = 1, 1
A_ : List[str] = 2
while True:
A_ : Dict = 0
A_ : Any = fa + fa
A_ , A_ : Union[str, Any] = fa, f
index += 1
for _ in str(__lowercase ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 192 | import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_UpperCAmelCase = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
"""name""",
"""cache_dir""",
"""data_dir""",
"""all_configs""",
"""save_infos""",
"""ignore_verifications""",
"""force_redownload""",
"""clear_cache""",
],
defaults=[None, None, None, False, False, False, False, False],
)
def UpperCamelCase ( __lowercase : Any ,__lowercase : str ):
'''simple docstring'''
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def UpperCamelCase ( __lowercase : Dict ):
'''simple docstring'''
A_ : Optional[Any] = _TestCommandArgs(dataset=__lowercase ,all_configs=__lowercase ,save_infos=__lowercase )
A_ : List[Any] = TestCommand(*__lowercase )
test_command.run()
A_ : Any = os.path.join(__lowercase ,'README.md' )
assert os.path.exists(__lowercase )
A_ : Tuple = DatasetInfosDict.from_directory(__lowercase )
A_ : Any = DatasetInfosDict(
{
'default': DatasetInfo(
features=Features(
{
'tokens': Sequence(Value('string' ) ),
'ner_tags': Sequence(
ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] ) ),
'langs': Sequence(Value('string' ) ),
'spans': Sequence(Value('string' ) ),
} ) ,splits=[
{
'name': 'train',
'num_bytes': 2_35_15_63,
'num_examples': 1_00_00,
},
{
'name': 'validation',
'num_bytes': 23_84_18,
'num_examples': 10_00,
},
] ,download_size=3_94_06_80 ,dataset_size=2_58_99_81 ,)
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
A_ , A_ : Union[str, Any] = getattr(dataset_infos['default'] ,__lowercase ), getattr(expected_dataset_infos['default'] ,__lowercase )
if key == "num_bytes":
assert is_apercent_close(__lowercase ,__lowercase )
elif key == "splits":
assert list(__lowercase ) == list(__lowercase )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes ,expected[split].num_bytes )
else:
result == expected
| 192 | 1 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase : List[str] = len(_UpperCAmelCase )
for i in range(length - 1 ):
lowercase : int = i
for k in range(i + 1 , _UpperCAmelCase ):
if collection[k] < collection[least]:
lowercase : List[Any] = k
if least != i:
lowercase , lowercase : List[str] = (collection[i], collection[least])
return collection
if __name__ == "__main__":
_UpperCamelCase: List[Any] = input('Enter numbers separated by a comma:\n').strip()
_UpperCamelCase: Dict = [int(item) for item in user_input.split(',')]
print(selection_sort(unsorted))
| 255 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase : Optional[int] = len(_UpperCAmelCase ) + 1
lowercase : Any = len(_UpperCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowercase : Tuple = [[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]
# since string of zero length match pattern of zero length
lowercase : List[Any] = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _UpperCAmelCase ):
lowercase : Tuple = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _UpperCAmelCase ):
lowercase : Tuple = dp[0][j - 2] if pattern[j - 1] == '*' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _UpperCAmelCase ):
for j in range(1 , _UpperCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowercase : List[str] = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowercase : Any = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowercase : List[Any] = dp[i - 1][j]
else:
lowercase : Optional[int] = 0
else:
lowercase : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
_UpperCamelCase: int = 'aab'
_UpperCamelCase: Tuple = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'''{input_string} matches the given pattern {pattern}''')
else:
print(f'''{input_string} does not match with the given pattern {pattern}''')
| 255 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowercase (_A , _A ):
"""simple docstring"""
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = SMALL_MODEL_IDENTIFIER
_lowerCAmelCase : Optional[int] = 'pt'
_lowerCAmelCase : Tuple = 'tf'
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(snake_case__ )
def a ( self , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Tuple = TFAutoModel.from_pretrained(self.test_model , from_pt=snake_case__ )
model_tf.save_pretrained(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Tuple = 'mock_framework'
# Framework provided - return whatever the user provides
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : int = FeaturesManager.determine_framework(snake_case__ , snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
def a ( self ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case__ )
_lowerCAmelCase : Tuple = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case__ )
_lowerCAmelCase : Optional[int] = FeaturesManager.determine_framework(snake_case__ )
self.assertEqual(snake_case__ , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : str = FeaturesManager.determine_framework(snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
_lowerCAmelCase : Any = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Union[str, Any] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_tf )
# Both in environment -> use PyTorch
_lowerCAmelCase : int = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[int] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
_lowerCAmelCase : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case__ , self.framework_pt )
# Both not in environment -> raise error
_lowerCAmelCase : str = MagicMock(return_value=snake_case__ )
_lowerCAmelCase : Optional[Any] = MagicMock(return_value=snake_case__ )
with patch('transformers.onnx.features.is_tf_available' , snake_case__ ), patch(
'transformers.onnx.features.is_torch_available' , snake_case__ ):
with self.assertRaises(snake_case__ ):
_lowerCAmelCase : Any = FeaturesManager.determine_framework(self.test_model )
| 25 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_a = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
]
_a = [
# (stable-diffusion, HF Diffusers)
("in_layers.0", "norm1"),
("in_layers.2", "conv1"),
("out_layers.0", "norm2"),
("out_layers.3", "conv2"),
("emb_layers.1", "time_emb_proj"),
("skip_connection", "conv_shortcut"),
]
_a = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
_a = f"""down_blocks.{i}.resnets.{j}."""
_a = f"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
_a = f"""down_blocks.{i}.attentions.{j}."""
_a = f"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
_a = f"""up_blocks.{i}.resnets.{j}."""
_a = f"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
_a = f"""up_blocks.{i}.attentions.{j}."""
_a = f"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
_a = f"""down_blocks.{i}.downsamplers.0.conv."""
_a = f"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
_a = f"""up_blocks.{i}.upsamplers.0."""
_a = f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
_a = "mid_block.attentions.0."
_a = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
_a = f"""mid_block.resnets.{j}."""
_a = f"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCAmelCase__(__snake_case ) -> List[str]:
'''simple docstring'''
lowerCamelCase__ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
lowerCamelCase__ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
lowerCamelCase__ = v.replace(__snake_case ,__snake_case )
lowerCamelCase__ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
lowerCamelCase__ = v.replace(__snake_case ,__snake_case )
lowerCamelCase__ = v
lowerCamelCase__ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
_a = [
# (stable-diffusion, HF Diffusers)
("nin_shortcut", "conv_shortcut"),
("norm_out", "conv_norm_out"),
("mid.attn_1.", "mid_block.attentions.0."),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
_a = f"""encoder.down_blocks.{i}.resnets.{j}."""
_a = f"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
_a = f"""down_blocks.{i}.downsamplers.0."""
_a = f"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
_a = f"""up_blocks.{i}.upsamplers.0."""
_a = f"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
_a = f"""decoder.up_blocks.{i}.resnets.{j}."""
_a = f"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
_a = f"""mid_block.resnets.{i}."""
_a = f"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
_a = [
# (stable-diffusion, HF Diffusers)
("norm.", "group_norm."),
("q.", "query."),
("k.", "key."),
("v.", "value."),
("proj_out.", "proj_attn."),
]
def lowerCAmelCase__(__snake_case ) -> Tuple:
'''simple docstring'''
return w.reshape(*w.shape ,1 ,1 )
def lowerCAmelCase__(__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
lowerCamelCase__ = v.replace(__snake_case ,__snake_case )
lowerCamelCase__ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
lowerCamelCase__ = v.replace(__snake_case ,__snake_case )
lowerCamelCase__ = v
lowerCamelCase__ = {v: vae_state_dict[k] for k, v in mapping.items()}
lowerCamelCase__ = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'mid.attn_1.{weight_name}.weight' in k:
print(F'Reshaping {k} for SD format' )
lowerCamelCase__ = reshape_weight_for_sd(__snake_case )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
_a = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
_a = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_a = re.compile("|".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_a = {"q": 0, "k": 1, "v": 2}
def lowerCAmelCase__(__snake_case ) -> List[Any]:
'''simple docstring'''
lowerCamelCase__ = {}
lowerCamelCase__ = {}
lowerCamelCase__ = {}
for k, v in text_enc_dict.items():
if (
k.endswith('''.self_attn.q_proj.weight''' )
or k.endswith('''.self_attn.k_proj.weight''' )
or k.endswith('''.self_attn.v_proj.weight''' )
):
lowerCamelCase__ = k[: -len('''.q_proj.weight''' )]
lowerCamelCase__ = k[-len('''q_proj.weight''' )]
if k_pre not in capture_qkv_weight:
lowerCamelCase__ = [None, None, None]
lowerCamelCase__ = v
continue
if (
k.endswith('''.self_attn.q_proj.bias''' )
or k.endswith('''.self_attn.k_proj.bias''' )
or k.endswith('''.self_attn.v_proj.bias''' )
):
lowerCamelCase__ = k[: -len('''.q_proj.bias''' )]
lowerCamelCase__ = k[-len('''q_proj.bias''' )]
if k_pre not in capture_qkv_bias:
lowerCamelCase__ = [None, None, None]
lowerCamelCase__ = v
continue
lowerCamelCase__ = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )] ,__snake_case )
lowerCamelCase__ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
lowerCamelCase__ = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )] ,__snake_case )
lowerCamelCase__ = torch.cat(__snake_case )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
lowerCamelCase__ = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )] ,__snake_case )
lowerCamelCase__ = torch.cat(__snake_case )
return new_state_dict
def lowerCAmelCase__(__snake_case ) -> Dict:
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
)
_a = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
_a = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
_a = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
_a = osp.join(args.model_path, "text_encoder", "model.safetensors")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
_a = load_file(unet_path, device="cpu")
else:
_a = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
_a = torch.load(unet_path, map_location="cpu")
if osp.exists(vae_path):
_a = load_file(vae_path, device="cpu")
else:
_a = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
_a = torch.load(vae_path, map_location="cpu")
if osp.exists(text_enc_path):
_a = load_file(text_enc_path, device="cpu")
else:
_a = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
_a = torch.load(text_enc_path, map_location="cpu")
# Convert the UNet model
_a = convert_unet_state_dict(unet_state_dict)
_a = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
_a = convert_vae_state_dict(vae_state_dict)
_a = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
_a = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
_a = {"transformer." + k: v for k, v in text_enc_dict.items()}
_a = convert_text_enc_state_dict_vaa(text_enc_dict)
_a = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
else:
_a = convert_text_enc_state_dict(text_enc_dict)
_a = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
_a = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
_a = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
_a = {"state_dict": state_dict}
torch.save(state_dict, args.checkpoint_path)
| 209 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_a = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = 16000 ) -> Any:
'''simple docstring'''
lowerCamelCase__ = int(round(sample_rate * max_length ) )
if len(__snake_case ) <= sample_length:
return wav
lowerCamelCase__ = randint(0 ,len(__snake_case ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase_ = field(default=lowerCAmelCase , metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the training audio paths and labels."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """A file containing the validation audio paths and labels."""} )
lowerCAmelCase_ = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
lowerCAmelCase_ = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
lowerCAmelCase_ = field(
default="""audio""" , metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""} , )
lowerCAmelCase_ = field(
default="""label""" , metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
lowerCAmelCase_ = field(
default=20 , metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""} , )
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase_ = field(
default="""facebook/wav2vec2-base""" , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""} )
lowerCAmelCase_ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Name or path of preprocessor config."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
lowerCAmelCase_ = field(
default=lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def __lowerCamelCase ( self ):
'''simple docstring'''
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __lowerCAmelCase , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def lowerCAmelCase__() -> Optional[int]:
'''simple docstring'''
lowerCamelCase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' ,__snake_case ,__snake_case )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,datefmt='''%m/%d/%Y %H:%M:%S''' ,handlers=[logging.StreamHandler(sys.stdout )] ,)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCamelCase__ = training_args.get_process_log_level()
logger.setLevel(__snake_case )
transformers.utils.logging.set_verbosity(__snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowerCamelCase__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase__ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
lowerCamelCase__ = DatasetDict()
lowerCamelCase__ = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.train_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
lowerCamelCase__ = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.eval_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
F'{", ".join(raw_datasets["train"].column_names )}.' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
'''Make sure to set `--label_column_name` to the correct text column - one of '''
F'{", ".join(raw_datasets["train"].column_names )}.' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowerCamelCase__ = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path ,return_attention_mask=model_args.attention_mask ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowerCamelCase__ = raw_datasets.cast_column(
data_args.audio_column_name ,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowerCamelCase__ = feature_extractor.model_input_names[0]
def train_transforms(__snake_case ):
lowerCamelCase__ = []
for audio in batch[data_args.audio_column_name]:
lowerCamelCase__ = random_subsample(
audio['''array'''] ,max_length=data_args.max_length_seconds ,sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__snake_case )
lowerCamelCase__ = feature_extractor(__snake_case ,sampling_rate=feature_extractor.sampling_rate )
lowerCamelCase__ = {model_input_name: inputs.get(__snake_case )}
lowerCamelCase__ = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(__snake_case ):
lowerCamelCase__ = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
lowerCamelCase__ = feature_extractor(__snake_case ,sampling_rate=feature_extractor.sampling_rate )
lowerCamelCase__ = {model_input_name: inputs.get(__snake_case )}
lowerCamelCase__ = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowerCamelCase__ = raw_datasets['''train'''].features[data_args.label_column_name].names
lowerCamelCase__ , lowerCamelCase__ = {}, {}
for i, label in enumerate(__snake_case ):
lowerCamelCase__ = str(__snake_case )
lowerCamelCase__ = label
# Load the accuracy metric from the datasets package
lowerCamelCase__ = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(__snake_case ):
lowerCamelCase__ = np.argmax(eval_pred.predictions ,axis=1 )
return metric.compute(predictions=__snake_case ,references=eval_pred.label_ids )
lowerCamelCase__ = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path ,num_labels=len(__snake_case ) ,labelaid=__snake_case ,idalabel=__snake_case ,finetuning_task='''audio-classification''' ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
lowerCamelCase__ = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,config=__snake_case ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCamelCase__ = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__snake_case ,output_all_columns=__snake_case )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCamelCase__ = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__snake_case ,output_all_columns=__snake_case )
# Initialize our trainer
lowerCamelCase__ = Trainer(
model=__snake_case ,args=__snake_case ,train_dataset=raw_datasets['''train'''] if training_args.do_train else None ,eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None ,compute_metrics=__snake_case ,tokenizer=__snake_case ,)
# Training
if training_args.do_train:
lowerCamelCase__ = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase__ = last_checkpoint
lowerCamelCase__ = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
trainer.log_metrics('''train''' ,train_result.metrics )
trainer.save_metrics('''train''' ,train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowerCamelCase__ = trainer.evaluate()
trainer.log_metrics('''eval''' ,__snake_case )
trainer.save_metrics('''eval''' ,__snake_case )
# Write model card and (optionally) push to hub
lowerCamelCase__ = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__snake_case )
else:
trainer.create_model_card(**__snake_case )
if __name__ == "__main__":
main()
| 209 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""pixel_values"""]
def __init__( self , A = True , A = 3_2 , A=PILImageResampling.BILINEAR , A = True , **A , ) -> None:
snake_case : Tuple = do_resize
snake_case : Optional[int] = do_rescale
snake_case : Union[str, Any] = size_divisor
snake_case : List[str] = resample
super().__init__(**A )
def UpperCAmelCase ( self , A , A , A , A = None , **A ) -> np.ndarray:
snake_case , snake_case : Optional[Any] = get_image_size(A )
# Rounds the height and width down to the closest multiple of size_divisor
snake_case : int = height // size_divisor * size_divisor
snake_case : Any = width // size_divisor * size_divisor
snake_case : List[Any] = resize(A , (new_h, new_w) , resample=A , data_format=A , **A )
return image
def UpperCAmelCase ( self , A , A , A = None , **A ) -> np.ndarray:
return rescale(image=A , scale=A , data_format=A , **A )
def UpperCAmelCase ( self , A , A = None , A = None , A=None , A = None , A = None , A = ChannelDimension.FIRST , **A , ) -> BatchFeature:
snake_case : Any = do_resize if do_resize is not None else self.do_resize
snake_case : Any = do_rescale if do_rescale is not None else self.do_rescale
snake_case : List[Any] = size_divisor if size_divisor is not None else self.size_divisor
snake_case : List[str] = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
snake_case : Union[str, Any] = make_list_of_images(A )
if not valid_images(A ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
snake_case : str = [to_numpy_array(A ) for img in images]
if do_resize:
snake_case : List[str] = [self.resize(A , size_divisor=A , resample=A ) for image in images]
if do_rescale:
snake_case : str = [self.rescale(A , scale=1 / 2_5_5 ) for image in images]
snake_case : List[Any] = [to_channel_dimension_format(A , A ) for image in images]
snake_case : Optional[Any] = {"""pixel_values""": images}
return BatchFeature(data=A , tensor_type=A )
| 176 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[Any] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Tuple = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Dict = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Any = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCamelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 176 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__snake_case =logging.get_logger(__name__)
# General docstring
__snake_case ="""RegNetConfig"""
# Base docstring
__snake_case ="""facebook/regnet-y-040"""
__snake_case =[1, 1_088, 7, 7]
# Image classification docstring
__snake_case ="""facebook/regnet-y-040"""
__snake_case ="""tabby, tabby cat"""
__snake_case =[
"""facebook/regnet-y-040""",
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 3 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : Optional[str] = "relu" , ) -> Any:
super().__init__()
lowerCAmelCase = nn.Convad(
UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=UpperCAmelCase__ , stride=UpperCAmelCase__ , padding=kernel_size // 2 , groups=UpperCAmelCase__ , bias=UpperCAmelCase__ , )
lowerCAmelCase = nn.BatchNormad(UpperCAmelCase__ )
lowerCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Tuple ) -> Tuple:
lowerCAmelCase = self.convolution(UpperCAmelCase__ )
lowerCAmelCase = self.normalization(UpperCAmelCase__ )
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , UpperCAmelCase__ : RegNetConfig ) -> Tuple:
super().__init__()
lowerCAmelCase = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
lowerCAmelCase = config.num_channels
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Optional[Any] ) -> Any:
lowerCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
lowerCAmelCase = self.embedder(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 2 ) -> Optional[int]:
super().__init__()
lowerCAmelCase = nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , stride=UpperCAmelCase__ , bias=UpperCAmelCase__ )
lowerCAmelCase = nn.BatchNormad(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Tensor ) -> Tensor:
lowerCAmelCase = self.convolution(UpperCAmelCase__ )
lowerCAmelCase = self.normalization(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : int ) -> Union[str, Any]:
super().__init__()
lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
lowerCAmelCase = nn.Sequential(
nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 ) , nn.Sigmoid() , )
def __UpperCAmelCase ( self : Tuple , UpperCAmelCase__ : str ) -> Optional[Any]:
# b c h w -> b c 1 1
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
lowerCAmelCase = self.attention(UpperCAmelCase__ )
lowerCAmelCase = hidden_state * attention
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Optional[int] , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 1 ) -> Optional[int]:
super().__init__()
lowerCAmelCase = in_channels != out_channels or stride != 1
lowerCAmelCase = max(1 , out_channels // config.groups_width )
lowerCAmelCase = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCAmelCase = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Any ) -> Union[str, Any]:
lowerCAmelCase = hidden_state
lowerCAmelCase = self.layer(UpperCAmelCase__ )
lowerCAmelCase = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : str , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 1 ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = in_channels != out_channels or stride != 1
lowerCAmelCase = max(1 , out_channels // config.groups_width )
lowerCAmelCase = (
RegNetShortCut(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
lowerCAmelCase = nn.Sequential(
RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , groups=UpperCAmelCase__ , activation=config.hidden_act ) , RegNetSELayer(UpperCAmelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(UpperCAmelCase__ , UpperCAmelCase__ , kernel_size=1 , activation=UpperCAmelCase__ ) , )
lowerCAmelCase = ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : Union[str, Any] ) -> Tuple:
lowerCAmelCase = hidden_state
lowerCAmelCase = self.layer(UpperCAmelCase__ )
lowerCAmelCase = self.shortcut(UpperCAmelCase__ )
hidden_state += residual
lowerCAmelCase = self.activation(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : RegNetConfig , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 2 , ) -> Optional[Any]:
super().__init__()
lowerCAmelCase = RegNetXLayer if config.layer_type == 'x' else RegNetYLayer
lowerCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , stride=UpperCAmelCase__ , ) , *[layer(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) for _ in range(depth - 1 )] , )
def __UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase__ : List[str] ) -> Tuple:
lowerCAmelCase = self.layers(UpperCAmelCase__ )
return hidden_state
class UpperCAmelCase_ ( nn.Module ):
def __init__( self : Any , UpperCAmelCase__ : RegNetConfig ) -> Dict:
super().__init__()
lowerCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
UpperCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCAmelCase__ , config.depths[1:] ):
self.stages.append(RegNetStage(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , depth=UpperCAmelCase__ ) )
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : bool = True ) -> BaseModelOutputWithNoAttention:
lowerCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
lowerCAmelCase = hidden_states + (hidden_state,)
lowerCAmelCase = stage_module(UpperCAmelCase__ )
if output_hidden_states:
lowerCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=UpperCAmelCase__ , hidden_states=UpperCAmelCase__ )
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : List[Any] = RegNetConfig
lowerCamelCase : Any = '''regnet'''
lowerCamelCase : Any = '''pixel_values'''
lowerCamelCase : Union[str, Any] = True
def __UpperCAmelCase ( self : List[str] , UpperCAmelCase__ : int ) -> Optional[int]:
if isinstance(UpperCAmelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(UpperCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def __UpperCAmelCase ( self : str , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple=False ) -> Any:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
lowerCAmelCase = value
__snake_case =R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
__snake_case =R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : List[str] , UpperCAmelCase__ : Optional[int] ) -> List[Any]:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config
lowerCAmelCase = RegNetEmbeddings(UpperCAmelCase__ )
lowerCAmelCase = RegNetEncoder(UpperCAmelCase__ )
lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self : Dict , UpperCAmelCase__ : Tensor , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase = self.embedder(UpperCAmelCase__ )
lowerCAmelCase = self.encoder(
UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCAmelCase = encoder_outputs[0]
lowerCAmelCase = self.pooler(UpperCAmelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCAmelCase__ , pooler_output=UpperCAmelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class UpperCAmelCase_ ( __lowercase ):
def __init__( self : Optional[int] , UpperCAmelCase__ : Union[str, Any] ) -> str:
super().__init__(UpperCAmelCase__ )
lowerCAmelCase = config.num_labels
lowerCAmelCase = RegNetModel(UpperCAmelCase__ )
# classification head
lowerCAmelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : Optional[torch.FloatTensor] = None , UpperCAmelCase__ : Optional[torch.LongTensor] = None , UpperCAmelCase__ : Optional[bool] = None , UpperCAmelCase__ : Optional[bool] = None , ) -> ImageClassifierOutputWithNoAttention:
lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase = self.regnet(UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , return_dict=UpperCAmelCase__ )
lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1]
lowerCAmelCase = self.classifier(UpperCAmelCase__ )
lowerCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
lowerCAmelCase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
lowerCAmelCase = 'single_label_classification'
else:
lowerCAmelCase = 'multi_label_classification'
if self.config.problem_type == "regression":
lowerCAmelCase = MSELoss()
if self.num_labels == 1:
lowerCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
lowerCAmelCase = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
lowerCAmelCase = CrossEntropyLoss()
lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
lowerCAmelCase = BCEWithLogitsLoss()
lowerCAmelCase = loss_fct(UpperCAmelCase__ , UpperCAmelCase__ )
if not return_dict:
lowerCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCAmelCase__ , logits=UpperCAmelCase__ , hidden_states=outputs.hidden_states )
| 4 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=True, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=64, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=3, lowerCamelCase=4, lowerCamelCase=None, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=1, ) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Dict = parent
_lowercase : Optional[Any] = batch_size
_lowercase : Any = seq_length
_lowercase : Optional[Any] = is_training
_lowercase : Optional[Any] = use_input_mask
_lowercase : List[Any] = use_token_type_ids
_lowercase : List[str] = use_labels
_lowercase : str = vocab_size
_lowercase : List[str] = hidden_size
_lowercase : Dict = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_act
_lowercase : int = hidden_dropout_prob
_lowercase : List[Any] = attention_probs_dropout_prob
_lowercase : Dict = max_position_embeddings
_lowercase : Union[str, Any] = type_vocab_size
_lowercase : List[Any] = type_sequence_label_size
_lowercase : Any = initializer_range
_lowercase : List[str] = num_labels
_lowercase : Any = num_choices
_lowercase : Tuple = scope
_lowercase : Optional[Any] = q_groups
_lowercase : List[str] = k_groups
_lowercase : Optional[int] = v_groups
_lowercase : List[str] = post_attention_groups
_lowercase : Union[str, Any] = intermediate_groups
_lowercase : int = output_groups
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : int = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowercase : Any = None
if self.use_input_mask:
_lowercase : Tuple = random_attention_mask([self.batch_size, self.seq_length])
_lowercase : Dict = None
_lowercase : int = None
_lowercase : List[Any] = None
if self.use_labels:
_lowercase : List[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowercase : int = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowercase : Dict = ids_tensor([self.batch_size], self.num_choices)
_lowercase : Optional[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
return SqueezeBertConfig(
embedding_size=self.hidden_size, 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, attention_probs_dropout_prob=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, q_groups=self.q_groups, k_groups=self.k_groups, v_groups=self.v_groups, post_attention_groups=self.post_attention_groups, intermediate_groups=self.intermediate_groups, output_groups=self.output_groups, )
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : List[str] = SqueezeBertModel(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Dict = model(lowerCamelCase, lowerCamelCase)
_lowercase : Any = model(lowerCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> int:
"""simple docstring"""
_lowercase : Dict = SqueezeBertForMaskedLM(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Optional[Any] = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = SqueezeBertForQuestionAnswering(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[Any] = model(
lowerCamelCase, attention_mask=lowerCamelCase, start_positions=lowerCamelCase, end_positions=lowerCamelCase)
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 UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = self.num_labels
_lowercase : int = SqueezeBertForSequenceClassification(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Any = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> List[Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.num_labels
_lowercase : List[str] = SqueezeBertForTokenClassification(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Union[str, Any] = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Dict:
"""simple docstring"""
_lowercase : str = self.num_choices
_lowercase : str = SqueezeBertForMultipleChoice(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Dict = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
_lowercase : int = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
_lowercase : Optional[Any] = model(
lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Optional[int] = self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase)) : Dict = config_and_inputs
_lowercase : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _lowerCamelCase( _a, _a, unittest.TestCase ):
lowercase_ : Union[str, Any] = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase_ : Optional[int] = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ : Tuple = False
lowercase_ : List[str] = True
lowercase_ : int = False
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : str = SqueezeBertModelTester(self)
_lowercase : Dict = ConfigTester(self, config_class=lowerCamelCase, dim=37)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase)
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase)
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase)
@slow
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : List[Any] = SqueezeBertModel.from_pretrained(lowerCamelCase)
self.assertIsNotNone(lowerCamelCase)
@require_sentencepiece
@require_tokenizers
@require_torch
class _lowerCamelCase( unittest.TestCase ):
@slow
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')
_lowercase : Optional[int] = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
_lowercase : List[str] = model(lowerCamelCase)[0]
_lowercase : Union[str, Any] = torch.Size((1, 3))
self.assertEqual(output.shape, lowerCamelCase)
_lowercase : Tuple = torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
self.assertTrue(torch.allclose(lowerCamelCase, lowerCamelCase, atol=1E-4))
| 21 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : int ) -> list:
'''simple docstring'''
UpperCAmelCase_ = word.split()
def justify(snake_case_ : list , snake_case_ : int , snake_case_ : int ) -> str:
UpperCAmelCase_ = max_width - width
UpperCAmelCase_ = len(snake_case_ )
if len(snake_case_ ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
UpperCAmelCase_ = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
UpperCAmelCase_ = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
UpperCAmelCase_ = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(snake_case_ ):
num_spaces_between_words_list[i] += 1
UpperCAmelCase_ = []
for i in range(snake_case_ ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * " " )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(snake_case_ )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
for word in words:
if width + len(snake_case_ ) + len(snake_case_ ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(snake_case_ )
width += len(snake_case_ )
else:
# justify the line and add it to result
answer.append(justify(snake_case_ , snake_case_ , snake_case_ ) )
# reset new line and new width
UpperCAmelCase_ , UpperCAmelCase_ = [word], len(snake_case_ )
UpperCAmelCase_ = max_width - width - len(snake_case_ )
answer.append(" ".join(snake_case_ ) + (remaining_spaces + 1) * " " )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 106 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_: int ={
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: List[str] =['MaskFormerFeatureExtractor']
SCREAMING_SNAKE_CASE_: Union[str, Any] =['MaskFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Dict =[
'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'MaskFormerForInstanceSegmentation',
'MaskFormerModel',
'MaskFormerPreTrainedModel',
]
SCREAMING_SNAKE_CASE_: List[str] =[
'MaskFormerSwinBackbone',
'MaskFormerSwinModel',
'MaskFormerSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: Dict =_LazyModule(__name__, globals()['__file__'], _import_structure)
| 106 | 1 |
"""simple docstring"""
_a : Dict = 0 # The first color of the flag.
_a : Union[str, Any] = 1 # The second color of the flag.
_a : Dict = 2 # The third color of the flag.
_a : Union[str, Any] = (red, white, blue)
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : list ) -> list:
if not sequence:
return []
if len(_lowerCamelCase ) == 1:
return list(_lowerCamelCase )
_lowerCAmelCase : List[Any] = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Tuple = 0
while mid <= high:
if sequence[mid] == colors[0]:
_lowerCAmelCase , _lowerCAmelCase : Any = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_lowerCAmelCase , _lowerCAmelCase : Any = sequence[high], sequence[mid]
high -= 1
else:
_lowerCAmelCase : Dict = f"The elements inside the sequence must contains only {colors} values"
raise ValueError(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
_a : int = input('Enter numbers separated by commas:\n').strip()
_a : Any = [int(item.strip()) for item in user_input.split(',')]
print(F"""{dutch_national_flag_sort(unsorted)}""")
| 44 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError('Relative densities cannot be greater than one' )
else:
a : Union[str, Any] = 1 - (matter_density + radiation_density + dark_energy)
a : Union[str, Any] = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
a : int = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowerCAmelCase: Optional[Any] = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1e-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
) | 297 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ (__a : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
_a : List[str] = BeautifulSoup(requests.get(__a ).text , 'html.parser' )
_a : Dict = soup.findAll('h1' )
_a : Union[str, Any] = soup.findAll('div' , {'class': 'maincounter-number'} )
keys += soup.findAll('span' , {'class': 'panel-title'} )
values += soup.findAll('div' , {'class': 'number-table-main'} )
return {key.text.strip(): value.text.strip() for key, value in zip(__a , __a )}
if __name__ == "__main__":
print("""\033[1m""" + """COVID-19 Status of the World""" + """\033[0m\n""")
for key, value in world_covidaa_stats().items():
print(f'''{key}\n{value}\n''')
| 5 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
_a : List[Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_a : Optional[int] = ''
_a : List[str] = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__a ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_a, _a : Optional[int] = 0, 0
# length[i] shows the length of palindromic substring with center i
_a : Optional[Any] = [1 for i in range(len(__a ) )]
# for each character in new_string find corresponding palindromic string
_a : Dict = 0
for j in range(len(__a ) ):
_a : Dict = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__a )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_a : Optional[int] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_a : str = j - k + 1 # noqa: E741
_a : Any = j + k - 1
# update max_length and start position
if max_length < length[j]:
_a : Union[str, Any] = length[j]
_a : List[str] = j
# create that string
_a : Tuple = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
A_ : List[Any] = f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(_UpperCAmelCase )
A_ : Optional[Any] = ''.join(bin(_UpperCAmelCase )[2:].zfill(8 ) for byte in data )
A_ : List[str] = len(_UpperCAmelCase ) % 6 != 0
if padding_needed:
# The padding that will be added later
A_ : int = B'=' * ((6 - len(_UpperCAmelCase ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(_UpperCAmelCase ) % 6)
else:
A_ : Union[str, Any] = B''
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(_UpperCAmelCase ) , 6 ) ).encode()
+ padding
)
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) and not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
A_ : Union[str, Any] = (
'argument should be a bytes-like object or ASCII string, '
f"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(_UpperCAmelCase )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
try:
A_ : List[Any] = encoded_data.decode('utf-8' )
except UnicodeDecodeError:
raise ValueError('base64 encoded data should only contain ASCII characters' )
A_ : Any = encoded_data.count('=' )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(_UpperCAmelCase ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
A_ : Optional[Any] = encoded_data[:-padding]
A_ : Optional[Any] = ''.join(
bin(B64_CHARSET.index(_UpperCAmelCase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
A_ : Union[str, Any] = ''.join(
bin(B64_CHARSET.index(_UpperCAmelCase ) )[2:].zfill(6 ) for char in encoded_data )
A_ : Tuple = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(_UpperCAmelCase ) , 8 )
]
return bytes(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 |
"""simple docstring"""
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
A_ : Any = hex_num[0] == '-'
if is_negative:
A_ : Optional[Any] = hex_num[1:]
try:
A_ : Tuple = int(_UpperCAmelCase , 16 )
except ValueError:
raise ValueError('Invalid value was passed to the function' )
A_ : Union[str, Any] = ''
while int_num > 0:
A_ : Optional[Any] = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('-' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 | 1 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=0 ):
UpperCamelCase_: str = 1.0 if scale is None else scale
UpperCamelCase_: Any = 0.0 if loc is None else loc
super().__init__(_lowerCamelCase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=_lowerCamelCase )] )
@property
def _a ( self ):
return self.base_dist.mean * self.scale + self.loc
@property
def _a ( self ):
return self.base_dist.variance * self.scale**2
@property
def _a ( self ):
return self.variance.sqrt()
class _lowerCAmelCase( nn.Module ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ):
super().__init__(**_lowerCamelCase )
UpperCamelCase_: List[str] = args_dim
UpperCamelCase_: str = nn.ModuleList([nn.Linear(_lowerCamelCase , _lowerCamelCase ) for dim in args_dim.values()] )
UpperCamelCase_: Optional[int] = domain_map
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: Optional[int] = [proj(_lowerCamelCase ) for proj in self.proj]
return self.domain_map(*_lowerCamelCase )
class _lowerCAmelCase( nn.Module ):
"""simple docstring"""
def __init__( self , _lowerCamelCase ):
super().__init__()
UpperCamelCase_: Optional[int] = function
def _a ( self , _lowerCamelCase , *_lowerCamelCase ):
return self.function(_lowerCamelCase , *_lowerCamelCase )
class _lowerCAmelCase:
"""simple docstring"""
a : type
a : int
a : Dict[str, int]
def __init__( self , _lowerCamelCase = 1 ):
UpperCamelCase_: List[Any] = dim
UpperCamelCase_: Optional[int] = {k: dim * self.args_dim[k] for k in self.args_dim}
def _a ( self , _lowerCamelCase ):
if self.dim == 1:
return self.distribution_class(*_lowerCamelCase )
else:
return Independent(self.distribution_class(*_lowerCamelCase ) , 1 )
def _a ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , ):
UpperCamelCase_: Tuple = self._base_distribution(_lowerCamelCase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(_lowerCamelCase , loc=_lowerCamelCase , scale=_lowerCamelCase , event_dim=self.event_dim )
@property
def _a ( self ):
return () if self.dim == 1 else (self.dim,)
@property
def _a ( self ):
return len(self.event_shape )
@property
def _a ( self ):
return 0.0
def _a ( self , _lowerCamelCase ):
return ParameterProjection(
in_features=_lowerCamelCase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def _a ( self , *_lowerCamelCase ):
raise NotImplementedError()
@staticmethod
def _a ( _lowerCamelCase ):
return (x + torch.sqrt(torch.square(_lowerCamelCase ) + 4.0 )) / 2.0
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Dict[str, int] ={"df": 1, "loc": 1, "scale": 1}
a : type =StudentT
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Tuple = cls.squareplus(_lowerCamelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
UpperCamelCase_: List[str] = 2.0 + cls.squareplus(_lowerCamelCase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Dict[str, int] ={"loc": 1, "scale": 1}
a : type =Normal
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: int = cls.squareplus(_lowerCamelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : Dict[str, int] ={"total_count": 1, "logits": 1}
a : type =NegativeBinomial
@classmethod
def _a ( cls , _lowerCamelCase , _lowerCamelCase ):
UpperCamelCase_: Any = cls.squareplus(_lowerCamelCase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: Optional[Any] = distr_args
if self.dim == 1:
return self.distribution_class(total_count=_lowerCamelCase , logits=_lowerCamelCase )
else:
return Independent(self.distribution_class(total_count=_lowerCamelCase , logits=_lowerCamelCase ) , 1 )
def _a ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ):
UpperCamelCase_: Optional[int] = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) ) | 364 |
# Copyright 2021 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 argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A_ : Any = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def snake_case () -> Union[str, Any]:
UpperCamelCase_: Tuple = _ask_options(
'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
UpperCamelCase_: List[str] = get_sagemaker_input()
else:
UpperCamelCase_: List[str] = get_cluster_input()
return config
def snake_case (UpperCAmelCase__=None ) -> Union[str, Any]:
if subparsers is not None:
UpperCamelCase_: List[Any] = subparsers.add_parser('config' , description=UpperCAmelCase__ )
else:
UpperCamelCase_: List[Any] = argparse.ArgumentParser('Accelerate config command' , description=UpperCAmelCase__ )
parser.add_argument(
'--config_file' , default=UpperCAmelCase__ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , )
if subparsers is not None:
parser.set_defaults(func=UpperCAmelCase__ )
return parser
def snake_case (UpperCAmelCase__ ) -> List[Any]:
UpperCamelCase_: Union[str, Any] = get_user_input()
if args.config_file is not None:
UpperCamelCase_: Tuple = args.config_file
else:
if not os.path.isdir(UpperCAmelCase__ ):
os.makedirs(UpperCAmelCase__ )
UpperCamelCase_: Dict = default_yaml_config_file
if config_file.endswith('.json' ):
config.to_json_file(UpperCAmelCase__ )
else:
config.to_yaml_file(UpperCAmelCase__ )
print(F'''accelerate configuration saved at {config_file}''' )
def snake_case () -> str:
UpperCamelCase_: Tuple = config_command_parser()
UpperCamelCase_: int = parser.parse_args()
config_command(UpperCAmelCase__ )
if __name__ == "__main__":
main() | 292 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
a = random.Random()
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : Optional[int]=1.0 , _snake_case : List[str]=None , _snake_case : str=None ) -> List[Any]:
'''simple docstring'''
if rng is None:
_A = global_rng
_A = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any]=7 , _UpperCAmelCase : Optional[int]=400 , _UpperCAmelCase : Tuple=2_000 , _UpperCAmelCase : List[Any]=10 , _UpperCAmelCase : Optional[int]=160 , _UpperCAmelCase : Optional[Any]=8 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Optional[int]=4_000 , _UpperCAmelCase : Union[str, Any]=False , _UpperCAmelCase : Optional[int]=True , ):
_A = parent
_A = batch_size
_A = min_seq_length
_A = max_seq_length
_A = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_A = padding_value
_A = sampling_rate
_A = return_attention_mask
_A = do_normalize
_A = feature_size
_A = chunk_length
_A = hop_length
def lowerCAmelCase_ ( self : Tuple ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Optional[Any]=False , _UpperCAmelCase : List[str]=False ):
def _flatten(_UpperCAmelCase : str ):
return list(itertools.chain(*_UpperCAmelCase ) )
if equal_length:
_A = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_A = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_A = [np.asarray(_UpperCAmelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase_ ( __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : Tuple = WhisperFeatureExtractor if is_speech_available() else None
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = WhisperFeatureExtractionTester(self )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = feat_extract_first.save_pretrained(_UpperCAmelCase )[0]
check_json_file_has_correct_format(_UpperCAmelCase )
_A = self.feature_extraction_class.from_pretrained(_UpperCAmelCase )
_A = feat_extract_first.to_dict()
_A = feat_extract_second.to_dict()
_A = feat_extract_first.mel_filters
_A = feat_extract_second.mel_filters
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_A = os.path.join(_UpperCAmelCase , 'feat_extract.json' )
feat_extract_first.to_json_file(_UpperCAmelCase )
_A = self.feature_extraction_class.from_json_file(_UpperCAmelCase )
_A = feat_extract_first.to_dict()
_A = feat_extract_second.to_dict()
_A = feat_extract_first.mel_filters
_A = feat_extract_second.mel_filters
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase ) )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_A = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
# Test feature size
_A = feature_extractor(_UpperCAmelCase , padding='max_length' , return_tensors='np' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_A = feature_extractor(speech_inputs[0] , return_tensors='np' ).input_features
_A = feature_extractor(np_speech_inputs[0] , return_tensors='np' ).input_features
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test batched
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_A = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_A = np.asarray(_UpperCAmelCase )
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
# Test truncation required
_A = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs]
_A = [x[: feature_extractor.n_samples] for x in speech_inputs]
_A = [np.asarray(_UpperCAmelCase ) for speech_input in speech_inputs_truncated]
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
_A = feature_extractor(_UpperCAmelCase , return_tensors='np' ).input_features
for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase , _UpperCAmelCase ):
self.assertTrue(np.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 ) )
def lowerCAmelCase_ ( self : Dict ):
import torch
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = np.random.rand(100 , 32 ).astype(np.floataa )
_A = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='np' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_A = feature_extractor.pad([{'input_features': inputs}] , return_tensors='pt' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Dict ):
_A = load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' )
# automatic decoding with librispeech
_A = ds.sort('id' ).select(range(_UpperCAmelCase ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def lowerCAmelCase_ ( self : List[Any] ):
# fmt: off
_A = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_A = self._load_datasamples(1 )
_A = WhisperFeatureExtractor()
_A = feature_extractor(_UpperCAmelCase , return_tensors='pt' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , _UpperCAmelCase , atol=1E-4 ) )
def lowerCAmelCase_ ( self : List[str] ):
_A = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_A = self._load_datasamples(1 )[0]
_A = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
_A = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=_UpperCAmelCase )[0]
self.assertTrue(np.all(np.mean(_UpperCAmelCase ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase ) - 1 ) < 1E-3 ) )
| 315 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_A = Rectangle(height=0.25 , width=0.25 )
_A = [mem.copy() for i in range(6 )]
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('CPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(4 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('GPU' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
gpu.move_to([-1, -1, 0] )
self.add(_UpperCAmelCase )
_A = [mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Model' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
model.move_to([3, -1.0, 0] )
self.add(_UpperCAmelCase )
_A = []
_A = []
for i, rect in enumerate(_UpperCAmelCase ):
_A = fill.copy().set_fill(_UpperCAmelCase , opacity=0.8 )
target.move_to(_UpperCAmelCase )
model_arr.append(_UpperCAmelCase )
_A = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_UpperCAmelCase , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(_UpperCAmelCase )
self.add(*_UpperCAmelCase , *_UpperCAmelCase )
_A = [meta_mem.copy() for i in range(6 )]
_A = [meta_mem.copy() for i in range(6 )]
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(*_UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = VGroup(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0 )
_A = Text('Disk' , font_size=24 )
_A = Group(_UpperCAmelCase , _UpperCAmelCase ).arrange(_UpperCAmelCase , buff=0.5 , aligned_edge=_UpperCAmelCase )
disk.move_to([-4, -1.25, 0] )
self.add(_UpperCAmelCase , _UpperCAmelCase )
_A = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_A = 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(_UpperCAmelCase , _UpperCAmelCase )
_A = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_UpperCAmelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_UpperCAmelCase )
_A = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase ) )
_A = Square(0.3 )
input.set_fill(_UpperCAmelCase , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , _UpperCAmelCase , buff=0.5 )
self.play(Write(_UpperCAmelCase ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=_UpperCAmelCase , buff=0.02 )
self.play(MoveToTarget(_UpperCAmelCase ) )
self.play(FadeOut(_UpperCAmelCase ) )
_A = Arrow(start=_UpperCAmelCase , end=_UpperCAmelCase , color=_UpperCAmelCase , buff=0.5 )
a.next_to(model_arr[0].get_left() , _UpperCAmelCase , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_A = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) )
_A = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(_UpperCAmelCase ) , Circumscribe(model_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_cpu_arr[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_A = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , _UpperCAmelCase , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_A = AnimationGroup(
FadeOut(_UpperCAmelCase , run_time=0.5 ) , MoveToTarget(_UpperCAmelCase , run_time=0.5 ) , FadeIn(_UpperCAmelCase , run_time=0.5 ) , lag_ratio=0.2 )
self.play(_UpperCAmelCase )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_A = 0.7
self.play(
Circumscribe(model_arr[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i] , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(model_arr[i + 1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(cpu_left_col_base[-1] , color=_UpperCAmelCase , **_UpperCAmelCase ) , Circumscribe(gpu_rect[0] , color=_UpperCAmelCase , **_UpperCAmelCase ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_A = a_c
_A = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(_UpperCAmelCase ) , FadeOut(_UpperCAmelCase , run_time=0.5 ) , )
_A = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_UpperCAmelCase , run_time=3 ) , MoveToTarget(_UpperCAmelCase ) )
self.wait()
| 315 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : List[Any] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
),
}
class a ( lowerCamelCase__ ):
_lowerCAmelCase = """dpr"""
def __init__( self , __magic_name__=3_05_22 , __magic_name__=7_68 , __magic_name__=12 , __magic_name__=12 , __magic_name__=30_72 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=5_12 , __magic_name__=2 , __magic_name__=0.0_2 , __magic_name__=1e-12 , __magic_name__=0 , __magic_name__="absolute" , __magic_name__ = 0 , **__magic_name__ , ) -> str:
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_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 = projection_dim
_a = position_embedding_type
| 362 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
a_ : Optional[int] = logging.get_logger(__name__)
a_ : List[Any] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a_ : List[str] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a :
_lowerCAmelCase = field(
default=_SCREAMING_SNAKE_CASE , metadata={"""help""": """Model type selected in the list: """ + """, """.join(_SCREAMING_SNAKE_CASE )} )
_lowerCAmelCase = field(
default=_SCREAMING_SNAKE_CASE , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
_lowerCAmelCase = field(
default=1_2_8 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_lowerCAmelCase = field(
default=1_2_8 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
_lowerCAmelCase = field(
default=6_4 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
_lowerCAmelCase = field(
default=3_0 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
_lowerCAmelCase = field(
default=_SCREAMING_SNAKE_CASE , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
_lowerCAmelCase = field(
default=_SCREAMING_SNAKE_CASE , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
_lowerCAmelCase = field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
_lowerCAmelCase = field(
default=2_0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
_lowerCAmelCase = field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
_lowerCAmelCase = field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = """train"""
_lowerCAmelCase = """dev"""
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
def __init__( self , __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = Split.train , __magic_name__ = False , __magic_name__ = None , __magic_name__ = "pt" , ) -> Any:
_a = args
_a = is_language_sensitive
_a = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__magic_name__ , __magic_name__ ):
try:
_a = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
_a = mode
# Load data features from cache or dataset file
_a = 'v2' if args.version_2_with_negative else 'v1'
_a = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_a = cached_features_file + '.lock'
with FileLock(__magic_name__ ):
if os.path.exists(__magic_name__ ) and not args.overwrite_cache:
_a = time.time()
_a = torch.load(__magic_name__ )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_a = self.old_features['features']
_a = self.old_features.get('dataset' , __magic_name__ )
_a = self.old_features.get('examples' , __magic_name__ )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
' future run' )
else:
if mode == Split.dev:
_a = self.processor.get_dev_examples(args.data_dir )
else:
_a = self.processor.get_train_examples(args.data_dir )
_a , _a = squad_convert_examples_to_features(
examples=self.examples , tokenizer=__magic_name__ , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=__magic_name__ , )
_a = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples} , __magic_name__ , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ) -> List[Any]:
return len(self.features )
def __getitem__( self , __magic_name__ ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
_a = self.features[i]
_a = torch.tensor(feature.input_ids , dtype=torch.long )
_a = torch.tensor(feature.attention_mask , dtype=torch.long )
_a = torch.tensor(feature.token_type_ids , dtype=torch.long )
_a = torch.tensor(feature.cls_index , dtype=torch.long )
_a = torch.tensor(feature.p_mask , dtype=torch.float )
_a = torch.tensor(feature.is_impossible , dtype=torch.float )
_a = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_a = torch.tensor(feature.start_position , dtype=torch.long )
_a = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 104 | 0 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class A_ (a_ ):
UpperCAmelCase__ = 42
UpperCAmelCase__ = jnp.floataa
UpperCAmelCase__ = True
def _lowercase ( self ):
'''simple docstring'''
super().setup()
UpperCAmelCase = nn.Dense(5 , dtype=self.dtype )
def __call__( self , *_A , **_A ):
'''simple docstring'''
UpperCAmelCase = super().__call__(*_A , **_A )
UpperCAmelCase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class A_ (a_ ):
UpperCAmelCase__ = FlaxBigBirdForNaturalQuestionsModule
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
def cross_entropy(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None ):
UpperCAmelCase = logits.shape[-1]
UpperCAmelCase = (labels[..., None] == jnp.arange(UpperCamelCase__ )[None]).astype('''f4''' )
UpperCAmelCase = jax.nn.log_softmax(UpperCamelCase__ , axis=-1 )
UpperCAmelCase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
UpperCAmelCase = reduction(UpperCamelCase__ )
return loss
UpperCAmelCase = partial(UpperCamelCase__ , reduction=jnp.mean )
UpperCAmelCase = cross_entropy(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = cross_entropy(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = cross_entropy(UpperCamelCase__ , UpperCamelCase__ )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class A_ :
UpperCAmelCase__ = "google/bigbird-roberta-base"
UpperCAmelCase__ = 3_0_0_0
UpperCAmelCase__ = 1_0_5_0_0
UpperCAmelCase__ = 1_2_8
UpperCAmelCase__ = 3
UpperCAmelCase__ = 1
UpperCAmelCase__ = 5
# tx_args
UpperCAmelCase__ = 3E-5
UpperCAmelCase__ = 0.0
UpperCAmelCase__ = 2_0_0_0_0
UpperCAmelCase__ = 0.0_095
UpperCAmelCase__ = "bigbird-roberta-natural-questions"
UpperCAmelCase__ = "training-expt"
UpperCAmelCase__ = "data/nq-training.jsonl"
UpperCAmelCase__ = "data/nq-validation.jsonl"
def _lowercase ( self ):
'''simple docstring'''
os.makedirs(self.base_dir , exist_ok=_A )
UpperCAmelCase = os.path.join(self.base_dir , self.save_dir )
UpperCAmelCase = self.batch_size_per_device * jax.device_count()
@dataclass
class A_ :
UpperCAmelCase__ = 42
UpperCAmelCase__ = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self , _A ):
'''simple docstring'''
UpperCAmelCase = self.collate_fn(_A )
UpperCAmelCase = jax.tree_util.tree_map(_A , _A )
return batch
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.fetch_inputs(features['''input_ids'''] )
UpperCAmelCase = {
'''input_ids''': jnp.array(_A , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(_A , dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] , dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] , dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] , dtype=jnp.intaa ),
}
return batch
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = [self._fetch_inputs(_A ) for ids in input_ids]
return zip(*_A )
def _lowercase ( self , _A ):
'''simple docstring'''
UpperCAmelCase = [1 for _ in range(len(_A ) )]
while len(_A ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None ) -> List[Any]:
'''simple docstring'''
if seed is not None:
UpperCAmelCase = dataset.shuffle(seed=UpperCamelCase__ )
for i in range(len(UpperCamelCase__ ) // batch_size ):
UpperCAmelCase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(UpperCamelCase__ )
@partial(jax.pmap , axis_name='''batch''' )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
'''simple docstring'''
def loss_fn(UpperCamelCase__ ):
UpperCAmelCase = model_inputs.pop('''start_labels''' )
UpperCAmelCase = model_inputs.pop('''end_labels''' )
UpperCAmelCase = model_inputs.pop('''pooled_labels''' )
UpperCAmelCase = state.apply_fn(**UpperCamelCase__ , params=UpperCamelCase__ , dropout_rng=UpperCamelCase__ , train=UpperCamelCase__ )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = outputs
return state.loss_fn(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , )
UpperCAmelCase , UpperCAmelCase = jax.random.split(UpperCamelCase__ )
UpperCAmelCase = jax.value_and_grad(UpperCamelCase__ )
UpperCAmelCase , UpperCAmelCase = grad_fn(state.params )
UpperCAmelCase = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
UpperCAmelCase = jax.lax.pmean(UpperCamelCase__ , '''batch''' )
UpperCAmelCase = state.apply_gradients(grads=UpperCamelCase__ )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''' )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , **UpperCamelCase__ ) -> str:
'''simple docstring'''
UpperCAmelCase = model_inputs.pop('''start_labels''' )
UpperCAmelCase = model_inputs.pop('''end_labels''' )
UpperCAmelCase = model_inputs.pop('''pooled_labels''' )
UpperCAmelCase = state.apply_fn(**UpperCamelCase__ , params=state.params , train=UpperCamelCase__ )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = outputs
UpperCAmelCase = state.loss_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
return metrics
class A_ (train_state.TrainState ):
UpperCAmelCase__ = struct.field(pytree_node=a_ )
@dataclass
class A_ :
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = 42
UpperCAmelCase__ = None
def _lowercase ( self , _A , _A , _A , _A=None ):
'''simple docstring'''
UpperCAmelCase = model.params
UpperCAmelCase = TrainState.create(
apply_fn=model.__call__ , params=_A , tx=_A , loss_fn=_A , )
if ckpt_dir is not None:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = restore_checkpoint(_A , _A )
UpperCAmelCase = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
UpperCAmelCase , UpperCAmelCase = build_tx(**_A )
UpperCAmelCase = train_state.TrainState(
step=_A , apply_fn=model.__call__ , params=_A , tx=_A , opt_state=_A , )
UpperCAmelCase = args
UpperCAmelCase = data_collator
UpperCAmelCase = lr
UpperCAmelCase = params
UpperCAmelCase = jax_utils.replicate(_A )
return state
def _lowercase ( self , _A , _A , _A ):
'''simple docstring'''
UpperCAmelCase = self.args
UpperCAmelCase = len(_A ) // args.batch_size
UpperCAmelCase = jax.random.PRNGKey(0 )
UpperCAmelCase = jax.random.split(_A , jax.device_count() )
for epoch in range(args.max_epochs ):
UpperCAmelCase = jnp.array(0 , dtype=jnp.floataa )
UpperCAmelCase = get_batched_dataset(_A , args.batch_size , seed=_A )
UpperCAmelCase = 0
for batch in tqdm(_A , total=_A , desc=F"""Running EPOCH-{epoch}""" ):
UpperCAmelCase = self.data_collator(_A )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self.train_step_fn(_A , _A , **_A )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
UpperCAmelCase = jax_utils.unreplicate(state.step )
UpperCAmelCase = running_loss.item() / i
UpperCAmelCase = self.scheduler_fn(state_step - 1 )
UpperCAmelCase = self.evaluate(_A , _A )
UpperCAmelCase = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(_A ) )
self.logger.log(_A , commit=_A )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}""" , state=_A )
def _lowercase ( self , _A , _A ):
'''simple docstring'''
UpperCAmelCase = get_batched_dataset(_A , self.args.batch_size )
UpperCAmelCase = len(_A ) // self.args.batch_size
UpperCAmelCase = jnp.array(0 , dtype=jnp.floataa )
UpperCAmelCase = 0
for batch in tqdm(_A , total=_A , desc='''Evaluating ... ''' ):
UpperCAmelCase = self.data_collator(_A )
UpperCAmelCase = self.val_step_fn(_A , **_A )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def _lowercase ( self , _A , _A ):
'''simple docstring'''
UpperCAmelCase = jax_utils.unreplicate(_A )
print(F"""SAVING CHECKPOINT IN {save_dir}""" , end=''' ... ''' )
self.model_save_fn(_A , params=state.params )
with open(os.path.join(_A , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(_A , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(_A , '''data_collator.joblib''' ) )
with open(os.path.join(_A , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , _A )
print('''DONE''' )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Any:
'''simple docstring'''
print(F"""RESTORING CHECKPOINT FROM {save_dir}""" , end=''' ... ''' )
with open(os.path.join(UpperCamelCase__ , '''flax_model.msgpack''' ) , '''rb''' ) as f:
UpperCAmelCase = from_bytes(state.params , f.read() )
with open(os.path.join(UpperCamelCase__ , '''opt_state.msgpack''' ) , '''rb''' ) as f:
UpperCAmelCase = from_bytes(state.opt_state , f.read() )
UpperCAmelCase = joblib.load(os.path.join(UpperCamelCase__ , '''args.joblib''' ) )
UpperCAmelCase = joblib.load(os.path.join(UpperCamelCase__ , '''data_collator.joblib''' ) )
with open(os.path.join(UpperCamelCase__ , '''training_state.json''' ) , '''r''' ) as f:
UpperCAmelCase = json.load(UpperCamelCase__ )
UpperCAmelCase = training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = num_train_steps - warmup_steps
UpperCAmelCase = optax.linear_schedule(init_value=UpperCamelCase__ , end_value=UpperCamelCase__ , transition_steps=UpperCamelCase__ )
UpperCAmelCase = optax.linear_schedule(init_value=UpperCamelCase__ , end_value=1E-7 , transition_steps=UpperCamelCase__ )
UpperCAmelCase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
'''simple docstring'''
def weight_decay_mask(UpperCamelCase__ ):
UpperCAmelCase = traverse_util.flatten_dict(UpperCamelCase__ )
UpperCAmelCase = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(UpperCamelCase__ )
UpperCAmelCase = scheduler_fn(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase = optax.adamw(learning_rate=UpperCamelCase__ , weight_decay=UpperCamelCase__ , mask=UpperCamelCase__ )
return tx, lr
| 273 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
__A : Optional[int] = logging.getLogger(__name__)
@dataclass
class A_ :
UpperCAmelCase__ = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCAmelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class A_ :
UpperCAmelCase__ = field(default=a_ , metadata={'''help''': '''The input training data file (a text file).'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
UpperCAmelCase__ = field(
default=a_ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase__ = field(
default=a_ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def _lowercase ( self ):
'''simple docstring'''
if self.train_file is not None:
UpperCAmelCase = self.train_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
UpperCAmelCase = self.validation_file.split('''.''' )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class A_ :
UpperCAmelCase__ = 42
UpperCAmelCase__ = True
UpperCAmelCase__ = None
UpperCAmelCase__ = None
def __call__( self , _A ):
'''simple docstring'''
UpperCAmelCase = '''label''' if '''label''' in features[0].keys() else '''labels'''
UpperCAmelCase = [feature.pop(_A ) for feature in features]
UpperCAmelCase = len(_A )
UpperCAmelCase = len(features[0]['''input_ids'''] )
UpperCAmelCase = [
[{k: v[i] for k, v in feature.items()} for i in range(_A )] for feature in features
]
UpperCAmelCase = list(chain(*_A ) )
UpperCAmelCase = self.tokenizer.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
# Un-flatten
UpperCAmelCase = {k: v.view(_A , _A , -1 ) for k, v in batch.items()}
# Add back labels
UpperCAmelCase = torch.tensor(_A , dtype=torch.intaa )
return batch
def __SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_swag''' , UpperCamelCase__ , UpperCamelCase__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase__ )
datasets.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.set_verbosity(UpperCamelCase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
UpperCAmelCase = {}
if data_args.train_file is not None:
UpperCAmelCase = data_args.train_file
if data_args.validation_file is not None:
UpperCAmelCase = data_args.validation_file
UpperCAmelCase = data_args.train_file.split('''.''' )[-1]
UpperCAmelCase = load_dataset(
UpperCamelCase__ , data_files=UpperCamelCase__ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
UpperCAmelCase = load_dataset(
'''swag''' , '''regular''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
UpperCAmelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
UpperCAmelCase = [F"""ending{i}""" for i in range(4 )]
UpperCAmelCase = '''sent1'''
UpperCAmelCase = '''sent2'''
if data_args.max_seq_length is None:
UpperCAmelCase = tokenizer.model_max_length
if max_seq_length > 1024:
logger.warning(
'''The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value'''
''' of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can'''
''' override this default with `--block_size xxx`.''' )
UpperCAmelCase = 1024
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"""
F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" )
UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(UpperCamelCase__ ):
UpperCAmelCase = [[context] * 4 for context in examples[context_name]]
UpperCAmelCase = examples[question_header_name]
UpperCAmelCase = [
[F"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(UpperCamelCase__ )
]
# Flatten out
UpperCAmelCase = list(chain(*UpperCamelCase__ ) )
UpperCAmelCase = list(chain(*UpperCamelCase__ ) )
# Tokenize
UpperCAmelCase = tokenizer(
UpperCamelCase__ , UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='''max_length''' if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(UpperCamelCase__ ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('''--do_train requires a train dataset''' )
UpperCAmelCase = raw_datasets['''train''']
if data_args.max_train_samples is not None:
UpperCAmelCase = min(len(UpperCamelCase__ ) , data_args.max_train_samples )
UpperCAmelCase = train_dataset.select(range(UpperCamelCase__ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
UpperCAmelCase = train_dataset.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError('''--do_eval requires a validation dataset''' )
UpperCAmelCase = raw_datasets['''validation''']
if data_args.max_eval_samples is not None:
UpperCAmelCase = min(len(UpperCamelCase__ ) , data_args.max_eval_samples )
UpperCAmelCase = eval_dataset.select(range(UpperCamelCase__ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
UpperCAmelCase = eval_dataset.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
UpperCAmelCase = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=UpperCamelCase__ , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(UpperCamelCase__ ):
UpperCAmelCase , UpperCAmelCase = eval_predictions
UpperCAmelCase = np.argmax(UpperCamelCase__ , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
UpperCAmelCase = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , compute_metrics=UpperCamelCase__ , )
# Training
if training_args.do_train:
UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase = last_checkpoint
UpperCAmelCase = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCAmelCase = train_result.metrics
UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__ )
)
UpperCAmelCase = min(UpperCamelCase__ , len(UpperCamelCase__ ) )
trainer.log_metrics('''train''' , UpperCamelCase__ )
trainer.save_metrics('''train''' , UpperCamelCase__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
UpperCAmelCase = trainer.evaluate()
UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__ )
UpperCAmelCase = min(UpperCamelCase__ , len(UpperCamelCase__ ) )
trainer.log_metrics('''eval''' , UpperCamelCase__ )
trainer.save_metrics('''eval''' , UpperCamelCase__ )
UpperCAmelCase = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''multiple-choice''',
'''dataset_tags''': '''swag''',
'''dataset_args''': '''regular''',
'''dataset''': '''SWAG''',
'''language''': '''en''',
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase__ )
else:
trainer.create_model_card(**UpperCamelCase__ )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> int:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 273 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( __A, unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = OpenAIGPTTokenizer
lowerCamelCase = OpenAIGPTTokenizerFast
lowerCamelCase = True
lowerCamelCase = False
def UpperCAmelCase_ ( self ) -> List[str]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
A_ : Any = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
A_ : List[Any] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
A_ : Union[str, Any] = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]
A_ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
A_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(_lowerCamelCase ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(_lowerCamelCase ) )
def UpperCAmelCase_ ( self , _lowerCamelCase ) -> Any:
return "lower newer", "lower newer"
def UpperCAmelCase_ ( self ) -> int:
A_ : List[Any] = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
A_ : Optional[Any] = """lower"""
A_ : Any = ["""low""", """er</w>"""]
A_ : List[str] = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = tokens + ["""<unk>"""]
A_ : Optional[int] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , _lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase=15 ) -> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
A_ : List[Any] = self.rust_tokenizer_class.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
# Simple input
A_ : int = """This is a simple input"""
A_ : List[str] = ["""This is a simple input 1""", """This is a simple input 2"""]
A_ : Dict = ("""This is a simple input""", """This is a pair""")
A_ : Optional[int] = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(_lowerCamelCase , tokenizer_r.encode , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" )
# Simple input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" )
# Simple input
self.assertRaises(
_lowerCamelCase , tokenizer_r.batch_encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" , )
# Pair input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" )
# Pair input
self.assertRaises(_lowerCamelCase , tokenizer_r.encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" )
# Pair input
self.assertRaises(
_lowerCamelCase , tokenizer_r.batch_encode_plus , _lowerCamelCase , max_length=_lowerCamelCase , padding="""max_length""" , )
def UpperCAmelCase_ ( self ) -> Tuple:
pass
@require_ftfy
@require_spacy
@require_tokenizers
class _lowerCAmelCase ( __A ):
"""simple docstring"""
pass
| 164 |
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
UpperCamelCase__ : List[Any] = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
UpperCamelCase__ : Optional[Any] = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
UpperCamelCase__ : List[Any] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
UpperCamelCase__ : Optional[Any] = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
UpperCamelCase__ : str = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
UpperCamelCase__ : int = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
UpperCamelCase__ : List[Any] = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 164 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Dict , *SCREAMING_SNAKE_CASE__ : Optional[int] , **SCREAMING_SNAKE_CASE__ : List[str] ) -> None:
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 229 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
_A : Optional[Any] = logging.get_logger(__name__)
# General docstring
_A : Optional[Any] = '''ResNetConfig'''
# Base docstring
_A : Tuple = '''microsoft/resnet-50'''
_A : List[str] = [1, 2048, 7, 7]
# Image classification docstring
_A : str = '''microsoft/resnet-50'''
_A : Dict = '''tiger cat'''
_A : List[Any] = [
'''microsoft/resnet-50''',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 3 , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : str = "relu" ) -> Any:
super().__init__()
__lowerCAmelCase = nn.Convad(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , padding=kernel_size // 2 , bias=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = nn.BatchNormad(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tensor ) -> Tensor:
__lowerCAmelCase = self.convolution(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.normalization(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.activation(SCREAMING_SNAKE_CASE__ )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : ResNetConfig ) -> List[str]:
super().__init__()
__lowerCAmelCase = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
__lowerCAmelCase = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
__lowerCAmelCase = config.num_channels
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tensor ) -> Tensor:
__lowerCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
__lowerCAmelCase = self.embedder(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.pooler(SCREAMING_SNAKE_CASE__ )
return embedding
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 2 ) -> Dict:
super().__init__()
__lowerCAmelCase = nn.Convad(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=1 , stride=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = nn.BatchNormad(SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tensor ) -> Tensor:
__lowerCAmelCase = self.convolution(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.normalization(SCREAMING_SNAKE_CASE__ )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : str = "relu" ) -> Dict:
super().__init__()
__lowerCAmelCase = in_channels != out_channels or stride != 1
__lowerCAmelCase = (
ResNetShortCut(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ ) if should_apply_shortcut else nn.Identity()
)
__lowerCAmelCase = nn.Sequential(
ResNetConvLayer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ ) , ResNetConvLayer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , activation=SCREAMING_SNAKE_CASE__ ) , )
__lowerCAmelCase = ACTaFN[activation]
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> int:
__lowerCAmelCase = hidden_state
__lowerCAmelCase = self.layer(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.shortcut(SCREAMING_SNAKE_CASE__ )
hidden_state += residual
__lowerCAmelCase = self.activation(SCREAMING_SNAKE_CASE__ )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : str = "relu" , SCREAMING_SNAKE_CASE__ : int = 4 ) -> int:
super().__init__()
__lowerCAmelCase = in_channels != out_channels or stride != 1
__lowerCAmelCase = out_channels // reduction
__lowerCAmelCase = (
ResNetShortCut(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ ) if should_apply_shortcut else nn.Identity()
)
__lowerCAmelCase = nn.Sequential(
ResNetConvLayer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=1 ) , ResNetConvLayer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ ) , ResNetConvLayer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=1 , activation=SCREAMING_SNAKE_CASE__ ) , )
__lowerCAmelCase = ACTaFN[activation]
def a ( self : Any , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Tuple:
__lowerCAmelCase = hidden_state
__lowerCAmelCase = self.layer(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.shortcut(SCREAMING_SNAKE_CASE__ )
hidden_state += residual
__lowerCAmelCase = self.activation(SCREAMING_SNAKE_CASE__ )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : ResNetConfig , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 2 , SCREAMING_SNAKE_CASE__ : int = 2 , ) -> int:
super().__init__()
__lowerCAmelCase = ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer
__lowerCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , stride=SCREAMING_SNAKE_CASE__ , activation=config.hidden_act ) , *[layer(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tensor ) -> Tensor:
__lowerCAmelCase = input
for layer in self.layers:
__lowerCAmelCase = layer(SCREAMING_SNAKE_CASE__ )
return hidden_state
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : ResNetConfig ) -> Optional[int]:
super().__init__()
__lowerCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
SCREAMING_SNAKE_CASE__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
__lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE__ , config.depths[1:] ):
self.stages.append(ResNetStage(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , depth=SCREAMING_SNAKE_CASE__ ) )
def a ( self : List[str] , SCREAMING_SNAKE_CASE__ : Tensor , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : bool = True ) -> BaseModelOutputWithNoAttention:
__lowerCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
__lowerCAmelCase = hidden_states + (hidden_state,)
__lowerCAmelCase = stage_module(SCREAMING_SNAKE_CASE__ )
if output_hidden_states:
__lowerCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , hidden_states=SCREAMING_SNAKE_CASE__ , )
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : int = ResNetConfig
_SCREAMING_SNAKE_CASE : Union[str, Any] = """resnet"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = """pixel_values"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = True
def a ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
if isinstance(SCREAMING_SNAKE_CASE__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(SCREAMING_SNAKE_CASE__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ) -> int:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCAmelCase = value
_A : Dict = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
_A : Optional[int] = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare ResNet model outputting raw features without any specific head on top.""" , UpperCAmelCase__ , )
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> List[str]:
super().__init__(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = config
__lowerCAmelCase = ResNetEmbeddings(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = ResNetEncoder(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tensor , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
__lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCAmelCase = self.embedder(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.encoder(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = encoder_outputs[0]
__lowerCAmelCase = self.pooler(SCREAMING_SNAKE_CASE__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ , pooler_output=SCREAMING_SNAKE_CASE__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , UpperCAmelCase__ , )
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : Tuple ) -> Any:
super().__init__(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = config.num_labels
__lowerCAmelCase = ResNetModel(SCREAMING_SNAKE_CASE__ )
# classification head
__lowerCAmelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def a ( self : int , SCREAMING_SNAKE_CASE__ : Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE__ : Optional[torch.LongTensor] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , ) -> ImageClassifierOutputWithNoAttention:
__lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCAmelCase = self.resnet(SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1]
__lowerCAmelCase = self.classifier(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__lowerCAmelCase = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__lowerCAmelCase = """single_label_classification"""
else:
__lowerCAmelCase = """multi_label_classification"""
if self.config.problem_type == "regression":
__lowerCAmelCase = MSELoss()
if self.num_labels == 1:
__lowerCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
__lowerCAmelCase = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.config.problem_type == "single_label_classification":
__lowerCAmelCase = CrossEntropyLoss()
__lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
__lowerCAmelCase = BCEWithLogitsLoss()
__lowerCAmelCase = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if not return_dict:
__lowerCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states )
@add_start_docstrings(
"""
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
""" , UpperCAmelCase__ , )
class _lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Tuple:
super().__init__(SCREAMING_SNAKE_CASE__ )
super()._init_backbone(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = [config.embedding_size] + config.hidden_sizes
__lowerCAmelCase = ResNetEmbeddings(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = ResNetEncoder(SCREAMING_SNAKE_CASE__ )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__ )
@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Tensor , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None ) -> BackboneOutput:
__lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCAmelCase = self.embedder(SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = self.encoder(SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = outputs.hidden_states
__lowerCAmelCase = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
__lowerCAmelCase = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=SCREAMING_SNAKE_CASE__ , )
| 229 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
"xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
"xlm-roberta-large-finetuned-conll02-dutch": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll02-spanish": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-english": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
),
"xlm-roberta-large-finetuned-conll03-german": (
"https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
),
}
class A ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase = '''xlm-roberta'''
def __init__( self : Optional[Any],lowercase_ : Optional[int]=3_0_5_2_2,lowercase_ : Optional[Any]=7_6_8,lowercase_ : Tuple=1_2,lowercase_ : Optional[int]=1_2,lowercase_ : List[Any]=3_0_7_2,lowercase_ : Tuple="gelu",lowercase_ : List[Any]=0.1,lowercase_ : List[Any]=0.1,lowercase_ : Union[str, Any]=5_1_2,lowercase_ : str=2,lowercase_ : Union[str, Any]=0.02,lowercase_ : Optional[int]=1E-12,lowercase_ : List[str]=1,lowercase_ : Any=0,lowercase_ : str=2,lowercase_ : Union[str, Any]="absolute",lowercase_ : Optional[int]=True,lowercase_ : Dict=None,**lowercase_ : str,)-> List[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase_,bos_token_id=lowerCAmelCase_,eos_token_id=lowerCAmelCase_,**lowerCAmelCase_ )
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = intermediate_size
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__ = position_embedding_type
A__ = use_cache
A__ = classifier_dropout
class A ( __lowerCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : List[str] )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A__ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 365 |
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : Optional[int],lowercase_ : Optional[int]=1_3,lowercase_ : int=7,lowercase_ : List[str]=True,lowercase_ : str=True,lowercase_ : List[str]=True,lowercase_ : Optional[Any]=True,lowercase_ : Dict=9_9,lowercase_ : Dict=2_4,lowercase_ : Union[str, Any]=2,lowercase_ : str=6,lowercase_ : Dict=3_7,lowercase_ : Optional[Any]="gelu",lowercase_ : Any=0.1,lowercase_ : Any=0.1,lowercase_ : Any=5_1_2,lowercase_ : Dict=1_6,lowercase_ : List[str]=2,lowercase_ : Dict=0.02,lowercase_ : Any=3,lowercase_ : Dict=None,lowercase_ : List[str]=1_0_0_0,)-> Optional[Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = 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__ = type_sequence_label_size
A__ = initializer_range
A__ = num_labels
A__ = scope
A__ = range_bbox
def snake_case__ ( self : List[Any] )-> Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
A__ = ids_tensor([self.batch_size, self.seq_length, 4],self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
A__ = bbox[i, j, 3]
A__ = bbox[i, j, 1]
A__ = t
if bbox[i, j, 2] < bbox[i, j, 0]:
A__ = bbox[i, j, 2]
A__ = bbox[i, j, 0]
A__ = t
A__ = None
if self.use_input_mask:
A__ = ids_tensor([self.batch_size, self.seq_length],vocab_size=2 )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size )
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size],self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.seq_length],self.num_labels )
A__ = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self : Dict )-> int:
'''simple docstring'''
return LiltConfig(
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,)
def snake_case__ ( self : Optional[Any],lowercase_ : Tuple,lowercase_ : str,lowercase_ : Optional[int],lowercase_ : Optional[Any],lowercase_ : str,lowercase_ : List[str],lowercase_ : Tuple,)-> Optional[Any]:
'''simple docstring'''
A__ = LiltModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,bbox=lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_ )
A__ = model(lowercase_,bbox=lowercase_,token_type_ids=lowercase_ )
A__ = model(lowercase_,bbox=lowercase_ )
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 snake_case__ ( self : Any,lowercase_ : Dict,lowercase_ : List[Any],lowercase_ : List[str],lowercase_ : Optional[int],lowercase_ : Optional[int],lowercase_ : Optional[int],lowercase_ : List[Any],)-> List[str]:
'''simple docstring'''
A__ = self.num_labels
A__ = LiltForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(
lowercase_,bbox=lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels) )
def snake_case__ ( self : int,lowercase_ : Union[str, Any],lowercase_ : int,lowercase_ : Tuple,lowercase_ : Any,lowercase_ : Optional[int],lowercase_ : Tuple,lowercase_ : List[str],)-> Any:
'''simple docstring'''
A__ = LiltForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(
lowercase_,bbox=lowercase_,attention_mask=lowercase_,token_type_ids=lowercase_,start_positions=lowercase_,end_positions=lowercase_,)
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 snake_case__ ( self : Optional[int] )-> Tuple:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class A ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
def snake_case__ ( self : List[str],lowercase_ : int,lowercase_ : List[str],lowercase_ : str,lowercase_ : Optional[Any],lowercase_ : Optional[Any] )-> Any:
'''simple docstring'''
return True
def snake_case__ ( self : int )-> Tuple:
'''simple docstring'''
A__ = LiltModelTester(self )
A__ = ConfigTester(self,config_class=lowercase_,hidden_size=3_7 )
def snake_case__ ( self : List[Any] )-> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self : Dict )-> Union[str, Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def snake_case__ ( self : Union[str, Any] )-> Dict:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A__ = type
self.model_tester.create_and_check_model(*lowercase_ )
def snake_case__ ( self : str )-> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase_ )
def snake_case__ ( self : List[Any] )-> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase_ )
@slow
def snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = LiltModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
@slow
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : List[Any] )-> Dict:
'''simple docstring'''
A__ = LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base' ).to(lowercase_ )
A__ = torch.tensor([[1, 2]],device=lowercase_ )
A__ = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]],device=lowercase_ )
# forward pass
with torch.no_grad():
A__ = model(input_ids=lowercase_,bbox=lowercase_ )
A__ = torch.Size([1, 2, 7_6_8] )
A__ = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],device=lowercase_,)
self.assertTrue(outputs.last_hidden_state.shape,lowercase_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3],lowercase_,atol=1E-3 ) )
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