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 |
|---|---|---|---|---|
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Union[str, Any] = '''data2vec-audio'''
def __init__( self , lowerCAmelCase_=32 , lowerCAmelCase_=7_68 , lowerCAmelCase_=12 , lowerCAmelCase_=12 , lowerCAmelCase_=30_72 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.02 , lowerCAmelCase_=1E-5 , lowerCAmelCase_="gelu" , lowerCAmelCase_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , lowerCAmelCase_=(5, 2, 2, 2, 2, 2, 2) , lowerCAmelCase_=(10, 3, 3, 3, 3, 2, 2) , lowerCAmelCase_=False , lowerCAmelCase_=16 , lowerCAmelCase_=19 , lowerCAmelCase_=5 , lowerCAmelCase_=0.05 , lowerCAmelCase_=10 , lowerCAmelCase_=2 , lowerCAmelCase_=0.0 , lowerCAmelCase_=10 , lowerCAmelCase_=0 , lowerCAmelCase_="sum" , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=2_56 , lowerCAmelCase_=(5_12, 5_12, 5_12, 5_12, 15_00) , lowerCAmelCase_=(5, 3, 3, 1, 1) , lowerCAmelCase_=(1, 2, 3, 1, 1) , lowerCAmelCase_=5_12 , lowerCAmelCase_=0 , lowerCAmelCase_=1 , lowerCAmelCase_=2 , lowerCAmelCase_=False , lowerCAmelCase_=3 , lowerCAmelCase_=2 , lowerCAmelCase_=3 , lowerCAmelCase_=None , **lowerCAmelCase_ , ) -> Any:
super().__init__(**lowerCAmelCase_ , pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ )
_A = hidden_size
_A = feat_extract_activation
_A = list(lowerCAmelCase_ )
_A = list(lowerCAmelCase_ )
_A = list(lowerCAmelCase_ )
_A = conv_bias
_A = num_conv_pos_embeddings
_A = num_conv_pos_embedding_groups
_A = conv_pos_kernel_size
_A = len(self.conv_dim )
_A = num_hidden_layers
_A = intermediate_size
_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
_A = use_weighted_layer_sum
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)`, but is `len(config.conv_dim) ="""
F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_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
# adapter
_A = add_adapter
_A = adapter_kernel_size
_A = adapter_stride
_A = num_adapter_layers
_A = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_A = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_A = list(lowerCAmelCase_ )
_A = list(lowerCAmelCase_ )
_A = list(lowerCAmelCase_ )
_A = xvector_output_dim
@property
def UpperCAmelCase ( self ) -> List[str]:
return math.prod(self.conv_stride )
| 180 | import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=13 , lowerCAmelCase_=30 , lowerCAmelCase_=2 , lowerCAmelCase_=3 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=32 , lowerCAmelCase_=5 , lowerCAmelCase_=4 , lowerCAmelCase_=37 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=10 , lowerCAmelCase_=0.02 , lowerCAmelCase_=3 , lowerCAmelCase_=0.6 , lowerCAmelCase_=None , ) -> int:
_A = parent
_A = batch_size
_A = image_size
_A = patch_size
_A = num_channels
_A = is_training
_A = use_labels
_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 = type_sequence_label_size
_A = initializer_range
_A = mask_ratio
_A = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
_A = (image_size // patch_size) ** 2
_A = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self ) -> str:
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCAmelCase_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
_A = ViTMAEModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_A = 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_ ) -> List[Any]:
_A = ViTMAEForPreTraining(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_A = model(lowerCAmelCase_ )
_A = (self.image_size // self.patch_size) ** 2
_A = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
_A = 1
_A = ViTMAEForPreTraining(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_A = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_A = model(lowerCAmelCase_ )
_A = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def UpperCAmelCase ( self ) -> Dict:
_A = self.prepare_config_and_inputs()
_A , _A , _A = config_and_inputs
_A = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class a ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Union[str, Any] = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowerCamelCase :List[Any] = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
lowerCamelCase :List[Any] = False
lowerCamelCase :Tuple = False
lowerCamelCase :int = False
lowerCamelCase :Any = False
def UpperCAmelCase ( self ) -> str:
_A = ViTMAEModelTester(self )
_A = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=37 )
def UpperCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def UpperCAmelCase ( self ) -> Optional[Any]:
pass
def UpperCAmelCase ( self ) -> str:
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(lowerCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_A = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase_ , nn.Linear ) )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(lowerCAmelCase_ )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Union[str, Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Dict:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_ )
def UpperCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
# make masks reproducible
np.random.seed(2 )
_A = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
_A = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
_A = torch.from_numpy(lowerCAmelCase_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
_A = pt_noise
super().check_pt_tf_models(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase ( self ) -> Any:
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_A = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_A = outputs[0].cpu().numpy()
_A = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase_ )
_A = model_class.from_pretrained(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
_A = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
# Make sure we don't have nans
_A = after_outputs[0].cpu().numpy()
_A = 0
_A = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowerCAmelCase_ , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def UpperCAmelCase ( self ) -> Tuple:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def UpperCAmelCase ( self ) -> str:
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def UpperCAmelCase ( self ) -> str:
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def UpperCAmelCase ( self ) -> Dict:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCAmelCase ( self ) -> str:
pass
@slow
def UpperCAmelCase ( self ) -> Optional[Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = ViTMAEModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def snake_case ( ) -> List[str]:
_A = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
return image
@require_torch
@require_vision
class a ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase ( self ) -> int:
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self ) -> Any:
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
_A = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(lowerCAmelCase_ )
_A = self.default_image_processor
_A = prepare_img()
_A = image_processor(images=lowerCAmelCase_ , return_tensors="""pt""" ).to(lowerCAmelCase_ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
_A = ViTMAEConfig()
_A = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
_A = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
_A = model(**lowerCAmelCase_ , noise=torch.from_numpy(lowerCAmelCase_ ).to(device=lowerCAmelCase_ ) )
# verify the logits
_A = torch.Size((1, 1_96, 7_68) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_A = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(lowerCAmelCase_ ) , atol=1E-4 ) )
| 180 | 1 |
from timeit import timeit
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase_= 0
while number:
number &= number - 1
result += 1
return result
def __a ( lowerCAmelCase_ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase_= 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def __a ( ) -> None:
'''simple docstring'''
def do_benchmark(lowerCAmelCase_ : int ) -> None:
UpperCAmelCase_= """import __main__ as z"""
print(F"""Benchmark when {number = }:""" )
print(F"""{get_set_bits_count_using_modulo_operator(lowerCAmelCase_ ) = }""" )
UpperCAmelCase_= timeit("""z.get_set_bits_count_using_modulo_operator(25)""" ,setup=lowerCAmelCase_ )
print(F"""timeit() runs in {timing} seconds""" )
print(F"""{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase_ ) = }""" )
UpperCAmelCase_= timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" ,setup=lowerCAmelCase_ ,)
print(F"""timeit() runs in {timing} seconds""" )
for number in (25, 37, 58, 0):
do_benchmark(lowerCAmelCase_ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 277 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase ( snake_case__):
"""simple docstring"""
def __init__( self : int , __UpperCAmelCase : pyspark.sql.DataFrame , __UpperCAmelCase : Optional[NamedSplit] = None , __UpperCAmelCase : Optional[Features] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : str = None , __UpperCAmelCase : bool = False , __UpperCAmelCase : str = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : str = "arrow" , **__UpperCAmelCase : str , ) -> Dict:
super().__init__(
split=__UpperCAmelCase , features=__UpperCAmelCase , cache_dir=__UpperCAmelCase , keep_in_memory=__UpperCAmelCase , streaming=__UpperCAmelCase , **__UpperCAmelCase , )
UpperCAmelCase_= load_from_cache_file
UpperCAmelCase_= file_format
UpperCAmelCase_= Spark(
df=__UpperCAmelCase , features=__UpperCAmelCase , cache_dir=__UpperCAmelCase , working_dir=__UpperCAmelCase , **__UpperCAmelCase , )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
UpperCAmelCase_= None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=__UpperCAmelCase , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 277 | 1 |
from __future__ import annotations
lowerCamelCase__ = 1.6021e-19 # units = C
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , ):
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302 |
import string
import numpy
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , _SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE :
__lowerCamelCase : List[str] =string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
__lowerCamelCase : List[Any] =numpy.vectorize(lambda lowerCamelCase__ : x % 36 )
__lowerCamelCase : Optional[Any] =numpy.vectorize(lowerCamelCase__ )
def __init__( self : Union[str, Any] , __lowercase : numpy.ndarray ):
'''simple docstring'''
__a = self.modulus(__lowercase ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
__a = encrypt_key.shape[0]
def UpperCamelCase_ ( self : Dict , __lowercase : str ):
'''simple docstring'''
return self.key_string.index(__lowercase )
def UpperCamelCase_ ( self : Dict , __lowercase : int ):
'''simple docstring'''
return self.key_string[round(__lowercase )]
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
__a = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
__a = det % len(self.key_string )
__a = len(self.key_string )
if greatest_common_divisor(__lowercase , len(self.key_string ) ) != 1:
__a = (
F"determinant modular {req_l} of encryption key({det}) "
F"is not co prime w.r.t {req_l}.\nTry another key."
)
raise ValueError(__lowercase )
def UpperCamelCase_ ( self : Dict , __lowercase : str ):
'''simple docstring'''
__a = [char for char in text.upper() if char in self.key_string]
__a = chars[-1]
while len(__lowercase ) % self.break_key != 0:
chars.append(__lowercase )
return "".join(__lowercase )
def UpperCamelCase_ ( self : List[str] , __lowercase : str ):
'''simple docstring'''
__a = self.process_text(text.upper() )
__a = """"""
for i in range(0 , len(__lowercase ) - self.break_key + 1 , self.break_key ):
__a = text[i : i + self.break_key]
__a = [self.replace_letters(__lowercase ) for char in batch]
__a = numpy.array([vec] ).T
__a = self.modulus(self.encrypt_key.dot(__lowercase ) ).T.tolist()[
0
]
__a = """""".join(
self.replace_digits(__lowercase ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
__a = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
__a = det % len(self.key_string )
__a = None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
__a = i
break
__a = (
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(__lowercase ) )
def UpperCamelCase_ ( self : Any , __lowercase : str ):
'''simple docstring'''
__a = self.make_decrypt_key()
__a = self.process_text(text.upper() )
__a = """"""
for i in range(0 , len(__lowercase ) - self.break_key + 1 , self.break_key ):
__a = text[i : i + self.break_key]
__a = [self.replace_letters(__lowercase ) for char in batch]
__a = numpy.array([vec] ).T
__a = self.modulus(decrypt_key.dot(__lowercase ) ).T.tolist()[0]
__a = """""".join(
self.replace_digits(__lowercase ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def lowerCAmelCase__ ( ):
"""simple docstring"""
__a = int(input("""Enter the order of the encryption key: """ ) )
__a = []
print("""Enter each row of the encryption key with space separated integers""" )
for _ in range(_SCREAMING_SNAKE_CASE ):
__a = [int(_SCREAMING_SNAKE_CASE ) for x in input().split()]
hill_matrix.append(_SCREAMING_SNAKE_CASE )
__a = HillCipher(numpy.array(_SCREAMING_SNAKE_CASE ) )
print("""Would you like to encrypt or decrypt some text? (1 or 2)""" )
__a = input("""\n1. Encrypt\n2. Decrypt\n""" )
if option == "1":
__a = input("""What text would you like to encrypt?: """ )
print("""Your encrypted text is:""" )
print(hc.encrypt(_SCREAMING_SNAKE_CASE ) )
elif option == "2":
__a = input("""What text would you like to decrypt?: """ )
print("""Your decrypted text is:""" )
print(hc.decrypt(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 302 | 1 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = DistilBertTokenizer
lowerCamelCase__ = DistilBertTokenizerFast
lowerCamelCase__ = True
@slow
def A_ ( self ):
_lowerCamelCase : Dict = DistilBertTokenizer.from_pretrained('distilbert-base-uncased' )
_lowerCamelCase : str = tokenizer.encode('sequence builders' , add_special_tokens=lowercase )
_lowerCamelCase : str = tokenizer.encode('multi-sequence build' , add_special_tokens=lowercase )
_lowerCamelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowercase )
_lowerCamelCase : int = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
] | 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def _snake_case ( lowercase__ , lowercase__ , lowercase__=None , lowercase__=None ):
if attention_mask is None:
_lowerCamelCase : List[str] = tf.cast(tf.math.not_equal(lowercase__ , config.pad_token_id ) , tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = OPTConfig
lowerCamelCase__ = {}
lowerCamelCase__ = """gelu"""
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=False , lowercase=99 , lowercase=16 , lowercase=2 , lowercase=4 , lowercase=4 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=20 , lowercase=2 , lowercase=1 , lowercase=0 , lowercase=16 , lowercase=16 , ):
_lowerCamelCase : Tuple = parent
_lowerCamelCase : Any = batch_size
_lowerCamelCase : Tuple = seq_length
_lowerCamelCase : str = is_training
_lowerCamelCase : Optional[int] = use_labels
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : Dict = hidden_size
_lowerCamelCase : str = num_hidden_layers
_lowerCamelCase : Optional[int] = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : Dict = hidden_act
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : List[str] = attention_probs_dropout_prob
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : List[Any] = eos_token_id
_lowerCamelCase : Tuple = pad_token_id
_lowerCamelCase : List[str] = bos_token_id
_lowerCamelCase : Optional[int] = embed_dim
_lowerCamelCase : List[str] = word_embed_proj_dim
_lowerCamelCase : Any = False
def A_ ( self ):
_lowerCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_lowerCamelCase : Optional[int] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_lowerCamelCase : str = tf.concat([input_ids, eos_tensor] , axis=1 )
_lowerCamelCase : Tuple = self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , 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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=lowercase , **self.config_updates , )
_lowerCamelCase : int = prepare_opt_inputs_dict(lowercase , lowercase )
return config, inputs_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Optional[Any] = TFOPTModel(config=lowercase )
_lowerCamelCase : Optional[Any] = inputs_dict['input_ids']
_lowerCamelCase : str = input_ids[:1, :]
_lowerCamelCase : Dict = inputs_dict['attention_mask'][:1, :]
_lowerCamelCase : Optional[Any] = 1
# first forward pass
_lowerCamelCase : Any = model(lowercase , attention_mask=lowercase , use_cache=lowercase )
_lowerCamelCase, _lowerCamelCase : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_lowerCamelCase : Optional[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCamelCase : Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
_lowerCamelCase : List[Any] = tf.concat([input_ids, next_tokens] , axis=-1 )
_lowerCamelCase : Optional[int] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
_lowerCamelCase : Optional[Any] = model(lowercase , attention_mask=lowercase )[0]
_lowerCamelCase : List[str] = model(lowercase , attention_mask=lowercase , past_key_values=lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
_lowerCamelCase : Any = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
_lowerCamelCase : Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
_lowerCamelCase : List[str] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowercase , lowercase , rtol=1E-3 )
@require_tf
class lowerCAmelCase__ ( lowercase, lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
lowerCamelCase__ = (TFOPTForCausalLM,) if is_tf_available() else ()
lowerCamelCase__ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = 10
def A_ ( self ):
_lowerCamelCase : int = TFOPTModelTester(self )
_lowerCamelCase : Tuple = ConfigTester(self , config_class=lowercase )
def A_ ( self ):
self.config_tester.run_common_tests()
def A_ ( self ):
_lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowercase )
def A_ ( self ):
_lowerCamelCase, _lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(lowercase , lowercase ):
if hasattr(lowercase , 'weight' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(lowercase , 'weight' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
_lowerCamelCase : Optional[int] = model_class(config=lowercase )
_lowerCamelCase : int = _get_word_embedding_weight(lowercase , model.get_input_embeddings() )
_lowerCamelCase : Tuple = _get_word_embedding_weight(lowercase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(lowercase )
_lowerCamelCase : str = _get_word_embedding_weight(lowercase , model.get_input_embeddings() )
_lowerCamelCase : Any = _get_word_embedding_weight(lowercase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
_lowerCamelCase : Union[str, Any] = size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , lowercase )
# check that weights remain the same after resizing
_lowerCamelCase : int = True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
_lowerCamelCase : Optional[Any] = False
self.assertTrue(lowercase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , lowercase )
_lowerCamelCase : Dict = True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
_lowerCamelCase : Union[str, Any] = False
self.assertTrue(lowercase )
def _snake_case ( lowercase__ ):
return tf.constant(lowercase__ , dtype=tf.intaa )
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = 99
def A_ ( self ):
_lowerCamelCase : Tuple = tf.ones((4, 1) , dtype=tf.intaa ) * 2
_lowerCamelCase : Tuple = tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
_lowerCamelCase : int = input_ids.shape[0]
_lowerCamelCase : List[Any] = OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : Tuple = TFOPTModel.from_pretrained('facebook/opt-350m' )
_lowerCamelCase : List[Any] = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
_lowerCamelCase : List[str] = tf.not_equal(lowercase , model.config.pad_token_id )
with tf.GradientTape():
_lowerCamelCase : List[str] = model(input_ids=lowercase , attention_mask=lowercase ).last_hidden_state
_lowerCamelCase : Optional[Any] = (1, 11, 512)
self.assertEqual(output.shape , lowercase )
_lowerCamelCase : List[str] = tf.constant(
[[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]] )
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=4E-3 ) )
_lowerCamelCase : List[str] = tf.function(lowercase , jit_compile=lowercase )
_lowerCamelCase : Union[str, Any] = xla_generate(lowercase , lowercase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , lowercase , atol=4E-2 ) )
@require_tf
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self ):
super().setUp()
_lowerCamelCase : List[Any] = 'facebook/opt-350m'
def A_ ( self ):
_lowerCamelCase : int = TFOPTForCausalLM.from_pretrained(self.path_model )
_lowerCamelCase : List[Any] = GPTaTokenizer.from_pretrained(self.path_model )
_lowerCamelCase : List[str] = [
'Today is a beautiful day and I want to',
'In the city of',
'Paris is the capital of France and',
'Computers and mobile phones have taken',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
_lowerCamelCase : List[Any] = tokenizer(lowercase , return_tensors='tf' , padding=lowercase , add_special_tokens=lowercase )
_lowerCamelCase : Optional[int] = tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
_lowerCamelCase : Any = tf.constant(
[
[1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
[-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
[0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
[6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
] )
self.assertTrue(np.allclose(lowercase , lowercase , atol=1E-4 ) )
_lowerCamelCase : Tuple = tf.function(lowercase , jit_compile=lowercase )
_lowerCamelCase : List[Any] = tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(lowercase , lowercase , atol=1E-4 ) )
@require_tf
@slow
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def A_ ( self ):
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def A_ ( self ):
_lowerCamelCase : str = 'facebook/opt-125m'
_lowerCamelCase : Dict = [
'Today is a beautiful day and I want to',
'In the city of New York, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Optional[int] = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Dict = TFOPTForCausalLM.from_pretrained(lowercase )
for prompt in self.prompts:
_lowerCamelCase : int = tokenizer(lowercase , return_tensors='tf' ).input_ids
_lowerCamelCase : int = model.generate(lowercase , max_length=10 )
_lowerCamelCase : Any = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
predicted_outputs += generated_string
self.assertListEqual(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : List[Any] = 'facebook/opt-350m'
_lowerCamelCase : int = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Optional[int] = TFOPTForCausalLM.from_pretrained(lowercase )
_lowerCamelCase : Any = 'left'
# use different length sentences to test batching
_lowerCamelCase : Optional[int] = [
'Hello, my dog is a little',
'Today, I',
]
_lowerCamelCase : Dict = tokenizer(lowercase , return_tensors='tf' , padding=lowercase )
_lowerCamelCase : int = inputs['input_ids']
_lowerCamelCase : Tuple = model.generate(input_ids=lowercase , attention_mask=inputs['attention_mask'] )
_lowerCamelCase : Optional[int] = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
_lowerCamelCase : Union[str, Any] = model.generate(input_ids=lowercase )
_lowerCamelCase : Dict = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['attention_mask'][-1] , tf.intaa ) )
_lowerCamelCase : int = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
_lowerCamelCase : Union[str, Any] = model.generate(input_ids=lowercase , max_length=model.config.max_length - num_paddings )
_lowerCamelCase : List[Any] = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
_lowerCamelCase : Union[str, Any] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowercase )
_lowerCamelCase : Optional[Any] = tokenizer.decode(output_padded[0] , skip_special_tokens=lowercase )
_lowerCamelCase : Optional[Any] = [
'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
'Today, I was in the middle of a conversation with a friend about the',
]
self.assertListEqual(lowercase , lowercase )
self.assertListEqual(lowercase , [non_padded_sentence, padded_sentence] )
def A_ ( self ):
_lowerCamelCase : Tuple = 'facebook/opt-350m'
_lowerCamelCase : List[Any] = [
'Today is a beautiful day and I want to',
'In the city of San Francisco, the city',
'Paris is the capital of France and the capital',
'Computers and mobile phones have taken over the',
]
_lowerCamelCase : Optional[int] = []
_lowerCamelCase : Optional[Any] = GPTaTokenizer.from_pretrained(lowercase )
_lowerCamelCase : Optional[Any] = TFOPTForCausalLM.from_pretrained(lowercase )
for prompt in self.prompts:
_lowerCamelCase : List[Any] = tokenizer(lowercase , return_tensors='tf' ).input_ids
_lowerCamelCase : Optional[Any] = model.generate(lowercase , max_length=10 )
_lowerCamelCase : Dict = tokenizer.batch_decode(lowercase , skip_special_tokens=lowercase )
predicted_outputs += generated_string
self.assertListEqual(lowercase , lowercase ) | 12 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 132 | """simple docstring"""
import copy
import re
class snake_case__ :
_snake_case : Dict = """hp"""
_snake_case : List[str] = {}
_snake_case : int = None
@classmethod
def a__ ( cls , lowerCamelCase , lowerCamelCase ):
__a = prefix
__a = defaults
cls.build_naming_info()
@staticmethod
def a__ ( lowerCamelCase , lowerCamelCase ):
if len(lowerCamelCase ) == 0:
return ""
__a = None
if any(char.isdigit() for char in word ):
raise Exception(F"Parameters should not contain numbers: '{word}' contains a number" )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(lowerCamelCase ) + 1 ):
__a = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
__a = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowerCamelCase ):
__a = ""
while integer != 0:
__a = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
__a = 0
while True:
__a = word + "#" + int_to_alphabetic(lowerCamelCase )
if sword in info["reverse_short_word"]:
continue
else:
__a = sword
break
__a = short_word
__a = word
return short_word
@staticmethod
def a__ ( lowerCamelCase , lowerCamelCase ):
__a = param_name.split("_" )
__a = [TrialShortNamer.shortname_for_word(lowerCamelCase , lowerCamelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
__a = ["", "_"]
for separator in separators:
__a = separator.join(lowerCamelCase )
if shortname not in info["reverse_short_param"]:
__a = shortname
__a = param_name
return shortname
return param_name
@staticmethod
def a__ ( lowerCamelCase , lowerCamelCase ):
__a = TrialShortNamer.shortname_for_key(lowerCamelCase , lowerCamelCase )
__a = short_name
__a = param_name
@classmethod
def a__ ( cls ):
if cls.NAMING_INFO is not None:
return
__a = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
__a = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(lowerCamelCase , lowerCamelCase )
__a = info
@classmethod
def a__ ( cls , lowerCamelCase ):
cls.build_naming_info()
assert cls.PREFIX is not None
__a = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F"You should provide a default value for the param name {k} with value {v}" )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
__a = cls.NAMING_INFO["short_param"][k]
if isinstance(lowerCamelCase , lowerCamelCase ):
__a = 1 if v else 0
__a = "" if isinstance(lowerCamelCase , (int, float) ) else "-"
__a = F"{key}{sep}{v}"
name.append(lowerCamelCase )
return "_".join(lowerCamelCase )
@classmethod
def a__ ( cls , lowerCamelCase ):
__a = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
__a = []
else:
__a = repr.split("_" )
__a = {}
for value in values:
if "-" in value:
__a , __a = value.split("-" )
else:
__a = re.sub("[0-9.]" , "" , lowerCamelCase )
__a = float(re.sub("[^0-9.]" , "" , lowerCamelCase ) )
__a = cls.NAMING_INFO["reverse_short_param"][p_k]
__a = p_v
for k in cls.DEFAULTS:
if k not in parameters:
__a = cls.DEFAULTS[k]
return parameters
| 261 | 0 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = None , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(
lowerCamelCase , split=lowerCamelCase , features=lowerCamelCase , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase , streaming=lowerCamelCase , num_proc=lowerCamelCase , **lowerCamelCase , )
_lowerCAmelCase = field
_lowerCAmelCase = path_or_paths if isinstance(lowerCamelCase , lowerCamelCase ) else {self.split: path_or_paths}
_lowerCAmelCase = Json(
cache_dir=lowerCamelCase , data_files=lowerCamelCase , features=lowerCamelCase , field=lowerCamelCase , **lowerCamelCase , )
def A__ (self ):
'''simple docstring'''
if self.streaming:
_lowerCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
self.builder.download_and_prepare(
download_config=lowerCamelCase , download_mode=lowerCamelCase , verification_mode=lowerCamelCase , base_path=lowerCamelCase , num_proc=self.num_proc , )
_lowerCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=lowerCamelCase , in_memory=self.keep_in_memory )
return dataset
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )
_lowerCAmelCase = dataset
_lowerCAmelCase = path_or_buf
_lowerCAmelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_lowerCAmelCase = num_proc
_lowerCAmelCase = """utf-8"""
_lowerCAmelCase = to_json_kwargs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.to_json_kwargs.pop("""path_or_buf""" , lowerCamelCase )
_lowerCAmelCase = self.to_json_kwargs.pop("""orient""" , """records""" )
_lowerCAmelCase = self.to_json_kwargs.pop("""lines""" , True if orient == """records""" else False )
_lowerCAmelCase = self.to_json_kwargs.pop("""index""" , False if orient in ["""split""", """table"""] else True )
_lowerCAmelCase = self.to_json_kwargs.pop("""compression""" , lowerCamelCase )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , """wb""" , compression=lowerCamelCase ) as buffer:
_lowerCAmelCase = self._write(file_obj=lowerCamelCase , orient=lowerCamelCase , lines=lowerCamelCase , index=lowerCamelCase , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
""" was passed. Please provide a local path instead.""" )
_lowerCAmelCase = self._write(
file_obj=self.path_or_buf , orient=lowerCamelCase , lines=lowerCamelCase , index=lowerCamelCase , **self.to_json_kwargs )
return written
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = args
_lowerCAmelCase = query_table(
table=self.dataset.data , key=slice(lowerCamelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
_lowerCAmelCase = batch.to_pandas().to_json(
path_or_buf=lowerCamelCase , orient=lowerCamelCase , lines=lowerCamelCase , index=lowerCamelCase , **lowerCamelCase )
if not json_str.endswith("""\n""" ):
json_str += "\n"
return json_str.encode(self.encoding )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating json from Arrow format""" , ):
_lowerCAmelCase = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(lowerCamelCase )
else:
_lowerCAmelCase , _lowerCAmelCase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , lowerCamelCase , lowerCamelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating json from Arrow format""" , ):
written += file_obj.write(lowerCamelCase )
return written
| 360 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" ) | 317 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
lowerCamelCase_ : str = {
'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
'mask_downscaling.0': 'mask_embed.conv1',
'mask_downscaling.1': 'mask_embed.layer_norm1',
'mask_downscaling.3': 'mask_embed.conv2',
'mask_downscaling.4': 'mask_embed.layer_norm2',
'mask_downscaling.6': 'mask_embed.conv3',
'point_embeddings': 'point_embed',
'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
'image_encoder': 'vision_encoder',
'neck.0': 'neck.conv1',
'neck.1': 'neck.layer_norm1',
'neck.2': 'neck.conv2',
'neck.3': 'neck.layer_norm2',
'patch_embed.proj': 'patch_embed.projection',
'.norm': '.layer_norm',
'blocks': 'layers',
}
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = {}
state_dict.pop('pixel_mean' , _UpperCAmelCase )
state_dict.pop('pixel_std' , _UpperCAmelCase )
A_ : List[str] = R'.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
A_ : Union[str, Any] = key.replace(_UpperCAmelCase , _UpperCAmelCase )
if re.match(_UpperCAmelCase , _UpperCAmelCase ):
A_ : Dict = int(re.match(_UpperCAmelCase , _UpperCAmelCase ).group(2 ) )
if layer_nb == 0:
A_ : Union[str, Any] = key.replace('layers.0' , 'proj_in' )
elif layer_nb == 1:
A_ : int = key.replace('layers.1' , 'layers.0' )
elif layer_nb == 2:
A_ : List[Any] = key.replace('layers.2' , 'proj_out' )
A_ : Optional[Any] = value
A_ : List[Any] = model_state_dict[
'prompt_encoder.shared_embedding.positional_embedding'
]
return model_state_dict
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase="ybelkada/segment-anything" ):
"""simple docstring"""
A_ : Dict = hf_hub_download(_UpperCAmelCase , f"""checkpoints/{model_name}.pth""" )
if "sam_vit_b" in model_name:
A_ : str = SamConfig()
elif "sam_vit_l" in model_name:
A_ : Tuple = SamVisionConfig(
hidden_size=1024 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
A_ : str = SamConfig(
vision_config=_UpperCAmelCase , )
elif "sam_vit_h" in model_name:
A_ : Tuple = SamVisionConfig(
hidden_size=1280 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
A_ : Any = SamConfig(
vision_config=_UpperCAmelCase , )
A_ : List[Any] = torch.load(_UpperCAmelCase , map_location='cpu' )
A_ : Dict = replace_keys(_UpperCAmelCase )
A_ : Tuple = SamImageProcessor()
A_ : Any = SamProcessor(image_processor=_UpperCAmelCase )
A_ : List[str] = SamModel(_UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
A_ : Dict = hf_model.to('cuda' )
A_ : str = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
A_ : Tuple = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
A_ : Union[str, Any] = [[[400, 650]]]
A_ : str = [[1]]
A_ : Optional[int] = processor(images=np.array(_UpperCAmelCase ) , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
A_ : int = hf_model(**_UpperCAmelCase )
A_ : int = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
A_ : List[str] = processor(
images=np.array(_UpperCAmelCase ) , input_points=_UpperCAmelCase , input_labels=_UpperCAmelCase , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
A_ : int = hf_model(**_UpperCAmelCase )
A_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
A_ : int = ((75, 275, 1725, 850),)
A_ : Optional[Any] = processor(images=np.array(_UpperCAmelCase ) , input_boxes=_UpperCAmelCase , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
A_ : Any = hf_model(**_UpperCAmelCase )
A_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
A_ : Union[str, Any] = [[[400, 650], [800, 650]]]
A_ : List[str] = [[1, 1]]
A_ : int = processor(
images=np.array(_UpperCAmelCase ) , input_points=_UpperCAmelCase , input_labels=_UpperCAmelCase , return_tensors='pt' ).to('cuda' )
with torch.no_grad():
A_ : Dict = hf_model(**_UpperCAmelCase )
A_ : Any = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
lowerCamelCase_ : Tuple = argparse.ArgumentParser()
lowerCamelCase_ : Tuple = ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
parser.add_argument(
'--model_name',
default='sam_vit_h_4b8939',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
parser.add_argument(
'--model_hub_id',
default='ybelkada/segment-anything',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
lowerCamelCase_ : Dict = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id) | 286 |
"""simple docstring"""
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()
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase=False ):
"""simple docstring"""
A_ : Optional[Any] = []
# 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"
A_ : List[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'),
] )
# fmt: on
return rename_keys
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
A_ : List[str] = ''
else:
A_ : Dict = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
A_ : str = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
A_ : List[Any] = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
A_ : List[Any] = in_proj_weight[
: config.hidden_size, :
]
A_ : Tuple = in_proj_bias[: config.hidden_size]
A_ : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
A_ : Dict = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
A_ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
A_ : Tuple = in_proj_bias[-config.hidden_size :]
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = dct.pop(_UpperCAmelCase )
A_ : Optional[int] = val
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Optional[int] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
A_ : int = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ):
"""simple docstring"""
A_ : List[Any] = BitConfig(
global_padding='same' , layer_type='bottleneck' , depths=(3, 4, 9) , out_features=['stage3'] , embedding_dynamic_padding=_UpperCAmelCase , )
A_ : Optional[int] = ViTHybridConfig(backbone_config=_UpperCAmelCase , image_size=384 , num_labels=1000 )
A_ : Union[str, Any] = False
# load original model from timm
A_ : List[Any] = timm.create_model(_UpperCAmelCase , pretrained=_UpperCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
A_ : Tuple = timm_model.state_dict()
if base_model:
remove_classification_head_(_UpperCAmelCase )
A_ : Any = create_rename_keys(_UpperCAmelCase , _UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_q_k_v(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
A_ : Union[str, Any] = 'huggingface/label-files'
A_ : Dict = 'imagenet-1k-id2label.json'
A_ : List[str] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) , 'r' ) )
A_ : str = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
A_ : Any = idalabel
A_ : Optional[int] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
A_ : List[Any] = ViTHybridModel(_UpperCAmelCase ).eval()
else:
A_ : str = ViTHybridForImageClassification(_UpperCAmelCase ).eval()
model.load_state_dict(_UpperCAmelCase )
# create image processor
A_ : Dict = create_transform(**resolve_data_config({} , model=_UpperCAmelCase ) )
A_ : List[str] = transform.transforms
A_ : List[str] = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
A_ : Tuple = 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() , )
A_ : Optional[Any] = prepare_img()
A_ : Any = transform(_UpperCAmelCase ).unsqueeze(0 )
A_ : Dict = processor(_UpperCAmelCase , return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase )
# verify logits
with torch.no_grad():
A_ : List[Any] = model(_UpperCAmelCase )
A_ : List[str] = outputs.logits
print('Predicted class:' , logits.argmax(-1 ).item() )
if base_model:
A_ : Union[str, Any] = 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:
A_ : Tuple = 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__":
lowerCamelCase_ : Optional[Any] = 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.'
)
lowerCamelCase_ : List[str] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub) | 286 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ ( __lowercase ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = "mvp"
_UpperCAmelCase : Optional[int] = ["past_key_values"]
_UpperCAmelCase : str = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase : List[Any]=50_267 , lowercase : Dict=1_024 , lowercase : Dict=12 , lowercase : Dict=4_096 , lowercase : List[str]=16 , lowercase : Union[str, Any]=12 , lowercase : Any=4_096 , lowercase : Any=16 , lowercase : Optional[int]=0.0 , lowercase : List[str]=0.0 , lowercase : Optional[Any]="gelu" , lowercase : Optional[Any]=1_024 , lowercase : Dict=0.1 , lowercase : int=0.0 , lowercase : Union[str, Any]=0.0 , lowercase : Optional[Any]=0.02 , lowercase : int=0.0 , lowercase : List[Any]=False , lowercase : str=True , lowercase : Any=1 , lowercase : Tuple=0 , lowercase : int=2 , lowercase : int=True , lowercase : int=2 , lowercase : Dict=2 , lowercase : str=False , lowercase : int=100 , lowercase : Tuple=800 , **lowercase : Any , ):
'''simple docstring'''
_snake_case = vocab_size
_snake_case = max_position_embeddings
_snake_case = d_model
_snake_case = encoder_ffn_dim
_snake_case = encoder_layers
_snake_case = encoder_attention_heads
_snake_case = decoder_ffn_dim
_snake_case = decoder_layers
_snake_case = decoder_attention_heads
_snake_case = dropout
_snake_case = attention_dropout
_snake_case = activation_dropout
_snake_case = activation_function
_snake_case = init_std
_snake_case = encoder_layerdrop
_snake_case = decoder_layerdrop
_snake_case = classifier_dropout
_snake_case = use_cache
_snake_case = encoder_layers
_snake_case = scale_embedding # scale factor will be sqrt(d_model) if True
_snake_case = use_prompt
_snake_case = prompt_length
_snake_case = prompt_mid_dim
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , decoder_start_token_id=_a , forced_eos_token_id=_a , **_a , )
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' , _a ):
_snake_case = self.bos_token_id
warnings.warn(
f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
'The config can simply be saved and uploaded again to be fixed.' ) | 365 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image
from transformers import DeformableDetrImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , lowercase : Union[str, Any] , lowercase : str=7 , lowercase : Union[str, Any]=3 , lowercase : Tuple=30 , lowercase : Optional[Any]=400 , lowercase : List[Any]=True , lowercase : Any=None , lowercase : str=True , lowercase : Tuple=[0.5, 0.5, 0.5] , lowercase : List[Any]=[0.5, 0.5, 0.5] , lowercase : Union[str, Any]=True , lowercase : List[Any]=1 / 255 , lowercase : int=True , ):
'''simple docstring'''
_snake_case = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
_snake_case = parent
_snake_case = batch_size
_snake_case = num_channels
_snake_case = min_resolution
_snake_case = max_resolution
_snake_case = do_resize
_snake_case = size
_snake_case = do_normalize
_snake_case = image_mean
_snake_case = image_std
_snake_case = do_rescale
_snake_case = rescale_factor
_snake_case = do_pad
def A ( self : str ):
'''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,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def A ( self : Optional[int] , lowercase : List[Any] , lowercase : Tuple=False ):
'''simple docstring'''
if not batched:
_snake_case = image_inputs[0]
if isinstance(lowercase , Image.Image ):
_snake_case , _snake_case = image.size
else:
_snake_case , _snake_case = image.shape[1], image.shape[2]
if w < h:
_snake_case = int(self.size['shortest_edge'] * h / w )
_snake_case = self.size['shortest_edge']
elif w > h:
_snake_case = self.size['shortest_edge']
_snake_case = int(self.size['shortest_edge'] * w / h )
else:
_snake_case = self.size['shortest_edge']
_snake_case = self.size['shortest_edge']
else:
_snake_case = []
for image in image_inputs:
_snake_case , _snake_case = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_snake_case = max(lowercase , key=lambda lowercase : item[0] )[0]
_snake_case = max(lowercase , key=lambda lowercase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ,unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase : Dict = DeformableDetrImageProcessor if is_vision_available() else None
def A ( self : List[Any] ):
'''simple docstring'''
_snake_case = DeformableDetrImageProcessingTester(self )
@property
def A ( self : int ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Dict ):
'''simple docstring'''
_snake_case = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , 'image_mean' ) )
self.assertTrue(hasattr(lowercase , 'image_std' ) )
self.assertTrue(hasattr(lowercase , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase , 'do_resize' ) )
self.assertTrue(hasattr(lowercase , 'do_rescale' ) )
self.assertTrue(hasattr(lowercase , 'do_pad' ) )
self.assertTrue(hasattr(lowercase , 'size' ) )
def A ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1_333} )
self.assertEqual(image_processor.do_pad , lowercase )
_snake_case = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowercase )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , lowercase )
def A ( self : Dict ):
'''simple docstring'''
pass
def A ( self : List[str] ):
'''simple docstring'''
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
_snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
_snake_case = image_processing(lowercase , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : List[str] ):
'''simple docstring'''
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , numpify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , np.ndarray )
# Test not batched input
_snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case = image_processing(lowercase , return_tensors='pt' ).pixel_values
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def A ( self : Optional[Any] ):
'''simple docstring'''
_snake_case = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_snake_case = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , torchify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , torch.Tensor )
# Test not batched input
_snake_case = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_snake_case = image_processing(lowercase , return_tensors='pt' ).pixel_values
_snake_case , _snake_case = self.image_processor_tester.get_expected_values(lowercase , batched=lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def A ( self : List[str] ):
'''simple docstring'''
_snake_case = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
_snake_case = json.loads(f.read() )
_snake_case = {'image_id': 39_769, 'annotations': target}
# encode them
_snake_case = DeformableDetrImageProcessor()
_snake_case = image_processing(images=lowercase , annotations=lowercase , return_tensors='pt' )
# verify pixel values
_snake_case = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , lowercase )
_snake_case = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowercase , atol=1E-4 ) )
# verify area
_snake_case = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowercase ) )
# verify boxes
_snake_case = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowercase )
_snake_case = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowercase , atol=1E-3 ) )
# verify image_id
_snake_case = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowercase ) )
# verify is_crowd
_snake_case = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowercase ) )
# verify class_labels
_snake_case = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowercase ) )
# verify orig_size
_snake_case = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowercase ) )
# verify size
_snake_case = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowercase ) )
@slow
def A ( self : Optional[Any] ):
'''simple docstring'''
_snake_case = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
_snake_case = json.loads(f.read() )
_snake_case = {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}
_snake_case = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
_snake_case = DeformableDetrImageProcessor(format='coco_panoptic' )
_snake_case = image_processing(images=lowercase , annotations=lowercase , masks_path=lowercase , return_tensors='pt' )
# verify pixel values
_snake_case = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , lowercase )
_snake_case = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowercase , atol=1E-4 ) )
# verify area
_snake_case = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowercase ) )
# verify boxes
_snake_case = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowercase )
_snake_case = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowercase , atol=1E-3 ) )
# verify image_id
_snake_case = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowercase ) )
# verify is_crowd
_snake_case = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowercase ) )
# verify class_labels
_snake_case = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowercase ) )
# verify masks
_snake_case = 822_873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , lowercase )
# verify orig_size
_snake_case = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowercase ) )
# verify size
_snake_case = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowercase ) ) | 130 | 0 |
import os
UpperCamelCase__ = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
def _a ( SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase = 0
__lowerCAmelCase = 0
while index < len(SCREAMING_SNAKE_CASE_ ) - 1:
__lowerCAmelCase = SYMBOLS[numerals[index]]
__lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _a ( SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = ""
__lowerCAmelCase = num // 10_00
numerals += m_count * "M"
num %= 10_00
__lowerCAmelCase = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
__lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _a ( SCREAMING_SNAKE_CASE_ : str = "/p089_roman.txt" ):
__lowerCAmelCase = 0
with open(os.path.dirname(SCREAMING_SNAKE_CASE_ ) + roman_numerals_filename ) as filea:
__lowerCAmelCase = filea.readlines()
for line in lines:
__lowerCAmelCase = line.strip()
__lowerCAmelCase = parse_roman_numerals(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = generate_roman_numerals(SCREAMING_SNAKE_CASE_ )
savings += len(SCREAMING_SNAKE_CASE_ ) - len(SCREAMING_SNAKE_CASE_ )
return savings
if __name__ == "__main__":
print(f'''{solution() = }''')
| 92 |
class a__ ( snake_case__ ):
pass
class a__ ( snake_case__ ):
pass
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = [
[],
[],
[],
]
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 1_0_0:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(_A )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self ):
"""simple docstring"""
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
if len(self.queue ) == 1_0_0:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
__lowerCAmelCase = min(self.queue )
self.queue.remove(_A )
return data
def __str__( self ):
"""simple docstring"""
return str(self.queue )
def _a ( ):
__lowerCAmelCase = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
__lowerCAmelCase = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(SCREAMING_SNAKE_CASE_ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 92 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int]=12 , UpperCAmelCase__ : Any=7 , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : int=99 , UpperCAmelCase__ : List[str]=32 , UpperCAmelCase__ : List[str]=32 , UpperCAmelCase__ : Tuple=2 , UpperCAmelCase__ : Any=4 , UpperCAmelCase__ : Dict=37 , UpperCAmelCase__ : Optional[int]=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Dict=512 , UpperCAmelCase__ : Dict=0.02 , UpperCAmelCase__ : Union[str, Any]=0 , UpperCAmelCase__ : Any=None , ) ->Union[str, Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = projection_dim
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = dropout
A__ = attention_dropout
A__ = max_position_embeddings
A__ = initializer_range
A__ = scope
A__ = bos_token_id
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Dict:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length])
if input_mask is not None:
A__ = input_mask.numpy()
A__ , A__ = input_mask.shape
A__ = np.random.randint(1 , seq_length - 1 , size=(batch_size,))
for batch_idx, start_index in enumerate(UpperCAmelCase__):
A__ = 1
A__ = 0
A__ = self.get_config()
return config, input_ids, tf.convert_to_tensor(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Any) ->Tuple:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[Any]) ->Dict:
'''simple docstring'''
A__ = TFBlipTextModel(config=UpperCAmelCase__)
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , training=UpperCAmelCase__)
A__ = model(UpperCAmelCase__ , training=UpperCAmelCase__)
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 SCREAMING_SNAKE_CASE ( self : int) ->str:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class UpperCamelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCAmelCase__ = False
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Optional[Any]:
'''simple docstring'''
A__ = BlipTextModelTester(self)
A__ = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37)
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Tuple) ->int:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->int:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''')
def SCREAMING_SNAKE_CASE ( self : int) ->Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''')
def SCREAMING_SNAKE_CASE ( self : Dict) ->Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''')
def SCREAMING_SNAKE_CASE ( self : Any) ->Union[str, Any]:
'''simple docstring'''
pass
@slow
def SCREAMING_SNAKE_CASE ( self : str) ->Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TFBlipTextModel.from_pretrained(UpperCAmelCase__)
self.assertIsNotNone(UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : str=True) ->str:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCAmelCase__)
| 231 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
"""simple docstring"""
if "cls_token" in name:
A__ = name.replace('''cls_token''' , '''vit.embeddings.cls_token''' )
if "mask_token" in name:
A__ = name.replace('''mask_token''' , '''decoder.mask_token''' )
if "decoder_pos_embed" in name:
A__ = name.replace('''decoder_pos_embed''' , '''decoder.decoder_pos_embed''' )
if "pos_embed" in name and "decoder" not in name:
A__ = name.replace('''pos_embed''' , '''vit.embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
A__ = name.replace('''patch_embed.proj''' , '''vit.embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
A__ = name.replace('''patch_embed.norm''' , '''vit.embeddings.norm''' )
if "decoder_blocks" in name:
A__ = name.replace('''decoder_blocks''' , '''decoder.decoder_layers''' )
if "blocks" in name:
A__ = name.replace('''blocks''' , '''vit.encoder.layer''' )
if "attn.proj" in name:
A__ = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
A__ = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
A__ = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
A__ = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
A__ = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
A__ = name.replace('''mlp.fc2''' , '''output.dense''' )
if "decoder_embed" in name:
A__ = name.replace('''decoder_embed''' , '''decoder.decoder_embed''' )
if "decoder_norm" in name:
A__ = name.replace('''decoder_norm''' , '''decoder.decoder_norm''' )
if "decoder_pred" in name:
A__ = name.replace('''decoder_pred''' , '''decoder.decoder_pred''' )
if "norm.weight" in name and "decoder" not in name:
A__ = name.replace('''norm.weight''' , '''vit.layernorm.weight''' )
if "norm.bias" in name and "decoder" not in name:
A__ = name.replace('''norm.bias''' , '''vit.layernorm.bias''' )
return name
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
A__ = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
A__ = key.split('''.''' )
A__ = int(key_split[1] )
if "decoder_blocks" in key:
A__ = config.decoder_hidden_size
A__ = '''decoder.decoder_layers.'''
if "weight" in key:
A__ = val[:dim, :]
A__ = val[dim : dim * 2, :]
A__ = val[-dim:, :]
elif "bias" in key:
A__ = val[:dim]
A__ = val[dim : dim * 2]
A__ = val[-dim:]
else:
A__ = config.hidden_size
A__ = '''vit.encoder.layer.'''
if "weight" in key:
A__ = val[:dim, :]
A__ = val[dim : dim * 2, :]
A__ = val[-dim:, :]
elif "bias" in key:
A__ = val[:dim]
A__ = val[dim : dim * 2]
A__ = val[-dim:]
else:
A__ = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Tuple:
"""simple docstring"""
A__ = ViTMAEConfig()
if "large" in checkpoint_url:
A__ = 1_024
A__ = 4_096
A__ = 24
A__ = 16
elif "huge" in checkpoint_url:
A__ = 14
A__ = 1_280
A__ = 5_120
A__ = 32
A__ = 16
A__ = ViTMAEForPreTraining(lowercase_ )
A__ = torch.hub.load_state_dict_from_url(lowercase_ , map_location='''cpu''' )['''model''']
A__ = ViTMAEImageProcessor(size=config.image_size )
A__ = convert_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ )
model.eval()
A__ = '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''
A__ = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
A__ = ViTMAEImageProcessor(size=config.image_size )
A__ = image_processor(images=lowercase_ , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
A__ = model(**lowercase_ )
A__ = outputs.logits
if "large" in checkpoint_url:
A__ = torch.tensor(
[[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]] )
elif "huge" in checkpoint_url:
A__ = torch.tensor(
[[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]] )
else:
A__ = torch.tensor(
[[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , lowercase_ , atol=1E-4 )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase_ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase_ )
if __name__ == "__main__":
_lowerCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
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."""
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 231 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = None
A : Union[str, Any] = namedtuple('CoinsDistribResult', 'moves excess')
def UpperCamelCase ( __magic_name__ : TreeNode | None ) -> int:
"""simple docstring"""
if root is None:
return 0
# Validation
def count_nodes(__magic_name__ : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__magic_name__ : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__magic_name__ ) != count_coins(__magic_name__ ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(__magic_name__ : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase__ , lowercase__ = get_distrib(node.left )
lowercase__ , lowercase__ = get_distrib(node.right )
lowercase__ = 1 - left_distrib_excess
lowercase__ = 1 - right_distrib_excess
lowercase__ = (
left_distrib_moves
+ right_distrib_moves
+ abs(__magic_name__ )
+ abs(__magic_name__ )
)
lowercase__ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__magic_name__ , __magic_name__ )
return get_distrib(__magic_name__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
A__ = ShapEImgaImgPipeline
A__ = ['''image''']
A__ = ['''image''']
A__ = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
A__ = False
@property
def lowerCamelCase__ (self : Optional[Any] ) -> List[str]:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
return 32
@property
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase__ (self : List[Any] ) -> Any:
"""simple docstring"""
return 8
@property
def lowerCamelCase__ (self : int ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
lowercase__ = CLIPVisionModel(_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Any ) -> List[Any]:
"""simple docstring"""
lowercase__ = CLIPImageProcessor(
crop_size=224 , do_center_crop=_UpperCAmelCase , do_normalize=_UpperCAmelCase , do_resize=_UpperCAmelCase , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def lowerCamelCase__ (self : int ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""embedding_proj_norm_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
lowercase__ = PriorTransformer(**_UpperCAmelCase )
return model
@property
def lowerCamelCase__ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
lowercase__ = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
lowercase__ = ShapERenderer(**_UpperCAmelCase )
return model
def lowerCamelCase__ (self : int ) -> Optional[int]:
"""simple docstring"""
lowercase__ = self.dummy_prior
lowercase__ = self.dummy_image_encoder
lowercase__ = self.dummy_image_processor
lowercase__ = self.dummy_renderer
lowercase__ = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=1024 , prediction_type="""sample""" , use_karras_sigmas=_UpperCAmelCase , clip_sample=_UpperCAmelCase , clip_sample_range=1.0 , )
lowercase__ = {
"""prior""": prior,
"""image_encoder""": image_encoder,
"""image_processor""": image_processor,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str=0 ) -> str:
"""simple docstring"""
lowercase__ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
if str(_UpperCAmelCase ).startswith("""mps""" ):
lowercase__ = torch.manual_seed(_UpperCAmelCase )
else:
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
lowercase__ = {
"""image""": input_image,
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ (self : str ) -> List[str]:
"""simple docstring"""
lowercase__ = """cpu"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = pipe(**self.get_dummy_inputs(_UpperCAmelCase ) )
lowercase__ = output.images[0]
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
lowercase__ = np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ (self : str ) -> Any:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ (self : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = torch_device == """cpu"""
lowercase__ = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_UpperCAmelCase , relax_max_difference=_UpperCAmelCase , )
def lowerCamelCase__ (self : Union[str, Any] ) -> int:
"""simple docstring"""
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_UpperCAmelCase )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = 1
lowercase__ = 2
lowercase__ = self.get_dummy_inputs(_UpperCAmelCase )
for key in inputs.keys():
if key in self.batch_params:
lowercase__ = batch_size * [inputs[key]]
lowercase__ = pipe(**_UpperCAmelCase , num_images_per_prompt=_UpperCAmelCase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Dict ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ (self : Any ) -> str:
"""simple docstring"""
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" )
lowercase__ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_img2img_out.npy""" )
lowercase__ = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" )
lowercase__ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
lowercase__ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
lowercase__ = pipe(
_UpperCAmelCase , generator=_UpperCAmelCase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_UpperCAmelCase , _UpperCAmelCase )
| 305 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=7 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=18 , __SCREAMING_SNAKE_CASE=30 , __SCREAMING_SNAKE_CASE=400 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=True , ):
"""simple docstring"""
UpperCamelCase : List[str] = size if size is not None else {'''height''': 18, '''width''': 18}
UpperCamelCase : int = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : Optional[int] = num_channels
UpperCamelCase : Union[str, Any] = image_size
UpperCamelCase : Union[str, Any] = min_resolution
UpperCamelCase : Tuple = max_resolution
UpperCamelCase : List[str] = do_resize
UpperCamelCase : List[str] = size
UpperCamelCase : int = apply_ocr
def _lowercase ( self ):
"""simple docstring"""
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class UpperCAmelCase_ ( _a, unittest.TestCase):
'''simple docstring'''
__UpperCamelCase : List[str] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : List[Any] = LayoutLMvaImageProcessingTester(self )
@property
def _lowercase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''do_resize''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''size''' ) )
self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE , '''apply_ocr''' ) )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 18} )
UpperCamelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , Image.Image )
# Test not batched input
UpperCamelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
self.assertIsInstance(encoding.words , __SCREAMING_SNAKE_CASE )
self.assertIsInstance(encoding.boxes , __SCREAMING_SNAKE_CASE )
# Test batched
UpperCamelCase : int = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , numpify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , np.ndarray )
# Test not batched input
UpperCamelCase : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCamelCase : List[str] = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__SCREAMING_SNAKE_CASE , torchify=__SCREAMING_SNAKE_CASE )
for image in image_inputs:
self.assertIsInstance(__SCREAMING_SNAKE_CASE , torch.Tensor )
# Test not batched input
UpperCamelCase : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCamelCase : Optional[int] = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
def _lowercase ( self ):
"""simple docstring"""
UpperCamelCase : List[str] = LayoutLMvaImageProcessor()
from datasets import load_dataset
UpperCamelCase : Dict = load_dataset('''hf-internal-testing/fixtures_docvqa''' , split='''test''' )
UpperCamelCase : List[Any] = Image.open(ds[0]['''file'''] ).convert('''RGB''' )
UpperCamelCase : int = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
UpperCamelCase : Union[str, Any] = [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231
UpperCamelCase : str = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , __SCREAMING_SNAKE_CASE )
self.assertListEqual(encoding.boxes , __SCREAMING_SNAKE_CASE )
# with apply_OCR = False
UpperCamelCase : Optional[Any] = LayoutLMvaImageProcessor(apply_ocr=__SCREAMING_SNAKE_CASE )
UpperCamelCase : int = image_processing(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 315 |
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(chr(ord(SCREAMING_SNAKE_CASE_ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 315 | 1 |
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_ ( *lowercase , **lowercase ):
"""simple docstring"""
pass
def UpperCamelCase ( __lowercase : List[Any] ):
'''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.
_UpperCAmelCase = (
"""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 , lowercase , lowercase , lowercase ):
"""simple docstring"""
A_ : List[str] = pipeline(
'document-question-answering' , model=lowercase , tokenizer=lowercase , image_processor=lowercase )
A_ : Optional[Any] = INVOICE_URL
A_ : int = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
A_ : Tuple = 'What is the placebo?'
A_ : Tuple = [
{
'image': load_image(lowercase ),
'question': question,
},
{
'image': image,
'question': question,
},
{
'image': image,
'question': question,
'word_boxes': word_boxes,
},
]
return dqa_pipeline, examples
def lowerCAmelCase_ ( self , lowercase , lowercase ):
"""simple docstring"""
A_ : Optional[int] = dqa_pipeline(lowercase , top_k=2 )
self.assertEqual(
lowercase , [
[
{'score': ANY(lowercase ), 'answer': ANY(lowercase ), 'start': ANY(lowercase ), 'end': ANY(lowercase )},
{'score': ANY(lowercase ), 'answer': ANY(lowercase ), 'start': ANY(lowercase ), 'end': ANY(lowercase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[str] = pipeline('document-question-answering' , model='hf-internal-testing/tiny-random-layoutlmv2' )
A_ : int = INVOICE_URL
A_ : int = 'How many cats are there?'
A_ : List[Any] = [
{'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},
]
A_ : Tuple = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , lowercase )
A_ : List[Any] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , lowercase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
A_ : List[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
A_ : Optional[int] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(lowercase , [] )
# We can optionnally pass directly the words and bounding boxes
A_ : Optional[Any] = './tests/fixtures/tests_samples/COCO/000000039769.png'
A_ : str = []
A_ : Optional[int] = []
A_ : Optional[int] = dqa_pipeline(image=lowercase , question=lowercase , words=lowercase , boxes=lowercase , top_k=2 )
self.assertEqual(lowercase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Tuple = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , )
A_ : Optional[Any] = INVOICE_URL
A_ : Union[str, Any] = 'What is the invoice number?'
A_ : Union[str, Any] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : Any = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : int = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 ):
"""simple docstring"""
A_ : Optional[int] = pipeline(
'document-question-answering' , model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' , revision='9977165' , max_seq_len=5_0 , )
A_ : Tuple = INVOICE_URL
A_ : int = 'What is the invoice number?'
A_ : Union[str, Any] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : Any = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : Any = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 ):
"""simple docstring"""
A_ : Dict = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowercase )
A_ : List[str] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowercase , revision='3dc6de3' , )
A_ : Any = INVOICE_URL
A_ : Union[str, Any] = 'What is the invoice number?'
A_ : Union[str, Any] = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : List[str] = dqa_pipeline({'image': image, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : List[Any] = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 , )
A_ : int = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
# This model should also work if `image` is set to None
A_ : Union[str, Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 ):
"""simple docstring"""
A_ : Optional[Any] = AutoTokenizer.from_pretrained(
'impira/layoutlm-document-qa' , revision='3dc6de3' , add_prefix_space=lowercase )
A_ : List[str] = pipeline(
'document-question-answering' , model='impira/layoutlm-document-qa' , tokenizer=lowercase , revision='3dc6de3' , max_seq_len=5_0 , )
A_ : Tuple = INVOICE_URL
A_ : Tuple = 'What is the invoice number?'
A_ : str = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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},
] , )
A_ : str = dqa_pipeline(
[{'image': image, 'question': question}, {'image': image, 'question': question}] , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 , )
A_ : List[Any] = list(zip(*apply_tesseract(load_image(lowercase ) , lowercase , '' ) ) )
# This model should also work if `image` is set to None
A_ : Union[str, Any] = dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question} , top_k=2 )
self.assertEqual(
nested_simplify(lowercase , 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 ):
"""simple docstring"""
A_ : Optional[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' , )
A_ : str = INVOICE_URL
A_ : Union[str, Any] = 'What is the invoice number?'
A_ : Tuple = dqa_pipeline(image=lowercase , question=lowercase , top_k=2 )
self.assertEqual(nested_simplify(lowercase , decimals=4 ) , [{'answer': 'us-001'}] )
@require_tf
@unittest.skip('Document question answering not implemented in TF' )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
pass
| 140 | from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase ( __lowercase : dict ,__lowercase : str ,__lowercase : set ,__lowercase : set ,__lowercase : dict ,__lowercase : dict ,__lowercase : PriorityQueue ,__lowercase : dict ,__lowercase : float | int ,):
'''simple docstring'''
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
A_ : List[str] = cst_fwd.get(__lowercase ,np.inf )
A_ : Any = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
A_ : Any = new_cost_f
A_ : Optional[int] = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
A_ : str = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def UpperCamelCase ( __lowercase : str ,__lowercase : str ,__lowercase : dict ,__lowercase : dict ):
'''simple docstring'''
A_ : List[str] = -1
A_ : List[Any] = set()
A_ : Union[str, Any] = set()
A_ : int = {source: 0}
A_ : List[Any] = {destination: 0}
A_ : Dict = {source: None}
A_ : Optional[int] = {destination: None}
A_ : PriorityQueue[Any] = PriorityQueue()
A_ : PriorityQueue[Any] = PriorityQueue()
A_ : Tuple = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
A_ , A_ : List[str] = queue_forward.get()
visited_forward.add(__lowercase )
A_ , A_ : Union[str, Any] = queue_backward.get()
visited_backward.add(__lowercase )
A_ : int = pass_and_relaxation(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,)
A_ : str = pass_and_relaxation(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase ,)
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
A_ : int = shortest_distance
return shortest_path_distance
_UpperCAmelCase = {
"""B""": [["""C""", 1]],
"""C""": [["""D""", 1]],
"""D""": [["""F""", 1]],
"""E""": [["""B""", 1], ["""G""", 2]],
"""F""": [],
"""G""": [["""F""", 1]],
}
_UpperCAmelCase = {
"""B""": [["""E""", 1]],
"""C""": [["""B""", 1]],
"""D""": [["""C""", 1]],
"""F""": [["""D""", 1], ["""G""", 1]],
"""E""": [[None, np.inf]],
"""G""": [["""E""", 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 140 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
UpperCAmelCase__ = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
UpperCAmelCase__ = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
UpperCAmelCase__ = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
UpperCAmelCase__ = train_datagen.flow_from_directory(
"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
UpperCAmelCase__ = test_datagen.flow_from_directory(
"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
UpperCAmelCase__ = tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(64, 64)
)
UpperCAmelCase__ = tf.keras.preprocessing.image.img_to_array(test_image)
UpperCAmelCase__ = np.expand_dims(test_image, axis=0)
UpperCAmelCase__ = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
UpperCAmelCase__ = "Normal"
if result[0][0] == 1:
UpperCAmelCase__ = "Abnormality detected"
| 290 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = CLIPTokenizer
UpperCamelCase = CLIPTokenizerFast
UpperCamelCase = True
UpperCamelCase = {}
UpperCamelCase = False
def _lowerCamelCase ( self : List[str]) -> List[str]:
"""simple docstring"""
super().setUp()
# fmt: off
_UpperCAmelCase = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
_UpperCAmelCase = dict(zip(A , range(len(A))))
_UpperCAmelCase = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
_UpperCAmelCase = {'unk_token': '<unk>'}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
_UpperCAmelCase = 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(A) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(A))
def _lowerCamelCase ( self : Optional[Any] , **A : str) -> Optional[int]:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : Any , **A : Dict) -> Union[str, Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map)
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A)
def _lowerCamelCase ( self : Optional[int] , A : Union[str, Any]) -> int:
"""simple docstring"""
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = 'lower newer'
return input_text, output_text
def _lowerCamelCase ( self : Dict) -> Any:
"""simple docstring"""
_UpperCAmelCase = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map)
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
_UpperCAmelCase = tokenizer.tokenize(A)
self.assertListEqual(A , A)
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A) , A)
@require_ftfy
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
_UpperCAmelCase = self.tokenizer_class.from_pretrained(A , **A)
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(A , **A)
_UpperCAmelCase = 'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
_UpperCAmelCase = tokenizer_s.tokenize(A)
_UpperCAmelCase = tokenizer_r.tokenize(A)
self.assertListEqual(A , A)
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
_UpperCAmelCase = 'xa\u0303y' + ' ' + 'x\xe3y'
_UpperCAmelCase = tokenizer_s.tokenize(A)
_UpperCAmelCase = tokenizer_r.tokenize(A)
self.assertListEqual(A , A)
# Test that the tokenization is identical on unicode of space type
_UpperCAmelCase = [
'\u0009', # (horizontal tab, '\t')
'\u000B', # (vertical tab)
'\u000C', # (form feed)
'\u0020', # (space, ' ')
'\u200E', # (left-to-right mark):w
'\u200F', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
_UpperCAmelCase = tokenizer_s.tokenize(A)
_UpperCAmelCase = tokenizer_r.tokenize(A)
self.assertListEqual(A , A)
# Test that the tokenization is identical on unicode of line break type
_UpperCAmelCase = [
'\u000A', # (line feed, '\n')
'\r\n', # (carriage return and line feed, '\r\n')
'\u000D', # (carriage return, '\r')
'\r', # (carriage return, '\r')
'\u000D', # (carriage return, '\r')
'\u2028', # (line separator)
'\u2029', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
_UpperCAmelCase = tokenizer_s.tokenize(A)
_UpperCAmelCase = tokenizer_r.tokenize(A)
self.assertListEqual(A , A)
def _lowerCamelCase ( self : str) -> Optional[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
_UpperCAmelCase = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
_UpperCAmelCase = F"{text_of_1_token} {text_of_1_token}"
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , )
_UpperCAmelCase = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A)
self.assertEqual(encoding.offset_mapping[0] , (0, len(A)))
self.assertEqual(
encoding.offset_mapping[1] , (len(A) + 1, len(A) + 1 + len(A)) , )
_UpperCAmelCase = F" {text}"
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(
A , use_fast=A , )
_UpperCAmelCase = tokenizer_r(A , return_offsets_mapping=A , add_special_tokens=A)
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(A)))
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(A) + 1, 1 + len(A) + 1 + len(A)) , )
def _lowerCamelCase ( self : Tuple) -> str:
"""simple docstring"""
with self.assertRaises(A) as context:
self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer')
self.assertTrue(
context.exception.args[0].startswith(
'The `backend_tokenizer` provided does not match the expected format.'))
@require_ftfy
def _lowerCamelCase ( self : int) -> int:
"""simple docstring"""
super().test_tokenization_python_rust_equals()
def _lowerCamelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
pass
| 290 | 1 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def UpperCamelCase_( ):
'''simple docstring'''
snake_case_ = input("Enter message: " )
snake_case_ = input("Enter key [alphanumeric]: " )
snake_case_ = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
snake_case_ = """encrypt"""
snake_case_ = encrypt_message(lowercase_ , lowercase_ )
elif mode.lower().startswith("d" ):
snake_case_ = """decrypt"""
snake_case_ = decrypt_message(lowercase_ , lowercase_ )
print(f'\n{mode.title()}ed message:' )
print(lowercase_ )
def UpperCamelCase_( snake_case : str , snake_case : str ):
'''simple docstring'''
return translate_message(lowercase_ , lowercase_ , "encrypt" )
def UpperCamelCase_( snake_case : str , snake_case : str ):
'''simple docstring'''
return translate_message(lowercase_ , lowercase_ , "decrypt" )
def UpperCamelCase_( snake_case : str , snake_case : str , snake_case : str ):
'''simple docstring'''
snake_case_ = []
snake_case_ = 0
snake_case_ = key.upper()
for symbol in message:
snake_case_ = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(lowercase_ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(lowercase_ ):
snake_case_ = 0
else:
translated.append(lowercase_ )
return "".join(lowercase_ )
if __name__ == "__main__":
main()
| 85 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : int = logging.get_logger(__name__)
A_ : Optional[Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = '''mgp-str'''
def __init__( self , A__=[32, 128] , A__=4 , A__=3 , A__=27 , A__=38 , A__=5_0257 , A__=3_0522 , A__=768 , A__=12 , A__=12 , A__=4.0 , A__=True , A__=False , A__=1e-5 , A__=0.0 , A__=0.0 , A__=0.0 , A__=False , A__=0.0_2 , **A__ , ):
super().__init__(**A__ )
A__ : Dict = image_size
A__ : int = patch_size
A__ : Dict = num_channels
A__ : List[Any] = max_token_length
A__ : str = num_character_labels
A__ : Tuple = num_bpe_labels
A__ : Optional[Any] = num_wordpiece_labels
A__ : Optional[int] = hidden_size
A__ : Tuple = num_hidden_layers
A__ : Any = num_attention_heads
A__ : List[Any] = mlp_ratio
A__ : Tuple = distilled
A__ : Union[str, Any] = layer_norm_eps
A__ : Tuple = drop_rate
A__ : List[str] = qkv_bias
A__ : Optional[Any] = attn_drop_rate
A__ : Union[str, Any] = drop_path_rate
A__ : Optional[Any] = output_aa_attentions
A__ : Optional[int] = initializer_range
| 192 | 0 |
'''simple docstring'''
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_ = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def SCREAMING_SNAKE_CASE_ ( __A : Dict ) -> Dict:
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__A )
def SCREAMING_SNAKE_CASE_ ( __A : List[Any] ) -> str:
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_SCREAMING_SNAKE_CASE = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(__A , id=__A )
| 353 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : Tuple , __A : List[str] , __A : List[str] ) -> List[Any]:
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys() )}.""" )
if tokenizer_name is None:
_SCREAMING_SNAKE_CASE = TOKENIZER_CLASSES
else:
_SCREAMING_SNAKE_CASE = {tokenizer_name: getattr(__A , tokenizer_name + "Fast" )}
logger.info(f"""Loading tokenizer classes: {tokenizer_names}""" )
for tokenizer_name in tokenizer_names:
_SCREAMING_SNAKE_CASE = TOKENIZER_CLASSES[tokenizer_name]
_SCREAMING_SNAKE_CASE = True
if checkpoint_name is None:
_SCREAMING_SNAKE_CASE = list(tokenizer_class.max_model_input_sizes.keys() )
else:
_SCREAMING_SNAKE_CASE = [checkpoint_name]
logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}""" )
for checkpoint in checkpoint_names:
logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}""" )
# Load tokenizer
_SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(__A , force_download=__A )
# Save fast tokenizer
logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}""" )
# For organization names we create sub-directories
if "/" in checkpoint:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = checkpoint.split("/" )
_SCREAMING_SNAKE_CASE = os.path.join(__A , __A )
elif add_prefix:
_SCREAMING_SNAKE_CASE = checkpoint
_SCREAMING_SNAKE_CASE = dump_path
else:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = dump_path
logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values() )[0]:
_SCREAMING_SNAKE_CASE = list(tokenizer.pretrained_vocab_files_map.values() )[0][checkpoint]
_SCREAMING_SNAKE_CASE = file_path.split(__A )[-1][0]
if next_char == "/":
_SCREAMING_SNAKE_CASE = os.path.join(__A , __A )
_SCREAMING_SNAKE_CASE = None
logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}""" )
_SCREAMING_SNAKE_CASE = tokenizer.save_pretrained(
__A , legacy_format=__A , filename_prefix=__A )
logger.info(f"""=> File names {file_names}""" )
for file_name in file_names:
if not file_name.endswith("tokenizer.json" ):
os.remove(__A )
logger.info(f"""=> removing {file_name}""" )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
)
parser.add_argument(
'--tokenizer_name',
default=None,
type=str,
help=(
f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
'download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--checkpoint_name',
default=None,
type=str,
help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
)
parser.add_argument(
'--force_download',
action='store_true',
help='Re-download checkpoints.',
)
lowerCamelCase_ = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
| 111 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
a =int(input("""Enter number: """).strip())
print(F"""{number} is {'' if perfect(number) else 'not '}a Perfect Number.""")
| 73 |
from __future__ import annotations
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , A : int = 6 ) ->None:
lowerCamelCase__ : Node | None = None
lowerCamelCase__ : Node | None = None
self.create_linked_list(A )
def __lowerCamelCase ( self : Optional[int] , A : int ) ->None:
lowerCamelCase__ : Optional[int] = Node()
lowerCamelCase__ : List[str] = current_node
lowerCamelCase__ : Union[str, Any] = current_node
lowerCamelCase__ : List[str] = current_node
for _ in range(1 , A ):
lowerCamelCase__ : List[str] = Node()
lowerCamelCase__ : List[Any] = current_node
lowerCamelCase__ : Optional[Any] = previous_node
lowerCamelCase__ : Dict = current_node
lowerCamelCase__ : Union[str, Any] = self.front
lowerCamelCase__ : int = previous_node
def __lowerCamelCase ( self : Optional[int] ) ->bool:
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def __lowerCamelCase ( self : Optional[int] ) ->Any | None:
self.check_can_perform_operation()
return self.front.data if self.front else None
def __lowerCamelCase ( self : Optional[int] , A : Any ) ->None:
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowerCamelCase__ : List[str] = self.rear.next
if self.rear:
lowerCamelCase__ : Optional[Any] = data
def __lowerCamelCase ( self : str ) ->Any:
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowerCamelCase__ : List[Any] = self.front.data
lowerCamelCase__ : Optional[Any] = None
return data
lowerCamelCase__ : Optional[int] = self.front
lowerCamelCase__ : Optional[int] = old_front.next
lowerCamelCase__ : Any = old_front.data
lowerCamelCase__ : List[str] = None
return data
def __lowerCamelCase ( self : Dict ) ->None:
if self.is_empty():
raise Exception('''Empty Queue''' )
def __lowerCamelCase ( self : int ) ->None:
if self.rear and self.rear.next == self.front:
raise Exception('''Full Queue''' )
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ) ->None:
lowerCamelCase__ : Any | None = None
lowerCamelCase__ : Node | None = None
lowerCamelCase__ : Node | None = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 142 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
'''simple docstring'''
__lowercase : Dict = BartphoTokenizer
__lowercase : Tuple = False
__lowercase : List[str] = True
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
super().setUp()
lowerCAmelCase__ : Tuple = ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
lowerCAmelCase__ : Optional[int] = dict(zip(__UpperCAmelCase ,range(len(__UpperCAmelCase ) ) ) )
lowerCAmelCase__ : Optional[Any] = {"""unk_token""": """<unk>"""}
lowerCAmelCase__ : int = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""] )
with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
for token in vocab_tokens:
fp.write(F"""{token} {vocab_tokens[token]}\n""" )
lowerCAmelCase__ : Union[str, Any] = BartphoTokenizer(__UpperCAmelCase ,self.monolingual_vocab_file ,**self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase_ ( self ,**__UpperCAmelCase ) -> List[str]:
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname ,**__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> int:
lowerCAmelCase__ : Optional[int] = """This is a là test"""
lowerCAmelCase__ : Any = """This is a<unk><unk> test"""
return input_text, output_text
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Optional[Any] = BartphoTokenizer(__UpperCAmelCase ,self.monolingual_vocab_file ,**self.special_tokens_map )
lowerCAmelCase__ : str = """This is a là test"""
lowerCAmelCase__ : int = """▁This ▁is ▁a ▁l à ▁t est""".split()
lowerCAmelCase__ : List[str] = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : str = tokens + [tokenizer.unk_token]
lowerCAmelCase__ : Any = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) ,__UpperCAmelCase )
| 184 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if any(not isinstance(UpperCamelCase , UpperCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(UpperCamelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(UpperCamelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 184 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Union[str, Any] ={
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2VisionConfig',
],
'processing_blip_2': ['Blip2Processor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Any =[
'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
'Blip2Model',
'Blip2QFormerModel',
'Blip2PreTrainedModel',
'Blip2ForConditionalGeneration',
'Blip2VisionModel',
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
__lowerCAmelCase : List[Any] =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 197 |
def SCREAMING_SNAKE_CASE__ ( ) -> list[list[int]]:
return [list(range(1000 - i ,-1000 - i ,-1 ) ) for i in range(1000 )]
lowerCamelCase : List[Any] = generate_large_matrix()
lowerCamelCase : Optional[int] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> None:
assert all(row == sorted(lowercase ,reverse=lowercase ) for row in grid )
assert all(list(lowercase ) == sorted(lowercase ,reverse=lowercase ) for col in zip(*lowercase ) )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Tuple = 0
snake_case : List[Any] = len(lowercase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
snake_case : Tuple = (left + right) // 2
snake_case : Dict = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
snake_case : List[Any] = mid + 1
else:
snake_case : str = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(lowercase )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Union[str, Any] = 0
snake_case : Dict = len(grid[0] )
for i in range(len(lowercase ) ):
snake_case : Tuple = find_negative_index(grid[i][:bound] )
total += bound
return (len(lowercase ) * len(grid[0] )) - total
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
return len([number for row in grid for number in row if number < 0] )
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Dict = 0
for row in grid:
for i, number in enumerate(lowercase ):
if number < 0:
total += len(lowercase ) - i
break
return total
def SCREAMING_SNAKE_CASE__ ( ) -> None:
from timeit import timeit
print("""Running benchmarks""" )
snake_case : List[Any] = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
snake_case : int = timeit(f"""{func}(grid=grid)""" ,setup=lowercase ,number=500 )
print(f"""{func}() took {time:0.4f} seconds""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 124 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase ( _UpperCamelCase : int = 1_00_00_00 , _UpperCamelCase : int = 10 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =defaultdict(_UpperCamelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_SCREAMING_SNAKE_CASE =max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_SCREAMING_SNAKE_CASE =1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(_UpperCamelCase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 370 |
'''simple docstring'''
import os
def _lowerCAmelCase ( ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =os.path.dirname(os.path.realpath(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =os.path.join(_UpperCamelCase , 'triangle.txt' )
with open(_UpperCamelCase ) as f:
_SCREAMING_SNAKE_CASE =f.readlines()
_SCREAMING_SNAKE_CASE =[]
for line in triangle:
_SCREAMING_SNAKE_CASE =[]
for number in line.strip().split(' ' ):
numbers_from_line.append(int(_UpperCamelCase ) )
a.append(_UpperCamelCase )
for i in range(1 , len(_UpperCamelCase ) ):
for j in range(len(a[i] ) ):
_SCREAMING_SNAKE_CASE =a[i - 1][j] if j != len(a[i - 1] ) else 0
_SCREAMING_SNAKE_CASE =a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(_UpperCamelCase , _UpperCamelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 114 | 0 |
from __future__ import annotations
__UpperCAmelCase = tuple[int, int, int]
__UpperCAmelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__UpperCAmelCase = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# -------------------------- default selection --------------------------
# rotors --------------------------
__UpperCAmelCase = '''EGZWVONAHDCLFQMSIPJBYUKXTR'''
__UpperCAmelCase = '''FOBHMDKEXQNRAULPGSJVTYICZW'''
__UpperCAmelCase = '''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
__UpperCAmelCase = {
'''A''': '''N''',
'''N''': '''A''',
'''B''': '''O''',
'''O''': '''B''',
'''C''': '''P''',
'''P''': '''C''',
'''D''': '''Q''',
'''Q''': '''D''',
'''E''': '''R''',
'''R''': '''E''',
'''F''': '''S''',
'''S''': '''F''',
'''G''': '''T''',
'''T''': '''G''',
'''H''': '''U''',
'''U''': '''H''',
'''I''': '''V''',
'''V''': '''I''',
'''J''': '''W''',
'''W''': '''J''',
'''K''': '''X''',
'''X''': '''K''',
'''L''': '''Y''',
'''Y''': '''L''',
'''M''': '''Z''',
'''Z''': '''M''',
}
# -------------------------- extra rotors --------------------------
__UpperCAmelCase = '''RMDJXFUWGISLHVTCQNKYPBEZOA'''
__UpperCAmelCase = '''SGLCPQWZHKXAREONTFBVIYJUDM'''
__UpperCAmelCase = '''HVSICLTYKQUBXDWAJZOMFGPREN'''
__UpperCAmelCase = '''RZWQHFMVDBKICJLNTUXAGYPSOE'''
__UpperCAmelCase = '''LFKIJODBEGAMQPXVUHYSTCZRWN'''
__UpperCAmelCase = '''KOAEGVDHXPQZMLFTYWJNBRCIUS'''
def UpperCamelCase ( snake_case__ : RotorPositionT , snake_case__ : RotorSelectionT , snake_case__ : str ) -> tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(snake_case__ ) )) < 3:
UpperCamelCase : Union[str, Any] = F"""Please use 3 unique rotors (not {unique_rotsel})"""
raise Exception(snake_case__ )
# Checks if rotor positions are valid
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = rotpos
if not 0 < rotorposa <= len(snake_case__ ):
UpperCamelCase : Optional[int] = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
raise ValueError(snake_case__ )
if not 0 < rotorposa <= len(snake_case__ ):
UpperCamelCase : int = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(snake_case__ )
if not 0 < rotorposa <= len(snake_case__ ):
UpperCamelCase : Union[str, Any] = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(snake_case__ )
# Validates string and returns dict
UpperCamelCase : Dict = _plugboard(snake_case__ )
return rotpos, rotsel, pbdict
def UpperCamelCase ( snake_case__ : str ) -> dict[str, str]:
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(snake_case__ , snake_case__ ):
UpperCamelCase : str = F"""Plugboard setting isn't type string ({type(snake_case__ )})"""
raise TypeError(snake_case__ )
elif len(snake_case__ ) % 2 != 0:
UpperCamelCase : Any = F"""Odd number of symbols ({len(snake_case__ )})"""
raise Exception(snake_case__ )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
UpperCamelCase : Tuple = set()
for i in pbstring:
if i not in abc:
UpperCamelCase : List[str] = F"""'{i}' not in list of symbols"""
raise Exception(snake_case__ )
elif i in tmppbl:
UpperCamelCase : Optional[int] = F"""Duplicate symbol ({i})"""
raise Exception(snake_case__ )
else:
tmppbl.add(snake_case__ )
del tmppbl
# Created the dictionary
UpperCamelCase : Tuple = {}
for j in range(0 , len(snake_case__ ) - 1 , 2 ):
UpperCamelCase : Dict = pbstring[j + 1]
UpperCamelCase : Dict = pbstring[j]
return pb
def UpperCamelCase ( snake_case__ : str , snake_case__ : RotorPositionT , snake_case__ : RotorSelectionT = (rotora, rotora, rotora) , snake_case__ : str = "" , ) -> str:
UpperCamelCase : Union[str, Any] = text.upper()
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[int] = _validator(
snake_case__ , snake_case__ , plugb.upper() )
UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = rotor_position
UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
UpperCamelCase : Dict = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
UpperCamelCase : str = plugboard[symbol]
# rotor ra --------------------------
UpperCamelCase : Tuple = abc.index(snake_case__ ) + rotorposa
UpperCamelCase : str = rotora[index % len(snake_case__ )]
# rotor rb --------------------------
UpperCamelCase : int = abc.index(snake_case__ ) + rotorposa
UpperCamelCase : Union[str, Any] = rotora[index % len(snake_case__ )]
# rotor rc --------------------------
UpperCamelCase : Dict = abc.index(snake_case__ ) + rotorposa
UpperCamelCase : Union[str, Any] = rotora[index % len(snake_case__ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
UpperCamelCase : Any = reflector[symbol]
# 2nd rotors
UpperCamelCase : Union[str, Any] = abc[rotora.index(snake_case__ ) - rotorposa]
UpperCamelCase : int = abc[rotora.index(snake_case__ ) - rotorposa]
UpperCamelCase : Union[str, Any] = abc[rotora.index(snake_case__ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
UpperCamelCase : Optional[Any] = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(snake_case__ ):
UpperCamelCase : str = 0
rotorposa += 1
if rotorposa >= len(snake_case__ ):
UpperCamelCase : List[str] = 0
rotorposa += 1
if rotorposa >= len(snake_case__ ):
UpperCamelCase : Dict = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(snake_case__ )
return "".join(snake_case__ )
if __name__ == "__main__":
__UpperCAmelCase = '''This is my Python script that emulates the Enigma machine from WWII.'''
__UpperCAmelCase = (1, 1, 1)
__UpperCAmelCase = '''pictures'''
__UpperCAmelCase = (rotora, rotora, rotora)
__UpperCAmelCase = enigma(message, rotor_pos, rotor_sel, pb)
print('''Encrypted message:''', en)
print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))
| 119 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Optional[Any] = "speech_to_text_2"
UpperCAmelCase__ : List[Any] = ["past_key_values"]
UpperCAmelCase__ : Any = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__( self, SCREAMING_SNAKE_CASE_=1_0000, SCREAMING_SNAKE_CASE_=6, SCREAMING_SNAKE_CASE_=2048, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="relu", SCREAMING_SNAKE_CASE_=256, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.02, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=0, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1024, **SCREAMING_SNAKE_CASE_, ) -> int:
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : List[str] = d_model
UpperCamelCase : List[str] = decoder_ffn_dim
UpperCamelCase : Optional[Any] = decoder_layers
UpperCamelCase : Any = decoder_attention_heads
UpperCamelCase : Tuple = dropout
UpperCamelCase : str = attention_dropout
UpperCamelCase : str = activation_dropout
UpperCamelCase : Union[str, Any] = activation_function
UpperCamelCase : Optional[int] = init_std
UpperCamelCase : Tuple = decoder_layerdrop
UpperCamelCase : Dict = use_cache
UpperCamelCase : Any = decoder_layers
UpperCamelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Union[str, Any] = max_target_positions
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_, bos_token_id=SCREAMING_SNAKE_CASE_, eos_token_id=SCREAMING_SNAKE_CASE_, decoder_start_token_id=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
| 119 | 1 |
SCREAMING_SNAKE_CASE__ = {str(digit): digit**5 for digit in range(1_0)}
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE_ ( ):
'''simple docstring'''
return sum(
number
for number in range(1000 , 100_0000 )
if number == digits_fifth_powers_sum(lowerCamelCase__ ) )
if __name__ == "__main__":
print(solution())
| 354 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class __lowerCamelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ = StableUnCLIPImgaImgPipeline
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
lowerCAmelCase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase__ = frozenset([] )
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = 32
lowercase_ = embedder_hidden_size
# image encoding components
lowercase_ = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
lowercase_ = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=UpperCAmelCase , projection_dim=UpperCAmelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
lowercase_ = StableUnCLIPImageNormalizer(embedding_dim=UpperCAmelCase )
lowercase_ = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
lowercase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
lowercase_ = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=UpperCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
lowercase_ = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=UpperCAmelCase , layers_per_block=1 , upcast_attention=UpperCAmelCase , use_linear_projection=UpperCAmelCase , )
torch.manual_seed(0 )
lowercase_ = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.00085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=UpperCAmelCase , steps_offset=1 , )
torch.manual_seed(0 )
lowercase_ = AutoencoderKL()
lowercase_ = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def A__ ( self , UpperCAmelCase , UpperCAmelCase=0 , UpperCAmelCase=True ) -> Tuple:
'''simple docstring'''
if str(UpperCAmelCase ).startswith("mps" ):
lowercase_ = torch.manual_seed(UpperCAmelCase )
else:
lowercase_ = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
lowercase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
if pil_image:
lowercase_ = input_image * 0.5 + 0.5
lowercase_ = input_image.clamp(0 , 1 )
lowercase_ = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowercase_ = DiffusionPipeline.numpy_to_pil(UpperCAmelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase_ = self.get_dummy_components()
lowercase_ = StableUnCLIPImgaImgPipeline(**UpperCAmelCase )
lowercase_ = sd_pipe.to(UpperCAmelCase )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase )
lowercase_ = self.get_dummy_inputs(UpperCAmelCase )
inputs.update({"image_embeds": None} )
lowercase_ = sd_pipe(**UpperCAmelCase ).images
lowercase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase_ = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def A__ ( self ) -> int:
'''simple docstring'''
lowercase_ = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase )
def A__ ( self ) -> Dict:
'''simple docstring'''
lowercase_ = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=UpperCAmelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def A__ ( self ) -> int:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCAmelCase )
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> int:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ) -> Tuple:
'''simple docstring'''
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
lowercase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="cpu" ).manual_seed(0 )
lowercase_ = pipe(UpperCAmelCase , "anime turle" , generator=UpperCAmelCase , output_type="np" )
lowercase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
lowercase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="cpu" ).manual_seed(0 )
lowercase_ = pipe(UpperCAmelCase , "anime turle" , generator=UpperCAmelCase , output_type="np" )
lowercase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCAmelCase , UpperCAmelCase )
def A__ ( self ) -> int:
'''simple docstring'''
lowercase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
lowercase_ = pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = pipe(
UpperCAmelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
lowercase_ = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 297 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
lowerCAmelCase: str = logging.getLogger(__name__)
@dataclass(frozen=lowerCamelCase__ )
class a__:
lowercase__ = 42
lowercase__ = 42
lowercase__ = None
lowercase__ = None
lowercase__ = None
@dataclass(frozen=lowerCamelCase__ )
class a__:
lowercase__ = 42
lowercase__ = None
lowercase__ = None
lowercase__ = None
lowercase__ = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class a__( lowerCamelCase__ ):
lowercase__ = 42
def __init__( self : Union[str, Any] , __snake_case : str , __snake_case : PreTrainedTokenizer , __snake_case : str , __snake_case : Optional[int] = None , __snake_case : Tuple=False , __snake_case : bool = False , ):
a : Optional[Any] = hans_processors[task]()
a : List[str] = os.path.join(
__snake_case , 'cached_{}_{}_{}_{}'.format(
'dev' if evaluate else 'train' , tokenizer.__class__.__name__ , str(__snake_case ) , __snake_case , ) , )
a : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
a , a : List[Any] = label_list[2], label_list[1]
a : List[str] = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
a : Dict = cached_features_file + '.lock'
with FileLock(__snake_case ):
if os.path.exists(__snake_case ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
a : List[Any] = torch.load(__snake_case )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
a : Optional[Any] = (
processor.get_dev_examples(__snake_case ) if evaluate else processor.get_train_examples(__snake_case )
)
logger.info('Training examples: %s' , len(__snake_case ) )
a : Optional[int] = hans_convert_examples_to_features(__snake_case , __snake_case , __snake_case , __snake_case )
logger.info('Saving features into cached file %s' , __snake_case )
torch.save(self.features , __snake_case )
def __len__( self : List[Any] ):
return len(self.features )
def __getitem__( self : Optional[int] , __snake_case : List[str] ):
return self.features[i]
def lowercase_ ( self : Optional[int] ):
return self.label_list
if is_tf_available():
import tensorflow as tf
class a__:
lowercase__ = 42
def __init__( self : str , __snake_case : str , __snake_case : PreTrainedTokenizer , __snake_case : str , __snake_case : Optional[int] = 1_28 , __snake_case : Optional[Any]=False , __snake_case : bool = False , ):
a : Union[str, Any] = hans_processors[task]()
a : List[str] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
a , a : Dict = label_list[2], label_list[1]
a : Any = label_list
a : Optional[int] = processor.get_dev_examples(__snake_case ) if evaluate else processor.get_train_examples(__snake_case )
a : List[Any] = hans_convert_examples_to_features(__snake_case , __snake_case , __snake_case , __snake_case )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc='convert examples to features' ):
if ex_index % 1_00_00 == 0:
logger.info('Writing example %d of %d' % (ex_index, len(__snake_case )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
a : Dict = tf.data.Dataset.from_generator(
__snake_case , (
{
'example_id': tf.intaa,
'input_ids': tf.intaa,
'attention_mask': tf.intaa,
'token_type_ids': tf.intaa,
},
tf.intaa,
) , (
{
'example_id': tf.TensorShape([] ),
'input_ids': tf.TensorShape([None, None] ),
'attention_mask': tf.TensorShape([None, None] ),
'token_type_ids': tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def lowercase_ ( self : str ):
return self.dataset
def __len__( self : int ):
return len(self.features )
def __getitem__( self : Optional[Any] , __snake_case : Optional[int] ):
return self.features[i]
def lowercase_ ( self : Union[str, Any] ):
return self.label_list
class a__( lowerCamelCase__ ):
def lowercase_ ( self : List[Any] , __snake_case : Union[str, Any] ):
return self._create_examples(self._read_tsv(os.path.join(__snake_case , 'heuristics_train_set.txt' ) ) , 'train' )
def lowercase_ ( self : List[Any] , __snake_case : Any ):
return self._create_examples(self._read_tsv(os.path.join(__snake_case , 'heuristics_evaluation_set.txt' ) ) , 'dev' )
def lowercase_ ( self : Union[str, Any] ):
return ["contradiction", "entailment", "neutral"]
def lowercase_ ( self : Dict , __snake_case : Tuple , __snake_case : Tuple ):
a : Union[str, Any] = []
for i, line in enumerate(__snake_case ):
if i == 0:
continue
a : Optional[Any] = '%s-%s' % (set_type, line[0])
a : Dict = line[5]
a : str = line[6]
a : Union[str, Any] = line[7][2:] if line[7].startswith('ex' ) else line[7]
a : Optional[Any] = line[0]
examples.append(InputExample(guid=__snake_case , text_a=__snake_case , text_b=__snake_case , label=__snake_case , pairID=__snake_case ) )
return examples
def lowerCamelCase__ ( _A , _A , _A , _A , ):
a : Optional[int] = {label: i for i, label in enumerate(_A )}
a : List[Any] = []
for ex_index, example in tqdm.tqdm(enumerate(_A ) , desc='convert examples to features' ):
if ex_index % 1_0000 == 0:
logger.info('Writing example %d' % (ex_index) )
a : Dict = tokenizer(
example.text_a , example.text_b , add_special_tokens=_A , max_length=_A , padding='max_length' , truncation=_A , return_overflowing_tokens=_A , )
a : List[Any] = label_map[example.label] if example.label in label_map else 0
a : Optional[int] = int(example.pairID )
features.append(InputFeatures(**_A , label=_A , pairID=_A ) )
for i, example in enumerate(examples[:5] ):
logger.info('*** Example ***' )
logger.info(f"""guid: {example}""" )
logger.info(f"""features: {features[i]}""" )
return features
lowerCAmelCase: List[Any] = {
'hans': 3,
}
lowerCAmelCase: Dict = {
'hans': HansProcessor,
} | 297 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = []
a : int = 0
a : str = 2_56
a : Union[str, Any] = 0
a : Any = 0
a : Optional[int] = 0
a : List[str] = 0
def lowercase_ ( self : str , __snake_case : str ):
a : Any = cva.imread(__snake_case , 0 )
a : Optional[Any] = copy.deepcopy(self.img )
a , a , a : int = plt.hist(self.img.ravel() , 2_56 , [0, 2_56] , label='x' )
a : Optional[int] = np.sum(__snake_case )
for i in range(len(__snake_case ) ):
a : Optional[Any] = x[i] / self.k
self.sk += prk
a : str = (self.L - 1) * self.sk
if self.rem != 0:
a : Optional[int] = int(last % last )
a : int = int(last + 1 if self.rem >= 0.5 else last )
self.last_list.append(__snake_case )
a : str = int(np.ma.count(self.img ) / self.img[1].size )
a : Optional[int] = self.img[1].size
for i in range(self.number_of_cols ):
for j in range(self.number_of_rows ):
a : Any = self.img[j][i]
if num != self.last_list[num]:
a : str = self.last_list[num]
cva.imwrite('output_data/output.jpg' , self.img )
def lowercase_ ( self : Dict ):
plt.hist(self.img.ravel() , 2_56 , [0, 2_56] )
def lowercase_ ( self : List[Any] ):
cva.imshow('Output-Image' , self.img )
cva.imshow('Input-Image' , self.original_image )
cva.waitKey(50_00 )
cva.destroyAllWindows()
if __name__ == "__main__":
lowerCAmelCase: Optional[Any] = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
lowerCAmelCase: Tuple = ConstantStretch()
stretcher.stretch(file_path)
stretcher.plot_histogram()
stretcher.show_image() | 297 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self : Optional[int] , **UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
UpperCamelCase = deprecated_arg[3:]
setattr(self , UpperCamelCase__ , not kwargs.pop(UpperCamelCase__ ) )
logger.warning(
f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
f""" {positive_arg}={kwargs[positive_arg]}""" )
UpperCamelCase = kwargs.pop('torchscript' , self.torchscript )
UpperCamelCase = kwargs.pop('torch_xla_tpu_print_metrics' , self.torch_xla_tpu_print_metrics )
UpperCamelCase = kwargs.pop('fp16_opt_level' , self.fpaa_opt_level )
super().__init__(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = field(default=_a , metadata={"""help""": """Trace the models using torchscript"""} )
_SCREAMING_SNAKE_CASE = field(default=_a , metadata={"""help""": """Print Xla/PyTorch tpu metrics"""} )
_SCREAMING_SNAKE_CASE = field(
default="""O1""" , metadata={
"""help""": (
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
"""See details at https://nvidia.github.io/apex/amp.html"""
)
} , )
@cached_property
def A ( self : List[str] ):
"""simple docstring"""
requires_backends(self , ['torch'] )
logger.info('PyTorch: setting up devices' )
if not self.cuda:
UpperCamelCase = torch.device('cpu' )
UpperCamelCase = 0
elif is_torch_tpu_available():
UpperCamelCase = xm.xla_device()
UpperCamelCase = 0
else:
UpperCamelCase = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
UpperCamelCase = torch.cuda.device_count()
return device, n_gpu
@property
def A ( self : Dict ):
"""simple docstring"""
return is_torch_tpu_available() and self.tpu
@property
def A ( self : List[str] ):
"""simple docstring"""
requires_backends(self , ['torch'] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def A ( self : Any ):
"""simple docstring"""
requires_backends(self , ['torch'] )
return self._setup_devices[0]
@property
def A ( self : Dict ):
"""simple docstring"""
requires_backends(self , ['torch'] )
return self._setup_devices[1]
@property
def A ( self : str ):
"""simple docstring"""
return self.n_gpu > 0
| 352 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowerCamelCase : List[Any] = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
"scale": 0.1,
"eta": 0.0,
"t_grad_cutoff": 2,
"device": "cpu",
}
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = "hopper-medium-v2"
_lowerCamelCase : Optional[int] = gym.make(env_name)
_lowerCamelCase : Any = ValueGuidedRLPipeline.from_pretrained(
"bglick13/hopper-medium-v2-value-function-hor32",
env=env,
)
env.seed(0)
_lowerCamelCase : Dict = env.reset()
_lowerCamelCase : Optional[int] = 0
_lowerCamelCase : str = 0
_lowerCamelCase : Dict = 1000
_lowerCamelCase : Tuple = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
_lowerCamelCase : List[str] = pipeline(obs, planning_horizon=32)
# execute action in environment
_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase : Dict = env.step(denorm_actions)
_lowerCamelCase : Optional[Any] = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
f''' {total_score}'''
)
# save observations for rendering
rollout.append(next_observation.copy())
_lowerCamelCase : Dict = next_observation
except KeyboardInterrupt:
pass
print(f'''Total reward: {total_reward}''')
| 249 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
UpperCAmelCase__ = get_tests_dir("""fixtures""")
class a ( unittest.TestCase ):
def lowerCAmelCase_ ( self : Optional[int] ):
# A mock response for an HTTP head request to emulate server down
_UpperCAmelCase = mock.Mock()
_UpperCAmelCase = 500
_UpperCAmelCase = {}
_UpperCAmelCase = HTTPError
_UpperCAmelCase = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2""" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("""requests.Session.request""" , return_value=__lowerCAmelCase ) as mock_head:
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained("""hf-internal-testing/tiny-random-wav2vec2""" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self : Dict ):
# This test is for deprecated behavior and can be removed in v5
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json""" )
@is_staging_test
class a ( unittest.TestCase ):
@classmethod
def lowerCAmelCase_ ( cls : Any ):
_UpperCAmelCase = TOKEN
HfFolder.save_token(__lowerCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : Optional[int] ):
try:
delete_repo(token=cls._token , repo_id="""test-feature-extractor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-feature-extractor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-feature-extractor""" )
except HTTPError:
pass
def lowerCAmelCase_ ( self : Tuple ):
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase )
feature_extractor.push_to_hub("""test-feature-extractor""" , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-feature-extractor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__lowerCAmelCase , repo_id="""test-feature-extractor""" , push_to_hub=__lowerCAmelCase , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase )
feature_extractor.push_to_hub("""valid_org/test-feature-extractor""" , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-feature-extractor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
__lowerCAmelCase , repo_id="""valid_org/test-feature-extractor-org""" , push_to_hub=__lowerCAmelCase , use_auth_token=self._token )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained("""valid_org/test-feature-extractor-org""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(__lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
CustomFeatureExtractor.register_for_auto_class()
_UpperCAmelCase = CustomFeatureExtractor.from_pretrained(__lowerCAmelCase )
feature_extractor.push_to_hub("""test-dynamic-feature-extractor""" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor"""} , )
_UpperCAmelCase = AutoFeatureExtractor.from_pretrained(
f'''{USER}/test-dynamic-feature-extractor''' , trust_remote_code=__lowerCAmelCase )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , """CustomFeatureExtractor""" )
| 289 | """simple docstring"""
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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class a ( lowerCAmelCase_ ):
_snake_case : Any = 'efficientnet'
def __init__( self : Any , __lowerCAmelCase : int = 3 , __lowerCAmelCase : int = 600 , __lowerCAmelCase : float = 2.0 , __lowerCAmelCase : float = 3.1 , __lowerCAmelCase : int = 8 , __lowerCAmelCase : List[int] = [3, 3, 5, 3, 5, 5, 3] , __lowerCAmelCase : List[int] = [32, 16, 24, 40, 80, 112, 192] , __lowerCAmelCase : List[int] = [16, 24, 40, 80, 112, 192, 320] , __lowerCAmelCase : List[int] = [] , __lowerCAmelCase : List[int] = [1, 2, 2, 2, 1, 2, 1] , __lowerCAmelCase : List[int] = [1, 2, 2, 3, 3, 4, 1] , __lowerCAmelCase : List[int] = [1, 6, 6, 6, 6, 6, 6] , __lowerCAmelCase : float = 0.25 , __lowerCAmelCase : str = "swish" , __lowerCAmelCase : int = 2560 , __lowerCAmelCase : str = "mean" , __lowerCAmelCase : float = 0.02 , __lowerCAmelCase : float = 0.001 , __lowerCAmelCase : float = 0.99 , __lowerCAmelCase : float = 0.5 , __lowerCAmelCase : float = 0.2 , **__lowerCAmelCase : List[Any] , ):
super().__init__(**__lowerCAmelCase )
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = width_coefficient
_UpperCAmelCase = depth_coefficient
_UpperCAmelCase = depth_divisor
_UpperCAmelCase = kernel_sizes
_UpperCAmelCase = in_channels
_UpperCAmelCase = out_channels
_UpperCAmelCase = depthwise_padding
_UpperCAmelCase = strides
_UpperCAmelCase = num_block_repeats
_UpperCAmelCase = expand_ratios
_UpperCAmelCase = squeeze_expansion_ratio
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dim
_UpperCAmelCase = pooling_type
_UpperCAmelCase = initializer_range
_UpperCAmelCase = batch_norm_eps
_UpperCAmelCase = batch_norm_momentum
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = drop_connect_rate
_UpperCAmelCase = sum(__lowerCAmelCase ) * 4
class a ( lowerCAmelCase_ ):
_snake_case : Dict = version.parse('1.11' )
@property
def lowerCAmelCase_ ( self : Any ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase_ ( self : int ):
return 1e-5
| 289 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
_lowerCAmelCase : Dict = True
except (ImportError, AttributeError):
_lowerCAmelCase : Any = object
def __snake_case ( *_lowerCAmelCase : Union[str, Any] , **_lowerCAmelCase : str ) -> Union[str, Any]:
pass
_lowerCAmelCase : Optional[int] = False
_lowerCAmelCase : int = logging.get_logger('''transformers-cli/serving''')
def __snake_case ( _lowerCAmelCase : Namespace ) -> Union[str, Any]:
A_ : Optional[int] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowerCamelCase__ , args.host , args.port , args.workers )
class __magic_name__ ( a_ ):
"""simple docstring"""
__UpperCamelCase = 42
class __magic_name__ ( a_ ):
"""simple docstring"""
__UpperCamelCase = 42
__UpperCamelCase = 42
class __magic_name__ ( a_ ):
"""simple docstring"""
__UpperCamelCase = 42
class __magic_name__ ( a_ ):
"""simple docstring"""
__UpperCamelCase = 42
class __magic_name__ ( a_ ):
"""simple docstring"""
@staticmethod
def SCREAMING_SNAKE_CASE ( snake_case :Optional[Any] ):
'''simple docstring'''
A_ : str = parser.add_parser(
"serve" , help="CLI tool to run inference requests through REST and GraphQL endpoints." )
serve_parser.add_argument(
"--task" , type=snake_case , choices=get_supported_tasks() , help="The task to run the pipeline on" , )
serve_parser.add_argument("--host" , type=snake_case , default="localhost" , help="Interface the server will listen on." )
serve_parser.add_argument("--port" , type=snake_case , default=8_888 , help="Port the serving will listen to." )
serve_parser.add_argument("--workers" , type=snake_case , default=1 , help="Number of http workers" )
serve_parser.add_argument("--model" , type=snake_case , help="Model's name or path to stored model." )
serve_parser.add_argument("--config" , type=snake_case , help="Model's config name or path to stored model." )
serve_parser.add_argument("--tokenizer" , type=snake_case , help="Tokenizer name to use." )
serve_parser.add_argument(
"--device" , type=snake_case , default=-1 , help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" , )
serve_parser.set_defaults(func=snake_case )
def __init__( self :Optional[int] , snake_case :List[Any] , snake_case :Dict , snake_case :str , snake_case :str ):
'''simple docstring'''
A_ : Optional[int] = pipeline
A_ : Optional[int] = host
A_ : Optional[Any] = port
A_ : Union[str, Any] = workers
if not _serve_dependencies_installed:
raise RuntimeError(
"Using serve command requires FastAPI and uvicorn. "
"Please install transformers with [serving]: pip install \"transformers[serving]\"."
"Or install FastAPI and uvicorn separately." )
else:
logger.info(f"Serving model over {host}:{port}" )
A_ : Any = FastAPI(
routes=[
APIRoute(
"/" , self.model_info , response_model=snake_case , response_class=snake_case , methods=["GET"] , ),
APIRoute(
"/tokenize" , self.tokenize , response_model=snake_case , response_class=snake_case , methods=["POST"] , ),
APIRoute(
"/detokenize" , self.detokenize , response_model=snake_case , response_class=snake_case , methods=["POST"] , ),
APIRoute(
"/forward" , self.forward , response_model=snake_case , response_class=snake_case , methods=["POST"] , ),
] , timeout=600 , )
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
run(self._app , host=self.host , port=self.port , workers=self.workers )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def SCREAMING_SNAKE_CASE ( self :str , snake_case :int = Body(snake_case , embed=snake_case ) , snake_case :Optional[int] = Body(snake_case , embed=snake_case ) ):
'''simple docstring'''
try:
A_ : Tuple = self._pipeline.tokenizer.tokenize(snake_case )
if return_ids:
A_ : List[Any] = self._pipeline.tokenizer.convert_tokens_to_ids(snake_case )
return ServeTokenizeResult(tokens=snake_case , tokens_ids=snake_case )
else:
return ServeTokenizeResult(tokens=snake_case )
except Exception as e:
raise HTTPException(status_code=500 , detail={"model": "", "error": str(snake_case )} )
def SCREAMING_SNAKE_CASE ( self :Optional[int] , snake_case :Any = Body(snake_case , embed=snake_case ) , snake_case :str = Body(snake_case , embed=snake_case ) , snake_case :Optional[Any] = Body(snake_case , embed=snake_case ) , ):
'''simple docstring'''
try:
A_ : Dict = self._pipeline.tokenizer.decode(snake_case , snake_case , snake_case )
return ServeDeTokenizeResult(model="" , text=snake_case )
except Exception as e:
raise HTTPException(status_code=500 , detail={"model": "", "error": str(snake_case )} )
async def SCREAMING_SNAKE_CASE ( self :Tuple , snake_case :Dict=Body(snake_case , embed=snake_case ) ):
'''simple docstring'''
if len(snake_case ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
A_ : Optional[Any] = self._pipeline(snake_case )
return ServeForwardResult(output=snake_case )
except Exception as e:
raise HTTPException(500 , {"error": str(snake_case )} )
| 350 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase = '''falcon'''
__UpperCamelCase = ['''past_key_values''']
def __init__( self :List[Any] , snake_case :Optional[int]=65_024 , snake_case :Tuple=4_544 , snake_case :Dict=32 , snake_case :Union[str, Any]=71 , snake_case :List[Any]=1e-5 , snake_case :Union[str, Any]=0.02 , snake_case :List[Any]=True , snake_case :Union[str, Any]=0.0 , snake_case :int=0.0 , snake_case :Union[str, Any]=None , snake_case :Dict=False , snake_case :int=False , snake_case :Tuple=True , snake_case :str=True , snake_case :List[Any]=False , snake_case :Optional[Any]=11 , snake_case :Tuple=11 , **snake_case :List[Any] , ):
'''simple docstring'''
A_ : Optional[int] = vocab_size
# Backward compatibility with n_embed kwarg
A_ : Any = kwargs.pop("n_embed" , snake_case )
A_ : str = hidden_size if n_embed is None else n_embed
A_ : List[str] = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : List[str] = layer_norm_epsilon
A_ : Optional[Any] = initializer_range
A_ : Optional[int] = use_cache
A_ : str = hidden_dropout
A_ : str = attention_dropout
A_ : str = bos_token_id
A_ : List[str] = eos_token_id
A_ : Union[str, Any] = num_attention_heads if num_kv_heads is None else num_kv_heads
A_ : int = alibi
A_ : str = new_decoder_architecture
A_ : Dict = multi_query # Ignored when new_decoder_architecture is True
A_ : Any = parallel_attn
A_ : Optional[Any] = bias
super().__init__(bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
@property
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
return self.hidden_size // self.num_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
return not self.alibi
| 70 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
UpperCamelCase__ : int = logging.get_logger(__name__)
@dataclass
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Any = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : int , **lowerCAmelCase__ : Tuple ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__SCREAMING_SNAKE_CASE : Optional[int] = deprecated_arg[3:]
setattr(self , lowerCAmelCase__ , not kwargs.pop(lowerCAmelCase__ ) )
logger.warning(
F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
F" {positive_arg}={kwargs[positive_arg]}" )
__SCREAMING_SNAKE_CASE : Dict = kwargs.pop("""torchscript""" , self.torchscript )
__SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
__SCREAMING_SNAKE_CASE : int = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowerCAmelCase__ )
_A : bool = field(default=lowerCamelCase__ , metadata={'''help''': '''Trace the models using torchscript'''} )
_A : bool = field(default=lowerCamelCase__ , metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''} )
_A : str = field(
default='''O1''' , metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
} , )
@cached_property
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
__SCREAMING_SNAKE_CASE : List[str] = torch.device("""cpu""" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
elif is_torch_tpu_available():
__SCREAMING_SNAKE_CASE : Any = xm.xla_device()
__SCREAMING_SNAKE_CASE : Any = 0
else:
__SCREAMING_SNAKE_CASE : Tuple = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
__SCREAMING_SNAKE_CASE : int = torch.cuda.device_count()
return device, n_gpu
@property
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
return is_torch_tpu_available() and self.tpu
@property
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def UpperCamelCase__ ( self : str ):
"""simple docstring"""
return self.n_gpu > 0 | 112 |
'''simple docstring'''
from math import ceil, sqrt
def lowerCAmelCase_ ( _lowerCamelCase: int = 1_00_00_00 ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__SCREAMING_SNAKE_CASE : Union[str, Any] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"{solution() = }") | 112 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
__UpperCamelCase : Optional[Any] = "0.12" # assumed parallelism: 8
@require_flax
@is_staging_test
class __magic_name__ ( unittest.TestCase):
@classmethod
def UpperCAmelCase__ ( cls : Dict ) -> int:
'''simple docstring'''
UpperCamelCase__ : str = TOKEN
HfFolder.save_token(lowerCamelCase__ )
@classmethod
def UpperCAmelCase__ ( cls : List[str] ) -> str:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Dict = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCamelCase__ : Union[str, Any] = FlaxBertModel(lowerCamelCase__ )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
UpperCamelCase__ : int = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
UpperCamelCase__ : int = flatten_dict(unfreeze(model.params ) )
UpperCamelCase__ : str = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase__ : Union[str, Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCamelCase__ , 1E-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowerCamelCase__ , repo_id='''test-model-flax''' , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
UpperCamelCase__ : Dict = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
UpperCamelCase__ : Optional[int] = flatten_dict(unfreeze(model.params ) )
UpperCamelCase__ : int = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase__ : Union[str, Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCamelCase__ , 1E-3 , msg=F"{key} not identical" )
def UpperCAmelCase__ ( self : Dict ) -> int:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
UpperCamelCase__ : Tuple = FlaxBertModel(lowerCamelCase__ )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
UpperCamelCase__ : Any = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
UpperCamelCase__ : Union[str, Any] = flatten_dict(unfreeze(model.params ) )
UpperCamelCase__ : List[Any] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase__ : Dict = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCamelCase__ , 1E-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
lowerCamelCase__ , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
UpperCamelCase__ : List[str] = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
UpperCamelCase__ : Tuple = flatten_dict(unfreeze(model.params ) )
UpperCamelCase__ : List[str] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
UpperCamelCase__ : List[Any] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(lowerCamelCase__ , 1E-3 , msg=F"{key} not identical" )
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
UpperCamelCase__ : Dict = True
UpperCamelCase__ : Optional[int] = flatten_dict(modela.params )
UpperCamelCase__ : List[str] = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1E-4:
UpperCamelCase__ : Optional[int] = False
return models_are_equal
@require_flax
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCamelCase__ : str = FlaxBertModel(lowerCamelCase__ )
UpperCamelCase__ : Tuple = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowerCamelCase__ , lowerCamelCase__ ) )
with self.assertRaises(lowerCamelCase__ ):
UpperCamelCase__ : int = FlaxBertModel.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ , subfolder=lowerCamelCase__ )
self.assertTrue(check_models_equal(lowerCamelCase__ , lowerCamelCase__ ) )
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCamelCase__ : Dict = FlaxBertModel(lowerCamelCase__ )
UpperCamelCase__ : List[str] = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(lowerCamelCase__ , lowerCamelCase__ ) , max_shard_size='''10KB''' )
with self.assertRaises(lowerCamelCase__ ):
UpperCamelCase__ : List[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Dict = FlaxBertModel.from_pretrained(lowerCamelCase__ , subfolder=lowerCamelCase__ )
self.assertTrue(check_models_equal(lowerCamelCase__ , lowerCamelCase__ ) )
def UpperCAmelCase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
UpperCamelCase__ : str = '''bert'''
UpperCamelCase__ : Union[str, Any] = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(lowerCamelCase__ ):
UpperCamelCase__ : Dict = FlaxBertModel.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : List[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ , subfolder=lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def UpperCAmelCase__ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = '''bert'''
UpperCamelCase__ : Union[str, Any] = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(lowerCamelCase__ ):
UpperCamelCase__ : Optional[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ , subfolder=lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
| 51 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : List[str] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"MobileNetV2OnnxConfig",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = ["MobileNetV2FeatureExtractor"]
__UpperCamelCase : List[str] = ["MobileNetV2ImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = [
"MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileNetV2ForImageClassification",
"MobileNetV2ForSemanticSegmentation",
"MobileNetV2Model",
"MobileNetV2PreTrainedModel",
"load_tf_weights_in_mobilenet_v2",
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
__UpperCamelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 51 | 1 |
from __future__ import annotations
_SCREAMING_SNAKE_CASE : Union[str, Any] = list[tuple[int, int]]
_SCREAMING_SNAKE_CASE : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_SCREAMING_SNAKE_CASE : int = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : float , __lowerCamelCase : Node | None , ) -> str:
SCREAMING_SNAKE_CASE__ = pos_x
SCREAMING_SNAKE_CASE__ = pos_y
SCREAMING_SNAKE_CASE__ = (pos_y, pos_x)
SCREAMING_SNAKE_CASE__ = goal_x
SCREAMING_SNAKE_CASE__ = goal_y
SCREAMING_SNAKE_CASE__ = g_cost
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = self.calculate_heuristic()
def lowercase_ ( self : str ) -> float:
SCREAMING_SNAKE_CASE__ = abs(self.pos_x - self.goal_x )
SCREAMING_SNAKE_CASE__ = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self : List[str] , __lowerCamelCase : Dict ) -> bool:
return self.f_cost < other.f_cost
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCamelCase : tuple[int, int] , __lowerCamelCase : tuple[int, int] ) -> Dict:
SCREAMING_SNAKE_CASE__ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __lowerCamelCase )
SCREAMING_SNAKE_CASE__ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , __lowerCamelCase )
SCREAMING_SNAKE_CASE__ = [self.start]
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = False
def lowercase_ ( self : Tuple ) -> Path | None:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
SCREAMING_SNAKE_CASE__ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
SCREAMING_SNAKE_CASE__ = True
return self.retrace_path(__lowerCamelCase )
self.closed_nodes.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE__ = self.get_successors(__lowerCamelCase )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(__lowerCamelCase )
else:
# retrieve the best current path
SCREAMING_SNAKE_CASE__ = self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(__lowerCamelCase )
else:
self.open_nodes.append(__lowerCamelCase )
if not self.reached:
return [self.start.pos]
return None
def lowercase_ ( self : str , __lowerCamelCase : Node ) -> list[Node]:
SCREAMING_SNAKE_CASE__ = []
for action in delta:
SCREAMING_SNAKE_CASE__ = parent.pos_x + action[1]
SCREAMING_SNAKE_CASE__ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__lowerCamelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
__lowerCamelCase , __lowerCamelCase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __lowerCamelCase , ) )
return successors
def lowercase_ ( self : Optional[Any] , __lowerCamelCase : Node | None ) -> Path:
SCREAMING_SNAKE_CASE__ = node
SCREAMING_SNAKE_CASE__ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
SCREAMING_SNAKE_CASE__ = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : str = (0, 0)
_SCREAMING_SNAKE_CASE : List[str] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('''------''')
_SCREAMING_SNAKE_CASE : str = GreedyBestFirst(init, goal)
_SCREAMING_SNAKE_CASE : Any = greedy_bf.search()
if path:
for pos_x, pos_y in path:
_SCREAMING_SNAKE_CASE : str = 2
for elem in grid:
print(elem)
| 314 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( _A = "AAPL" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE__ = BeautifulSoup(requests.get(_A ).text , '''html.parser''' )
SCREAMING_SNAKE_CASE__ = '''My(6px) Pos(r) smartphone_Mt(6px)'''
return soup.find('''div''' , class_=class_ ).find('''span''' ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"Current {symbol:<4} stock price is {stock_price(symbol):>8}")
| 314 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__UpperCAmelCase : str = field(
metadata={"help": "The output directory where the model will be written."} ,)
__UpperCAmelCase : str = field(
metadata={
"help": (
"The encoder model checkpoint for weights initialization."
"Don\'t set if you want to train an encoder model from scratch."
)
} ,)
__UpperCAmelCase : str = field(
metadata={
"help": (
"The decoder model checkpoint for weights initialization."
"Don\'t set if you want to train a decoder model from scratch."
)
} ,)
__UpperCAmelCase : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"} )
__UpperCAmelCase : Optional[str] = field(
default=_UpperCAmelCase ,metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"} )
def _A ( ) -> List[Any]:
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments,))
(__lowercase ) = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
__lowercase = AutoConfig.from_pretrained(model_args.encoder_config_name)
# Use pretrained encoder model's config
else:
__lowercase = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)
# Use explicit specified decoder config
if model_args.decoder_config_name:
__lowercase = AutoConfig.from_pretrained(model_args.decoder_config_name)
# Use pretrained decoder model's config
else:
__lowercase = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
__lowercase = True
__lowercase = True
__lowercase = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path, decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path, encoder_config=snake_case_, decoder_config=snake_case_, )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
__lowercase = decoder_config.decoder_start_token_id
__lowercase = decoder_config.pad_token_id
if decoder_start_token_id is None:
__lowercase = decoder_config.bos_token_id
if pad_token_id is None:
__lowercase = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
__lowercase = decoder_config.eos_token_id
__lowercase = decoder_start_token_id
__lowercase = pad_token_id
__lowercase = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)
__lowercase = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
__lowercase = tokenizer.convert_ids_to_tokens(model.config.pad_token_id)
model.save_pretrained(model_args.output_dir)
image_processor.save_pretrained(model_args.output_dir)
tokenizer.save_pretrained(model_args.output_dir)
if __name__ == "__main__":
main()
| 355 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_a = 5_00_00
_a = 50_00
_a , _a = os.path.split(__file__)
_a = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def _A ( UpperCamelCase_ : datasets.Dataset, UpperCamelCase_ : List[str]) -> List[str]:
'''simple docstring'''
for i in range(UpperCamelCase_):
__lowercase = dataset[i]
@get_duration
def _A ( UpperCamelCase_ : datasets.Dataset, UpperCamelCase_ : List[Any], UpperCamelCase_ : int) -> Dict:
'''simple docstring'''
for i in range(0, len(UpperCamelCase_), UpperCamelCase_):
__lowercase = dataset[i : i + batch_size]
@get_duration
def _A ( UpperCamelCase_ : datasets.Dataset, UpperCamelCase_ : Any, UpperCamelCase_ : Optional[int]) -> List[str]:
'''simple docstring'''
with dataset.formatted_as(type=UpperCamelCase_):
for i in range(UpperCamelCase_):
__lowercase = dataset[i]
@get_duration
def _A ( UpperCamelCase_ : datasets.Dataset, UpperCamelCase_ : str, UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : Union[str, Any]) -> Dict:
'''simple docstring'''
with dataset.formatted_as(type=UpperCamelCase_):
for i in range(0, UpperCamelCase_, UpperCamelCase_):
__lowercase = dataset[i : i + batch_size]
def _A ( ) -> List[str]:
'''simple docstring'''
__lowercase = {"num examples": SPEED_TEST_N_EXAMPLES}
__lowercase = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted, {"type": "pandas", "length": SMALL_TEST}),
(read_formatted, {"type": "torch", "length": SMALL_TEST}),
(read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
__lowercase = [
(read, {"length": SMALL_TEST}),
(read, {"length": SPEED_TEST_N_EXAMPLES}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
(read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
(read_formatted, {"type": "numpy", "length": SMALL_TEST}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
(read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print("generating dataset")
__lowercase = datasets.Features(
{"list": datasets.Sequence(datasets.Value("float32")), "numbers": datasets.Value("float32")})
__lowercase = generate_example_dataset(
os.path.join(UpperCamelCase_, "dataset.arrow"), UpperCamelCase_, num_examples=UpperCamelCase_, seq_shapes={"list": (100,)}, )
print("first set of iterations")
for func, kwargs in functions:
print(func.__name__, str(UpperCamelCase_))
__lowercase = func(UpperCamelCase_, **UpperCamelCase_)
print("shuffling dataset")
__lowercase = dataset.shuffle()
print("Second set of iterations (after shuffling")
for func, kwargs in functions_shuffled:
print("shuffled ", func.__name__, str(UpperCamelCase_))
__lowercase = func(
UpperCamelCase_, **UpperCamelCase_)
with open(UpperCamelCase_, "wb") as f:
f.write(json.dumps(UpperCamelCase_).encode("utf-8"))
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 144 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a_ = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a_ = {
# fairseq:
"""wmt19-ru-en""": {"""length_penalty""": 1.1},
"""wmt19-en-ru""": {"""length_penalty""": 1.15},
"""wmt19-en-de""": {"""length_penalty""": 1.0},
"""wmt19-de-en""": {"""length_penalty""": 1.1},
# allenai:
"""wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
"""wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
"""wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}
# this remaps the different models to their organization names
a_ = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a_ = """facebook"""
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a_ = """allenai"""
def a__ ( _UpperCamelCase : Union[str, Any] ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
__lowerCamelCase = dict((re.sub(R'''@@$''' ,'''''' ,_UpperCamelCase ), v) if k.endswith('''@@''' ) else (re.sub(R'''$''' ,'''</w>''' ,_UpperCamelCase ), v) for k, v in d.items() )
__lowerCamelCase = '''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
__lowerCamelCase = d[k] # restore
return da
def a__ ( _UpperCamelCase : str ,_UpperCamelCase : List[str] ):
# prep
assert os.path.exists(_UpperCamelCase )
os.makedirs(_UpperCamelCase ,exist_ok=_UpperCamelCase )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
__lowerCamelCase = basename(_UpperCamelCase )
__lowerCamelCase = dirname(_UpperCamelCase )
__lowerCamelCase = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
__lowerCamelCase = cls.hub_models()
__lowerCamelCase = {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
__lowerCamelCase = '''.'''
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F"""using checkpoint {checkpoint_file}""" )
__lowerCamelCase = hub_utils.from_pretrained(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,archive_map=_UpperCamelCase ,**_UpperCamelCase )
__lowerCamelCase = vars(chkpt['''args''']['''model'''] )
__lowerCamelCase = args['''source_lang''']
__lowerCamelCase = args['''target_lang''']
__lowerCamelCase = dirname(_UpperCamelCase )
__lowerCamelCase = basename(_UpperCamelCase )
# dicts
__lowerCamelCase = os.path.join(_UpperCamelCase ,F"""dict.{src_lang}.txt""" )
__lowerCamelCase = os.path.join(_UpperCamelCase ,F"""dict.{tgt_lang}.txt""" )
__lowerCamelCase = Dictionary.load(_UpperCamelCase )
__lowerCamelCase = rewrite_dict_keys(src_dict.indices )
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = os.path.join(_UpperCamelCase ,'''vocab-src.json''' )
print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""" )
with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(_UpperCamelCase ,ensure_ascii=_UpperCamelCase ,indent=_UpperCamelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
__lowerCamelCase = True
for k in src_vocab.keys():
if not k.islower():
__lowerCamelCase = False
break
__lowerCamelCase = Dictionary.load(_UpperCamelCase )
__lowerCamelCase = rewrite_dict_keys(tgt_dict.indices )
__lowerCamelCase = len(_UpperCamelCase )
__lowerCamelCase = os.path.join(_UpperCamelCase ,'''vocab-tgt.json''' )
print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""" )
with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(_UpperCamelCase ,ensure_ascii=_UpperCamelCase ,indent=_UpperCamelCase ) )
# merges_file (bpecodes)
__lowerCamelCase = os.path.join(_UpperCamelCase ,VOCAB_FILES_NAMES['''merges_file'''] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
__lowerCamelCase = os.path.join(_UpperCamelCase ,_UpperCamelCase )
if os.path.exists(_UpperCamelCase ):
break
with open(_UpperCamelCase ,encoding='''utf-8''' ) as fin:
__lowerCamelCase = fin.read()
__lowerCamelCase = re.sub(R''' \d+$''' ,'''''' ,_UpperCamelCase ,0 ,re.M ) # remove frequency number
print(F"""Generating {merges_file}""" )
with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as fout:
fout.write(_UpperCamelCase )
# model config
__lowerCamelCase = os.path.join(_UpperCamelCase ,'''config.json''' )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args["bpe"]}"""
assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args["tokenizer"]}"""
__lowerCamelCase = {
'''architectures''': ['''FSMTForConditionalGeneration'''],
'''model_type''': '''fsmt''',
'''activation_dropout''': args['''activation_dropout'''],
'''activation_function''': '''relu''',
'''attention_dropout''': args['''attention_dropout'''],
'''d_model''': args['''decoder_embed_dim'''],
'''dropout''': args['''dropout'''],
'''init_std''': 0.02,
'''max_position_embeddings''': args['''max_source_positions'''],
'''num_hidden_layers''': args['''encoder_layers'''],
'''src_vocab_size''': src_vocab_size,
'''tgt_vocab_size''': tgt_vocab_size,
'''langs''': [src_lang, tgt_lang],
'''encoder_attention_heads''': args['''encoder_attention_heads'''],
'''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
'''encoder_layerdrop''': args['''encoder_layerdrop'''],
'''encoder_layers''': args['''encoder_layers'''],
'''decoder_attention_heads''': args['''decoder_attention_heads'''],
'''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
'''decoder_layerdrop''': args['''decoder_layerdrop'''],
'''decoder_layers''': args['''decoder_layers'''],
'''bos_token_id''': 0,
'''pad_token_id''': 1,
'''eos_token_id''': 2,
'''is_encoder_decoder''': True,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_all_embeddings'''],
}
# good hparam defaults to start with
__lowerCamelCase = 5
__lowerCamelCase = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
__lowerCamelCase = best_score_hparams[model_dir]['''length_penalty''']
else:
__lowerCamelCase = 1.0
print(F"""Generating {fsmt_model_config_file}""" )
with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(_UpperCamelCase ,ensure_ascii=_UpperCamelCase ,indent=_UpperCamelCase ) )
# tokenizer config
__lowerCamelCase = os.path.join(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = {
'''langs''': [src_lang, tgt_lang],
'''model_max_length''': 10_24,
'''do_lower_case''': do_lower_case,
}
print(F"""Generating {fsmt_tokenizer_config_file}""" )
with open(_UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(_UpperCamelCase ,ensure_ascii=_UpperCamelCase ,indent=_UpperCamelCase ) )
# model
__lowerCamelCase = chkpt['''models'''][0]
__lowerCamelCase = model.state_dict()
# rename keys to start with 'model.'
__lowerCamelCase = OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
__lowerCamelCase = [
'''model.model''',
'''model.encoder.version''',
'''model.decoder.version''',
'''model.encoder_embed_tokens.weight''',
'''model.decoder_embed_tokens.weight''',
'''model.encoder.embed_positions._float_tensor''',
'''model.decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
model_state_dict.pop(_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase = FSMTConfig.from_pretrained(_UpperCamelCase )
__lowerCamelCase = FSMTForConditionalGeneration(_UpperCamelCase )
# check that it loads ok
model_new.load_state_dict(_UpperCamelCase ,strict=_UpperCamelCase )
# save
__lowerCamelCase = os.path.join(_UpperCamelCase ,_UpperCamelCase )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(_UpperCamelCase ,_UpperCamelCase )
print('''Conversion is done!''' )
print('''\nLast step is to upload the files to s3''' )
print(F"""cd {data_root}""" )
print(F"""transformers-cli upload {model_dir}""" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fsmt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a_ = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 330 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=10 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=37 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=10 , __UpperCAmelCase=0.02 , __UpperCAmelCase="divided_space_time" , __UpperCAmelCase=None , ):
'''simple docstring'''
__lowerCamelCase = parent
__lowerCamelCase = batch_size
__lowerCamelCase = image_size
__lowerCamelCase = num_channels
__lowerCamelCase = patch_size
__lowerCamelCase = num_frames
__lowerCamelCase = is_training
__lowerCamelCase = use_labels
__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 = attention_type
__lowerCamelCase = initializer_range
__lowerCamelCase = scope
__lowerCamelCase = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__lowerCamelCase = (image_size // patch_size) ** 2
__lowerCamelCase = (num_frames) * self.num_patches_per_frame + 1
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__lowerCamelCase = None
if self.use_labels:
__lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
__lowerCamelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__lowerCamelCase = self.num_labels
return config
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
__lowerCamelCase = model(__UpperCAmelCase )
# verify the logits shape
__lowerCamelCase = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.prepare_config_and_inputs()
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = config_and_inputs
__lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowerCAmelCase__ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerModelTester(self )
__lowerCamelCase = ConfigTester(
self , config_class=__UpperCAmelCase , has_text_modality=__UpperCAmelCase , hidden_size=37 )
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ):
'''simple docstring'''
__lowerCamelCase = copy.deepcopy(__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
__lowerCamelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase , nn.Linear ) )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = model_class(__UpperCAmelCase )
__lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCamelCase = [*signature.parameters.keys()]
__lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__UpperCAmelCase )
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCamelCase = TimesformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
if not self.has_attentions:
pass
else:
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCamelCase = True
for model_class in self.all_model_classes:
__lowerCamelCase = self.model_tester.seq_length
__lowerCamelCase = self.model_tester.num_frames
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__lowerCamelCase = len(__UpperCAmelCase )
# Check attention is always last and order is fine
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
__lowerCamelCase = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def lowerCamelCase ( self ):
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
__lowerCamelCase = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
__lowerCamelCase = outputs.hidden_states
__lowerCamelCase = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
__lowerCamelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__lowerCamelCase ,__lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCamelCase = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def a__ ( ):
__lowerCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''' ,filename='''eating_spaghetti.npy''' ,repo_type='''dataset''' )
__lowerCamelCase = np.load(_UpperCamelCase )
return list(_UpperCamelCase )
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def lowerCamelCase ( self ):
'''simple docstring'''
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__UpperCAmelCase )
__lowerCamelCase = self.default_image_processor
__lowerCamelCase = prepare_video()
__lowerCamelCase = image_processor(video[:8] , return_tensors='''pt''' ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
__lowerCamelCase = model(**__UpperCAmelCase )
# verify the logits
__lowerCamelCase = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
__lowerCamelCase = torch.tensor([-0.3_016, -0.7_713, -0.4_205] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCAmelCase , atol=1E-4 ) )
| 330 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class UpperCamelCase :
def __init__(self : List[str] , _A : Any , ) -> Optional[Any]:
__snake_case : Any = parent
__snake_case : Union[str, Any] = 13
__snake_case : Union[str, Any] = 7
__snake_case : List[str] = True
__snake_case : str = True
__snake_case : str = False
__snake_case : Union[str, Any] = True
__snake_case : Any = 99
__snake_case : Any = 32
__snake_case : int = 2
__snake_case : Tuple = 4
__snake_case : Tuple = 37
__snake_case : Optional[Any] = 'gelu'
__snake_case : Optional[int] = 0.1
__snake_case : Optional[int] = 0.1
__snake_case : Optional[Any] = 5_12
__snake_case : str = 16
__snake_case : Optional[Any] = 2
__snake_case : Any = 0.02
__snake_case : int = 3
__snake_case : Dict = 4
__snake_case : List[Any] = None
def _lowercase (self : int) -> List[str]:
__snake_case : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__snake_case : List[str] = None
if self.use_input_mask:
__snake_case : int = random_attention_mask([self.batch_size, self.seq_length])
__snake_case : List[str] = None
__snake_case : Dict = None
__snake_case : Dict = None
if self.use_labels:
__snake_case : List[Any] = 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 : List[str] = ids_tensor([self.batch_size] , self.num_choices)
__snake_case : Dict = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase (self : List[Any] , _A : Tuple , _A : int , _A : Any , _A : int , _A : List[str] , _A : str) -> int:
__snake_case : Tuple = TFDistilBertModel(config=_A)
__snake_case : Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
__snake_case : Optional[int] = model(_A)
__snake_case : Union[str, Any] = [input_ids, input_mask]
__snake_case : Any = model(_A)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _lowercase (self : List[str] , _A : List[Any] , _A : Dict , _A : str , _A : List[Any] , _A : List[str] , _A : Optional[int]) -> int:
__snake_case : Optional[Any] = TFDistilBertForMaskedLM(config=_A)
__snake_case : Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
__snake_case : Dict = model(_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _lowercase (self : str , _A : int , _A : int , _A : Dict , _A : Union[str, Any] , _A : Optional[int] , _A : List[Any]) -> Optional[Any]:
__snake_case : int = TFDistilBertForQuestionAnswering(config=_A)
__snake_case : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
}
__snake_case : Dict = model(_A)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def _lowercase (self : List[Any] , _A : Any , _A : Tuple , _A : Dict , _A : Union[str, Any] , _A : Any , _A : List[str]) -> int:
__snake_case : int = self.num_labels
__snake_case : str = TFDistilBertForSequenceClassification(_A)
__snake_case : Any = {'input_ids': input_ids, 'attention_mask': input_mask}
__snake_case : Optional[int] = model(_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowercase (self : Optional[int] , _A : List[Any] , _A : List[str] , _A : int , _A : Optional[Any] , _A : Optional[Any] , _A : int) -> Tuple:
__snake_case : str = self.num_choices
__snake_case : List[Any] = TFDistilBertForMultipleChoice(_A)
__snake_case : int = tf.tile(tf.expand_dims(_A , 1) , (1, self.num_choices, 1))
__snake_case : Union[str, Any] = tf.tile(tf.expand_dims(_A , 1) , (1, self.num_choices, 1))
__snake_case : int = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
}
__snake_case : str = model(_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _lowercase (self : List[Any] , _A : str , _A : Any , _A : Optional[int] , _A : Tuple , _A : Dict , _A : Union[str, Any]) -> Optional[Any]:
__snake_case : Any = self.num_labels
__snake_case : Optional[int] = TFDistilBertForTokenClassification(_A)
__snake_case : int = {'input_ids': input_ids, 'attention_mask': input_mask}
__snake_case : Optional[Any] = model(_A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _lowercase (self : Tuple) -> Any:
__snake_case : Union[str, Any] = self.prepare_config_and_inputs()
((__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case) , (__snake_case)) : Optional[Any] = config_and_inputs
__snake_case : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class UpperCamelCase ( lowercase , lowercase , unittest.TestCase ):
UpperCAmelCase : Optional[Any] = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
UpperCAmelCase : str = (
{
"""feature-extraction""": TFDistilBertModel,
"""fill-mask""": TFDistilBertForMaskedLM,
"""question-answering""": TFDistilBertForQuestionAnswering,
"""text-classification""": TFDistilBertForSequenceClassification,
"""token-classification""": TFDistilBertForTokenClassification,
"""zero-shot""": TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase : Tuple = False
UpperCAmelCase : List[str] = False
def _lowercase (self : Optional[int]) -> Optional[int]:
__snake_case : Dict = TFDistilBertModelTester(self)
__snake_case : List[Any] = ConfigTester(self , config_class=_A , dim=37)
def _lowercase (self : Tuple) -> Union[str, Any]:
self.config_tester.run_common_tests()
def _lowercase (self : Optional[int]) -> List[str]:
__snake_case : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_A)
def _lowercase (self : List[str]) -> Optional[Any]:
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_A)
def _lowercase (self : Union[str, Any]) -> Dict:
__snake_case : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_A)
def _lowercase (self : List[str]) -> str:
__snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_A)
def _lowercase (self : Dict) -> Dict:
__snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_A)
def _lowercase (self : Dict) -> Dict:
__snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_A)
@slow
def _lowercase (self : Optional[int]) -> List[str]:
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]):
__snake_case : int = TFDistilBertModel.from_pretrained(_A)
self.assertIsNotNone(_A)
@require_tf
class UpperCamelCase ( unittest.TestCase ):
@slow
def _lowercase (self : Optional[Any]) -> List[str]:
__snake_case : Dict = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
__snake_case : List[Any] = tf.constant([[0, 1, 2, 3, 4, 5]])
__snake_case : Dict = model(_A)[0]
__snake_case : Optional[Any] = [1, 6, 7_68]
self.assertEqual(output.shape , _A)
__snake_case : str = tf.constant(
[
[
[0.19_261_885, -0.13_732_955, 0.4_119_799],
[0.22_150_156, -0.07_422_661, 0.39_037_204],
[0.22_756_018, -0.0_896_414, 0.3_701_467],
]
])
tf.debugging.assert_near(output[:, :3, :3] , _A , atol=1E-4)
| 95 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
_a : List[str]= False
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : List[str]) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _lowercase (self : Optional[Any]) -> Union[str, Any]:
return 12
@property
def _lowercase (self : Dict) -> Union[str, Any]:
return 12
@property
def _lowercase (self : int) -> Tuple:
return 32
@property
def _lowercase (self : Optional[int]) -> Dict:
torch.manual_seed(0)
__snake_case : 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 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def _lowercase (self : List[Any]) -> Optional[int]:
__snake_case : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
return tokenizer
@property
def _lowercase (self : Union[str, Any]) -> Optional[int]:
torch.manual_seed(0)
__snake_case : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(_A)
@property
def _lowercase (self : Union[str, Any]) -> Dict:
torch.manual_seed(0)
__snake_case : Any = 12
__snake_case : int = 12
__snake_case : List[Any] = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__snake_case : Union[str, Any] = TransformeraDModel(**_A)
return model
def _lowercase (self : Union[str, Any]) -> Dict:
__snake_case : Tuple = 'cpu'
__snake_case : List[str] = self.dummy_vqvae
__snake_case : str = self.dummy_text_encoder
__snake_case : Optional[Any] = self.dummy_tokenizer
__snake_case : Dict = self.dummy_transformer
__snake_case : Optional[int] = VQDiffusionScheduler(self.num_embed)
__snake_case : List[Any] = LearnedClassifierFreeSamplingEmbeddings(learnable=_A)
__snake_case : List[Any] = VQDiffusionPipeline(
vqvae=_A , text_encoder=_A , tokenizer=_A , transformer=_A , scheduler=_A , learned_classifier_free_sampling_embeddings=_A , )
__snake_case : List[Any] = pipe.to(_A)
pipe.set_progress_bar_config(disable=_A)
__snake_case : Optional[Any] = 'teddy bear playing in the pool'
__snake_case : str = torch.Generator(device=_A).manual_seed(0)
__snake_case : Union[str, Any] = pipe([prompt] , generator=_A , num_inference_steps=2 , output_type='np')
__snake_case : Optional[int] = output.images
__snake_case : int = torch.Generator(device=_A).manual_seed(0)
__snake_case : Tuple = pipe(
[prompt] , generator=_A , output_type='np' , return_dict=_A , num_inference_steps=2)[0]
__snake_case : str = image[0, -3:, -3:, -1]
__snake_case : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__snake_case : str = np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
def _lowercase (self : Tuple) -> Optional[int]:
__snake_case : Optional[Any] = 'cpu'
__snake_case : Optional[int] = self.dummy_vqvae
__snake_case : List[str] = self.dummy_text_encoder
__snake_case : Optional[int] = self.dummy_tokenizer
__snake_case : Optional[Any] = self.dummy_transformer
__snake_case : Union[str, Any] = VQDiffusionScheduler(self.num_embed)
__snake_case : Optional[int] = LearnedClassifierFreeSamplingEmbeddings(
learnable=_A , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length)
__snake_case : Union[str, Any] = VQDiffusionPipeline(
vqvae=_A , text_encoder=_A , tokenizer=_A , transformer=_A , scheduler=_A , learned_classifier_free_sampling_embeddings=_A , )
__snake_case : Union[str, Any] = pipe.to(_A)
pipe.set_progress_bar_config(disable=_A)
__snake_case : Union[str, Any] = 'teddy bear playing in the pool'
__snake_case : Optional[int] = torch.Generator(device=_A).manual_seed(0)
__snake_case : Tuple = pipe([prompt] , generator=_A , num_inference_steps=2 , output_type='np')
__snake_case : Optional[Any] = output.images
__snake_case : str = torch.Generator(device=_A).manual_seed(0)
__snake_case : Dict = pipe(
[prompt] , generator=_A , output_type='np' , return_dict=_A , num_inference_steps=2)[0]
__snake_case : int = image[0, -3:, -3:, -1]
__snake_case : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__snake_case : Optional[Any] = np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])
assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
@slow
@require_torch_gpu
class UpperCamelCase ( unittest.TestCase ):
def _lowercase (self : Any) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase (self : Tuple) -> Optional[int]:
__snake_case : List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')
__snake_case : Union[str, Any] = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
__snake_case : Tuple = pipeline.to(_A)
pipeline.set_progress_bar_config(disable=_A)
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__snake_case : Optional[int] = torch.Generator(device=_A).manual_seed(0)
__snake_case : int = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=_A , output_type='np' , )
__snake_case : int = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert np.abs(expected_image - image).max() < 2.0
| 95 | 1 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def _lowerCAmelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[0] * no_of_processes
_SCREAMING_SNAKE_CASE =[0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =burst_time[i]
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =-1
for i in range(_UpperCamelCase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
_SCREAMING_SNAKE_CASE =ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
_SCREAMING_SNAKE_CASE =i
total_time += burst_time[target_process]
completed += 1
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =(
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def _lowerCAmelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : int , _UpperCamelCase : list[int] ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[0] * no_of_processes
for i in range(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print("[TEST CASE 01]")
lowerCamelCase : Optional[Any] = 4
lowerCamelCase : List[str] = [2, 5, 3, 7]
lowerCamelCase : int = [0, 0, 0, 0]
lowerCamelCase : Optional[int] = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
lowerCamelCase : int = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
for i, process_id in enumerate(list(range(1, 5))):
print(
f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
)
print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')
| 47 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowerCamelCase : int = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class A__ ( unittest.TestCase ):
def __init__( self : List[str] , _a : List[Any] , _a : List[str]=7 , _a : List[str]=3 , _a : Tuple=18 , _a : Tuple=30 , _a : str=400 , _a : Tuple=None , _a : Union[str, Any]=True , _a : List[str]=True , _a : Optional[int]=None , ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =size if size is not None else {'height': 20, 'width': 20}
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =batch_size
_SCREAMING_SNAKE_CASE =num_channels
_SCREAMING_SNAKE_CASE =image_size
_SCREAMING_SNAKE_CASE =min_resolution
_SCREAMING_SNAKE_CASE =max_resolution
_SCREAMING_SNAKE_CASE =size
_SCREAMING_SNAKE_CASE =do_normalize
_SCREAMING_SNAKE_CASE =do_convert_rgb
_SCREAMING_SNAKE_CASE =[512, 1024, 2048, 4096]
_SCREAMING_SNAKE_CASE =patch_size if patch_size is not None else {'height': 16, 'width': 16}
def A ( self : Any ) -> List[str]:
'''simple docstring'''
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def A ( self : int ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
_SCREAMING_SNAKE_CASE =Image.open(requests.get(_a , stream=_a ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class A__ ( A__ , unittest.TestCase ):
A__ = PixaStructImageProcessor if is_vision_available() else None
def A ( self : Dict ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =PixaStructImageProcessingTester(self )
@property
def A ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : Any ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , 'do_normalize' ) )
self.assertTrue(hasattr(_a , 'do_convert_rgb' ) )
def A ( self : Any ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processor_tester.prepare_dummy_image()
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
_SCREAMING_SNAKE_CASE =2048
_SCREAMING_SNAKE_CASE =image_processor(_a , return_tensors='pt' , max_patches=_a )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.06_06 ) , atol=1e-3 , rtol=1e-3 ) )
def A ( self : Any ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE =(
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processor(
_a , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE =(
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
_SCREAMING_SNAKE_CASE =True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(_a ):
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a ).flattened_patches
_SCREAMING_SNAKE_CASE ='Hello'
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a , header_text=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processor(
_a , return_tensors='pt' , max_patches=_a , header_text=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : List[Any] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
_SCREAMING_SNAKE_CASE =(
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processor(
_a , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def A ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
_SCREAMING_SNAKE_CASE =(
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processor(
_a , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11 , reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' , )
@require_torch
@require_vision
class A__ ( A__ , unittest.TestCase ):
A__ = PixaStructImageProcessor if is_vision_available() else None
def A ( self : str ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =PixaStructImageProcessingTester(self , num_channels=4 )
_SCREAMING_SNAKE_CASE =3
@property
def A ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[str] ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , 'do_normalize' ) )
self.assertTrue(hasattr(_a , 'do_convert_rgb' ) )
def A ( self : Dict ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE =prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE =(
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_SCREAMING_SNAKE_CASE =image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_SCREAMING_SNAKE_CASE =image_processor(
_a , return_tensors='pt' , max_patches=_a ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 47 | 1 |
"""simple docstring"""
import random
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCAmelCase__ :str = a[left_index]
lowerCAmelCase__ :List[Any] = left_index + 1
for j in range(left_index + 1 , _SCREAMING_SNAKE_CASE ):
if a[j] < pivot:
lowerCAmelCase__ :int = a[i], a[j]
i += 1
lowerCAmelCase__ :Optional[Any] = a[i - 1], a[left_index]
return i - 1
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if left < right:
lowerCAmelCase__ :str = random.randint(_SCREAMING_SNAKE_CASE , right - 1 )
lowerCAmelCase__ :List[str] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
lowerCAmelCase__ :Tuple = 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 __A ():
"""simple docstring"""
lowerCAmelCase__ :str = input('Enter numbers separated by a comma:\n' ).strip()
lowerCAmelCase__ :str = [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()
| 350 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) ->str:
"""simple docstring"""
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release:
# old versions of hfh don't url-encode the file path
lowerCAmelCase__ :Optional[int] = quote(_SCREAMING_SNAKE_CASE )
return hfh.hf_hub_url(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' , revision=_SCREAMING_SNAKE_CASE )
| 254 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_A : Tuple ='''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
_A : int ='''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''
_A : Dict ='''
Computes SQuAD scores (F1 and EM).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': the text of the answer
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the SQuAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
Examples:
>>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
>>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
>>> squad_metric = datasets.load_metric("squad")
>>> results = squad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
def lowerCamelCase_ ( self: List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {"""id""": datasets.Value("""string""" ), """prediction_text""": datasets.Value("""string""" )},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""] , reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""] , )
def lowerCamelCase_ ( self: Union[str, Any] , UpperCamelCase__: List[Any] , UpperCamelCase__: Tuple ):
lowerCamelCase__ : List[str] = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
lowerCamelCase__ : Tuple = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
lowerCamelCase__ : Any = evaluate(dataset=UpperCamelCase__ , predictions=UpperCamelCase__ )
return score
| 41 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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 PIL import Image
from transformers import DonutImageProcessor
class _lowercase ( unittest.TestCase ):
def __init__( self: str , UpperCamelCase__: Optional[Any] , UpperCamelCase__: Any=7 , UpperCamelCase__: Optional[int]=3 , UpperCamelCase__: List[str]=18 , UpperCamelCase__: Union[str, Any]=30 , UpperCamelCase__: List[str]=400 , UpperCamelCase__: Any=True , UpperCamelCase__: Union[str, Any]=None , UpperCamelCase__: List[Any]=True , UpperCamelCase__: List[Any]=False , UpperCamelCase__: Tuple=True , UpperCamelCase__: Tuple=True , UpperCamelCase__: Any=[0.5, 0.5, 0.5] , UpperCamelCase__: Optional[Any]=[0.5, 0.5, 0.5] , ):
lowerCamelCase__ : int = parent
lowerCamelCase__ : Any = batch_size
lowerCamelCase__ : Optional[int] = num_channels
lowerCamelCase__ : Union[str, Any] = image_size
lowerCamelCase__ : Optional[int] = min_resolution
lowerCamelCase__ : Optional[Any] = max_resolution
lowerCamelCase__ : Union[str, Any] = do_resize
lowerCamelCase__ : Optional[Any] = size if size is not None else {"""height""": 18, """width""": 20}
lowerCamelCase__ : Dict = do_thumbnail
lowerCamelCase__ : Optional[int] = do_align_axis
lowerCamelCase__ : Any = do_pad
lowerCamelCase__ : Optional[Any] = do_normalize
lowerCamelCase__ : Union[str, Any] = image_mean
lowerCamelCase__ : Union[str, Any] = image_std
def lowerCamelCase_ ( self: str ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class _lowercase ( _lowercase , unittest.TestCase ):
a = DonutImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : Any = DonutImageProcessingTester(self )
@property
def lowerCamelCase_ ( self: Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """size""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """do_thumbnail""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """do_align_long_axis""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """do_pad""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase__ , """image_std""" ) )
def lowerCamelCase_ ( self: Optional[Any] ):
lowerCamelCase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 18, """width""": 20} )
lowerCamelCase__ : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"""height""": 42, """width""": 42} )
# Previous config had dimensions in (width, height) order
lowerCamelCase__ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {"""height""": 84, """width""": 42} )
def lowerCamelCase_ ( self: List[str] ):
pass
@is_flaky()
def lowerCamelCase_ ( self: Union[str, Any] ):
# Initialize image_processing
lowerCamelCase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
lowerCamelCase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowerCamelCase__ : List[str] = image_processing(UpperCamelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def lowerCamelCase_ ( self: Optional[int] ):
# Initialize image_processing
lowerCamelCase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase__ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
lowerCamelCase__ : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowerCamelCase__ : Optional[Any] = image_processing(UpperCamelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
@is_flaky()
def lowerCamelCase_ ( self: Dict ):
# Initialize image_processing
lowerCamelCase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase__ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
lowerCamelCase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
lowerCamelCase__ : Tuple = image_processing(UpperCamelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
| 41 | 1 |
"""simple docstring"""
class A__ :
'''simple docstring'''
def __init__( self: List[str] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: Dict) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Any = name
__lowerCAmelCase : Optional[Any] = val
def __str__( self: Tuple) -> Dict:
"""simple docstring"""
return F"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self: Tuple , _SCREAMING_SNAKE_CASE: int) -> Union[str, Any]:
"""simple docstring"""
return self.val < other.val
class A__ :
'''simple docstring'''
def __init__( self: Any , _SCREAMING_SNAKE_CASE: List[str]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = {}
__lowerCAmelCase : str = {}
__lowerCAmelCase : Dict = self.build_heap(_SCREAMING_SNAKE_CASE)
def __getitem__( self: Any , _SCREAMING_SNAKE_CASE: Optional[Any]) -> Optional[Any]:
"""simple docstring"""
return self.get_value(_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: Optional[Any]) -> Tuple:
"""simple docstring"""
return (idx - 1) // 2
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Tuple) -> List[str]:
"""simple docstring"""
return idx * 2 + 1
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: Optional[int]) -> List[str]:
"""simple docstring"""
return idx * 2 + 2
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Any) -> Tuple:
"""simple docstring"""
return self.heap_dict[key]
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: Tuple) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : str = len(_SCREAMING_SNAKE_CASE) - 1
__lowerCAmelCase : List[Any] = self.get_parent_idx(_SCREAMING_SNAKE_CASE)
for idx, i in enumerate(_SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[str] = idx
__lowerCAmelCase : int = i.val
for i in range(_SCREAMING_SNAKE_CASE , -1 , -1):
self.sift_down(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
return array
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any]) -> List[str]:
"""simple docstring"""
while True:
__lowerCAmelCase : int = self.get_left_child_idx(_SCREAMING_SNAKE_CASE) # noqa: E741
__lowerCAmelCase : str = self.get_right_child_idx(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = idx
if l < len(_SCREAMING_SNAKE_CASE) and array[l] < array[idx]:
__lowerCAmelCase : Dict = l
if r < len(_SCREAMING_SNAKE_CASE) and array[r] < array[smallest]:
__lowerCAmelCase : Tuple = r
if smallest != idx:
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = array[smallest], array[idx]
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : List[Any] = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
__lowerCAmelCase : int = smallest
else:
break
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: Dict) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : int = self.get_parent_idx(_SCREAMING_SNAKE_CASE)
while p >= 0 and self.heap[p] > self.heap[idx]:
__lowerCAmelCase , __lowerCAmelCase : Dict = self.heap[idx], self.heap[p]
__lowerCAmelCase , __lowerCAmelCase : int = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
__lowerCAmelCase : Dict = p
__lowerCAmelCase : Union[str, Any] = self.get_parent_idx(_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> List[Any]:
"""simple docstring"""
return self.heap[0]
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> int:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : str = self.heap[-1], self.heap[0]
__lowerCAmelCase , __lowerCAmelCase : Dict = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
__lowerCAmelCase : str = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap)
return x
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: str) -> List[str]:
"""simple docstring"""
self.heap.append(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = len(self.heap) - 1
__lowerCAmelCase : List[str] = node.val
self.sift_up(len(self.heap) - 1)
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Any:
"""simple docstring"""
return len(self.heap) == 0
def _SCREAMING_SNAKE_CASE ( self: int , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Any]) -> int:
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
__lowerCAmelCase : Union[str, Any] = new_value
__lowerCAmelCase : Optional[int] = new_value
self.sift_up(self.idx_of_element[node])
__snake_case : int = Node('R', -1)
__snake_case : Optional[Any] = Node('B', 6)
__snake_case : List[Any] = Node('A', 3)
__snake_case : Optional[Any] = Node('X', 1)
__snake_case : List[Any] = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__snake_case : List[str] = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod() | 58 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[Any] = logging.get_logger(__name__)
# TODO Update this
__snake_case : Optional[int] = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'esm'
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: str=None , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: int=768 , _SCREAMING_SNAKE_CASE: Any=12 , _SCREAMING_SNAKE_CASE: Optional[Any]=12 , _SCREAMING_SNAKE_CASE: Optional[int]=3072 , _SCREAMING_SNAKE_CASE: List[Any]=0.1 , _SCREAMING_SNAKE_CASE: Tuple=0.1 , _SCREAMING_SNAKE_CASE: Optional[Any]=1026 , _SCREAMING_SNAKE_CASE: List[Any]=0.02 , _SCREAMING_SNAKE_CASE: Optional[Any]=1e-12 , _SCREAMING_SNAKE_CASE: List[Any]="absolute" , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[Any]=False , _SCREAMING_SNAKE_CASE: List[Any]=False , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: Tuple=None , **_SCREAMING_SNAKE_CASE: str , ) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , mask_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = vocab_size
__lowerCAmelCase : Any = hidden_size
__lowerCAmelCase : Dict = num_hidden_layers
__lowerCAmelCase : List[str] = num_attention_heads
__lowerCAmelCase : List[Any] = intermediate_size
__lowerCAmelCase : List[Any] = hidden_dropout_prob
__lowerCAmelCase : int = attention_probs_dropout_prob
__lowerCAmelCase : List[Any] = max_position_embeddings
__lowerCAmelCase : int = initializer_range
__lowerCAmelCase : List[Any] = layer_norm_eps
__lowerCAmelCase : List[Any] = position_embedding_type
__lowerCAmelCase : Optional[Any] = use_cache
__lowerCAmelCase : List[str] = emb_layer_norm_before
__lowerCAmelCase : Tuple = token_dropout
__lowerCAmelCase : List[str] = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values.")
__lowerCAmelCase : str = EsmFoldConfig()
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : int = EsmFoldConfig(**_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!")
__lowerCAmelCase : List[Any] = get_default_vocab_list()
else:
__lowerCAmelCase : Tuple = vocab_list
else:
__lowerCAmelCase : Union[str, Any] = None
__lowerCAmelCase : List[Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , _SCREAMING_SNAKE_CASE):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!")
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Dict:
"""simple docstring"""
__lowerCAmelCase : List[str] = super().to_dict()
if isinstance(self.esmfold_config , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[str] = self.esmfold_config.to_dict()
return output
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = 1_2_8
SCREAMING_SNAKE_CASE = None
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Any:
"""simple docstring"""
if self.trunk is None:
__lowerCAmelCase : List[str] = TrunkConfig()
elif isinstance(self.trunk , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : List[str] = TrunkConfig(**self.trunk)
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = asdict(self)
__lowerCAmelCase : Tuple = self.trunk.to_dict()
return output
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = 4_8
SCREAMING_SNAKE_CASE = 1_0_2_4
SCREAMING_SNAKE_CASE = 1_2_8
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = 3_2
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 1_2_8
SCREAMING_SNAKE_CASE = None
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Optional[int]:
"""simple docstring"""
if self.structure_module is None:
__lowerCAmelCase : Optional[Any] = StructureModuleConfig()
elif isinstance(self.structure_module , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : Any = StructureModuleConfig(**self.structure_module)
if self.max_recycles <= 0:
raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}.""")
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F""" {self.sequence_state_dim} and {self.sequence_state_dim}.""")
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""")
__lowerCAmelCase : int = self.sequence_state_dim // self.sequence_head_width
__lowerCAmelCase : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""")
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""")
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""")
if self.dropout >= 0.4:
raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}.""")
def _SCREAMING_SNAKE_CASE ( self: Dict) -> str:
"""simple docstring"""
__lowerCAmelCase : int = asdict(self)
__lowerCAmelCase : Union[str, Any] = self.structure_module.to_dict()
return output
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = 3_8_4
SCREAMING_SNAKE_CASE = 1_2_8
SCREAMING_SNAKE_CASE = 1_6
SCREAMING_SNAKE_CASE = 1_2_8
SCREAMING_SNAKE_CASE = 1_2
SCREAMING_SNAKE_CASE = 4
SCREAMING_SNAKE_CASE = 8
SCREAMING_SNAKE_CASE = 0.1
SCREAMING_SNAKE_CASE = 8
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = 7
SCREAMING_SNAKE_CASE = 1_0
SCREAMING_SNAKE_CASE = 1e-8
SCREAMING_SNAKE_CASE = 1e5
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> Union[str, Any]:
"""simple docstring"""
return asdict(self)
def _lowercase ( ) -> List[Any]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
) | 58 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase_ : Optional[int] = logging.get_logger(__name__)
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : int = b.T
A_ : List[Any] = np.sum(np.square(_UpperCAmelCase ) , axis=1 )
A_ : Optional[int] = np.sum(np.square(_UpperCAmelCase ) , axis=0 )
A_ : List[Any] = np.matmul(_UpperCAmelCase , _UpperCAmelCase )
A_ : Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Dict = x.reshape(-1 , 3 )
A_ : Any = squared_euclidean_distance(_UpperCAmelCase , _UpperCAmelCase )
return np.argmin(_UpperCAmelCase , axis=1 )
class _UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
lowercase_ : int = ["""pixel_values"""]
def __init__( self , snake_case_ = None , snake_case_ = True , snake_case_ = None , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = True , snake_case_ = True , **snake_case_ , ):
"""simple docstring"""
super().__init__(**snake_case_ )
A_ : Dict = size if size is not None else {'height': 2_5_6, 'width': 2_5_6}
A_ : Tuple = get_size_dict(snake_case_ )
A_ : Dict = np.array(snake_case_ ) if clusters is not None else None
A_ : Any = do_resize
A_ : Tuple = size
A_ : Optional[int] = resample
A_ : Optional[Any] = do_normalize
A_ : Tuple = do_color_quantize
def lowerCamelCase_ ( self , snake_case_ , snake_case_ , snake_case_ = PILImageResampling.BILINEAR , snake_case_ = None , **snake_case_ , ):
"""simple docstring"""
A_ : Any = get_size_dict(snake_case_ )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}""" )
return resize(
snake_case_ , size=(size['height'], size['width']) , resample=snake_case_ , data_format=snake_case_ , **snake_case_ )
def lowerCamelCase_ ( self , snake_case_ , snake_case_ = None , ):
"""simple docstring"""
A_ : Any = rescale(image=snake_case_ , scale=1 / 1_27.5 , data_format=snake_case_ )
A_ : str = image - 1
return image
def lowerCamelCase_ ( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = ChannelDimension.FIRST , **snake_case_ , ):
"""simple docstring"""
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : List[str] = size if size is not None else self.size
A_ : Tuple = get_size_dict(snake_case_ )
A_ : Optional[Any] = resample if resample is not None else self.resample
A_ : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
A_ : List[str] = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
A_ : Union[str, Any] = clusters if clusters is not None else self.clusters
A_ : Tuple = np.array(snake_case_ )
A_ : Optional[int] = make_list_of_images(snake_case_ )
if not valid_images(snake_case_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_color_quantize and clusters is None:
raise ValueError('Clusters must be specified if do_color_quantize is True.' )
# All transformations expect numpy arrays.
A_ : List[Any] = [to_numpy_array(snake_case_ ) for image in images]
if do_resize:
A_ : Dict = [self.resize(image=snake_case_ , size=snake_case_ , resample=snake_case_ ) for image in images]
if do_normalize:
A_ : int = [self.normalize(image=snake_case_ ) for image in images]
if do_color_quantize:
A_ : List[str] = [to_channel_dimension_format(snake_case_ , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
A_ : Tuple = np.array(snake_case_ )
A_ : int = color_quantize(snake_case_ , snake_case_ ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
A_ : Dict = images.shape[0]
A_ : str = images.reshape(snake_case_ , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
A_ : Optional[int] = list(snake_case_ )
else:
A_ : Union[str, Any] = [to_channel_dimension_format(snake_case_ , snake_case_ ) for image in images]
A_ : List[str] = {'input_ids': images}
return BatchFeature(data=snake_case_ , tensor_type=snake_case_ ) | 286 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = R'\w+[.]\d+'
A_ : int = re.findall(_UpperCAmelCase , _UpperCAmelCase )
for pat in pats:
A_ : Optional[int] = key.replace(_UpperCAmelCase , '_'.join(pat.split('.' ) ) )
return key
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = pt_tuple_key[:-1] + ('scale',)
if (
any('norm' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
A_ : Union[str, Any] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
A_ : List[str] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
A_ : Optional[Any] = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
A_ : int = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
A_ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
A_ : Optional[Any] = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
A_ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
A_ : Tuple = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
A_ : Optional[int] = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=42 ):
"""simple docstring"""
A_ : int = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
A_ : Union[str, Any] = flax_model.init_weights(PRNGKey(_UpperCAmelCase ) )
A_ : Optional[Any] = flatten_dict(_UpperCAmelCase )
A_ : Tuple = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
A_ : Any = rename_key(_UpperCAmelCase )
A_ : List[str] = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
A_ , A_ : Union[str, Any] = rename_key_and_reshape_tensor(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
A_ : str = jnp.asarray(_UpperCAmelCase )
return unflatten_dict(_UpperCAmelCase ) | 286 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
_UpperCAmelCase : str = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Any = "git_vision_model"
def __init__( self , A_=768 , A_=3_072 , A_=12 , A_=12 , A_=3 , A_=224 , A_=16 , A_="quick_gelu" , A_=1e-5 , A_=0.0 , A_=0.02 , **A_ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**A_ )
UpperCamelCase = hidden_size
UpperCamelCase = intermediate_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = num_channels
UpperCamelCase = patch_size
UpperCamelCase = image_size
UpperCamelCase = initializer_range
UpperCamelCase = attention_dropout
UpperCamelCase = layer_norm_eps
UpperCamelCase = hidden_act
@classmethod
def __UpperCamelCase ( cls , A_ , **A_ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(A_ )
UpperCamelCase , UpperCamelCase = cls.get_config_dict(A_ , **A_ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get('model_type' ) == "git":
UpperCamelCase = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(A_ , **A_ )
class lowercase ( _SCREAMING_SNAKE_CASE ):
__lowercase : Optional[Any] = "git"
def __init__( self , A_=None , A_=30_522 , A_=768 , A_=6 , A_=12 , A_=3_072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=1_024 , A_=0.02 , A_=1e-12 , A_=0 , A_="absolute" , A_=True , A_=False , A_=101 , A_=102 , A_=None , **A_ , ) -> Optional[int]:
"""simple docstring"""
super().__init__(bos_token_id=A_ , eos_token_id=A_ , pad_token_id=A_ , **A_ )
if vision_config is None:
UpperCamelCase = {}
logger.info('vision_config is None. initializing the GitVisionConfig with default values.' )
UpperCamelCase = GitVisionConfig(**A_ )
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 = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = position_embedding_type
UpperCamelCase = use_cache
UpperCamelCase = tie_word_embeddings
UpperCamelCase = num_image_with_embedding
UpperCamelCase = bos_token_id
UpperCamelCase = eos_token_id
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase = copy.deepcopy(self.__dict__ )
UpperCamelCase = self.vision_config.to_dict()
UpperCamelCase = self.__class__.model_type
return output
| 366 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 110 | 0 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ ):
if not input_list:
return []
_lowerCamelCase : Dict = [input_list.count(lowercase__ ) for value in input_list]
_lowerCamelCase : List[str] = max(lowercase__ ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(lowercase__ ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 96 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """new-model"""
if is_tf_available():
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = NewModelConfig
@require_tf
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
_lowerCamelCase : List[str] = 'bert-base-cased'
_lowerCamelCase : int = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForPreTraining.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : int = TFAutoModelForCausalLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : str = TFAutoModelForCausalLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : List[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : str = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Tuple = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForMaskedLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase : Optional[int] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : str = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Union[str, Any] = TFAutoModelForSequenceClassification.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
def A_ ( self ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
_lowerCamelCase : Optional[Any] = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
@slow
@require_tensorflow_probability
def A_ ( self ):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
_lowerCamelCase : Dict = AutoConfig.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Dict = TFAutoModelForTableQuestionAnswering.from_pretrained(lowercase )
_lowerCamelCase, _lowerCamelCase : List[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(
lowercase , output_loading_info=lowercase )
self.assertIsNotNone(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : int = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
_lowerCamelCase : Any = TFAutoModelWithLMHead.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=lowercase ) , 14410 )
def A_ ( self ):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
_lowerCamelCase : List[str] = TFAutoModel.from_pretrained('sgugger/funnel-random-tiny' )
self.assertIsInstance(lowercase , lowercase )
_lowerCamelCase : Optional[int] = copy.deepcopy(model.config )
_lowerCamelCase : Dict = ['FunnelBaseModel']
_lowerCamelCase : List[Any] = TFAutoModel.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
def A_ ( self ):
try:
AutoConfig.register('new-model' , lowercase )
_lowerCamelCase : Tuple = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
auto_class.register(lowercase , lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase ):
auto_class.register(lowercase , lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
_lowerCamelCase : Optional[Any] = BertModelTester(self ).get_config()
_lowerCamelCase : Dict = NewModelConfig(**tiny_config.to_dict() )
_lowerCamelCase : int = auto_class.from_config(lowercase )
self.assertIsInstance(lowercase , lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(lowercase )
_lowerCamelCase : List[Any] = auto_class.from_pretrained(lowercase )
self.assertIsInstance(lowercase , lowercase )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'bert-base is not a local folder and is not a valid model identifier' ):
_lowerCamelCase : Union[str, Any] = TFAutoModel.from_pretrained('bert-base' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_lowerCamelCase : str = TFAutoModel.from_pretrained(lowercase , revision='aaaaaa' )
def A_ ( self ):
with self.assertRaisesRegex(
lowercase , 'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin' , ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def A_ ( self ):
with self.assertRaisesRegex(lowercase , 'Use `from_pt=True` to load this model' ):
_lowerCamelCase : Tuple = TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
def A_ ( self ):
# Make sure we have cached the model.
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
with RequestCounter() as counter:
_lowerCamelCase : Optional[int] = TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
_lowerCamelCase : int = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
with RequestCounter() as counter:
_lowerCamelCase : List[Any] = TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 ) | 96 | 1 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
def get_matched_characters(__a , __a ) -> str:
snake_case_ : Dict = []
snake_case_ : int = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ : str = int(max(0 , i - limit ) )
snake_case_ : List[Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__a )
snake_case_ : str = f"""{_stra[0:_stra.index(__a )]} {_stra[_stra.index(__a ) + 1:]}"""
return "".join(__a )
# matching characters
snake_case_ : Optional[Any] = get_matched_characters(__a , __a )
snake_case_ : Any = get_matched_characters(__a , __a )
snake_case_ : Optional[int] = len(__a )
# transposition
snake_case_ : Tuple = (
len([(ca, ca) for ca, ca in zip(__a , __a ) if ca != ca] ) // 2
)
if not match_count:
snake_case_ : Union[str, Any] = 0.0
else:
snake_case_ : Tuple = (
1
/ 3
* (
match_count / len(__a )
+ match_count / len(__a )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
snake_case_ : Optional[Any] = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 88 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Optional[int] = int(__a )
# Initialize Result
snake_case_ : Tuple = []
# Traverse through all denomination
for denomination in reversed(__a ):
# Find denominations
while int(__a ) >= int(__a ):
total_value -= int(__a )
answer.append(__a ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """0"""
if (
input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
== "y"
):
_SCREAMING_SNAKE_CASE = int(input("""Enter the number of denominations you want to add: """).strip())
for i in range(0, n):
denominations.append(int(input(F'''Denomination {i}: ''').strip()))
_SCREAMING_SNAKE_CASE = input("""Enter the change you want to make in Indian Currency: """).strip()
else:
# All denominations of Indian Currency if user does not enter
_SCREAMING_SNAKE_CASE = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
_SCREAMING_SNAKE_CASE = input("""Enter the change you want to make: """).strip()
if int(value) == 0 or int(value) < 0:
print("""The total value cannot be zero or negative.""")
else:
print(F'''Following is minimal change for {value}: ''')
_SCREAMING_SNAKE_CASE = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=""" """)
| 88 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=13 , UpperCamelCase__=30 , UpperCamelCase__=2 , UpperCamelCase__=3 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=32 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=37 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=10 , UpperCamelCase__=0.02 , UpperCamelCase__=3 , UpperCamelCase__=None , UpperCamelCase__=2 , ) -> Union[str, Any]:
lowerCamelCase : int = parent
lowerCamelCase : Optional[int] = batch_size
lowerCamelCase : Any = image_size
lowerCamelCase : List[str] = patch_size
lowerCamelCase : Tuple = num_channels
lowerCamelCase : List[Any] = is_training
lowerCamelCase : List[str] = use_labels
lowerCamelCase : int = hidden_size
lowerCamelCase : Tuple = num_hidden_layers
lowerCamelCase : Tuple = num_attention_heads
lowerCamelCase : List[Any] = intermediate_size
lowerCamelCase : Any = hidden_act
lowerCamelCase : Dict = hidden_dropout_prob
lowerCamelCase : Tuple = attention_probs_dropout_prob
lowerCamelCase : Optional[Any] = type_sequence_label_size
lowerCamelCase : Union[str, Any] = initializer_range
lowerCamelCase : List[str] = scope
lowerCamelCase : List[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCamelCase : List[str] = (image_size // patch_size) ** 2
lowerCamelCase : Dict = num_patches + 2
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase : Optional[int] = None
if self.use_labels:
lowerCamelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase : Tuple = self.get_config()
return config, pixel_values, labels
def _lowercase ( self ) -> Optional[int]:
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
lowerCamelCase : List[str] = TFDeiTModel(config=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
lowerCamelCase : int = TFDeiTForMaskedImageModeling(config=UpperCamelCase__ )
lowerCamelCase : List[str] = model(UpperCamelCase__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCamelCase : str = 1
lowerCamelCase : Optional[int] = TFDeiTForMaskedImageModeling(UpperCamelCase__ )
lowerCamelCase : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase : Dict = model(UpperCamelCase__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
lowerCamelCase : Optional[Any] = self.type_sequence_label_size
lowerCamelCase : Optional[int] = TFDeiTForImageClassification(UpperCamelCase__ )
lowerCamelCase : Tuple = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase : List[str] = 1
lowerCamelCase : str = TFDeiTForImageClassification(UpperCamelCase__ )
lowerCamelCase : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase : Optional[int] = model(UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _lowercase ( self ) -> str:
lowerCamelCase : int = self.prepare_config_and_inputs()
lowerCamelCase , lowerCamelCase , lowerCamelCase : Any = config_and_inputs
lowerCamelCase : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase__ (lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : List[Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
lowerCamelCase_ : Optional[Any] = (
{
"""feature-extraction""": TFDeiTModel,
"""image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
lowerCamelCase_ : Dict = False
lowerCamelCase_ : Tuple = False
lowerCamelCase_ : str = False
lowerCamelCase_ : List[Any] = False
def _lowercase ( self ) -> Union[str, Any]:
lowerCamelCase : Optional[int] = TFDeiTModelTester(self )
lowerCamelCase : Any = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ , hidden_size=37 )
def _lowercase ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def _lowercase ( self ) -> int:
pass
def _lowercase ( self ) -> List[Any]:
lowerCamelCase , lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase : Optional[Any] = model_class(UpperCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCamelCase : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase__ , tf.keras.layers.Dense ) )
def _lowercase ( self ) -> Tuple:
lowerCamelCase , lowerCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase : Optional[Any] = model_class(UpperCamelCase__ )
lowerCamelCase : int = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase : Any = [*signature.parameters.keys()]
lowerCamelCase : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def _lowercase ( self ) -> str:
lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def _lowercase ( self ) -> Tuple:
lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__ )
def _lowercase ( self ) -> Optional[int]:
lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False ) -> List[str]:
lowerCamelCase : Optional[Any] = super()._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def _lowercase ( self ) -> Optional[Any]:
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : Any = TFDeiTModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def A ( ) -> List[str]:
lowerCamelCase : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowercase ( self ) -> List[str]:
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def _lowercase ( self ) -> str:
lowerCamelCase : int = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" )
lowerCamelCase : str = self.default_image_processor
lowerCamelCase : Optional[Any] = prepare_img()
lowerCamelCase : Tuple = image_processor(images=UpperCamelCase__ , return_tensors="tf" )
# forward pass
lowerCamelCase : Optional[int] = model(**UpperCamelCase__ )
# verify the logits
lowerCamelCase : str = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
lowerCamelCase : Optional[Any] = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 48 |
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ) -> Any:
lowerCamelCase : Any = []
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'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "deit.embeddings.cls_token"),
("dist_token", "deit.embeddings.distillation_token"),
("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "deit.embeddings.position_embeddings"),
] )
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 "deit" from all keys that start with "deit"
lowerCamelCase : Union[str, Any] = [(pair[0], pair[1][4:]) if pair[1].startswith("deit" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("norm.weight", "deit.layernorm.weight"),
("norm.bias", "deit.layernorm.bias"),
("head.weight", "cls_classifier.weight"),
("head.bias", "cls_classifier.bias"),
("head_dist.weight", "distillation_classifier.weight"),
("head_dist.bias", "distillation_classifier.bias"),
] )
return rename_keys
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ) -> str:
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase : Optional[int] = ""
else:
lowerCamelCase : List[str] = "deit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
lowerCamelCase : Optional[int] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase : List[Any] = in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase : Any = in_proj_bias[: config.hidden_size]
lowerCamelCase : List[str] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase : List[Any] = in_proj_bias[-config.hidden_size :]
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> str:
lowerCamelCase : List[str] = dct.pop(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Any = val
def A ( ) -> List[str]:
lowerCamelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowerCamelCase : str = Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Union[str, Any] = DeiTConfig()
# all deit models have fine-tuned heads
lowerCamelCase : Optional[int] = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
lowerCamelCase : Dict = 1000
lowerCamelCase : Tuple = "huggingface/label-files"
lowerCamelCase : List[str] = "imagenet-1k-id2label.json"
lowerCamelCase : List[Any] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset" ) ,"r" ) )
lowerCamelCase : Optional[int] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowerCamelCase : Tuple = idalabel
lowerCamelCase : str = {v: k for k, v in idalabel.items()}
lowerCamelCase : Dict = int(deit_name[-6:-4] )
lowerCamelCase : Optional[Any] = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("tiny" ):
lowerCamelCase : Optional[Any] = 192
lowerCamelCase : List[str] = 768
lowerCamelCase : Tuple = 12
lowerCamelCase : Optional[Any] = 3
elif deit_name[9:].startswith("small" ):
lowerCamelCase : str = 384
lowerCamelCase : Optional[Any] = 1536
lowerCamelCase : Dict = 12
lowerCamelCase : Optional[int] = 6
if deit_name[9:].startswith("base" ):
pass
elif deit_name[4:].startswith("large" ):
lowerCamelCase : str = 1024
lowerCamelCase : List[str] = 4096
lowerCamelCase : Any = 24
lowerCamelCase : Dict = 16
# load original model from timm
lowerCamelCase : List[Any] = timm.create_model(_SCREAMING_SNAKE_CASE ,pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCamelCase : Dict = timm_model.state_dict()
lowerCamelCase : Dict = create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# load HuggingFace model
lowerCamelCase : Optional[Any] = DeiTForImageClassificationWithTeacher(_SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by DeiTImageProcessor
lowerCamelCase : Any = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
lowerCamelCase : Union[str, Any] = DeiTImageProcessor(size=_SCREAMING_SNAKE_CASE ,crop_size=config.image_size )
lowerCamelCase : str = image_processor(images=prepare_img() ,return_tensors="pt" )
lowerCamelCase : int = encoding["pixel_values"]
lowerCamelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = timm_model(_SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_SCREAMING_SNAKE_CASE ,outputs.logits ,atol=1e-3 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--deit_name',
default='vit_deit_base_distilled_patch16_224',
type=str,
help='Name of the DeiT 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.'
)
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 48 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
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__)
def __lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCamelCase = argparse.ArgumentParser(
description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' )
parser.add_argument('''--file_path''' , type=UpperCamelCase__ , default='''data/dump.txt''' , help='''The path to the data.''' )
parser.add_argument('''--tokenizer_type''' , type=UpperCamelCase__ , default='''bert''' , choices=['''bert''', '''roberta''', '''gpt2'''] )
parser.add_argument('''--tokenizer_name''' , type=UpperCamelCase__ , default='''bert-base-uncased''' , help='''The tokenizer to use.''' )
parser.add_argument('''--dump_file''' , type=UpperCamelCase__ , default='''data/dump''' , help='''The dump file prefix.''' )
__lowerCamelCase = parser.parse_args()
logger.info(f"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
__lowerCamelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
__lowerCamelCase = tokenizer.special_tokens_map['''cls_token'''] # `[CLS]`
__lowerCamelCase = tokenizer.special_tokens_map['''sep_token'''] # `[SEP]`
elif args.tokenizer_type == "roberta":
__lowerCamelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
__lowerCamelCase = tokenizer.special_tokens_map['''cls_token'''] # `<s>`
__lowerCamelCase = tokenizer.special_tokens_map['''sep_token'''] # `</s>`
elif args.tokenizer_type == "gpt2":
__lowerCamelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
__lowerCamelCase = tokenizer.special_tokens_map['''bos_token'''] # `<|endoftext|>`
__lowerCamelCase = tokenizer.special_tokens_map['''eos_token'''] # `<|endoftext|>`
logger.info(f"""Loading text from {args.file_path}""" )
with open(args.file_path , '''r''' , encoding='''utf8''' ) as fp:
__lowerCamelCase = fp.readlines()
logger.info('''Start encoding''' )
logger.info(f"""{len(UpperCamelCase__ )} examples to process.""" )
__lowerCamelCase = []
__lowerCamelCase = 0
__lowerCamelCase = 1_00_00
__lowerCamelCase = time.time()
for text in data:
__lowerCamelCase = f"""{bos} {text.strip()} {sep}"""
__lowerCamelCase = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
rslt.append(UpperCamelCase__ )
iter += 1
if iter % interval == 0:
__lowerCamelCase = time.time()
logger.info(f"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
__lowerCamelCase = time.time()
logger.info('''Finished binarization''' )
logger.info(f"""{len(UpperCamelCase__ )} examples processed.""" )
__lowerCamelCase = f"""{args.dump_file}.{args.tokenizer_name}.pickle"""
__lowerCamelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
__lowerCamelCase = [np.uintaa(UpperCamelCase__ ) for d in rslt]
else:
__lowerCamelCase = [np.intaa(UpperCamelCase__ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f"""Dump to {dp_file}""" )
with open(UpperCamelCase__ , '''wb''' ) as handle:
pickle.dump(rslt_ , UpperCamelCase__ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 237 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
return "".join(chr(ord(UpperCamelCase__ ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 237 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def __lowerCAmelCase (_UpperCamelCase ):
if is_torch_version('<' , '2.0.0' ) or not hasattr(_UpperCamelCase , '_dynamo' ):
return False
return isinstance(_UpperCamelCase , torch._dynamo.eval_frame.OptimizedModule )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase = True ):
__lowerCAmelCase : List[str] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
__lowerCAmelCase : str = is_compiled_module(_UpperCamelCase )
if is_compiled:
__lowerCAmelCase : List[str] = model
__lowerCAmelCase : Optional[int] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : List[Any] = model.module
if not keep_fpaa_wrapper:
__lowerCAmelCase : Optional[Any] = getattr(_UpperCamelCase , 'forward' )
__lowerCAmelCase : List[str] = model.__dict__.pop('_original_forward' , _UpperCamelCase )
if original_forward is not None:
while hasattr(_UpperCamelCase , '__wrapped__' ):
__lowerCAmelCase : List[str] = forward.__wrapped__
if forward == original_forward:
break
__lowerCAmelCase : Dict = forward
if getattr(_UpperCamelCase , '_converted_to_transformer_engine' , _UpperCamelCase ):
convert_model(_UpperCamelCase , to_transformer_engine=_UpperCamelCase )
if is_compiled:
__lowerCAmelCase : Dict = model
__lowerCAmelCase : Tuple = compiled_model
return model
def __lowerCAmelCase ():
PartialState().wait_for_everyone()
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(_UpperCamelCase , _UpperCamelCase )
elif PartialState().local_process_index == 0:
torch.save(_UpperCamelCase , _UpperCamelCase )
@contextmanager
def __lowerCAmelCase (**_UpperCamelCase ):
for key, value in kwargs.items():
__lowerCAmelCase : List[str] = str(_UpperCamelCase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def __lowerCAmelCase (_UpperCamelCase ):
if not hasattr(_UpperCamelCase , '__qualname__' ) and not hasattr(_UpperCamelCase , '__name__' ):
__lowerCAmelCase : Optional[Any] = getattr(_UpperCamelCase , '__class__' , _UpperCamelCase )
if hasattr(_UpperCamelCase , '__qualname__' ):
return obj.__qualname__
if hasattr(_UpperCamelCase , '__name__' ):
return obj.__name__
return str(_UpperCamelCase )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
for key, value in source.items():
if isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Optional[Any] = destination.setdefault(_UpperCamelCase , {} )
merge_dicts(_UpperCamelCase , _UpperCamelCase )
else:
__lowerCAmelCase : Optional[Any] = value
return destination
def __lowerCAmelCase (_UpperCamelCase = None ):
if port is None:
__lowerCAmelCase : Tuple = 2_9500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0 | 86 |
"""simple docstring"""
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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class A__ ( _lowerCamelCase):
A_ : Optional[int] = 'poolformer'
def __init__( self , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4.0 , _SCREAMING_SNAKE_CASE=[2, 2, 6, 2] , _SCREAMING_SNAKE_CASE=[64, 1_28, 3_20, 5_12] , _SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , _SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , _SCREAMING_SNAKE_CASE=[2, 1, 1, 1] , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=0.02 , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : int = num_channels
__lowerCAmelCase : str = patch_size
__lowerCAmelCase : Optional[Any] = stride
__lowerCAmelCase : Optional[int] = padding
__lowerCAmelCase : List[Any] = pool_size
__lowerCAmelCase : int = hidden_sizes
__lowerCAmelCase : str = mlp_ratio
__lowerCAmelCase : Optional[int] = depths
__lowerCAmelCase : str = patch_sizes
__lowerCAmelCase : str = strides
__lowerCAmelCase : Optional[int] = num_encoder_blocks
__lowerCAmelCase : Any = drop_path_rate
__lowerCAmelCase : Any = hidden_act
__lowerCAmelCase : Dict = use_layer_scale
__lowerCAmelCase : Union[str, Any] = layer_scale_init_value
__lowerCAmelCase : Dict = initializer_range
super().__init__(**_SCREAMING_SNAKE_CASE )
class A__ ( _lowerCamelCase):
A_ : List[str] = version.parse('1.11')
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __lowerCamelCase ( self ):
return 2E-3 | 86 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Any = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlenderbotConfig",
"BlenderbotOnnxConfig",
],
"tokenization_blenderbot": ["BlenderbotTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ["BlenderbotTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = [
"BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlenderbotForCausalLM",
"BlenderbotForConditionalGeneration",
"BlenderbotModel",
"BlenderbotPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = [
"TFBlenderbotForConditionalGeneration",
"TFBlenderbotModel",
"TFBlenderbotPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
"FlaxBlenderbotForConditionalGeneration",
"FlaxBlenderbotModel",
"FlaxBlenderbotPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 363 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : List[str] = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : List[str] ="""distilbert"""
__UpperCAmelCase : int ={
"""hidden_size""": """dim""",
"""num_attention_heads""": """n_heads""",
"""num_hidden_layers""": """n_layers""",
}
def __init__( self , __a=3_05_22 , __a=5_12 , __a=False , __a=6 , __a=12 , __a=7_68 , __a=4 * 7_68 , __a=0.1 , __a=0.1 , __a="gelu" , __a=0.0_2 , __a=0.1 , __a=0.2 , __a=0 , **__a , ):
__lowerCAmelCase = vocab_size
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = sinusoidal_pos_embds
__lowerCAmelCase = n_layers
__lowerCAmelCase = n_heads
__lowerCAmelCase = dim
__lowerCAmelCase = hidden_dim
__lowerCAmelCase = dropout
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = activation
__lowerCAmelCase = initializer_range
__lowerCAmelCase = qa_dropout
__lowerCAmelCase = seq_classif_dropout
super().__init__(**__a , pad_token_id=__a )
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
@property
def snake_case ( self ):
if self.task == "multiple-choice":
__lowerCAmelCase = {0: "batch", 1: "choice", 2: "sequence"}
else:
__lowerCAmelCase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 259 | 0 |
"""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 : str = logging.get_logger(__name__)
_a : Optional[int] = {
'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 : List[Any] = [
'ctc_proj',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any ,_lowerCamelCase : List[Any] ,_lowerCamelCase : Optional[int] ,_lowerCamelCase : Union[str, Any] ,_lowerCamelCase : str ) -> List[str]:
for attribute in key.split(""".""" ):
_lowerCAmelCase : Tuple = getattr(_lowerCamelCase ,_lowerCamelCase )
if weight_type is not None:
_lowerCAmelCase : int = getattr(_lowerCamelCase ,_lowerCamelCase ).shape
else:
_lowerCAmelCase : str = 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 : Optional[Any] = value
elif weight_type == "weight_g":
_lowerCAmelCase : str = value
elif weight_type == "weight_v":
_lowerCAmelCase : Union[str, Any] = value
elif weight_type == "bias":
_lowerCAmelCase : Optional[int] = value
else:
_lowerCAmelCase : List[Any] = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict ,_lowerCamelCase : Dict ) -> Any:
_lowerCAmelCase : int = []
_lowerCAmelCase : Dict = fairseq_model.state_dict()
_lowerCAmelCase : int = hf_model.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase : int = False
if "conv_layers" in name:
load_conv_layer(
_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,hf_model.config.feat_extract_norm == """group""" ,)
_lowerCAmelCase : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase : List[Any] = True
if "*" in mapped_key:
_lowerCAmelCase : int = name.split(_lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase : Any = mapped_key.replace("""*""" ,_lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase : int = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase : List[str] = """weight_v"""
elif "bias" in name and "relative_attention_bias" not in name:
_lowerCAmelCase : Tuple = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase : str = """weight"""
else:
_lowerCAmelCase : Dict = None
set_recursively(_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase )
continue
if not is_used:
unused_weights.append(_lowerCamelCase )
logger.warning(f"Unused weights: {unused_weights}" )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ,_lowerCamelCase : Any ,_lowerCamelCase : Tuple ,_lowerCamelCase : Optional[Any] ,_lowerCamelCase : Optional[Any] ) -> List[str]:
_lowerCAmelCase : List[Any] = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase : Optional[Any] = name.split(""".""" )
_lowerCAmelCase : Dict = int(items[0] )
_lowerCAmelCase : List[str] = 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 : List[str] = 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 : Any = 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 : List[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 : int = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_lowerCamelCase )
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : List[Any] ,_lowerCamelCase : str=None ) -> Dict:
# load the pre-trained checkpoints
_lowerCAmelCase : int = torch.load(_lowerCamelCase )
_lowerCAmelCase : List[str] = WavLMConfigOrig(checkpoint["""cfg"""] )
_lowerCAmelCase : Tuple = WavLMOrig(_lowerCamelCase )
model.load_state_dict(checkpoint["""model"""] )
model.eval()
if config_path is not None:
_lowerCAmelCase : Any = WavLMConfig.from_pretrained(_lowerCamelCase )
else:
_lowerCAmelCase : Any = WavLMConfig()
_lowerCAmelCase : Union[str, Any] = WavLMModel(_lowerCamelCase )
recursively_load_weights(_lowerCamelCase ,_lowerCamelCase )
hf_wavlm.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
_a : Optional[int] = 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 : int = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 44 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.get_logger(__name__)
a__ : Dict = {
'''openai/imagegpt-small''': '''''',
'''openai/imagegpt-medium''': '''''',
'''openai/imagegpt-large''': '''''',
}
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = 'imagegpt'
__SCREAMING_SNAKE_CASE : Optional[Any] = ['past_key_values']
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , _lowerCamelCase=512 + 1 , _lowerCamelCase=32 * 32 , _lowerCamelCase=512 , _lowerCamelCase=24 , _lowerCamelCase=8 , _lowerCamelCase=None , _lowerCamelCase="quick_gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=1e-5 , _lowerCamelCase=0.0_2 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase=False , _lowerCamelCase=False , **_lowerCamelCase , ) ->str:
SCREAMING_SNAKE_CASE : Any = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = n_positions
SCREAMING_SNAKE_CASE : Optional[int] = n_embd
SCREAMING_SNAKE_CASE : List[Any] = n_layer
SCREAMING_SNAKE_CASE : List[Any] = n_head
SCREAMING_SNAKE_CASE : int = n_inner
SCREAMING_SNAKE_CASE : Dict = activation_function
SCREAMING_SNAKE_CASE : Union[str, Any] = resid_pdrop
SCREAMING_SNAKE_CASE : Dict = embd_pdrop
SCREAMING_SNAKE_CASE : List[str] = attn_pdrop
SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_epsilon
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : int = scale_attn_weights
SCREAMING_SNAKE_CASE : Optional[int] = use_cache
SCREAMING_SNAKE_CASE : Optional[Any] = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE : str = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE : List[str] = tie_word_embeddings
super().__init__(tie_word_embeddings=_lowerCamelCase , **_lowerCamelCase )
class a_ ( a__ ):
"""simple docstring"""
@property
def __lowerCAmelCase ( self ) ->Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
] )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = 1 , _lowerCamelCase = -1 , _lowerCamelCase = False , _lowerCamelCase = None , _lowerCamelCase = 3 , _lowerCamelCase = 32 , _lowerCamelCase = 32 , ) ->Mapping[str, Any]:
SCREAMING_SNAKE_CASE : Union[str, Any] = self._generate_dummy_images(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE : Tuple = dict(preprocessor(images=_lowerCamelCase , return_tensors=_lowerCamelCase ) )
return inputs
| 313 | 0 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__A = logging.get_logger(__name__)
class _snake_case :
snake_case__ = None
@experimental
def lowercase_ ( _lowerCamelCase: Optional[Any] , _lowerCamelCase: List[Any] , _lowerCamelCase: Tuple , _lowerCamelCase: int , _lowerCamelCase: str , _lowerCamelCase: Union[str, Any] , _lowerCamelCase: Optional[Any] ) -> List[str]:
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return _map_with_joblib(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def lowercase_ ( _lowerCamelCase: int , _lowerCamelCase: Optional[int] , _lowerCamelCase: Optional[Any] , _lowerCamelCase: List[Any] , _lowerCamelCase: List[Any] , _lowerCamelCase: int , _lowerCamelCase: List[Any] ) -> List[str]:
'''simple docstring'''
__lowerCamelCase : Any = num_proc if num_proc <= len(_lowerCamelCase ) else len(_lowerCamelCase )
__lowerCamelCase : str = [] # We organize the splits ourselve (contiguous splits)
for index in range(_lowerCamelCase ):
__lowerCamelCase : Dict = len(_lowerCamelCase ) // num_proc
__lowerCamelCase : Union[str, Any] = len(_lowerCamelCase ) % num_proc
__lowerCamelCase : Tuple = div * index + min(_lowerCamelCase , _lowerCamelCase )
__lowerCamelCase : Optional[Any] = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(_lowerCamelCase ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F"""Error dividing inputs iterable among processes. """
F"""Total number of objects {len(_lowerCamelCase )}, """
F"""length: {sum(len(i[1] ) for i in split_kwds )}""" )
logger.info(
F"""Spawning {num_proc} processes for {len(_lowerCamelCase )} objects in slices of {[len(i[1] ) for i in split_kwds]}""" )
__lowerCamelCase : Optional[Any] = None, None
if not disable_tqdm:
__lowerCamelCase : Optional[int] = (RLock(),), tqdm.set_lock
with Pool(_lowerCamelCase , initargs=_lowerCamelCase , initializer=_lowerCamelCase ) as pool:
__lowerCamelCase : Dict = pool.map(_lowerCamelCase , _lowerCamelCase )
logger.info(F"""Finished {num_proc} processes""" )
__lowerCamelCase : Union[str, Any] = [obj for proc_res in mapped for obj in proc_res]
logger.info(F"""Unpacked {len(_lowerCamelCase )} objects""" )
return mapped
def lowercase_ ( _lowerCamelCase: Any , _lowerCamelCase: str , _lowerCamelCase: Union[str, Any] , _lowerCamelCase: Any , _lowerCamelCase: Any , _lowerCamelCase: List[str] , _lowerCamelCase: Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=_lowerCamelCase ):
return joblib.Parallel()(
joblib.delayed(_lowerCamelCase )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def lowercase_ ( _lowerCamelCase: str ) -> int:
'''simple docstring'''
__lowerCamelCase : Optional[int] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
__lowerCamelCase : int = None | 351 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
class _snake_case ( a__ ):
snake_case__ = "bert-generation"
def __init__( self : Optional[int] , UpperCAmelCase : Dict=50358 , UpperCAmelCase : int=1024 , UpperCAmelCase : Optional[int]=24 , UpperCAmelCase : str=16 , UpperCAmelCase : str=4096 , UpperCAmelCase : List[Any]="gelu" , UpperCAmelCase : str=0.1 , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : Union[str, Any]=512 , UpperCAmelCase : Optional[Any]=0.0_2 , UpperCAmelCase : int=1E-12 , UpperCAmelCase : Tuple=0 , UpperCAmelCase : int=2 , UpperCAmelCase : Optional[int]=1 , UpperCAmelCase : Union[str, Any]="absolute" , UpperCAmelCase : Tuple=True , **UpperCAmelCase : Optional[Any] , ):
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
__lowerCamelCase : Union[str, Any] = vocab_size
__lowerCamelCase : List[Any] = hidden_size
__lowerCamelCase : Any = num_hidden_layers
__lowerCamelCase : List[Any] = num_attention_heads
__lowerCamelCase : int = hidden_act
__lowerCamelCase : List[str] = intermediate_size
__lowerCamelCase : Tuple = hidden_dropout_prob
__lowerCamelCase : List[str] = attention_probs_dropout_prob
__lowerCamelCase : Optional[Any] = max_position_embeddings
__lowerCamelCase : List[Any] = initializer_range
__lowerCamelCase : Union[str, Any] = layer_norm_eps
__lowerCamelCase : List[str] = position_embedding_type
__lowerCamelCase : Optional[Any] = use_cache | 64 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
),
}
class __a ( lowerCAmelCase_ ):
__lowercase : Optional[int] = 'pix2struct_text_model'
__lowercase : Dict = ['past_key_values']
__lowercase : Union[str, Any] = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , lowerCAmelCase__=50_244 , lowerCAmelCase__=768 , lowerCAmelCase__=64 , lowerCAmelCase__=2_048 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=32 , lowerCAmelCase__=128 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1E-6 , lowerCAmelCase__=1.0 , lowerCAmelCase__="gelu_new" , lowerCAmelCase__=0 , lowerCAmelCase__=False , lowerCAmelCase__=0 , lowerCAmelCase__=1 , lowerCAmelCase__=False , lowerCAmelCase__=True , **lowerCAmelCase__ , ) -> List[Any]:
'''simple docstring'''
lowercase__: int = vocab_size
lowercase__: Tuple = hidden_size
lowercase__: Optional[int] = d_kv
lowercase__: Optional[Any] = d_ff
lowercase__: Any = num_layers
lowercase__: Optional[Any] = num_heads
lowercase__: List[str] = relative_attention_num_buckets
lowercase__: Optional[Any] = relative_attention_max_distance
lowercase__: Tuple = dropout_rate
lowercase__: Tuple = layer_norm_epsilon
lowercase__: Any = initializer_factor
lowercase__: Optional[int] = use_cache
lowercase__: Union[str, Any] = eos_token_id
lowercase__: Tuple = decoder_start_token_id
# for backwards compatibility
lowercase__: Any = dense_act_fn
super().__init__(
pad_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , tie_word_embeddings=__lowerCAmelCase , is_decoder=__lowerCAmelCase , **__lowerCAmelCase , )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , **lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
cls._set_token_in_kwargs(__lowerCAmelCase )
lowercase__ , lowercase__: str = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
lowercase__: Union[str, Any] = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
class __a ( lowerCAmelCase_ ):
__lowercase : Tuple = 'pix2struct_vision_model'
def __init__( self , lowerCAmelCase__=768 , lowerCAmelCase__=768 , lowerCAmelCase__=2_048 , lowerCAmelCase__=64 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__="gelu_new" , lowerCAmelCase__=1E-6 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=1E-10 , lowerCAmelCase__=1.0 , lowerCAmelCase__=4_096 , lowerCAmelCase__=32 , lowerCAmelCase__=128 , **lowerCAmelCase__ , ) -> int:
'''simple docstring'''
super().__init__(**__lowerCAmelCase )
lowercase__: str = hidden_size
lowercase__: Tuple = patch_embed_hidden_size
lowercase__: Dict = d_ff
lowercase__: str = dropout_rate
lowercase__: Union[str, Any] = num_hidden_layers
lowercase__: str = num_attention_heads
lowercase__: Union[str, Any] = initializer_range
lowercase__: str = initializer_factor
lowercase__: int = attention_dropout
lowercase__: Tuple = layer_norm_eps
lowercase__: Tuple = dense_act_fn
lowercase__: List[str] = seq_len
lowercase__: str = relative_attention_num_buckets
lowercase__: Union[str, Any] = relative_attention_max_distance
lowercase__: List[Any] = d_kv
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , **lowerCAmelCase__ ) -> str:
'''simple docstring'''
cls._set_token_in_kwargs(__lowerCAmelCase )
lowercase__ , lowercase__: Optional[Any] = cls.get_config_dict(__lowerCAmelCase , **__lowerCAmelCase )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type' ) == "pix2struct":
lowercase__: Union[str, Any] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__lowerCAmelCase , **__lowerCAmelCase )
class __a ( lowerCAmelCase_ ):
__lowercase : Any = 'pix2struct'
__lowercase : Any = True
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=1.0 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=True , **lowerCAmelCase__ , ) -> Dict:
'''simple docstring'''
super().__init__(tie_word_embeddings=__lowerCAmelCase , is_encoder_decoder=__lowerCAmelCase , **__lowerCAmelCase )
if text_config is None:
lowercase__: Optional[Any] = {}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.' )
if vision_config is None:
lowercase__: Any = {}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.' )
lowercase__: List[str] = PixaStructTextConfig(**__lowerCAmelCase )
lowercase__: Optional[Any] = PixaStructVisionConfig(**__lowerCAmelCase )
lowercase__: int = self.text_config.decoder_start_token_id
lowercase__: Union[str, Any] = self.text_config.pad_token_id
lowercase__: Optional[Any] = self.text_config.eos_token_id
lowercase__: Optional[Any] = initializer_factor
lowercase__: List[Any] = initializer_range
lowercase__: Union[str, Any] = self.initializer_range
lowercase__: str = self.initializer_range
lowercase__: List[str] = is_vqa
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: str = copy.deepcopy(self.__dict__ )
lowercase__: int = self.text_config.to_dict()
lowercase__: Tuple = self.vision_config.to_dict()
lowercase__: Optional[int] = self.__class__.model_type
return output
| 196 | """simple docstring"""
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
UpperCAmelCase__ = logging.get_logger(__name__)
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
def run_func(lowercase ):
@wraps(lowercase )
def run_in_eager_mode(*lowercase ,**lowercase ):
return func(*lowercase ,**lowercase )
@wraps(lowercase )
@tf.function(experimental_compile=lowercase )
def run_in_graph_mode(*lowercase ,**lowercase ):
return func(*lowercase ,**lowercase )
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 __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = random.Random()
_UpperCAmelCase = [rng.randint(0 ,vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowercase ,shape=(batch_size, sequence_length) ,dtype=tf.intaa )
class a ( lowerCAmelCase_ ):
_snake_case : TensorFlowBenchmarkArguments
_snake_case : PretrainedConfig
_snake_case : str = "TensorFlow"
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
return tf.__version__
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ):
# initialize GPU on separate process
_UpperCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_UpperCAmelCase = self._prepare_inference_func(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self._measure_speed(_inference )
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ):
_UpperCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_UpperCAmelCase = self._prepare_train_func(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self._measure_speed(_train )
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : 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] , __lowerCAmelCase )
_UpperCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_UpperCAmelCase = self._prepare_inference_func(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self._measure_memory(_inference )
def lowerCAmelCase_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , __lowerCAmelCase )
_UpperCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_UpperCAmelCase = self._prepare_train_func(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return self._measure_memory(_train )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ):
_UpperCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_UpperCAmelCase = (
hasattr(__lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , __lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_UpperCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_UpperCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_UpperCAmelCase = getattr(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = model_cls(__lowerCAmelCase )
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:
_UpperCAmelCase = TF_MODEL_MAPPING[config.__class__](__lowerCAmelCase )
# encoder-decoder has vocab size saved differently
_UpperCAmelCase = config.vocab_size if hasattr(__lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
_UpperCAmelCase = random_input_ids(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase , training=__lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(__lowerCAmelCase , training=__lowerCAmelCase )
_UpperCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : int ):
_UpperCAmelCase = 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.""" )
_UpperCAmelCase = (
hasattr(__lowerCAmelCase , """architectures""" )
and isinstance(config.architectures , __lowerCAmelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_UpperCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_UpperCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_UpperCAmelCase = getattr(__lowerCAmelCase , __lowerCAmelCase )
_UpperCAmelCase = model_cls(__lowerCAmelCase )
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:
_UpperCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCAmelCase )
# encoder-decoder has vocab size saved differently
_UpperCAmelCase = config.vocab_size if hasattr(__lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
_UpperCAmelCase = random_input_ids(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_UpperCAmelCase = model(__lowerCAmelCase , decoder_input_ids=__lowerCAmelCase , labels=__lowerCAmelCase , training=__lowerCAmelCase )[0]
_UpperCAmelCase = tf.gradients(__lowerCAmelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_UpperCAmelCase = model(__lowerCAmelCase , labels=__lowerCAmelCase , training=__lowerCAmelCase )[0]
_UpperCAmelCase = tf.gradients(__lowerCAmelCase , model.trainable_variables )
return gradients
_UpperCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : 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(__lowerCAmelCase , 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
_UpperCAmelCase = timeit.repeat(
__lowerCAmelCase , repeat=self.args.repeat , number=10 , )
return min(__lowerCAmelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : 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.""" )
_UpperCAmelCase = 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.""" )
_UpperCAmelCase = """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()
_UpperCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_UpperCAmelCase = nvml.nvmlDeviceGetMemoryInfo(__lowerCAmelCase )
_UpperCAmelCase = meminfo.used
_UpperCAmelCase = Memory(__lowerCAmelCase )
# 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.""" )
_UpperCAmelCase = None
else:
_UpperCAmelCase = measure_peak_memory_cpu(__lowerCAmelCase )
_UpperCAmelCase = Memory(__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_UpperCAmelCase = stop_memory_tracing(__lowerCAmelCase )
if memory is None:
_UpperCAmelCase = summary.total
else:
_UpperCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 289 | 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_fnet import FNetTokenizer
else:
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_lowerCAmelCase : Dict = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
_lowerCAmelCase : str = {
"google/fnet-base": 5_12,
"google/fnet-large": 5_12,
}
_lowerCAmelCase : List[str] = "▁"
class UpperCAmelCase_ ( _UpperCamelCase ):
__SCREAMING_SNAKE_CASE : str = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['input_ids', 'token_type_ids']
__SCREAMING_SNAKE_CASE : Tuple = FNetTokenizer
def __init__( self : str , A : int=None , A : int=None , A : Optional[int]=False , A : Optional[int]=True , A : Union[str, Any]=True , A : Optional[Any]="<unk>" , A : List[str]="[SEP]" , A : Tuple="<pad>" , A : int="[CLS]" , A : int="[MASK]" , **A : Optional[int] , ):
# 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.
_UpperCAmelCase : Union[str, Any] = (
AddedToken(A , lstrip=A , rstrip=A , normalized=A )
if isinstance(A , A )
else mask_token
)
super().__init__(
A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , **A , )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = remove_space
_UpperCAmelCase : Any = keep_accents
_UpperCAmelCase : Tuple = vocab_file
_UpperCAmelCase : Union[str, Any] = False if not self.vocab_file else True
def snake_case_ ( self : List[Any] , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : List[str] = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_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 snake_case_ ( self : Any , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Dict = [self.sep_token_id]
_UpperCAmelCase : Dict = [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 snake_case_ ( self : int , A : str , A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 202 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
_lowerCAmelCase : List[str] = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def __snake_case ( SCREAMING_SNAKE_CASE__ : Dict ) -> List[str]:
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_UpperCAmelCase : str = k.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return k
def __snake_case ( SCREAMING_SNAKE_CASE__ : dict , SCREAMING_SNAKE_CASE__ : dict ) -> PegasusForConditionalGeneration:
'''simple docstring'''
_UpperCAmelCase : List[Any] = DEFAULTS.copy()
cfg_kwargs.update(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[Any] = PegasusConfig(**SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[Any] = PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[int] = torch_model.model.state_dict()
_UpperCAmelCase : Union[str, Any] = {}
for k, v in tf_weights.items():
_UpperCAmelCase : Union[str, Any] = rename_state_dict_key(SCREAMING_SNAKE_CASE__ )
if new_k not in sd:
raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})' )
if "dense" in k or "proj" in new_k:
_UpperCAmelCase : Any = v.T
_UpperCAmelCase : str = torch.tensor(SCREAMING_SNAKE_CASE__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
# make sure embedding.padding_idx is respected
_UpperCAmelCase : Tuple = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1] )
_UpperCAmelCase : Any = mapping["shared.weight"]
_UpperCAmelCase : Dict = mapping["shared.weight"]
_UpperCAmelCase : Dict = {k: torch.zeros_like(SCREAMING_SNAKE_CASE__ ) for k, v in sd.items() if k.endswith("bias" ) and k not in mapping}
mapping.update(**SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase , _UpperCAmelCase : List[Any] = torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__ , strict=SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : int = [
k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], f'no matches found for the following tf keys {extra}'
return torch_model
def __snake_case ( SCREAMING_SNAKE_CASE__ : Dict="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Optional[int] = tf.train.list_variables(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : Optional[Any] = ["Adafactor", "global_step"]
for name, shape in tqdm(SCREAMING_SNAKE_CASE__ , desc="converting tf checkpoint to dict" ):
_UpperCAmelCase : Union[str, Any] = any(pat in name for pat in ignore_name )
if skip_key:
continue
_UpperCAmelCase : int = tf.train.load_variable(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Dict = array
return tf_weights
def __snake_case ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict = Path(SCREAMING_SNAKE_CASE__ ).parent.name
_UpperCAmelCase : Tuple = task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
_UpperCAmelCase : Dict = PegasusTokenizer.from_pretrained("sshleifer/pegasus" , model_max_length=SCREAMING_SNAKE_CASE__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(SCREAMING_SNAKE_CASE__ )
# convert model
_UpperCAmelCase : Union[str, Any] = get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[Any] = task_specific_params[f'summarization_{dataset}']
if dataset == "large":
_UpperCAmelCase : Optional[int] = task_specific_params
_UpperCAmelCase : str = convert_pegasus(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
torch_model.save_pretrained(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : Optional[Any] = torch_model.state_dict()
sd.pop("model.decoder.embed_positions.weight" )
sd.pop("model.encoder.embed_positions.weight" )
torch.save(SCREAMING_SNAKE_CASE__ , Path(SCREAMING_SNAKE_CASE__ ) / "pytorch_model.bin" )
if __name__ == "__main__":
_lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
_lowerCAmelCase : Union[str, Any] = parser.parse_args()
if args.save_dir is None:
_lowerCAmelCase : Tuple = Path(args.tf_ckpt_path).parent.name
_lowerCAmelCase : Dict = os.path.join("pegasus", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 202 | 1 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase ( lowerCAmelCase_ ) -> tuple:
return (data["data"], data["target"])
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> XGBClassifier:
_a : int = XGBClassifier()
classifier.fit(lowerCAmelCase_ , lowerCAmelCase_ )
return classifier
def __lowerCamelCase ( ) -> None:
_a : Optional[Any] = load_iris()
_a , _a : List[Any] = data_handling(lowerCAmelCase_ )
_a , _a , _a , _a : int = train_test_split(
lowerCAmelCase_ , lowerCAmelCase_ , test_size=0.25 )
_a : str = iris['target_names']
# Create an XGBoost Classifier from the training data
_a : Any = xgboost(lowerCAmelCase_ , lowerCAmelCase_ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , display_labels=lowerCAmelCase_ , cmap='Blues' , normalize='true' , )
plt.title('Normalized Confusion Matrix - IRIS Dataset' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 89 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase :int = logging.get_logger(__name__)
_lowerCAmelCase :Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''mgp-str'''
def __init__( self , A=[3_2, 1_2_8] , A=4 , A=3 , A=2_7 , A=3_8 , A=5_0_2_5_7 , A=3_0_5_2_2 , A=7_6_8 , A=1_2 , A=1_2 , A=4.0 , A=True , A=False , A=1E-5 , A=0.0 , A=0.0 , A=0.0 , A=False , A=0.02 , **A , ) -> Union[str, Any]:
super().__init__(**A )
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : str = patch_size
_UpperCAmelCase : Dict = num_channels
_UpperCAmelCase : Dict = max_token_length
_UpperCAmelCase : Optional[Any] = num_character_labels
_UpperCAmelCase : int = num_bpe_labels
_UpperCAmelCase : List[str] = num_wordpiece_labels
_UpperCAmelCase : Optional[int] = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : List[Any] = mlp_ratio
_UpperCAmelCase : List[str] = distilled
_UpperCAmelCase : Optional[int] = layer_norm_eps
_UpperCAmelCase : str = drop_rate
_UpperCAmelCase : List[Any] = qkv_bias
_UpperCAmelCase : List[str] = attn_drop_rate
_UpperCAmelCase : Dict = drop_path_rate
_UpperCAmelCase : Union[str, Any] = output_aa_attentions
_UpperCAmelCase : List[str] = initializer_range
| 263 | 0 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 186 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def snake_case (A_ :Dict ):
'''simple docstring'''
a : str = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def snake_case (A_ :Any , A_ :List[Any] ):
'''simple docstring'''
a : Union[str, Any] = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def snake_case (A_ :Dict ):
'''simple docstring'''
a : int = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token') )
return token
def snake_case ():
'''simple docstring'''
a : int = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def snake_case (A_ :int , A_ :Optional[int] , A_ :Dict , A_ :Dict ):
'''simple docstring'''
a : Optional[Any] = 'imagenet-1k-id2label.json'
a : Dict = 1_0_0_0
a : Tuple = 'huggingface/label-files'
a : List[Any] = num_labels
a : List[str] = json.load(open(cached_download(hf_hub_url(A_ , A_ , repo_type='dataset' ) ) , 'r' ) )
a : int = {int(A_ ): v for k, v in idalabel.items()}
a : str = idalabel
a : Optional[int] = {v: k for k, v in idalabel.items()}
a : Tuple = CvtConfig(num_labels=A_ , idalabel=A_ , labelaid=A_ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
a : int = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
a : List[Any] = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
a : Optional[int] = [2, 2, 2_0]
a : Any = [3, 1_2, 1_6]
a : str = [1_9_2, 7_6_8, 1_0_2_4]
a : List[Any] = CvtForImageClassification(A_ )
a : Optional[int] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
a : Union[str, Any] = image_size
a : Optional[Any] = torch.load(A_ , map_location=torch.device('cpu' ) )
a : int = OrderedDict()
a : Any = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
a : Dict = list_of_state_dict + cls_token(A_ )
a : Any = list_of_state_dict + embeddings(A_ )
for cnt in range(config.depth[idx] ):
a : Dict = list_of_state_dict + attention(A_ , A_ )
a : Any = list_of_state_dict + final()
for gg in list_of_state_dict:
print(A_ )
for i in range(len(A_ ) ):
a : List[Any] = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(A_ )
model.save_pretrained(A_ )
image_processor.save_pretrained(A_ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_UpperCamelCase : int = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 186 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
A_ = logging.get_logger(__name__)
A_ = OrderedDict(
[
("audio-spectrogram-transformer", "ASTFeatureExtractor"),
("beit", "BeitFeatureExtractor"),
("chinese_clip", "ChineseCLIPFeatureExtractor"),
("clap", "ClapFeatureExtractor"),
("clip", "CLIPFeatureExtractor"),
("clipseg", "ViTFeatureExtractor"),
("conditional_detr", "ConditionalDetrFeatureExtractor"),
("convnext", "ConvNextFeatureExtractor"),
("cvt", "ConvNextFeatureExtractor"),
("data2vec-audio", "Wav2Vec2FeatureExtractor"),
("data2vec-vision", "BeitFeatureExtractor"),
("deformable_detr", "DeformableDetrFeatureExtractor"),
("deit", "DeiTFeatureExtractor"),
("detr", "DetrFeatureExtractor"),
("dinat", "ViTFeatureExtractor"),
("donut-swin", "DonutFeatureExtractor"),
("dpt", "DPTFeatureExtractor"),
("encodec", "EncodecFeatureExtractor"),
("flava", "FlavaFeatureExtractor"),
("glpn", "GLPNFeatureExtractor"),
("groupvit", "CLIPFeatureExtractor"),
("hubert", "Wav2Vec2FeatureExtractor"),
("imagegpt", "ImageGPTFeatureExtractor"),
("layoutlmv2", "LayoutLMv2FeatureExtractor"),
("layoutlmv3", "LayoutLMv3FeatureExtractor"),
("levit", "LevitFeatureExtractor"),
("maskformer", "MaskFormerFeatureExtractor"),
("mctct", "MCTCTFeatureExtractor"),
("mobilenet_v1", "MobileNetV1FeatureExtractor"),
("mobilenet_v2", "MobileNetV2FeatureExtractor"),
("mobilevit", "MobileViTFeatureExtractor"),
("nat", "ViTFeatureExtractor"),
("owlvit", "OwlViTFeatureExtractor"),
("perceiver", "PerceiverFeatureExtractor"),
("poolformer", "PoolFormerFeatureExtractor"),
("regnet", "ConvNextFeatureExtractor"),
("resnet", "ConvNextFeatureExtractor"),
("segformer", "SegformerFeatureExtractor"),
("sew", "Wav2Vec2FeatureExtractor"),
("sew-d", "Wav2Vec2FeatureExtractor"),
("speech_to_text", "Speech2TextFeatureExtractor"),
("speecht5", "SpeechT5FeatureExtractor"),
("swiftformer", "ViTFeatureExtractor"),
("swin", "ViTFeatureExtractor"),
("swinv2", "ViTFeatureExtractor"),
("table-transformer", "DetrFeatureExtractor"),
("timesformer", "VideoMAEFeatureExtractor"),
("tvlt", "TvltFeatureExtractor"),
("unispeech", "Wav2Vec2FeatureExtractor"),
("unispeech-sat", "Wav2Vec2FeatureExtractor"),
("van", "ConvNextFeatureExtractor"),
("videomae", "VideoMAEFeatureExtractor"),
("vilt", "ViltFeatureExtractor"),
("vit", "ViTFeatureExtractor"),
("vit_mae", "ViTFeatureExtractor"),
("vit_msn", "ViTFeatureExtractor"),
("wav2vec2", "Wav2Vec2FeatureExtractor"),
("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
("wavlm", "Wav2Vec2FeatureExtractor"),
("whisper", "WhisperFeatureExtractor"),
("xclip", "CLIPFeatureExtractor"),
("yolos", "YolosFeatureExtractor"),
]
)
A_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def A_ ( snake_case ):
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
SCREAMING_SNAKE_CASE:Any = model_type_to_module_name(snake_case )
SCREAMING_SNAKE_CASE:Optional[Any] = importlib.import_module(F'''.{module_name}''' , "transformers.models" )
try:
return getattr(snake_case , snake_case )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(snake_case , "__name__" , snake_case ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
SCREAMING_SNAKE_CASE:Optional[int] = importlib.import_module("transformers" )
if hasattr(snake_case , snake_case ):
return getattr(snake_case , snake_case )
return None
def A_ ( snake_case , snake_case = None , snake_case = False , snake_case = False , snake_case = None , snake_case = None , snake_case = None , snake_case = False , **snake_case , ):
SCREAMING_SNAKE_CASE:Optional[int] = get_file_from_repo(
snake_case , snake_case , cache_dir=snake_case , force_download=snake_case , resume_download=snake_case , proxies=snake_case , use_auth_token=snake_case , revision=snake_case , local_files_only=snake_case , )
if resolved_config_file is None:
logger.info(
"Could not locate the feature extractor configuration file, will try to use the model config instead." )
return {}
with open(snake_case , encoding="utf-8" ) as reader:
return json.load(snake_case )
class _snake_case :
def __init__( self : Optional[int] ):
raise EnvironmentError(
"AutoFeatureExtractor is designed to be instantiated "
"using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( cls : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : str ,**SCREAMING_SNAKE_CASE__ : Optional[Any] ):
SCREAMING_SNAKE_CASE:int = kwargs.pop("config" ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = kwargs.pop("trust_remote_code" ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[int] = True
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:List[Any] = FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:List[str] = config_dict.get("feature_extractor_type" ,SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = None
if "AutoFeatureExtractor" in config_dict.get("auto_map" ,{} ):
SCREAMING_SNAKE_CASE:int = config_dict["auto_map"]["AutoFeatureExtractor"]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE:str = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
# It could be in `config.feature_extractor_type``
SCREAMING_SNAKE_CASE:List[str] = getattr(SCREAMING_SNAKE_CASE__ ,"feature_extractor_type" ,SCREAMING_SNAKE_CASE__ )
if hasattr(SCREAMING_SNAKE_CASE__ ,"auto_map" ) and "AutoFeatureExtractor" in config.auto_map:
SCREAMING_SNAKE_CASE:List[str] = config.auto_map["AutoFeatureExtractor"]
if feature_extractor_class is not None:
SCREAMING_SNAKE_CASE:Union[str, Any] = feature_extractor_class_from_name(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = feature_extractor_auto_map is not None
SCREAMING_SNAKE_CASE:Union[str, Any] = feature_extractor_class is not None or type(SCREAMING_SNAKE_CASE__ ) in FEATURE_EXTRACTOR_MAPPING
SCREAMING_SNAKE_CASE:List[str] = resolve_trust_remote_code(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
if has_remote_code and trust_remote_code:
SCREAMING_SNAKE_CASE:int = get_class_from_dynamic_module(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[Any] = kwargs.pop("code_revision" ,SCREAMING_SNAKE_CASE__ )
if os.path.isdir(SCREAMING_SNAKE_CASE__ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(SCREAMING_SNAKE_CASE__ ) in FEATURE_EXTRACTOR_MAPPING:
SCREAMING_SNAKE_CASE:Union[str, Any] = FEATURE_EXTRACTOR_MAPPING[type(SCREAMING_SNAKE_CASE__ )]
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
raise ValueError(
F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' )
@staticmethod
def __UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : int ):
FEATURE_EXTRACTOR_MAPPING.register(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
| 139 |
'''simple docstring'''
import numpy
# List of input, output pairs
A_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A_ = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
A_ = [2, 4, 1, 5]
A_ = len(train_data)
A_ = 0.009
def A_ ( snake_case , snake_case="train" ):
return calculate_hypothesis_value(snake_case , snake_case ) - output(
snake_case , snake_case )
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Any = 0
for i in range(len(snake_case ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def A_ ( snake_case , snake_case ):
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def A_ ( snake_case , snake_case ):
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def A_ ( snake_case , snake_case=m ):
SCREAMING_SNAKE_CASE:Dict = 0
for i in range(snake_case ):
if index == -1:
summation_value += _error(snake_case )
else:
summation_value += _error(snake_case ) * train_data[i][0][index]
return summation_value
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:int = summation_of_cost_derivative(snake_case , snake_case ) / m
return cost_derivative_value
def A_ ( ):
global parameter_vector
# Tune these values to set a tolerance value for predicted output
SCREAMING_SNAKE_CASE:List[str] = 0.00_0002
SCREAMING_SNAKE_CASE:Union[str, Any] = 0
SCREAMING_SNAKE_CASE:Union[str, Any] = 0
while True:
j += 1
SCREAMING_SNAKE_CASE:List[str] = [0, 0, 0, 0]
for i in range(0 , len(snake_case ) ):
SCREAMING_SNAKE_CASE:Union[str, Any] = get_cost_derivative(i - 1 )
SCREAMING_SNAKE_CASE:Union[str, Any] = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
snake_case , snake_case , atol=snake_case , rtol=snake_case , ):
break
SCREAMING_SNAKE_CASE:List[str] = temp_parameter_vector
print(("Number of iterations:", j) )
def A_ ( ):
for i in range(len(snake_case ) ):
print(("Actual output value:", output(snake_case , "test" )) )
print(("Hypothesis output:", calculate_hypothesis_value(snake_case , "test" )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 139 | 1 |
'''simple docstring'''
lowerCAmelCase : int = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
lowerCAmelCase : Optional[int] = frozenset(["""prompt""", """negative_prompt"""])
lowerCAmelCase : Optional[int] = frozenset([])
lowerCAmelCase : int = frozenset(["""image"""])
lowerCAmelCase : Dict = frozenset(
[
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
lowerCAmelCase : Optional[int] = frozenset(["""image"""])
lowerCAmelCase : List[Any] = frozenset(
[
"""prompt""",
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
lowerCAmelCase : List[Any] = frozenset(["""prompt""", """image""", """negative_prompt"""])
lowerCAmelCase : List[str] = frozenset(
[
# Text guided image variation with an image mask
"""prompt""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
lowerCAmelCase : Dict = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])
lowerCAmelCase : List[str] = frozenset(
[
# image variation with an image mask
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
lowerCAmelCase : Any = frozenset(["""image""", """mask_image"""])
lowerCAmelCase : Any = frozenset(
[
"""example_image""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
lowerCAmelCase : str = frozenset(["""example_image""", """image""", """mask_image"""])
lowerCAmelCase : Dict = frozenset(["""class_labels"""])
lowerCAmelCase : Any = frozenset(["""class_labels"""])
lowerCAmelCase : str = frozenset(["""batch_size"""])
lowerCAmelCase : Dict = frozenset([])
lowerCAmelCase : str = frozenset(["""batch_size"""])
lowerCAmelCase : Any = frozenset([])
lowerCAmelCase : int = frozenset(
[
"""prompt""",
"""audio_length_in_s""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
lowerCAmelCase : List[Any] = frozenset(["""prompt""", """negative_prompt"""])
lowerCAmelCase : Dict = frozenset(["""input_tokens"""])
lowerCAmelCase : str = frozenset(["""input_tokens"""])
| 25 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase : Union[str, Any] = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Dict = [
"""RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ResNetForImageClassification""",
"""ResNetModel""",
"""ResNetPreTrainedModel""",
"""ResNetBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : str = [
"""TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFResNetForImageClassification""",
"""TFResNetModel""",
"""TFResNetPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[Any] = [
"""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
lowerCAmelCase : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 25 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
"""SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'deformable_detr'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__(self , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=3 , _lowerCamelCase=300 , _lowerCamelCase=1024 , _lowerCamelCase=6 , _lowerCamelCase=1024 , _lowerCamelCase=8 , _lowerCamelCase=6 , _lowerCamelCase=1024 , _lowerCamelCase=8 , _lowerCamelCase=0.0 , _lowerCamelCase=True , _lowerCamelCase="relu" , _lowerCamelCase=256 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.02 , _lowerCamelCase=1.0 , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase="sine" , _lowerCamelCase="resnet50" , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase=4 , _lowerCamelCase=4 , _lowerCamelCase=4 , _lowerCamelCase=False , _lowerCamelCase=300 , _lowerCamelCase=False , _lowerCamelCase=1 , _lowerCamelCase=5 , _lowerCamelCase=2 , _lowerCamelCase=1 , _lowerCamelCase=1 , _lowerCamelCase=5 , _lowerCamelCase=2 , _lowerCamelCase=0.1 , _lowerCamelCase=0.25 , _lowerCamelCase=False , **_lowerCamelCase , ):
"""simple docstring"""
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.""" )
UpperCAmelCase__ : List[Any] = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
UpperCAmelCase__ : Dict = backbone_config.get("""model_type""" )
UpperCAmelCase__ : Any = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : str = config_class.from_dict(_lowerCamelCase )
UpperCAmelCase__ : Union[str, Any] = use_timm_backbone
UpperCAmelCase__ : str = backbone_config
UpperCAmelCase__ : str = num_channels
UpperCAmelCase__ : Optional[Any] = num_queries
UpperCAmelCase__ : Dict = max_position_embeddings
UpperCAmelCase__ : Optional[int] = d_model
UpperCAmelCase__ : Any = encoder_ffn_dim
UpperCAmelCase__ : Union[str, Any] = encoder_layers
UpperCAmelCase__ : int = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : List[str] = decoder_layers
UpperCAmelCase__ : List[str] = decoder_attention_heads
UpperCAmelCase__ : Optional[Any] = dropout
UpperCAmelCase__ : List[str] = attention_dropout
UpperCAmelCase__ : str = activation_dropout
UpperCAmelCase__ : Any = activation_function
UpperCAmelCase__ : Tuple = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : Optional[int] = encoder_layerdrop
UpperCAmelCase__ : Any = auxiliary_loss
UpperCAmelCase__ : Optional[Any] = position_embedding_type
UpperCAmelCase__ : List[Any] = backbone
UpperCAmelCase__ : Tuple = use_pretrained_backbone
UpperCAmelCase__ : Union[str, Any] = dilation
# deformable attributes
UpperCAmelCase__ : List[Any] = num_feature_levels
UpperCAmelCase__ : Optional[int] = encoder_n_points
UpperCAmelCase__ : Any = decoder_n_points
UpperCAmelCase__ : Dict = two_stage
UpperCAmelCase__ : List[str] = two_stage_num_proposals
UpperCAmelCase__ : int = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
# Hungarian matcher
UpperCAmelCase__ : List[Any] = class_cost
UpperCAmelCase__ : str = bbox_cost
UpperCAmelCase__ : List[str] = giou_cost
# Loss coefficients
UpperCAmelCase__ : Union[str, Any] = mask_loss_coefficient
UpperCAmelCase__ : Union[str, Any] = dice_loss_coefficient
UpperCAmelCase__ : List[Any] = bbox_loss_coefficient
UpperCAmelCase__ : Optional[Any] = giou_loss_coefficient
UpperCAmelCase__ : Optional[Any] = eos_coefficient
UpperCAmelCase__ : List[Any] = focal_alpha
UpperCAmelCase__ : Union[str, Any] = disable_custom_kernels
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a (self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a (self ):
"""simple docstring"""
return self.d_model
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : str = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
UpperCAmelCase__ : Optional[int] = self.backbone_config.to_dict()
UpperCAmelCase__ : Tuple = self.__class__.model_type
return output
| 171 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_A = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": 10_00,
"""block_out_channels""": [32, 64],
"""attention_head_dim""": 8,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
_A = {
"""sample_size""": 64,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 3,
"""num_class_embeds""": 10_00,
"""block_out_channels""": [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """scale_shift""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
_A = {
"""sample_size""": 2_56,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_class_embeds""": None,
"""block_out_channels""": [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
"""attention_head_dim""": 64,
"""down_block_types""": [
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""ResnetDownsampleBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
"""AttnDownBlock2D""",
],
"""up_block_types""": [
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""AttnUpBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
"""ResnetUpsampleBlock2D""",
],
"""resnet_time_scale_shift""": """default""",
"""upsample_type""": """resnet""",
"""downsample_type""": """resnet""",
}
_A = {
"""num_train_timesteps""": 40,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
_A = {
"""num_train_timesteps""": 2_01,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
_A = {
"""num_train_timesteps""": 1_51,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
def a__ ( lowerCAmelCase ) -> Tuple:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("""boolean value expected""" )
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=False ) -> List[str]:
UpperCAmelCase__ : int = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase__ : Optional[int] = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase__ : Optional[Any] = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase__ : str = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase__ : Any = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase__ : Optional[int] = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase__ : str = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase__ : List[Any] = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase__ : Dict = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase__ : Union[str, Any] = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase__ : int = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase__ : Dict = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None ) -> Optional[int]:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : int = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase__ : List[Any] = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase__ : str = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase__ : Union[str, Any] = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Optional[Any] = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Any = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : int = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Dict = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : int = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase__ : Any = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase__ : str = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> str:
UpperCAmelCase__ : Optional[Any] = torch.load(lowerCAmelCase , map_location="""cpu""" )
UpperCAmelCase__ : List[Any] = {}
UpperCAmelCase__ : List[Any] = checkpoint["""time_embed.0.weight"""]
UpperCAmelCase__ : str = checkpoint["""time_embed.0.bias"""]
UpperCAmelCase__ : List[str] = checkpoint["""time_embed.2.weight"""]
UpperCAmelCase__ : Dict = checkpoint["""time_embed.2.bias"""]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase__ : Dict = checkpoint["""label_emb.weight"""]
UpperCAmelCase__ : str = checkpoint["""input_blocks.0.0.weight"""]
UpperCAmelCase__ : List[str] = checkpoint["""input_blocks.0.0.bias"""]
UpperCAmelCase__ : List[str] = unet_config["""down_block_types"""]
UpperCAmelCase__ : Tuple = unet_config["""layers_per_block"""]
UpperCAmelCase__ : int = unet_config["""attention_head_dim"""]
UpperCAmelCase__ : Union[str, Any] = unet_config["""block_out_channels"""]
UpperCAmelCase__ : Union[str, Any] = 1
UpperCAmelCase__ : Union[str, Any] = channels_list[0]
for i, layer_type in enumerate(lowerCAmelCase ):
UpperCAmelCase__ : Union[str, Any] = channels_list[i]
UpperCAmelCase__ : int = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(lowerCAmelCase ):
UpperCAmelCase__ : Tuple = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase__ : List[Any] = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase__ : Dict = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase__ : Optional[Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(lowerCAmelCase ):
UpperCAmelCase__ : Any = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase__ : Optional[Any] = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase__ : int = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase__ : Union[str, Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
UpperCAmelCase__ : Dict = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase__ : int = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase__ : Union[str, Any] = convert_attention(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : Any = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase__ : List[str] = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase__ : Tuple = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
UpperCAmelCase__ : Tuple = current_channels
# hardcoded the mid-block for now
UpperCAmelCase__ : List[Any] = """mid_block.resnets.0"""
UpperCAmelCase__ : str = """middle_block.0"""
UpperCAmelCase__ : List[str] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = """mid_block.attentions.0"""
UpperCAmelCase__ : Any = """middle_block.1"""
UpperCAmelCase__ : Optional[int] = convert_attention(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[Any] = """mid_block.resnets.1"""
UpperCAmelCase__ : Tuple = """middle_block.2"""
UpperCAmelCase__ : Union[str, Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Any = 0
UpperCAmelCase__ : Dict = unet_config["""up_block_types"""]
for i, layer_type in enumerate(lowerCAmelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase__ : Tuple = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase__ : Optional[Any] = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase__ : Dict = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : List[str] = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase__ : Any = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase__ : Union[str, Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase__ : List[str] = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase__ : Dict = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase__ : Any = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , has_skip=lowerCAmelCase )
UpperCAmelCase__ : Union[str, Any] = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase__ : List[str] = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase__ : Dict = convert_attention(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
current_layer += 1
if i != len(lowerCAmelCase ) - 1:
UpperCAmelCase__ : int = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase__ : int = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase__ : Union[str, Any] = convert_resnet(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : Optional[Any] = checkpoint["""out.0.weight"""]
UpperCAmelCase__ : List[Any] = checkpoint["""out.0.bias"""]
UpperCAmelCase__ : Tuple = checkpoint["""out.2.weight"""]
UpperCAmelCase__ : Optional[Any] = checkpoint["""out.2.bias"""]
return new_checkpoint
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
parser.add_argument(
"""--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
)
parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")
_A = parser.parse_args()
_A = strabool(args.class_cond)
_A = os.path.basename(args.unet_path)
print(f'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
_A = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_A = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
_A = TEST_UNET_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
_A = None
_A = con_pt_to_diffuser(args.unet_path, unet_config)
_A = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
_A = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
_A = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_A = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')
_A = CMStochasticIterativeScheduler(**scheduler_config)
_A = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 171 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase=13 ,__UpperCAmelCase=32 ,__UpperCAmelCase=2 ,__UpperCAmelCase=3 ,__UpperCAmelCase=16 ,__UpperCAmelCase=[1, 2, 1] ,__UpperCAmelCase=[2, 2, 4] ,__UpperCAmelCase=2 ,__UpperCAmelCase=2.0 ,__UpperCAmelCase=True ,__UpperCAmelCase=0.0 ,__UpperCAmelCase=0.0 ,__UpperCAmelCase=0.1 ,__UpperCAmelCase="gelu" ,__UpperCAmelCase=False ,__UpperCAmelCase=True ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=1E-5 ,__UpperCAmelCase=True ,__UpperCAmelCase=None ,__UpperCAmelCase=True ,__UpperCAmelCase=10 ,__UpperCAmelCase=8 ,) -> Optional[int]:
lowerCAmelCase__ : List[str] = parent
lowerCAmelCase__ : Union[str, Any] = batch_size
lowerCAmelCase__ : Any = image_size
lowerCAmelCase__ : Dict = patch_size
lowerCAmelCase__ : Dict = num_channels
lowerCAmelCase__ : List[Any] = embed_dim
lowerCAmelCase__ : str = depths
lowerCAmelCase__ : Dict = num_heads
lowerCAmelCase__ : str = window_size
lowerCAmelCase__ : int = mlp_ratio
lowerCAmelCase__ : Union[str, Any] = qkv_bias
lowerCAmelCase__ : Dict = hidden_dropout_prob
lowerCAmelCase__ : str = attention_probs_dropout_prob
lowerCAmelCase__ : Optional[int] = drop_path_rate
lowerCAmelCase__ : List[str] = hidden_act
lowerCAmelCase__ : Optional[int] = use_absolute_embeddings
lowerCAmelCase__ : Any = patch_norm
lowerCAmelCase__ : Union[str, Any] = layer_norm_eps
lowerCAmelCase__ : Optional[int] = initializer_range
lowerCAmelCase__ : Tuple = is_training
lowerCAmelCase__ : Any = scope
lowerCAmelCase__ : Optional[Any] = use_labels
lowerCAmelCase__ : Optional[int] = type_sequence_label_size
lowerCAmelCase__ : int = encoder_stride
def UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ : Tuple = None
if self.use_labels:
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowerCAmelCase__ : Optional[int] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ) -> Dict:
return SwinvaConfig(
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 ,)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> List[Any]:
lowerCAmelCase__ : Tuple = SwinvaModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Union[str, Any] = model(__UpperCAmelCase )
lowerCAmelCase__ : Tuple = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowerCAmelCase__ : List[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 UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple:
lowerCAmelCase__ : Any = SwinvaForMaskedImageModeling(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : List[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCAmelCase__ : Optional[Any] = 1
lowerCAmelCase__ : Dict = SwinvaForMaskedImageModeling(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase__ : str = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, 1, self.image_size, self.image_size) )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Dict:
lowerCAmelCase__ : str = self.type_sequence_label_size
lowerCAmelCase__ : str = SwinvaForImageClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ : Any = model(__UpperCAmelCase ,labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ ( self ) -> Optional[int]:
lowerCAmelCase__ : List[Any] = self.prepare_config_and_inputs()
lowerCAmelCase__ : List[Any] = config_and_inputs
lowerCAmelCase__ : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
__lowercase : List[str] = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__lowercase : List[str] = (
{"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__lowercase : Dict = False
__lowercase : Optional[Any] = False
__lowercase : Union[str, Any] = False
__lowercase : Optional[Any] = False
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : List[str] = SwinvaModelTester(self )
lowerCAmelCase__ : Any = ConfigTester(self ,config_class=__UpperCAmelCase ,embed_dim=37 )
def UpperCAmelCase_ ( self ) -> Any:
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 UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
pass
@unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ) -> Dict:
pass
def UpperCAmelCase_ ( self ) -> Optional[Any]:
lowerCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Union[str, Any] = model_class(__UpperCAmelCase )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
lowerCAmelCase__ : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCAmelCase ,nn.Linear ) )
def UpperCAmelCase_ ( self ) -> Any:
lowerCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Tuple = model_class(__UpperCAmelCase )
lowerCAmelCase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ : str = [*signature.parameters.keys()]
lowerCAmelCase__ : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> int:
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Optional[Any] = True
for model_class in self.all_model_classes:
lowerCAmelCase__ : Union[str, Any] = True
lowerCAmelCase__ : Optional[Any] = False
lowerCAmelCase__ : Optional[int] = True
lowerCAmelCase__ : int = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : List[Any] = model(**self._prepare_for_class(__UpperCAmelCase ,__UpperCAmelCase ) )
lowerCAmelCase__ : str = outputs.attentions
lowerCAmelCase__ : Any = len(self.model_tester.depths )
self.assertEqual(len(__UpperCAmelCase ) ,__UpperCAmelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ : Dict = True
lowerCAmelCase__ : int = config.window_size**2
lowerCAmelCase__ : Any = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : int = model(**self._prepare_for_class(__UpperCAmelCase ,__UpperCAmelCase ) )
lowerCAmelCase__ : Dict = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) ,__UpperCAmelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)
lowerCAmelCase__ : Dict = len(__UpperCAmelCase )
# Check attention is always last and order is fine
lowerCAmelCase__ : Any = True
lowerCAmelCase__ : Any = True
lowerCAmelCase__ : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : List[str] = model(**self._prepare_for_class(__UpperCAmelCase ,__UpperCAmelCase ) )
if hasattr(self.model_tester ,"""num_hidden_states_types""" ):
lowerCAmelCase__ : Any = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
lowerCAmelCase__ : Optional[int] = 2
self.assertEqual(out_len + added_hidden_states ,len(__UpperCAmelCase ) )
lowerCAmelCase__ : Tuple = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) ,__UpperCAmelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> List[Any]:
lowerCAmelCase__ : Optional[int] = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : Optional[Any] = model(**self._prepare_for_class(__UpperCAmelCase ,__UpperCAmelCase ) )
lowerCAmelCase__ : List[Any] = outputs.hidden_states
lowerCAmelCase__ : List[Any] = getattr(
self.model_tester ,"""expected_num_hidden_layers""" ,len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCAmelCase ) ,__UpperCAmelCase )
# Swinv2 has a different seq_length
lowerCAmelCase__ : List[str] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase__ : Union[str, Any] = (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] ,)
lowerCAmelCase__ : int = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCAmelCase ) ,__UpperCAmelCase )
lowerCAmelCase__ : str = reshaped_hidden_states[0].shape
lowerCAmelCase__ : Any = (
reshaped_hidden_states[0].view(__UpperCAmelCase ,__UpperCAmelCase ,height * width ).permute(0 ,2 ,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Tuple = (
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:
lowerCAmelCase__ : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ : Union[str, Any] = True
self.check_hidden_states_output(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Tuple = 3
lowerCAmelCase__ : str = (
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)
)
lowerCAmelCase__ : List[str] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCAmelCase__ : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowerCAmelCase__ : Union[str, Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowerCAmelCase__ : int = True
self.check_hidden_states_output(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ : Tuple = True
self.check_hidden_states_output(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,(padded_height, padded_width) )
def UpperCAmelCase_ ( self ) -> List[str]:
lowerCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase )
@slow
def UpperCAmelCase_ ( self ) -> Optional[Any]:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ : Dict = SwinvaModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> Any:
lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Tuple = _config_zero_init(__UpperCAmelCase )
for model_class in self.all_model_classes:
lowerCAmelCase__ : List[Any] = model_class(config=__UpperCAmelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,)
@require_vision
@require_torch
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase_ ( self ) -> int:
return (
AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
lowerCAmelCase__ : Tuple = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
__UpperCAmelCase )
lowerCAmelCase__ : Tuple = self.default_image_processor
lowerCAmelCase__ : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCAmelCase__ : Any = image_processor(images=__UpperCAmelCase ,return_tensors="""pt""" ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
lowerCAmelCase__ : Optional[int] = model(**__UpperCAmelCase )
# verify the logits
lowerCAmelCase__ : int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape ,__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__UpperCAmelCase ,atol=1E-4 ) )
| 366 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCAmelCase_:
'''simple docstring'''
__lowercase : Optional[Union[str, Path]] = None
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = False
__lowercase : Optional[Dict] = None
__lowercase : Optional[str] = None
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = False
__lowercase : bool = True
__lowercase : Optional[int] = None
__lowercase : int = 1
__lowercase : Optional[Union[str, bool]] = None
__lowercase : bool = False
__lowercase : Optional[Dict] = None
__lowercase : Optional[str] = None
def UpperCAmelCase_ ( self ) -> "DownloadConfig":
return self.__class__(**{k: copy.deepcopy(__UpperCAmelCase ) for k, v in self.__dict__.items()} )
| 184 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( lowerCAmelCase ):
def __init__( self ,snake_case ,snake_case = None ,snake_case = None ,snake_case = None ,snake_case = False ,snake_case = False ,snake_case = None ,snake_case = None ,**snake_case ,):
'''simple docstring'''
super().__init__(
snake_case ,split=snake_case ,features=snake_case ,cache_dir=snake_case ,keep_in_memory=snake_case ,streaming=snake_case ,num_proc=snake_case ,**snake_case ,)
lowercase : Optional[Any] = field
lowercase : str = path_or_paths if isinstance(snake_case ,snake_case ) else {self.split: path_or_paths}
lowercase : Dict = Json(
cache_dir=snake_case ,data_files=snake_case ,features=snake_case ,field=snake_case ,**snake_case ,)
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
if self.streaming:
lowercase : List[Any] = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
lowercase : List[str] = None
lowercase : Any = None
lowercase : List[str] = None
lowercase : str = None
self.builder.download_and_prepare(
download_config=snake_case ,download_mode=snake_case ,verification_mode=snake_case ,base_path=snake_case ,num_proc=self.num_proc ,)
lowercase : Optional[int] = self.builder.as_dataset(
split=self.split ,verification_mode=snake_case ,in_memory=self.keep_in_memory )
return dataset
class __snake_case :
def __init__( self ,snake_case ,snake_case ,snake_case = None ,snake_case = None ,**snake_case ,):
'''simple docstring'''
if num_proc is not None and num_proc <= 0:
raise ValueError(f"num_proc {num_proc} must be an integer > 0." )
lowercase : Tuple = dataset
lowercase : Optional[int] = path_or_buf
lowercase : Optional[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowercase : Optional[Any] = num_proc
lowercase : Tuple = """utf-8"""
lowercase : Tuple = to_json_kwargs
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
lowercase : Dict = self.to_json_kwargs.pop("""path_or_buf""" ,snake_case )
lowercase : Tuple = self.to_json_kwargs.pop("""orient""" ,"""records""" )
lowercase : List[str] = self.to_json_kwargs.pop("""lines""" ,True if orient == """records""" else False )
lowercase : Optional[int] = self.to_json_kwargs.pop("""index""" ,False if orient in ["""split""", """table"""] else True )
lowercase : str = self.to_json_kwargs.pop("""compression""" ,snake_case )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(f"`datasets` currently does not support {compression} compression" )
if isinstance(self.path_or_buf ,(str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf ,"""wb""" ,compression=snake_case ) as buffer:
lowercase : Union[str, Any] = self._write(file_obj=snake_case ,orient=snake_case ,lines=snake_case ,index=snake_case ,**self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
f"The compression parameter is not supported when writing to a buffer, but compression={compression}"
""" was passed. Please provide a local path instead.""" )
lowercase : Tuple = self._write(
file_obj=self.path_or_buf ,orient=snake_case ,lines=snake_case ,index=snake_case ,**self.to_json_kwargs )
return written
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
lowercase , lowercase , lowercase , lowercase , lowercase : Any = args
lowercase : Optional[Any] = query_table(
table=self.dataset.data ,key=slice(snake_case ,offset + self.batch_size ) ,indices=self.dataset._indices ,)
lowercase : Any = batch.to_pandas().to_json(
path_or_buf=snake_case ,orient=snake_case ,lines=snake_case ,index=snake_case ,**snake_case )
if not json_str.endswith("""\n""" ):
json_str += "\n"
return json_str.encode(self.encoding )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ,snake_case ,**snake_case ,):
'''simple docstring'''
lowercase : Optional[int] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 ,len(self.dataset ) ,self.batch_size ) ,unit="""ba""" ,disable=not logging.is_progress_bar_enabled() ,desc="""Creating json from Arrow format""" ,):
lowercase : Any = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(snake_case )
else:
lowercase , lowercase : int = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json ,[(offset, orient, lines, index, to_json_kwargs) for offset in range(0 ,snake_case ,snake_case )] ,) ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,unit="""ba""" ,disable=not logging.is_progress_bar_enabled() ,desc="""Creating json from Arrow format""" ,):
written += file_obj.write(snake_case )
return written
| 20 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Any:
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
lowercase : Union[str, Any] = tmp_path / """cache"""
lowercase : Optional[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase : Any = SqlDatasetReader(
"""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ , keep_in_memory=SCREAMING_SNAKE_CASE__ ).read()
_check_sql_dataset(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@require_sqlalchemy
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Tuple:
lowercase : Union[str, Any] = tmp_path / """cache"""
lowercase : int = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
lowercase : str = features.copy() if features else default_expected_features
lowercase : Optional[Any] = (
Features({feature: Value(SCREAMING_SNAKE_CASE__ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase : Optional[int] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=SCREAMING_SNAKE_CASE__ , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
_check_sql_dataset(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> List[Any]:
with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE__ ) ) as con:
lowercase : Optional[int] = con.cursor()
cur.execute("""SELECT * FROM dataset""" )
for row in cur:
yield row
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
lowercase : Any = tmp_path / """cache"""
lowercase : int = os.path.join(SCREAMING_SNAKE_CASE__ , """tmp.sql""" )
lowercase : List[str] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write()
lowercase : List[str] = iter_sql_file(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = iter_sql_file(SCREAMING_SNAKE_CASE__ )
for rowa, rowa in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
assert rowa == rowa
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
lowercase : Dict = tmp_path / """cache"""
lowercase : List[str] = os.path.join(SCREAMING_SNAKE_CASE__ , """tmp.sql""" )
lowercase : List[str] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write()
lowercase : Optional[int] = iter_sql_file(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = iter_sql_file(SCREAMING_SNAKE_CASE__ )
for rowa, rowa in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
assert rowa == rowa
@require_sqlalchemy
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
lowercase : str = tmp_path / """cache"""
lowercase : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE__ , """tmp.sql""" )
lowercase : Optional[Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=SCREAMING_SNAKE_CASE__ ).read()
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
SqlDatasetWriter(SCREAMING_SNAKE_CASE__ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
| 20 | 1 |
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(_UpperCAmelCase) - 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
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0, 0
# length[i] shows the length of palindromic substring with center i
SCREAMING_SNAKE_CASE = [1 for i in range(len(_UpperCAmelCase))]
# for each character in new_string find corresponding palindromic string
SCREAMING_SNAKE_CASE = 0
for j in range(len(_UpperCAmelCase)):
SCREAMING_SNAKE_CASE = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1)
while (
j - k >= 0
and j + k < len(_UpperCAmelCase)
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
SCREAMING_SNAKE_CASE = 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:
SCREAMING_SNAKE_CASE = j - k + 1 # noqa: E741
SCREAMING_SNAKE_CASE = j + k - 1
# update max_length and start position
if max_length < length[j]:
SCREAMING_SNAKE_CASE = length[j]
SCREAMING_SNAKE_CASE = j
# create that string
SCREAMING_SNAKE_CASE = 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()
| 327 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
class _snake_case :
def __init__( self , a) -> List[Any]:
SCREAMING_SNAKE_CASE = metric_id
class _snake_case :
_lowercase : Optional[Any] = [MetricMock(A__ ) for metric_id in ['''accuracy''', '''mse''', '''precision''', '''codeparrot/apps_metric''']]
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[int]:
return self._metrics
monkeypatch.setattr('datasets.inspect.huggingface_hub' , HfhMock())
@pytest.mark.parametrize(
'func, args' , [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))])
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
if "tmp_path" in args:
SCREAMING_SNAKE_CASE = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args)
with pytest.warns(_UpperCAmelCase , match='https://huggingface.co/docs/evaluate'):
func(*_UpperCAmelCase)
| 327 | 1 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"
# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
_A : str ='''facebook/wmt19-en-de'''
_A : List[Any] =FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A : Any =FSMTConfig.from_pretrained(mname)
config.update(
dict(
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
)
_A : List[Any] =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')
# Test
_A : Tuple =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A : Optional[int] =tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
_A : Any ='''tiny-wmt19-en-de'''
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'Generated {mname_tiny}')
# Upload
# transformers-cli upload tiny-wmt19-en-de
| 41 |
_lowercase : Optional[Any] =[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def lowerCAmelCase_ ( _lowercase : int) -> int:
"""simple docstring"""
a__ : Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000]
number //= 10_0000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_lowercase : list[bool | None] =[None] * 1000_0000
_lowercase : Tuple =True
_lowercase : int =False
def lowerCAmelCase_ ( _lowercase : int) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
a__ : Optional[Any] = chain(next_number(_lowercase))
a__ : Dict = number_chain
while number < 1000_0000:
a__ : Any = number_chain
number *= 10
return number_chain
def lowerCAmelCase_ ( _lowercase : int = 1000_0000) -> int:
"""simple docstring"""
for i in range(1 , _lowercase):
if CHAINS[i] is None:
chain(i + 1)
return CHAINS[:number].count(_lowercase)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'{solution() = }')
| 170 | 0 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : List[Any] = logging.get_logger(__name__)
snake_case__ : Tuple = '''https://openaipublic.azureedge.net/jukebox/models/'''
snake_case__ : int = {
'''jukebox-1b-lyrics''': [
'''5b/vqvae.pth.tar''',
'''5b/prior_level_0.pth.tar''',
'''5b/prior_level_1.pth.tar''',
'''1b_lyrics/prior_level_2.pth.tar''',
],
'''jukebox-5b-lyrics''': [
'''5b/vqvae.pth.tar''',
'''5b/prior_level_0.pth.tar''',
'''5b/prior_level_1.pth.tar''',
'''5b_lyrics/prior_level_2.pth.tar''',
],
}
def _snake_case ( _snake_case : Union[str, Any] ):
if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
lowerCAmelCase : Optional[Any] = key.replace('''.model.1.bias''' , '''.conv1d_1.bias''' )
elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
lowerCAmelCase : List[str] = key.replace('''.model.1.weight''' , '''.conv1d_1.weight''' )
elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
lowerCAmelCase : Optional[Any] = key.replace('''.model.3.bias''' , '''.conv1d_2.bias''' )
elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
lowerCAmelCase : Tuple = key.replace('''.model.3.weight''' , '''.conv1d_2.weight''' )
if "conditioner_blocks.0." in key:
lowerCAmelCase : Optional[Any] = key.replace('''conditioner_blocks.0''' , '''conditioner_blocks''' )
if "prime_prior" in key:
lowerCAmelCase : Union[str, Any] = key.replace('''prime_prior''' , '''encoder''' )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
lowerCAmelCase : Union[str, Any] = key.replace('''.emb.''' , '''.''' )
if key.endswith('''k''' ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace('''.k''' , '''.codebook''' )
if "y_emb." in key:
return key.replace('''y_emb.''' , '''metadata_embedding.''' )
if "x_emb.emb." in key:
lowerCAmelCase : int = key.replace('''0.x_emb.emb''' , '''embed_tokens''' )
if "prime_state_ln" in key:
return key.replace('''prime_state_ln''' , '''encoder.final_layer_norm''' )
if ".ln" in key:
return key.replace('''.ln''' , '''.layer_norm''' )
if "_ln" in key:
return key.replace('''_ln''' , '''_layer_norm''' )
if "prime_state_proj" in key:
return key.replace('''prime_state_proj''' , '''encoder.proj_in''' )
if "prime_x_out" in key:
return key.replace('''prime_x_out''' , '''encoder.lm_head''' )
if "prior.x_out" in key:
return key.replace('''x_out''' , '''fc_proj_out''' )
if "x_emb" in key:
return key.replace('''x_emb''' , '''embed_tokens''' )
return key
def _snake_case ( _snake_case : List[Any] , _snake_case : Optional[Any] , _snake_case : Tuple , _snake_case : List[Any] ):
lowerCAmelCase : List[Any] = {}
import re
lowerCAmelCase : List[str] = re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowerCAmelCase : List[str] = re.compile(
r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCAmelCase : Union[str, Any] = re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowerCAmelCase : Optional[int] = re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
lowerCAmelCase : Union[str, Any] = re.compile(
r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCAmelCase : Tuple = re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )
lowerCAmelCase : Dict = re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
lowerCAmelCase : Optional[Any] = re.compile(
r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
lowerCAmelCase : Tuple = re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)''' )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_snake_case ):
lowerCAmelCase : Optional[int] = re_encoder_block_conv_in.match(_snake_case )
lowerCAmelCase : Tuple = regex_match.groups()
lowerCAmelCase : Tuple = int(groups[2] ) * 2 + int(groups[3] )
lowerCAmelCase : Union[str, Any] = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
lowerCAmelCase : Optional[int] = re_encoder_block_conv_in.sub(_snake_case , _snake_case )
elif re_encoder_block_resnet.fullmatch(_snake_case ):
lowerCAmelCase : Union[str, Any] = re_encoder_block_resnet.match(_snake_case )
lowerCAmelCase : Any = regex_match.groups()
lowerCAmelCase : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] )
lowerCAmelCase : Dict = {'''1''': 1, '''3''': 2}[groups[-2]]
lowerCAmelCase : Optional[int] = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
lowerCAmelCase : Optional[int] = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCAmelCase : int = prefix + resnet_block
lowerCAmelCase : Dict = re_encoder_block_resnet.sub(_snake_case , _snake_case )
elif re_encoder_block_proj_out.fullmatch(_snake_case ):
lowerCAmelCase : List[str] = re_encoder_block_proj_out.match(_snake_case )
lowerCAmelCase : List[Any] = regex_match.groups()
lowerCAmelCase : str = f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
lowerCAmelCase : str = re_encoder_block_proj_out.sub(_snake_case , _snake_case )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_snake_case ):
lowerCAmelCase : List[str] = re_decoder_block_conv_out.match(_snake_case )
lowerCAmelCase : int = regex_match.groups()
lowerCAmelCase : Union[str, Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCAmelCase : List[Any] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
lowerCAmelCase : Optional[Any] = re_decoder_block_conv_out.sub(_snake_case , _snake_case )
elif re_decoder_block_resnet.fullmatch(_snake_case ):
lowerCAmelCase : int = re_decoder_block_resnet.match(_snake_case )
lowerCAmelCase : Dict = regex_match.groups()
lowerCAmelCase : Optional[Any] = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowerCAmelCase : List[str] = {'''1''': 1, '''3''': 2}[groups[-2]]
lowerCAmelCase : Any = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
lowerCAmelCase : Dict = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCAmelCase : Optional[Any] = prefix + resnet_block
lowerCAmelCase : Any = re_decoder_block_resnet.sub(_snake_case , _snake_case )
elif re_decoder_block_proj_in.fullmatch(_snake_case ):
lowerCAmelCase : Tuple = re_decoder_block_proj_in.match(_snake_case )
lowerCAmelCase : int = regex_match.groups()
lowerCAmelCase : List[str] = f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
lowerCAmelCase : Tuple = re_decoder_block_proj_in.sub(_snake_case , _snake_case )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_snake_case ):
lowerCAmelCase : Any = re_prior_cond_conv_out.match(_snake_case )
lowerCAmelCase : List[str] = regex_match.groups()
lowerCAmelCase : str = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCAmelCase : Any = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
lowerCAmelCase : int = re_prior_cond_conv_out.sub(_snake_case , _snake_case )
elif re_prior_cond_resnet.fullmatch(_snake_case ):
lowerCAmelCase : List[str] = re_prior_cond_resnet.match(_snake_case )
lowerCAmelCase : Any = regex_match.groups()
lowerCAmelCase : int = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowerCAmelCase : Optional[int] = {'''1''': 1, '''3''': 2}[groups[-2]]
lowerCAmelCase : Tuple = f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
lowerCAmelCase : str = f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
lowerCAmelCase : int = prefix + resnet_block
lowerCAmelCase : Any = re_prior_cond_resnet.sub(_snake_case , _snake_case )
elif re_prior_cond_proj_in.fullmatch(_snake_case ):
lowerCAmelCase : int = re_prior_cond_proj_in.match(_snake_case )
lowerCAmelCase : int = regex_match.groups()
lowerCAmelCase : int = f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
lowerCAmelCase : Optional[int] = re_prior_cond_proj_in.sub(_snake_case , _snake_case )
# keep original key
else:
lowerCAmelCase : int = original_key
lowerCAmelCase : Dict = replace_key(_snake_case )
if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(f'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
lowerCAmelCase : Any = model_state_dict[f'''{key_prefix}.{key}''']
print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
lowerCAmelCase : int = original_key
lowerCAmelCase : Tuple = original_key
lowerCAmelCase : List[str] = value
return new_dict
@torch.no_grad()
def _snake_case ( _snake_case : Tuple=None , _snake_case : Dict=None ):
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' ):
lowerCAmelCase : Tuple = requests.get(f'''{PREFIX}{file}''' , allow_redirects=_snake_case )
os.makedirs(f'''{pytorch_dump_folder_path}/''' , exist_ok=_snake_case )
open(f'''{pytorch_dump_folder_path}/{file.split("/" )[-1]}''' , '''wb''' ).write(r.content )
lowerCAmelCase : Any = MODEL_MAPPING[model_name.split('''/''' )[-1]]
lowerCAmelCase : Tuple = JukeboxConfig.from_pretrained(_snake_case )
lowerCAmelCase : Optional[Any] = JukeboxModel(_snake_case )
lowerCAmelCase : Tuple = []
lowerCAmelCase : Optional[int] = {}
for i, dict_name in enumerate(_snake_case ):
lowerCAmelCase : Optional[Any] = torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}''' )['''model''']
lowerCAmelCase : List[Any] = {}
for k in old_dic.keys():
if k.endswith('''.b''' ):
lowerCAmelCase : Dict = old_dic[k]
elif k.endswith('''.w''' ):
lowerCAmelCase : Optional[int] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
lowerCAmelCase : str = old_dic[k]
else:
lowerCAmelCase : int = old_dic[k]
lowerCAmelCase : Dict = '''vqvae''' if i == 0 else f'''priors.{3 - i}'''
lowerCAmelCase : List[Any] = fix_jukebox_keys(_snake_case , model.state_dict() , _snake_case , _snake_case )
weight_dict.append(_snake_case )
lowerCAmelCase : Dict = weight_dict.pop(0 )
model.vqvae.load_state_dict(_snake_case )
for i in range(len(_snake_case ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_snake_case ).mkdir(exist_ok=_snake_case )
with open(f'''{pytorch_dump_folder_path}/mapping.json''' , '''w''' ) as txtfile:
json.dump(_snake_case , _snake_case )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
return weight_dict
if __name__ == "__main__":
snake_case__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''jukebox-5b-lyrics''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''jukebox-5b-lyrics-converted''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
snake_case__ : int = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 367 |
"""simple docstring"""
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case__ : Optional[Any] = '''
import os
'''
snake_case__ : Tuple = '''
def foo():
import os
return False
'''
snake_case__ : Any = '''
def foo():
def bar():
if True:
import os
return False
return bar()
'''
snake_case__ : Any = '''
import os
try:
import bar
except ImportError:
raise ValueError()
'''
snake_case__ : int = '''
import os
def foo():
try:
import bar
except ImportError:
raise ValueError()
'''
snake_case__ : Any = '''
import os
try:
import bar
except (ImportError, AttributeError):
raise ValueError()
'''
snake_case__ : List[str] = '''
import os
try:
import bar
except ImportError as e:
raise ValueError()
'''
snake_case__ : int = '''
import os
try:
import bar
except:
raise ValueError()
'''
snake_case__ : List[Any] = '''
import os
try:
import bar
import baz
except ImportError:
raise ValueError()
'''
snake_case__ : Optional[int] = '''
import os
try:
import bar
import baz
except ImportError:
x = 1
raise ValueError()
'''
snake_case__ : Any = [
TOP_LEVEL_IMPORT,
IMPORT_IN_FUNCTION,
DEEPLY_NESTED_IMPORT,
TOP_LEVEL_TRY_IMPORT,
GENERIC_EXCEPT_IMPORT,
MULTILINE_TRY_IMPORT,
MULTILINE_BOTH_IMPORT,
MULTIPLE_EXCEPTS_IMPORT,
EXCEPT_AS_IMPORT,
TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case''' , _snake_case )
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : List[str] ):
lowerCAmelCase : Dict = os.path.join(_snake_case , '''test_file.py''' )
with open(_snake_case , '''w''' ) as _tmp_file:
_tmp_file.write(_snake_case )
lowerCAmelCase : Tuple = get_imports(_snake_case )
assert parsed_imports == ["os"]
| 314 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def A ( snake_case :str ) -> Any:
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def A ( snake_case :Optional[Any] ) -> List[Any]:
__UpperCamelCase = create_tensor(snake_case )
__UpperCamelCase = gather(snake_case )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def A ( snake_case :int ) -> Tuple:
__UpperCamelCase = [state.process_index]
__UpperCamelCase = gather_object(snake_case )
assert len(snake_case ) == state.num_processes, f'{gathered_obj}, {len(snake_case )} != {state.num_processes}'
assert gathered_obj == list(range(state.num_processes ) ), f'{gathered_obj} != {list(range(state.num_processes ) )}'
def A ( snake_case :Any ) -> Dict:
__UpperCamelCase = create_tensor(snake_case )
__UpperCamelCase = broadcast(snake_case )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def A ( snake_case :Union[str, Any] ) -> Optional[Any]:
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
__UpperCamelCase = torch.arange(state.num_processes + 1 ).to(state.device )
else:
__UpperCamelCase = torch.arange(state.num_processes ).to(state.device )
__UpperCamelCase = pad_across_processes(snake_case )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def A ( snake_case :Optional[Any] ) -> Dict:
# For now runs on only two processes
if state.num_processes != 2:
return
__UpperCamelCase = create_tensor(snake_case )
__UpperCamelCase = reduce(snake_case , 'sum' )
__UpperCamelCase = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(snake_case , snake_case ), f'{reduced_tensor} != {truth_tensor}'
def A ( snake_case :Tuple ) -> List[str]:
# For now runs on only two processes
if state.num_processes != 2:
return
__UpperCamelCase = create_tensor(snake_case )
__UpperCamelCase = reduce(snake_case , 'mean' )
__UpperCamelCase = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(snake_case , snake_case ), f'{reduced_tensor} != {truth_tensor}'
def A ( snake_case :int ) -> List[Any]:
# For xla_spawn (TPUs)
main()
def A ( ) -> Optional[int]:
__UpperCamelCase = PartialState()
state.print(f'State: {state}' )
state.print('testing gather' )
test_gather(snake_case )
state.print('testing gather_object' )
test_gather_object(snake_case )
state.print('testing broadcast' )
test_broadcast(snake_case )
state.print('testing pad_across_processes' )
test_pad_across_processes(snake_case )
state.print('testing reduce_sum' )
test_reduce_sum(snake_case )
state.print('testing reduce_mean' )
test_reduce_mean(snake_case )
if __name__ == "__main__":
main()
| 316 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A ( snake_case :str , snake_case :tuple , snake_case :Path , snake_case :Dict , snake_case :int , snake_case :List[str] , snake_case :Union[str, Any] , snake_case :Union[str, Any]=False , ) -> str:
output_path.parent.mkdir(parents=snake_case , exist_ok=snake_case )
# 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(
snake_case , snake_case , f=output_path.as_posix() , input_names=snake_case , output_names=snake_case , dynamic_axes=snake_case , do_constant_folding=snake_case , use_external_data_format=snake_case , enable_onnx_checker=snake_case , opset_version=snake_case , )
else:
export(
snake_case , snake_case , f=output_path.as_posix() , input_names=snake_case , output_names=snake_case , dynamic_axes=snake_case , do_constant_folding=snake_case , opset_version=snake_case , )
@torch.no_grad()
def A ( snake_case :str , snake_case :str , snake_case :int , snake_case :bool = False ) -> List[str]:
__UpperCamelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__UpperCamelCase = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__UpperCamelCase = 'cpu'
__UpperCamelCase = Path(snake_case )
# VAE DECODER
__UpperCamelCase = AutoencoderKL.from_pretrained(model_path + '/vae' )
__UpperCamelCase = vae_decoder.config.latent_channels
# forward only through the decoder part
__UpperCamelCase = vae_decoder.decode
onnx_export(
snake_case , model_args=(
torch.randn(1 , snake_case , 2_5 , 2_5 ).to(device=snake_case , dtype=snake_case ),
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=snake_case , )
del vae_decoder
if __name__ == "__main__":
UpperCamelCase : Dict = 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=1_4,
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 : List[Any] = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 316 | 1 |
# Copyright 2022 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
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
lowerCamelCase : Union[str, Any] = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def _SCREAMING_SNAKE_CASE ( lowercase : str=None ):
'''simple docstring'''
if subparsers is not None:
lowerCamelCase_ = subparsers.add_parser('tpu-config' , description=_description )
else:
lowerCamelCase_ = argparse.ArgumentParser('Accelerate tpu-config command' , description=_description )
# Core arguments
lowerCamelCase_ = parser.add_argument_group(
'Config Arguments' , 'Arguments that can be configured through `accelerate config`.' )
config_args.add_argument(
'--config_file' , type=lowercase , default=lowercase , help='Path to the config file to use for accelerate.' , )
config_args.add_argument(
'--tpu_name' , default=lowercase , help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.' , )
config_args.add_argument(
'--tpu_zone' , default=lowercase , help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.' , )
lowerCamelCase_ = parser.add_argument_group('TPU Arguments' , 'Arguments for options ran inside the TPU.' )
pod_args.add_argument(
'--use_alpha' , action='store_true' , help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.' , )
pod_args.add_argument(
'--command_file' , default=lowercase , help='The path to the file containing the commands to run on the pod on startup.' , )
pod_args.add_argument(
'--command' , action='append' , nargs='+' , help='A command to run on the pod. Can be passed multiple times.' , )
pod_args.add_argument(
'--install_accelerate' , action='store_true' , help='Whether to install accelerate on the pod. Defaults to False.' , )
pod_args.add_argument(
'--accelerate_version' , default='latest' , help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.' , )
pod_args.add_argument(
'--debug' , action='store_true' , help='If set, will print the command that would be run instead of running it.' )
if subparsers is not None:
parser.set_defaults(func=lowercase )
return parser
def _SCREAMING_SNAKE_CASE ( lowercase : List[Any] ):
'''simple docstring'''
lowerCamelCase_ = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(lowercase ):
lowerCamelCase_ = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
lowerCamelCase_ = defaults.command_file
if not args.command and defaults.commands is not None:
lowerCamelCase_ = defaults.commands
if not args.tpu_name:
lowerCamelCase_ = defaults.tpu_name
if not args.tpu_zone:
lowerCamelCase_ = defaults.tpu_zone
if args.accelerate_version == "dev":
lowerCamelCase_ = 'git+https://github.com/huggingface/accelerate.git'
elif args.accelerate_version == "latest":
lowerCamelCase_ = 'accelerate -U'
elif isinstance(parse(args.accelerate_version ) , lowercase ):
lowerCamelCase_ = f"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError('You must specify either a command file or a command to run on the pod.' )
if args.command_file:
with open(args.command_file , 'r' ) as f:
lowerCamelCase_ = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , lowercase ):
lowerCamelCase_ = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
lowerCamelCase_ = ['cd /usr/share']
if args.install_accelerate:
new_cmd += [f"""pip install {args.accelerate_version}"""]
new_cmd += args.command
lowerCamelCase_ = '; '.join(lowercase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
lowerCamelCase_ = ['gcloud']
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(f"""Running {" ".join(lowercase )}""" )
return
subprocess.run(lowercase )
print('Successfully setup pod.' )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = tpu_command_parser()
lowerCamelCase_ = parser.parse_args()
tpu_command_launcher(lowercase )
| 356 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase : dict , lowercase : str ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = set(lowercase ), [start]
while stack:
lowerCamelCase_ = stack.pop()
explored.add(lowercase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(lowercase )
return explored
lowerCamelCase : int = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 208 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class snake_case ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict =RoCBertTokenizer
SCREAMING_SNAKE_CASE_ : int =None
SCREAMING_SNAKE_CASE_ : Any =False
SCREAMING_SNAKE_CASE_ : int =True
SCREAMING_SNAKE_CASE_ : Optional[Any] =filter_non_english
def _lowerCamelCase ( self : int ):
super().setUp()
__UpperCamelCase = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
__UpperCamelCase = {}
__UpperCamelCase = {}
for i, value in enumerate(__A ):
__UpperCamelCase = i
__UpperCamelCase = i
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(__A , __A , ensure_ascii=__A )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(__A , __A , ensure_ascii=__A )
def _lowerCamelCase ( self : Tuple ):
__UpperCamelCase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__UpperCamelCase = tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(__A , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__A ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__A ) , [5, 6, 2, 5, 7, 8] )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def _lowerCamelCase ( self : Tuple ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _lowerCamelCase ( self : int ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A , strip_accents=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def _lowerCamelCase ( self : int ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A , strip_accents=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _lowerCamelCase ( self : Any ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def _lowerCamelCase ( self : Optional[int] ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _lowerCamelCase ( self : Any ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A , strip_accents=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _lowerCamelCase ( self : Optional[Any] ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A , strip_accents=__A )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def _lowerCamelCase ( self : Any ):
__UpperCamelCase = RoCBertBasicTokenizer(do_lower_case=__A , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def _lowerCamelCase ( self : Dict ):
__UpperCamelCase = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__UpperCamelCase = {}
for i, token in enumerate(__A ):
__UpperCamelCase = i
__UpperCamelCase = RoCBertWordpieceTokenizer(vocab=__A , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def _lowerCamelCase ( self : Dict ):
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def _lowerCamelCase ( self : Dict ):
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def _lowerCamelCase ( self : int ):
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def _lowerCamelCase ( self : Tuple ):
__UpperCamelCase = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__A ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
__UpperCamelCase = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(__A ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def _lowerCamelCase ( self : Dict ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__A , **__A )
__UpperCamelCase = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
__UpperCamelCase = tokenizer_r.encode_plus(
__A , return_attention_mask=__A , return_token_type_ids=__A , return_offsets_mapping=__A , add_special_tokens=__A , )
__UpperCamelCase = tokenizer_r.do_lower_case if hasattr(__A , 'do_lower_case' ) else False
__UpperCamelCase = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), 'Allen'),
((2_1, 2_3), '##NL'),
((2_3, 2_4), '##P'),
((2_5, 3_3), 'sentence'),
((3_3, 3_4), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 1_5), tokenizer_r.mask_token),
((1_6, 2_1), 'allen'),
((2_1, 2_3), '##nl'),
((2_3, 2_4), '##p'),
((2_5, 3_3), 'sentence'),
((3_3, 3_4), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def _lowerCamelCase ( self : str ):
__UpperCamelCase = ['的', '人', '有']
__UpperCamelCase = ''.join(__A )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__UpperCamelCase = True
__UpperCamelCase = self.tokenizer_class.from_pretrained(__A , **__A )
__UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__A , **__A )
__UpperCamelCase = tokenizer_p.encode(__A , add_special_tokens=__A )
__UpperCamelCase = tokenizer_r.encode(__A , add_special_tokens=__A )
__UpperCamelCase = tokenizer_r.convert_ids_to_tokens(__A )
__UpperCamelCase = tokenizer_p.convert_ids_to_tokens(__A )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
__UpperCamelCase = False
__UpperCamelCase = self.rust_tokenizer_class.from_pretrained(__A , **__A )
__UpperCamelCase = self.tokenizer_class.from_pretrained(__A , **__A )
__UpperCamelCase = tokenizer_r.encode(__A , add_special_tokens=__A )
__UpperCamelCase = tokenizer_p.encode(__A , add_special_tokens=__A )
__UpperCamelCase = tokenizer_r.convert_ids_to_tokens(__A )
__UpperCamelCase = tokenizer_p.convert_ids_to_tokens(__A )
# it is expected that only the first Chinese character is not preceded by "##".
__UpperCamelCase = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(__A )
]
self.assertListEqual(__A , __A )
self.assertListEqual(__A , __A )
@slow
def _lowerCamelCase ( self : Optional[int] ):
__UpperCamelCase = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__UpperCamelCase = tokenizer.encode('你好' , add_special_tokens=__A )
__UpperCamelCase = tokenizer.encode('你是谁' , add_special_tokens=__A )
__UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__A )
__UpperCamelCase = tokenizer.build_inputs_with_special_tokens(__A , __A )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _lowerCamelCase ( self : List[str] ):
__UpperCamelCase = self.get_tokenizers(do_lower_case=__A )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
__UpperCamelCase = '你好，你是谁'
__UpperCamelCase = tokenizer.tokenize(__A )
__UpperCamelCase = tokenizer.convert_tokens_to_ids(__A )
__UpperCamelCase = tokenizer.convert_tokens_to_shape_ids(__A )
__UpperCamelCase = tokenizer.convert_tokens_to_pronunciation_ids(__A )
__UpperCamelCase = tokenizer.prepare_for_model(
__A , __A , __A , add_special_tokens=__A )
__UpperCamelCase = tokenizer.encode_plus(__A , add_special_tokens=__A )
self.assertEqual(__A , __A )
| 53 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class lowercase__ ( lowercase ):
lowercase__ = """openai/whisper-base"""
lowercase__ = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
lowercase__ = """transcriber"""
lowercase__ = WhisperProcessor
lowercase__ = WhisperForConditionalGeneration
lowercase__ = ["""audio"""]
lowercase__ = ["""text"""]
def UpperCamelCase_ ( self : Dict ,lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ ,return_tensors='pt' ).input_features
def UpperCamelCase_ ( self : Dict ,lowerCamelCase__ : Tuple ):
'''simple docstring'''
return self.model.generate(inputs=lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )[0]
| 83 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
lowerCamelCase = trt.Logger(trt.Logger.WARNING)
lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
lowerCamelCase = logging.getLogger(__name__)
lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--onnx_model_path""",
default=None,
type=str,
required=True,
help="""Path to ONNX model: """,
)
parser.add_argument(
"""--output_dir""",
default=None,
type=str,
required=True,
help="""The output directory where the model checkpoints and predictions will be written.""",
)
# Other parameters
parser.add_argument(
"""--tokenizer_name""",
default="""""",
type=str,
required=True,
help="""Pretrained tokenizer name or path if not the same as model_name""",
)
parser.add_argument(
"""--version_2_with_negative""",
action="""store_true""",
help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
"""--null_score_diff_threshold""",
type=float,
default=0.0,
help="""If null_score - best_non_null is greater than the threshold predict null.""",
)
parser.add_argument(
"""--max_seq_length""",
default=384,
type=int,
help=(
"""The maximum total input sequence length after WordPiece tokenization. Sequences """
"""longer than this will be truncated, and sequences shorter than this will be padded."""
),
)
parser.add_argument(
"""--doc_stride""",
default=128,
type=int,
help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)
parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")
parser.add_argument(
"""--n_best_size""",
default=20,
type=int,
help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
"""--max_answer_length""",
default=30,
type=int,
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."""
),
)
parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")
parser.add_argument(
"""--dataset_name""",
type=str,
default=None,
required=True,
help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
"""--dataset_config_name""",
type=str,
default=None,
help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
"""--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
"""--fp16""",
action="""store_true""",
help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
"""--int8""",
action="""store_true""",
help="""Whether to use INT8""",
)
lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name."""
)
logger.info("""Training/evaluation parameters %s""", args)
lowerCamelCase = args.per_device_eval_batch_size
lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
lowerCamelCase = True
lowerCamelCase = """temp_engine/bert-fp32.engine"""
if args.fpaa:
lowerCamelCase = """temp_engine/bert-fp16.engine"""
if args.inta:
lowerCamelCase = """temp_engine/bert-int8.engine"""
# import ONNX file
if not os.path.exists("""temp_engine"""):
os.makedirs("""temp_engine""")
lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, """rb""") as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, """wb""") as f:
f.write(engine.serialize())
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = np.asarray(inputs["input_ids"] , dtype=np.intaa )
UpperCAmelCase_ = np.asarray(inputs["attention_mask"] , dtype=np.intaa )
UpperCAmelCase_ = np.asarray(inputs["token_type_ids"] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowerCAmelCase__ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowerCAmelCase__ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowerCAmelCase__ )
# start time
UpperCAmelCase_ = time.time()
# Run inference
context.execute_async(
bindings=[int(lowerCAmelCase__ ) for d_inp in d_inputs] + [int(lowerCAmelCase__ ), int(lowerCAmelCase__ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
cuda.memcpy_dtoh_async(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Synchronize the stream and take time
stream.synchronize()
# end time
UpperCAmelCase_ = time.time()
UpperCAmelCase_ = end_time - start_time
UpperCAmelCase_ = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
datefmt="""%m/%d/%Y %H:%M:%S""",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(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).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError("""Evaluation requires a dataset name""")
# 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.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
lowerCamelCase = raw_datasets["""validation"""].column_names
lowerCamelCase = """question""" if """question""" in column_names else column_names[0]
lowerCamelCase = """context""" if """context""" in column_names else column_names[1]
lowerCamelCase = """answers""" if """answers""" in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase = tokenizer.padding_side == """right"""
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({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}."
)
lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def a__ ( lowerCAmelCase__ ):
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
# left whitespace
UpperCAmelCase_ = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
UpperCAmelCase_ = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation="only_second" if pad_on_right else "only_first" , max_length=lowerCAmelCase__ , stride=args.doc_stride , return_overflowing_tokens=lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , padding="max_length" , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
UpperCAmelCase_ = tokenized_examples.pop("overflow_to_sample_mapping" )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
UpperCAmelCase_ = []
for i in range(len(tokenized_examples["input_ids"] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
UpperCAmelCase_ = tokenized_examples.sequence_ids(lowerCAmelCase__ )
UpperCAmelCase_ = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
UpperCAmelCase_ = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
UpperCAmelCase_ = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i] )
]
return tokenized_examples
lowerCamelCase = raw_datasets["""validation"""]
# Validation Feature Creation
lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="""Running tokenizer on validation dataset""",
)
lowerCamelCase = default_data_collator
lowerCamelCase = eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="eval" ):
# Post-processing: we match the start logits and end logits to answers in the original context.
UpperCAmelCase_ = postprocess_qa_predictions(
examples=lowerCAmelCase__ , features=lowerCAmelCase__ , predictions=lowerCAmelCase__ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowerCAmelCase__ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
UpperCAmelCase_ = [
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
]
else:
UpperCAmelCase_ = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
UpperCAmelCase_ = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowerCAmelCase__ , label_ids=lowerCAmelCase__ )
lowerCamelCase = load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")
# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def a__ ( lowerCAmelCase__ ):
return trt.volume(engine.get_binding_shape(lowerCAmelCase__ ) ) * engine.get_binding_dtype(lowerCAmelCase__ ).itemsize
# Allocate device memory for inputs and outputs.
lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
lowerCamelCase = cuda.Stream()
# Evaluation
logger.info("""***** Running Evaluation *****""")
logger.info(F" Num examples = {len(eval_dataset)}")
logger.info(F" Batch size = {args.per_device_eval_batch_size}")
lowerCamelCase = 0.0
lowerCamelCase = 0
lowerCamelCase = timeit.default_timer()
lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
lowerCamelCase , lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
lowerCamelCase , lowerCamelCase = outputs
lowerCamelCase = torch.tensor(start_logits)
lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)
lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
if all_preds is not None:
lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
lowerCamelCase = timeit.default_timer() - start_time
logger.info(""" Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1_000 / niter))
logger.info("""Total Inference Time = {:.3f} ms""".format(total_time * 1_000))
logger.info("""Total Number of Inference = %d""", niter)
lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(F"Evaluation metrics: {eval_metric}")
| 241 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] , *_UpperCAmelCase : List[Any] , **_UpperCAmelCase : Union[str, Any] ) -> None:
'''simple docstring'''
warnings.warn(
"The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use FlavaImageProcessor instead." , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 241 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def lowerCAmelCase__(__snake_case ) -> Any:
'''simple docstring'''
lowerCamelCase__ = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCamelCase__ = flatten_dict(lowercase__ )
return flax_params
def lowerCAmelCase__(__snake_case ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase__ = {}
lowerCamelCase__ = {
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase__ = {
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase__ = '''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase__ = new_key.replace(lowercase__ ,lowercase__ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase__ = new_key.replace(lowercase__ ,lowercase__ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase__ = re.sub(R'''layers_(\d+)''' ,R'''layer.\1''' ,lowercase__ )
lowerCamelCase__ = new_key.replace('''encoder''' ,'''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase__ = re.sub(R'''layers_(\d+)''' ,R'''layer.\1''' ,lowercase__ )
lowerCamelCase__ = flax_dict[key]
lowerCamelCase__ = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase__ = torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase__ = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False ,__snake_case=False ) -> Tuple:
'''simple docstring'''
lowerCamelCase__ = get_flax_param(lowercase__ )
if not use_large:
lowerCamelCase__ = PixaStructVisionConfig()
lowerCamelCase__ = PixaStructTextConfig()
else:
lowerCamelCase__ = PixaStructVisionConfig(
hidden_size=1536 ,d_ff=3968 ,num_attention_heads=24 ,num_hidden_layers=18 )
lowerCamelCase__ = PixaStructTextConfig(hidden_size=1536 ,d_ff=3968 ,num_heads=24 ,num_layers=18 )
lowerCamelCase__ = PixaStructConfig(
vision_config=encoder_config.to_dict() ,text_config=decoder_config.to_dict() ,is_vqa=lowercase__ )
lowerCamelCase__ = PixaStructForConditionalGeneration(lowercase__ )
lowerCamelCase__ = rename_and_convert_flax_params(lowercase__ )
model.load_state_dict(lowercase__ )
lowerCamelCase__ = AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase__ = PixaStructImageProcessor()
lowerCamelCase__ = PixaStructProcessor(image_processor=lowercase__ ,tokenizer=lowercase__ )
if use_large:
lowerCamelCase__ = 4096
lowerCamelCase__ = True
# mkdir if needed
os.makedirs(lowercase__ ,exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
print('''Model saved in {}'''.format(lowercase__ ) )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--use_large", action="store_true", help="Use large model.")
parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
_a = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 209 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfig",
"BlipTextConfig",
"BlipVisionConfig",
],
"processing_blip": ["BlipProcessor"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["BlipImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"BlipModel",
"BlipPreTrainedModel",
"BlipForConditionalGeneration",
"BlipForQuestionAnswering",
"BlipVisionModel",
"BlipTextModel",
"BlipForImageTextRetrieval",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFBlipModel",
"TFBlipPreTrainedModel",
"TFBlipForConditionalGeneration",
"TFBlipForQuestionAnswering",
"TFBlipVisionModel",
"TFBlipTextModel",
"TFBlipForImageTextRetrieval",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 164 | 0 |
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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a : List[Any] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
a : str = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
]
)
def lowerCAmelCase_ (lowerCAmelCase__: Optional[Any] , lowerCAmelCase__: Optional[Any] , lowerCAmelCase__: Any ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_: int = val
def lowerCAmelCase_ (lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: List[Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase_: Optional[int] = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
UpperCAmelCase_: str = value
else:
UpperCAmelCase_: int = value
return new_state_dict
def lowerCAmelCase_ (lowerCAmelCase__: Tuple , lowerCAmelCase__: Any=False ):
"""simple docstring"""
UpperCAmelCase_: Any = ''''''
if is_panoptic:
UpperCAmelCase_: Optional[Any] = '''conditional_detr.'''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase_: List[Any] = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
UpperCAmelCase_: Tuple = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_: List[str] = in_proj_weight[:2_5_6, :]
UpperCAmelCase_: str = in_proj_bias[:2_5_6]
UpperCAmelCase_: Optional[Any] = in_proj_weight[2_5_6:5_1_2, :]
UpperCAmelCase_: List[Any] = in_proj_bias[2_5_6:5_1_2]
UpperCAmelCase_: Optional[Any] = in_proj_weight[-2_5_6:, :]
UpperCAmelCase_: int = in_proj_bias[-2_5_6:]
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase_: List[str] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ (lowerCAmelCase__: int , lowerCAmelCase__: Dict ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
UpperCAmelCase_: Tuple = '''resnet101'''
if "dc5" in model_name:
UpperCAmelCase_: int = True
UpperCAmelCase_: List[Any] = '''panoptic''' in model_name
if is_panoptic:
UpperCAmelCase_: Optional[Any] = 2_5_0
else:
UpperCAmelCase_: Optional[int] = 9_1
UpperCAmelCase_: str = '''huggingface/label-files'''
UpperCAmelCase_: Optional[int] = '''coco-detection-id2label.json'''
UpperCAmelCase_: Tuple = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type="""dataset""" ) , """r""" ) )
UpperCAmelCase_: List[Any] = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
UpperCAmelCase_: int = idalabel
UpperCAmelCase_: Union[str, Any] = {v: k for k, v in idalabel.items()}
# load image processor
UpperCAmelCase_: Optional[int] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
UpperCAmelCase_: Optional[int] = ConditionalDetrImageProcessor(format=lowerCAmelCase__ )
# prepare image
UpperCAmelCase_: Union[str, Any] = prepare_img()
UpperCAmelCase_: Tuple = image_processor(images=lowerCAmelCase__ , return_tensors="""pt""" )
UpperCAmelCase_: int = encoding['''pixel_values''']
logger.info(F'Converting model {model_name}...' )
# load original model from torch hub
UpperCAmelCase_: List[str] = torch.hub.load("""DeppMeng/ConditionalDETR""" , lowerCAmelCase__ , pretrained=lowerCAmelCase__ ).eval()
UpperCAmelCase_: Tuple = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
UpperCAmelCase_: Any = '''conditional_detr.''' + src
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_: int = rename_backbone_keys(lowerCAmelCase__ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCAmelCase__ , is_panoptic=lowerCAmelCase__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_: Optional[Any] = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
UpperCAmelCase_: int = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_: Optional[Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
UpperCAmelCase_: Union[str, Any] = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_: Any = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
UpperCAmelCase_: str = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_: str = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
UpperCAmelCase_: Optional[int] = state_dict.pop(lowerCAmelCase__ )
UpperCAmelCase_: str = val
# finally, create HuggingFace model and load state dict
UpperCAmelCase_: List[Any] = ConditionalDetrForSegmentation(lowerCAmelCase__ ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
model.eval()
model.push_to_hub(repo_id=lowerCAmelCase__ , organization="""DepuMeng""" , commit_message="""Add model""" )
# verify our conversion
UpperCAmelCase_: Any = conditional_detr(lowerCAmelCase__ )
UpperCAmelCase_: str = model(lowerCAmelCase__ )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
# Save model and image processor
logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
model.save_pretrained(lowerCAmelCase__ )
image_processor.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
a : int = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='conditional_detr_resnet50',
type=str,
help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
a : Dict = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 351 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
a : Optional[Any] = logging.getLogger(__name__)
a : List[Any] = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
a : List[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _a :
A = field(
default=_lowerCAmelCase , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowerCAmelCase )} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class _a :
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''The input training data file (a text file).'''} )
A = field(
default=_lowerCAmelCase , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
A = field(default=_lowerCAmelCase , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
A = field(
default=0.15 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
A = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
A = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
A = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
A = field(
default=_lowerCAmelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def lowerCAmelCase_ (lowerCAmelCase__: DataTrainingArguments , lowerCAmelCase__: PreTrainedTokenizer , lowerCAmelCase__: bool = False , lowerCAmelCase__: Optional[str] = None , ):
"""simple docstring"""
def _dataset(lowerCAmelCase__: int , lowerCAmelCase__: Optional[Any]=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("""You need to set world whole masking and mlm to True for Chinese Whole Word Mask""" )
return LineByLineWithRefDataset(
tokenizer=lowerCAmelCase__ , file_path=lowerCAmelCase__ , block_size=args.block_size , ref_path=lowerCAmelCase__ , )
return LineByLineTextDataset(tokenizer=lowerCAmelCase__ , file_path=lowerCAmelCase__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=lowerCAmelCase__ , file_path=lowerCAmelCase__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=lowerCAmelCase__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(lowerCAmelCase__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_: int = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"""Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file """
"""or remove the --do_eval argument.""" )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCAmelCase__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
UpperCAmelCase_: Dict = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
UpperCAmelCase_: Any = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
UpperCAmelCase_: int = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.tokenizer_name:
UpperCAmelCase_: Optional[int] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
UpperCAmelCase_: List[str] = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"""
""" script, save it,and load it from here, using --tokenizer_name""" )
if model_args.model_name_or_path:
UpperCAmelCase_: int = AutoModelWithLMHead.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 , )
else:
logger.info("""Training new model from scratch""" )
UpperCAmelCase_: Union[str, Any] = AutoModelWithLMHead.from_config(lowerCAmelCase__ )
model.resize_token_embeddings(len(lowerCAmelCase__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"""BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"""
"""--mlm flag (masked language modeling).""" )
if data_args.block_size <= 0:
UpperCAmelCase_: List[str] = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
UpperCAmelCase_: Any = min(data_args.block_size , tokenizer.max_len )
# Get datasets
UpperCAmelCase_: str = (
get_dataset(lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
UpperCAmelCase_: List[Any] = (
get_dataset(lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , evaluate=lowerCAmelCase__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
UpperCAmelCase_: Dict = DataCollatorForPermutationLanguageModeling(
tokenizer=lowerCAmelCase__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
UpperCAmelCase_: str = DataCollatorForWholeWordMask(
tokenizer=lowerCAmelCase__ , mlm_probability=data_args.mlm_probability )
else:
UpperCAmelCase_: Optional[int] = DataCollatorForLanguageModeling(
tokenizer=lowerCAmelCase__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCAmelCase_: Union[str, Any] = Trainer(
model=lowerCAmelCase__ , args=lowerCAmelCase__ , data_collator=lowerCAmelCase__ , train_dataset=lowerCAmelCase__ , eval_dataset=lowerCAmelCase__ , prediction_loss_only=lowerCAmelCase__ , )
# Training
if training_args.do_train:
UpperCAmelCase_: Dict = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=lowerCAmelCase__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
UpperCAmelCase_: Union[str, Any] = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCAmelCase_: List[Any] = trainer.evaluate()
UpperCAmelCase_: Optional[Any] = math.exp(eval_output["""eval_loss"""] )
UpperCAmelCase_: Optional[Any] = {"""perplexity""": perplexity}
UpperCAmelCase_: Any = os.path.join(training_args.output_dir , """eval_results_lm.txt""" )
if trainer.is_world_master():
with open(lowerCAmelCase__ , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , lowerCAmelCase__ , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
results.update(lowerCAmelCase__ )
return results
def lowerCAmelCase_ (lowerCAmelCase__: List[Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 82 | 0 |
'''simple docstring'''
from string import ascii_uppercase
A_ : List[Any] = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""int() can't convert non-string with explicit base""" )
if num < 0:
raise ValueError("""parameter must be positive int""" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if base in (0, 1):
raise ValueError("""base must be >= 2""" )
if base > 36:
raise ValueError("""base must be <= 36""" )
_UpperCAmelCase : List[str] = """"""
_UpperCAmelCase : List[Any] = 0
_UpperCAmelCase : Any = 0
while div != 1:
_UpperCAmelCase ,_UpperCAmelCase : Optional[int] = divmod(lowerCAmelCase_ , lowerCAmelCase_ )
if base >= 11 and 9 < mod < 36:
_UpperCAmelCase : Union[str, Any] = ALPHABET_VALUES[str(lowerCAmelCase_ )]
else:
_UpperCAmelCase : Tuple = str(lowerCAmelCase_ )
new_value += actual_value
_UpperCAmelCase : Optional[int] = num // base
_UpperCAmelCase : Union[str, Any] = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(lowerCAmelCase_ )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 3_7):
for num in range(1_0_0_0):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 215 |
'''simple docstring'''
class lowercase :
"""simple docstring"""
def __init__( self ) -> List[str]:
_UpperCAmelCase : int = 0
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Optional[int] = {}
def _snake_case ( self ,a_ ) -> Optional[Any]:
if vertex not in self.adjacency:
_UpperCAmelCase : int = {}
self.num_vertices += 1
def _snake_case ( self ,a_ ,a_ ,a_ ) -> int:
self.add_vertex(a_ )
self.add_vertex(a_ )
if head == tail:
return
_UpperCAmelCase : List[Any] = weight
_UpperCAmelCase : Dict = weight
def _snake_case ( self ) -> Dict:
_UpperCAmelCase : Optional[int] = self.get_edges()
for edge in edges:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = edge
edges.remove((tail, head, weight) )
for i in range(len(a_ ) ):
_UpperCAmelCase : str = list(edges[i] )
edges.sort(key=lambda a_ : e[2] )
for i in range(len(a_ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
_UpperCAmelCase : Optional[Any] = edges[i][2] + 1
for edge in edges:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Union[str, Any] = edge
_UpperCAmelCase : str = weight
_UpperCAmelCase : List[str] = weight
def __str__( self ) -> Any:
_UpperCAmelCase : List[Any] = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
_UpperCAmelCase : List[str] = self.adjacency[head][tail]
string += f'''{head} -> {tail} == {weight}\n'''
return string.rstrip("""\n""" )
def _snake_case ( self ) -> str:
_UpperCAmelCase : int = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def _snake_case ( self ) -> Optional[int]:
return self.adjacency.keys()
@staticmethod
def _snake_case ( a_=None ,a_=None ) -> Tuple:
_UpperCAmelCase : List[Any] = Graph()
if vertices is None:
_UpperCAmelCase : List[str] = []
if edges is None:
_UpperCAmelCase : Optional[Any] = []
for vertex in vertices:
g.add_vertex(a_ )
for edge in edges:
g.add_edge(*a_ )
return g
class lowercase :
"""simple docstring"""
def __init__( self ) -> int:
_UpperCAmelCase : List[str] = {}
_UpperCAmelCase : int = {}
def __len__( self ) -> Tuple:
return len(self.parent )
def _snake_case ( self ,a_ ) -> str:
if item in self.parent:
return self.find(a_ )
_UpperCAmelCase : Optional[Any] = item
_UpperCAmelCase : List[Any] = 0
return item
def _snake_case ( self ,a_ ) -> List[str]:
if item not in self.parent:
return self.make_set(a_ )
if item != self.parent[item]:
_UpperCAmelCase : List[Any] = self.find(self.parent[item] )
return self.parent[item]
def _snake_case ( self ,a_ ,a_ ) -> Union[str, Any]:
_UpperCAmelCase : Any = self.find(a_ )
_UpperCAmelCase : List[str] = self.find(a_ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
_UpperCAmelCase : Any = roota
return roota
if self.rank[roota] < self.rank[roota]:
_UpperCAmelCase : Any = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
_UpperCAmelCase : List[str] = roota
return roota
return None
@staticmethod
def _snake_case ( a_ ) -> List[Any]:
_UpperCAmelCase : int = graph.num_vertices
_UpperCAmelCase : int = Graph.UnionFind()
_UpperCAmelCase : Optional[int] = []
while num_components > 1:
_UpperCAmelCase : int = {}
for vertex in graph.get_vertices():
_UpperCAmelCase : Union[str, Any] = -1
_UpperCAmelCase : Tuple = graph.get_edges()
for edge in edges:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = edge
edges.remove((tail, head, weight) )
for edge in edges:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = edge
_UpperCAmelCase : Any = union_find.find(a_ )
_UpperCAmelCase : Any = union_find.find(a_ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_UpperCAmelCase : Tuple = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_UpperCAmelCase : List[str] = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = cheap_edge[vertex]
if union_find.find(a_ ) != union_find.find(a_ ):
union_find.union(a_ ,a_ )
mst_edges.append(cheap_edge[vertex] )
_UpperCAmelCase : Tuple = num_components - 1
_UpperCAmelCase : Optional[int] = Graph.build(edges=a_ )
return mst
| 215 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowercase ( ) ->List[str]:
'''simple docstring'''
__A : List[Any] = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch '''
'''helper utility that will spawn up '''
'''multiple distributed processes'''
) )
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' ,type=snake_case_ ,default=1 ,help='''Number of TPU cores to use (1 or 8).''' )
# positional
parser.add_argument(
'''training_script''' ,type=snake_case_ ,help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) ,)
# rest from the training program
parser.add_argument('''training_script_args''' ,nargs=snake_case_ )
return parser.parse_args()
def __lowercase ( ) ->int:
'''simple docstring'''
__A : str = parse_args()
# Import training_script as a module.
__A : Union[str, Any] = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
__A : List[str] = script_fpath.stem
__A : int = importlib.import_module(snake_case_ )
# Patch sys.argv
__A : Tuple = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores )]
xmp.spawn(mod._mp_fn ,args=() ,nprocs=args.num_cores )
if __name__ == "__main__":
main() | 353 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = """"""
_lowerCamelCase = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , __lowerCamelCase = None , __lowerCamelCase = None , **__lowerCamelCase , ):
'''simple docstring'''
super().__init__(self , **__lowerCamelCase )
__A : int = repo_info
__A : Optional[int] = token
__A : int = None
def UpperCamelCase__( self ):
'''simple docstring'''
if self.dir_cache is None:
__A : int = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
__A : Tuple = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(__lowerCamelCase ): {'''name''': str(__lowerCamelCase ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase = "rb" , **__lowerCamelCase , ):
'''simple docstring'''
if not isinstance(self.repo_info , __lowerCamelCase ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
__A : Union[str, Any] = hf_hub_url(self.repo_info.id , __lowerCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__lowerCamelCase , mode=__lowerCamelCase , headers=get_authentication_headers_for_url(__lowerCamelCase , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def UpperCamelCase__( self , __lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
self._get_dirs()
__A : Optional[Any] = self._strip_protocol(__lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__lowerCamelCase )
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase=False , **__lowerCamelCase ):
'''simple docstring'''
self._get_dirs()
__A : Any = PurePosixPath(path.strip('''/''' ) )
__A : Any = {}
for p, f in self.dir_cache.items():
__A : List[Any] = PurePosixPath(p.strip('''/''' ) )
__A : Dict = p.parent
if root == path:
__A : Union[str, Any] = f
__A : List[str] = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 291 | 0 |
"""simple docstring"""
import pprint
import requests
a :Union[str, Any] = "https://zenquotes.io/api"
def _lowercase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def _lowercase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
a :int = random_quotes()
pprint.pprint(response)
| 132 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
a :Optional[int] = ["text", "image", "audio"]
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
inputs.append(create_inputs(__lowerCAmelCase ) )
else:
raise ValueError(F'''Invalid type requested: {input_type}''' )
return inputs
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple = []
for output in outputs:
if isinstance(__lowerCAmelCase , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(__lowerCAmelCase , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(__lowerCAmelCase , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(F'''Invalid output: {output}''' )
return output_types
@is_tool_test
class __a :
'''simple docstring'''
def _a ( self ) -> str:
"""simple docstring"""
self.assertTrue(hasattr(self.tool , """inputs""" ) )
self.assertTrue(hasattr(self.tool , """outputs""" ) )
SCREAMING_SNAKE_CASE__ : List[Any] = self.tool.inputs
for _input in inputs:
if isinstance(_input , _a ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
SCREAMING_SNAKE_CASE__ : Dict = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tool(*_a )
# There is a single output
if len(self.tool.outputs ) == 1:
SCREAMING_SNAKE_CASE__ : List[Any] = [outputs]
self.assertListEqual(output_types(_a ) , self.tool.outputs )
def _a ( self ) -> List[Any]:
"""simple docstring"""
self.assertTrue(hasattr(self.tool , """description""" ) )
self.assertTrue(hasattr(self.tool , """default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE__ : Dict = self.tool(*_a )
if not isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [outputs]
self.assertEqual(len(_a ) , len(self.tool.outputs ) )
for output, output_type in zip(_a , self.tool.outputs ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(_a , _a ) )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = create_inputs(self.tool.inputs )
SCREAMING_SNAKE_CASE__ : List[Any] = []
for _input, input_type in zip(_a , self.tool.inputs ):
if isinstance(_a , _a ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tool(*_a )
if not isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = [outputs]
self.assertEqual(len(_a ) , len(self.tool.outputs ) )
| 132 | 1 |
import datasets
from .evaluate import evaluate
lowercase : Dict = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
'''
lowercase : int = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''
lowercase : int = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the CUAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
\'aupr\': Area Under the Precision-Recall curve
\'prec_at_80_recall\': Precision at 80% recall
\'prec_at_90_recall\': Precision at 90% recall
Examples:
>>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> cuad_metric = datasets.load_metric("cuad")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {
"id": datasets.Value("string" ),
"prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://www.atticusprojectai.org/cuad"] , reference_urls=["https://www.atticusprojectai.org/cuad"] , )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
snake_case_ : Union[str, Any] = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
snake_case_ : Optional[Any] = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
snake_case_ : Any = evaluate(dataset=_SCREAMING_SNAKE_CASE , predictions=_SCREAMING_SNAKE_CASE )
return score
| 36 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = ['image_processor', 'tokenizer']
A : Tuple = 'AutoImageProcessor'
A : Dict = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ : Union[str, Any] = self.image_processor
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> List[str]:
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:
snake_case_ : Tuple = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if images is not None:
snake_case_ : Tuple = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None and images is not None:
snake_case_ : List[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE ) , tensor_type=_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Any:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def _lowerCAmelCase ( self ) -> Dict:
return ["input_ids", "attention_mask", "pixel_values"]
| 36 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :str , snake_case_ :list[str] | None = None , snake_case_ :dict[str, float] | None = None , snake_case_ :bool = False , ):
__UpperCAmelCase = cipher_alphabet or [chr(snake_case_ ) for i in range(97 , 123 )]
# If the argument is None or the user provided an empty dictionary
if not frequencies_dict:
# Frequencies of letters in the english language (how much they show up)
__UpperCAmelCase = {
'''a''': 0.08497,
'''b''': 0.01492,
'''c''': 0.02202,
'''d''': 0.04253,
'''e''': 0.11162,
'''f''': 0.02228,
'''g''': 0.02015,
'''h''': 0.06094,
'''i''': 0.07546,
'''j''': 0.00153,
'''k''': 0.01292,
'''l''': 0.04025,
'''m''': 0.02406,
'''n''': 0.06749,
'''o''': 0.07507,
'''p''': 0.01929,
'''q''': 0.00095,
'''r''': 0.07587,
'''s''': 0.06327,
'''t''': 0.09356,
'''u''': 0.02758,
'''v''': 0.00978,
'''w''': 0.02560,
'''x''': 0.00150,
'''y''': 0.01994,
'''z''': 0.00077,
}
else:
# Custom frequencies dictionary
__UpperCAmelCase = frequencies_dict
if not case_sensitive:
__UpperCAmelCase = ciphertext.lower()
# Chi squared statistic values
__UpperCAmelCase = {}
# cycle through all of the shifts
for shift in range(len(snake_case_ ) ):
__UpperCAmelCase = ''''''
# decrypt the message with the shift
for letter in ciphertext:
try:
# Try to index the letter in the alphabet
__UpperCAmelCase = (alphabet_letters.index(letter.lower() ) - shift) % len(
snake_case_ )
decrypted_with_shift += (
alphabet_letters[new_key].upper()
if case_sensitive and letter.isupper()
else alphabet_letters[new_key]
)
except ValueError:
# Append the character if it isn't in the alphabet
decrypted_with_shift += letter
__UpperCAmelCase = 0.0
# Loop through each letter in the decoded message with the shift
for letter in decrypted_with_shift:
if case_sensitive:
__UpperCAmelCase = letter.lower()
if letter in frequencies:
# Get the amount of times the letter occurs in the message
__UpperCAmelCase = decrypted_with_shift.lower().count(snake_case_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
__UpperCAmelCase = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
__UpperCAmelCase = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
else:
if letter.lower() in frequencies:
# Get the amount of times the letter occurs in the message
__UpperCAmelCase = decrypted_with_shift.count(snake_case_ )
# Get the excepcted amount of times the letter should appear based
# on letter frequencies
__UpperCAmelCase = frequencies[letter] * occurrences
# Complete the chi squared statistic formula
__UpperCAmelCase = ((occurrences - expected) ** 2) / expected
# Add the margin of error to the total chi squared statistic
chi_squared_statistic += chi_letter_value
# Add the data to the chi_squared_statistic_values dictionary
__UpperCAmelCase = (
chi_squared_statistic,
decrypted_with_shift,
)
# Get the most likely cipher by finding the cipher with the smallest chi squared
# statistic
def chi_squared_statistic_values_sorting_key(snake_case_ :int ) -> tuple[float, str]:
return chi_squared_statistic_values[key]
__UpperCAmelCase = min(
snake_case_ , key=snake_case_ , )
# Get all the data from the most likely cipher (key, decoded message)
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) = chi_squared_statistic_values[most_likely_cipher]
# Return the data on the most likely shift
return (
most_likely_cipher,
most_likely_cipher_chi_squared_value,
decoded_most_likely_cipher,
)
| 332 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Tuple = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_lowercase : List[str] = 25_00_04
_lowercase : int = 25_00_20
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( _lowerCAmelCase , unittest.TestCase ):
a__ : Union[str, Any] = MBartaaTokenizer
a__ : List[str] = MBartaaTokenizerFast
a__ : Any = True
a__ : List[str] = True
def a ( self : str ):
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase = MBartaaTokenizer(_lowercase , src_lang='''en_XX''' , tgt_lang='''ro_RO''' , keep_accents=_lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def a ( self : Dict ):
__UpperCAmelCase = '''<s>'''
__UpperCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(_lowercase ) , 10_54 )
def a ( self : Tuple ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_54 )
def a ( self : str ):
__UpperCAmelCase = MBartaaTokenizer(_lowercase , src_lang='''en_XX''' , tgt_lang='''ro_RO''' , keep_accents=_lowercase )
__UpperCAmelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__UpperCAmelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowercase , [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''', '''.'''] , )
__UpperCAmelCase = tokenizer.convert_tokens_to_ids(_lowercase )
self.assertListEqual(
_lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__UpperCAmelCase = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [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>''', '''.'''] , )
@slow
def a ( self : str ):
# fmt: off
__UpperCAmelCase = {'''input_ids''': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''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, 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, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 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=_lowercase , model_name='''facebook/mbart-large-50''' , revision='''d3913889c59cd5c9e456b269c376325eabad57e2''' , )
def a ( self : str ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__UpperCAmelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
__UpperCAmelCase = self.tokenizer_class.from_pretrained(_lowercase , **_lowercase )
__UpperCAmelCase = tempfile.mkdtemp()
__UpperCAmelCase = tokenizer_r.save_pretrained(_lowercase )
__UpperCAmelCase = tokenizer_p.save_pretrained(_lowercase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__UpperCAmelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(_lowercase , _lowercase )
# Checks everything loads correctly in the same way
__UpperCAmelCase = tokenizer_r.from_pretrained(_lowercase )
__UpperCAmelCase = tokenizer_p.from_pretrained(_lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowercase , _lowercase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(_lowercase )
# Save tokenizer rust, legacy_format=True
__UpperCAmelCase = tempfile.mkdtemp()
__UpperCAmelCase = tokenizer_r.save_pretrained(_lowercase , legacy_format=_lowercase )
__UpperCAmelCase = tokenizer_p.save_pretrained(_lowercase )
# Checks it save with the same files
self.assertSequenceEqual(_lowercase , _lowercase )
# Checks everything loads correctly in the same way
__UpperCAmelCase = tokenizer_r.from_pretrained(_lowercase )
__UpperCAmelCase = tokenizer_p.from_pretrained(_lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowercase , _lowercase ) )
shutil.rmtree(_lowercase )
# Save tokenizer rust, legacy_format=False
__UpperCAmelCase = tempfile.mkdtemp()
__UpperCAmelCase = tokenizer_r.save_pretrained(_lowercase , legacy_format=_lowercase )
__UpperCAmelCase = tokenizer_p.save_pretrained(_lowercase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__UpperCAmelCase = tokenizer_r.from_pretrained(_lowercase )
__UpperCAmelCase = tokenizer_p.from_pretrained(_lowercase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowercase , _lowercase ) )
shutil.rmtree(_lowercase )
@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( unittest.TestCase ):
a__ : str = "facebook/mbart-large-50-one-to-many-mmt"
a__ : Union[str, Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
a__ : Any = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
a__ : Any = [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]
@classmethod
def a ( cls : Tuple ):
__UpperCAmelCase = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
__UpperCAmelCase = 1
return cls
def a ( self : Union[str, Any] ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 25_00_20 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN'''] , 25_00_38 )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _lowercase )
def a ( self : Optional[Any] ):
self.assertIn(_lowercase , self.tokenizer.all_special_ids )
__UpperCAmelCase = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
__UpperCAmelCase = self.tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
__UpperCAmelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertNotIn(self.tokenizer.eos_token , _lowercase )
def a ( self : Optional[Any] ):
__UpperCAmelCase = ['''this is gunna be a long sentence ''' * 20]
assert isinstance(src_text[0] , _lowercase )
__UpperCAmelCase = 10
__UpperCAmelCase = self.tokenizer(_lowercase , max_length=_lowercase , truncation=_lowercase ).input_ids[0]
self.assertEqual(ids[0] , _lowercase )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(_lowercase ) , _lowercase )
def a ( self : Optional[int] ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [25_00_53, 25_00_01] )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = tempfile.mkdtemp()
__UpperCAmelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_lowercase )
__UpperCAmelCase = MBartaaTokenizer.from_pretrained(_lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , _lowercase )
@require_torch
def a ( self : Dict ):
__UpperCAmelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_lowercase , return_tensors='''pt''' )
__UpperCAmelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def a ( self : Union[str, Any] ):
__UpperCAmelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=_lowercase , truncation=_lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
__UpperCAmelCase = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
__UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , _lowercase )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def a ( self : Union[str, Any] ):
__UpperCAmelCase = self.tokenizer(self.src_text , padding=_lowercase , truncation=_lowercase , max_length=3 , return_tensors='''pt''' )
__UpperCAmelCase = self.tokenizer(
text_target=self.tgt_text , padding=_lowercase , truncation=_lowercase , max_length=10 , return_tensors='''pt''' )
__UpperCAmelCase = targets['''input_ids''']
__UpperCAmelCase = shift_tokens_right(_lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def a ( self : Dict ):
__UpperCAmelCase = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(_lowercase ) , {
# en_XX, A, test, EOS
'''input_ids''': [[25_00_04, 62, 30_34, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 25_00_01,
} , )
| 332 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
__a = logging.get_logger("transformers.models.encodec")
__a = {
"quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
"quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
"quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
"quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
__a = {
"encoder.model.0.conv.conv": "encoder.layers.0.conv",
"encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
"encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
"encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
"encoder.model.3.conv.conv": "encoder.layers.3.conv",
"encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
"encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
"encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
"encoder.model.6.conv.conv": "encoder.layers.6.conv",
"encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
"encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
"encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
"encoder.model.9.conv.conv": "encoder.layers.9.conv",
"encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
"encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
"encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
"encoder.model.12.conv.conv": "encoder.layers.12.conv",
"encoder.model.13.lstm": "encoder.layers.13.lstm",
"encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
__a = {
"encoder.model.0.conv.norm": "encoder.layers.0.norm",
"encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
"encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
"encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
"encoder.model.3.conv.norm": "encoder.layers.3.norm",
"encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
"encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
"encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
"encoder.model.6.conv.norm": "encoder.layers.6.norm",
"encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
"encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
"encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
"encoder.model.9.conv.norm": "encoder.layers.9.norm",
"encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
"encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
"encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
"encoder.model.12.conv.norm": "encoder.layers.12.norm",
"encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
__a = {
"decoder.model.0.conv.conv": "decoder.layers.0.conv",
"decoder.model.1.lstm": "decoder.layers.1.lstm",
"decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
"decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
"decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
"decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
"decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
"decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
"decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
"decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
"decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
"decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
"decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
"decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
"decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
"decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
"decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
"decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
"decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
__a = {
"decoder.model.0.conv.norm": "decoder.layers.0.norm",
"decoder.model.3.convtr.norm": "decoder.layers.3.norm",
"decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
"decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
"decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
"decoder.model.6.convtr.norm": "decoder.layers.6.norm",
"decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
"decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
"decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
"decoder.model.9.convtr.norm": "decoder.layers.9.norm",
"decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
"decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
"decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
"decoder.model.12.convtr.norm": "decoder.layers.12.norm",
"decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
"decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
"decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
"decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
__a = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
__a = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
__a = []
__a = []
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
for attribute in key.split(""".""" ):
snake_case__ : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
if weight_type is not None:
snake_case__ : Any = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
else:
snake_case__ : int = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}" )
if weight_type == "weight":
snake_case__ : Dict = value
elif weight_type == "weight_g":
snake_case__ : List[str] = value
elif weight_type == "weight_v":
snake_case__ : Optional[Any] = value
elif weight_type == "bias":
snake_case__ : Dict = value
elif weight_type == "running_mean":
snake_case__ : int = value
elif weight_type == "running_var":
snake_case__ : Dict = value
elif weight_type == "num_batches_tracked":
snake_case__ : Tuple = value
elif weight_type == "weight_ih_l0":
snake_case__ : Optional[int] = value
elif weight_type == "weight_hh_l0":
snake_case__ : Dict = value
elif weight_type == "bias_ih_l0":
snake_case__ : Optional[Any] = value
elif weight_type == "bias_hh_l0":
snake_case__ : Dict = value
elif weight_type == "weight_ih_l1":
snake_case__ : Dict = value
elif weight_type == "weight_hh_l1":
snake_case__ : int = value
elif weight_type == "bias_ih_l1":
snake_case__ : int = value
elif weight_type == "bias_hh_l1":
snake_case__ : List[Any] = value
else:
snake_case__ : Tuple = value
logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}." )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
snake_case__ : Union[str, Any] = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
snake_case__ : List[Any] = []
if model_name == "encodec_24khz" or "encodec_32khz":
snake_case__ : Optional[Any] = MAPPING_24K
elif model_name == "encodec_48khz":
snake_case__ : Dict = MAPPING_48K
else:
raise ValueError(f"Unsupported model: {model_name}" )
for name, value in orig_dict.items():
if should_ignore(_lowerCAmelCase , _lowerCAmelCase ):
logger.info(f"{name} was ignored" )
continue
snake_case__ : Optional[Any] = False
for key, mapped_key in MAPPING.items():
if "*" in key:
snake_case__ : Dict = key.split(""".*.""" )
if prefix in name and suffix in name:
snake_case__ : Union[str, Any] = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("""embed""" ) and name.endswith("""embed_avg""" ):
continue
snake_case__ : Optional[int] = True
if "*" in mapped_key:
snake_case__ : int = name.split(_lowerCAmelCase )[0].split(""".""" )[-2]
snake_case__ : Tuple = mapped_key.replace("""*""" , _lowerCAmelCase )
if "weight_g" in name:
snake_case__ : Dict = """weight_g"""
elif "weight_v" in name:
snake_case__ : int = """weight_v"""
elif "weight_ih_l0" in name:
snake_case__ : Tuple = """weight_ih_l0"""
elif "weight_hh_l0" in name:
snake_case__ : Union[str, Any] = """weight_hh_l0"""
elif "bias_ih_l0" in name:
snake_case__ : int = """bias_ih_l0"""
elif "bias_hh_l0" in name:
snake_case__ : Optional[Any] = """bias_hh_l0"""
elif "weight_ih_l1" in name:
snake_case__ : str = """weight_ih_l1"""
elif "weight_hh_l1" in name:
snake_case__ : Tuple = """weight_hh_l1"""
elif "bias_ih_l1" in name:
snake_case__ : List[Any] = """bias_ih_l1"""
elif "bias_hh_l1" in name:
snake_case__ : Optional[int] = """bias_hh_l1"""
elif "bias" in name:
snake_case__ : Dict = """bias"""
elif "weight" in name:
snake_case__ : Dict = """weight"""
elif "running_mean" in name:
snake_case__ : List[Any] = """running_mean"""
elif "running_var" in name:
snake_case__ : int = """running_var"""
elif "num_batches_tracked" in name:
snake_case__ : List[Any] = """num_batches_tracked"""
else:
snake_case__ : Dict = None
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
continue
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"Unused weights: {unused_weights}" )
@torch.no_grad()
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , ) -> Any:
if config_path is not None:
snake_case__ : Union[str, Any] = EncodecConfig.from_pretrained(_lowerCAmelCase )
else:
snake_case__ : Dict = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
snake_case__ : Tuple = [8, 5, 4, 4]
snake_case__ : str = [2.2]
snake_case__ : str = 64
snake_case__ : List[str] = 32_000
snake_case__ : int = 2_048
snake_case__ : Union[str, Any] = False
snake_case__ : Dict = False
snake_case__ : Optional[Any] = False
elif model_name == "encodec_48khz":
snake_case__ : Union[str, Any] = [8, 5, 4, 2]
snake_case__ : Tuple = [3.0, 6.0, 12.0, 24.0]
snake_case__ : List[Any] = 48_000
snake_case__ : Dict = 2
snake_case__ : Dict = False
snake_case__ : Optional[int] = """time_group_norm"""
snake_case__ : str = True
snake_case__ : Optional[int] = 1.0
snake_case__ : Tuple = 0.01
else:
raise ValueError(f"Unknown model name: {model_name}" )
snake_case__ : Any = EncodecModel(_lowerCAmelCase )
snake_case__ : List[str] = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(_lowerCAmelCase )
snake_case__ : Optional[Any] = torch.load(_lowerCAmelCase )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
snake_case__ : Optional[Any] = original_checkpoint["""best_state"""]
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
if repo_id:
print("""Pushing to the hub...""" )
feature_extractor.push_to_hub(_lowerCAmelCase )
model.push_to_hub(_lowerCAmelCase )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
parser.add_argument(
"--model",
default="encodec_24khz",
type=str,
help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
)
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
__a = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 358 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[Any] = DonutProcessor.from_pretrained(snake_case_ )
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Any = {
"""name""": """John Doe""",
"""age""": """99""",
"""city""": """Atlanta""",
"""state""": """GA""",
"""zip""": """30301""",
"""phone""": """123-4567""",
"""nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
}
snake_case__ : str = (
"""<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
"""<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
"""<s_nicknames><s_nickname>Johnny</s_nickname>"""
"""<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
)
snake_case__ : Optional[Any] = self.processor.tokenajson(snake_case_ )
self.assertDictEqual(snake_case_ , snake_case_ )
| 43 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCamelCase__ = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `" / "`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `" // "`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `"train"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `"compressed"`)
The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
`"compressed"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a "dummy" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
'''
@add_start_docstrings(__a )
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = 'rag'
lowerCAmelCase__ = True
def __init__( self : List[Any] , _A : List[Any]=None , _A : int=True , _A : Optional[int]=None , _A : Dict=None , _A : Any=None , _A : Any=None , _A : str=None , _A : Dict=" / " , _A : Optional[int]=" // " , _A : Optional[int]=5 , _A : List[str]=300 , _A : Dict=768 , _A : Dict=8 , _A : Union[str, Any]="wiki_dpr" , _A : List[str]="train" , _A : Optional[Any]="compressed" , _A : Optional[Any]=None , _A : Tuple=None , _A : Optional[int]=False , _A : List[str]=False , _A : Optional[Any]=0.0 , _A : Optional[int]=True , _A : List[str]=False , _A : Dict=False , _A : Dict=False , _A : Union[str, Any]=True , _A : Tuple=None , **_A : int , ):
'''simple docstring'''
super().__init__(
bos_token_id=_A , pad_token_id=_A , eos_token_id=_A , decoder_start_token_id=_A , forced_eos_token_id=_A , is_encoder_decoder=_A , prefix=_A , vocab_size=_A , **_A , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
UpperCAmelCase__ : Dict = kwargs.pop('''question_encoder''' )
UpperCAmelCase__ : str = question_encoder_config.pop('''model_type''' )
UpperCAmelCase__ : List[Any] = kwargs.pop('''generator''' )
UpperCAmelCase__ : Dict = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
UpperCAmelCase__ : Any = AutoConfig.for_model(_A , **_A )
UpperCAmelCase__ : Optional[Any] = AutoConfig.for_model(_A , **_A )
UpperCAmelCase__ : int = reduce_loss
UpperCAmelCase__ : List[str] = label_smoothing
UpperCAmelCase__ : Optional[Any] = exclude_bos_score
UpperCAmelCase__ : List[Any] = do_marginalize
UpperCAmelCase__ : List[Any] = title_sep
UpperCAmelCase__ : List[str] = doc_sep
UpperCAmelCase__ : Optional[int] = n_docs
UpperCAmelCase__ : str = max_combined_length
UpperCAmelCase__ : Optional[Any] = dataset
UpperCAmelCase__ : Tuple = dataset_split
UpperCAmelCase__ : Dict = index_name
UpperCAmelCase__ : str = retrieval_vector_size
UpperCAmelCase__ : List[str] = retrieval_batch_size
UpperCAmelCase__ : Any = passages_path
UpperCAmelCase__ : Optional[int] = index_path
UpperCAmelCase__ : Union[str, Any] = use_dummy_dataset
UpperCAmelCase__ : str = output_retrieved
UpperCAmelCase__ : Optional[Any] = do_deduplication
UpperCAmelCase__ : Optional[Any] = use_cache
if self.forced_eos_token_id is None:
UpperCAmelCase__ : Optional[Any] = getattr(self.generator , '''forced_eos_token_id''' , _A )
@classmethod
def lowercase_ ( cls : str , _A : PretrainedConfig , _A : PretrainedConfig , **_A : Dict ):
'''simple docstring'''
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **_A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : int = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Tuple = self.question_encoder.to_dict()
UpperCAmelCase__ : List[str] = self.generator.to_dict()
UpperCAmelCase__ : Dict = self.__class__.model_type
return output
| 181 |
'''simple docstring'''
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 a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
return (abs(source - target ) / target) < 0.0_1
@pytest.mark.integration
def a__ ( lowerCAmelCase__ ) -> Optional[int]:
UpperCAmelCase__ : str = _TestCommandArgs(dataset=lowerCAmelCase__ , all_configs=lowerCAmelCase__ , save_infos=lowerCAmelCase__ )
UpperCAmelCase__ : Tuple = TestCommand(*lowerCAmelCase__ )
test_command.run()
UpperCAmelCase__ : List[Any] = os.path.join(lowerCAmelCase__ , '''README.md''' )
assert os.path.exists(lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = DatasetInfosDict.from_directory(lowerCAmelCase__ )
UpperCAmelCase__ : 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:
UpperCAmelCase__ , UpperCAmelCase__ : str = getattr(dataset_infos['''default'''] , lowerCAmelCase__ ), getattr(expected_dataset_infos['''default'''] , lowerCAmelCase__ )
if key == "num_bytes":
assert is_apercent_close(lowerCAmelCase__ , lowerCAmelCase__ )
elif key == "splits":
assert list(lowerCAmelCase__ ) == list(lowerCAmelCase__ )
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
| 181 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class _A ( lowerCAmelCase__ , unittest.TestCase ):
lowercase__: Optional[int] = XLMProphetNetTokenizer
lowercase__: str = False
lowercase__: List[Any] = True
def lowercase__ ( self : List[str] ) -> str:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__snake_case : List[str] = XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Dict = """[PAD]"""
__snake_case : Tuple = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def lowercase__ ( self : str ) -> List[str]:
"""simple docstring"""
__snake_case : List[str] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 10_12 )
def lowercase__ ( self : Any ) -> Tuple:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_12 )
def lowercase__ ( self : List[str] ) -> int:
"""simple docstring"""
__snake_case : Optional[Any] = XLMProphetNetTokenizer(_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE )
__snake_case : Optional[int] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_SCREAMING_SNAKE_CASE , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__snake_case : Dict = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_SCREAMING_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""",
""".""",
] , )
__snake_case : Any = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
__snake_case : Any = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE )
self.assertListEqual(
_SCREAMING_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 : Any ) -> str:
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def lowercase__ ( self : List[Any] ) -> Any:
"""simple docstring"""
__snake_case : Any = """Hello World!"""
__snake_case : Any = [3_53_89, 66_72, 49, 2]
self.assertListEqual(_SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE ) )
@slow
def lowercase__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__snake_case : Union[str, Any] = {"""input_ids""": [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 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=_SCREAMING_SNAKE_CASE , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 354 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> Dict:
"""simple docstring"""
__snake_case : str = 0
__snake_case : Optional[int] = len(_lowerCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , _lowerCamelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( _lowerCamelCase ) -> Tuple:
"""simple docstring"""
if len(_lowerCamelCase ) <= 1:
return arr, 0
__snake_case : Any = len(_lowerCamelCase ) // 2
__snake_case : List[str] = arr[0:mid]
__snake_case : int = arr[mid:]
__snake_case , __snake_case : List[Any] = count_inversions_recursive(_lowerCamelCase )
__snake_case , __snake_case : Tuple = count_inversions_recursive(_lowerCamelCase )
__snake_case , __snake_case : str = _count_cross_inversions(_lowerCamelCase , _lowerCamelCase )
__snake_case : str = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : Any = []
__snake_case : List[str] = 0
while i < len(_lowerCamelCase ) and j < len(_lowerCamelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(_lowerCamelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(_lowerCamelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> Optional[int]:
"""simple docstring"""
__snake_case : Optional[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__snake_case : Optional[Any] = count_inversions_bf(_lowerCamelCase )
__snake_case , __snake_case : Union[str, Any] = count_inversions_recursive(_lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ , _lowerCamelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__snake_case : Any = count_inversions_bf(_lowerCamelCase )
__snake_case , __snake_case : Union[str, Any] = count_inversions_recursive(_lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , _lowerCamelCase )
# an empty list should also have zero inversions
__snake_case : List[Any] = []
__snake_case : List[Any] = count_inversions_bf(_lowerCamelCase )
__snake_case , __snake_case : List[Any] = count_inversions_recursive(_lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , _lowerCamelCase )
if __name__ == "__main__":
main()
| 13 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}
class lowercase__ ( _UpperCAmelCase ):
a_ ="""xlnet"""
a_ =["""mems"""]
a_ ={
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __UpperCAmelCase=32000 , __UpperCAmelCase=1024 , __UpperCAmelCase=24 , __UpperCAmelCase=16 , __UpperCAmelCase=4096 , __UpperCAmelCase="gelu" , __UpperCAmelCase=True , __UpperCAmelCase="bi" , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=None , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=-1 , __UpperCAmelCase=False , __UpperCAmelCase="last" , __UpperCAmelCase=True , __UpperCAmelCase="tanh" , __UpperCAmelCase=0.1 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=5 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , **__UpperCAmelCase , )-> int:
'''simple docstring'''
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = d_model
lowerCAmelCase__ = n_layer
lowerCAmelCase__ = n_head
if d_model % n_head != 0:
raise ValueError(F"'d_model % n_head' ({d_model % n_head}) should be equal to 0" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})" )
lowerCAmelCase__ = d_model // n_head
lowerCAmelCase__ = ff_activation
lowerCAmelCase__ = d_inner
lowerCAmelCase__ = untie_r
lowerCAmelCase__ = attn_type
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = dropout
lowerCAmelCase__ = mem_len
lowerCAmelCase__ = reuse_len
lowerCAmelCase__ = bi_data
lowerCAmelCase__ = clamp_len
lowerCAmelCase__ = same_length
lowerCAmelCase__ = summary_type
lowerCAmelCase__ = summary_use_proj
lowerCAmelCase__ = summary_activation
lowerCAmelCase__ = summary_last_dropout
lowerCAmelCase__ = start_n_top
lowerCAmelCase__ = end_n_top
lowerCAmelCase__ = bos_token_id
lowerCAmelCase__ = pad_token_id
lowerCAmelCase__ = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs["use_cache"]
lowerCAmelCase__ = use_mems_eval
lowerCAmelCase__ = use_mems_train
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError(
F"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 340 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a_ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_std''': True,
'''scale''': 0.1,
'''eta''': 0.0,
'''t_grad_cutoff''': 2,
'''device''': '''cpu''',
}
if __name__ == "__main__":
a_ = '''hopper-medium-v2'''
a_ = gym.make(env_name)
a_ = ValueGuidedRLPipeline.from_pretrained(
'''bglick13/hopper-medium-v2-value-function-hor32''',
env=env,
)
env.seed(0)
a_ = env.reset()
a_ = 0
a_ = 0
a_ = 1000
a_ = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
a_ = pipeline(obs, planning_horizon=32)
# execute action in environment
a_, a_, a_, a_ = env.step(denorm_actions)
a_ = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
F" {total_score}"
)
# save observations for rendering
rollout.append(next_observation.copy())
a_ = next_observation
except KeyboardInterrupt:
pass
print(F"Total reward: {total_reward}")
| 340 | 1 |
from __future__ import annotations
def _a ( _lowerCamelCase , _lowerCamelCase ) -> set[str]:
"""simple docstring"""
__snake_case : str = set(_snake_case ), [start]
while stack:
__snake_case : Any = stack.pop()
explored.add(_snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_snake_case )
return explored
__UpperCamelCase = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 368 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase = logging.getLogger(__name__)
class _A ( __lowercase ):
def __init__( self : List[Any] , __magic_name__ : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : List[Any] , __magic_name__ : List[str]=None ) -> int:
"""simple docstring"""
super().__init__(
__magic_name__ , question_encoder_tokenizer=__magic_name__ , generator_tokenizer=__magic_name__ , index=__magic_name__ , init_retrieval=__magic_name__ , )
__snake_case : List[str] = None
def lowercase__ ( self : int , __magic_name__ : int ) -> List[str]:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__snake_case : List[Any] = self._infer_socket_ifname()
# avoid clash with the NCCL port
__snake_case : List[str] = str(distributed_port + 1 )
__snake_case : Any = dist.new_group(ranks=__magic_name__ , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def lowercase__ ( self : int ) -> int:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def lowercase__ ( self : Dict , __magic_name__ : int , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int]=torch.floataa ) -> List[str]:
"""simple docstring"""
__snake_case : Optional[int] = torch.empty(__magic_name__ , dtype=__magic_name__ )
dist.scatter(__magic_name__ , src=0 , scatter_list=__magic_name__ , group=self.process_group )
return target_tensor
def lowercase__ ( self : Optional[int] ) -> Any:
"""simple docstring"""
__snake_case : int = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__snake_case : Union[str, Any] = next((addr for addr in addrs if addr.startswith("""e""" )) , __magic_name__ )
return ifname
def lowercase__ ( self : Union[str, Any] , __magic_name__ : np.ndarray , __magic_name__ : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__snake_case , __snake_case : List[Any] = self._main_retrieve(__magic_name__ , __magic_name__ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__magic_name__ )
# distributed training
__snake_case : Union[str, Any] = dist.get_world_size(group=self.process_group )
# gather logic
__snake_case : Tuple = None
if self._is_main():
__snake_case : Dict = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(__magic_name__ )]
dist.gather(torch.tensor(__magic_name__ ) , dst=0 , gather_list=__magic_name__ , group=self.process_group )
# scatter logic
__snake_case : Optional[int] = question_hidden_states.shape[0]
__snake_case : Optional[Any] = []
__snake_case : Any = []
if self._is_main():
assert len(__magic_name__ ) == world_size
__snake_case , __snake_case : Optional[int] = self._main_retrieve(torch.cat(__magic_name__ ).numpy() , __magic_name__ )
__snake_case , __snake_case : Tuple = torch.tensor(__magic_name__ ), torch.tensor(__magic_name__ )
__snake_case : Any = self._chunk_tensor(__magic_name__ , __magic_name__ )
__snake_case : Any = self._chunk_tensor(__magic_name__ , __magic_name__ )
__snake_case : Optional[Any] = self._scattered(__magic_name__ , [n_queries, n_docs] , target_type=torch.intaa )
__snake_case : Any = self._scattered(__magic_name__ , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__magic_name__ )
| 13 | 0 |
"""simple docstring"""
_a : Tuple = [
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,
]
_a : 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,
]
_a : 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,
]
_a : Dict = [
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,
]
_a : 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,
]
_a : Optional[int] = [
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,
]
_a : Union[str, Any] = [
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,
]
_a : int = [
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,
]
| 44 | import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCAmelCase = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize
_UpperCAmelCase = """\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
"""
_UpperCAmelCase = """\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
"""
_UpperCAmelCase = """
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
'meteor': meteor score.
Examples:
>>> meteor = datasets.load_metric('meteor')
>>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]
>>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results[\"meteor\"], 4))
0.6944
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase_ ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
import nltk
nltk.download('wordnet' )
if NLTK_VERSION >= version.Version('3.6.5' ):
nltk.download('punkt' )
if NLTK_VERSION >= version.Version('3.6.6' ):
nltk.download('omw-1.4' )
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase=0.9 , lowercase=3 , lowercase=0.5 ):
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5' ):
A_ : List[Any] = [
meteor_score.single_meteor_score(
word_tokenize(lowercase ) , word_tokenize(lowercase ) , alpha=lowercase , beta=lowercase , gamma=lowercase )
for ref, pred in zip(lowercase , lowercase )
]
else:
A_ : Optional[Any] = [
meteor_score.single_meteor_score(lowercase , lowercase , alpha=lowercase , beta=lowercase , gamma=lowercase )
for ref, pred in zip(lowercase , lowercase )
]
return {"meteor": np.mean(lowercase )}
| 140 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase ( ) -> List[Any]:
lowercase_ : Tuple = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=UpperCAmelCase__ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=UpperCAmelCase__ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=UpperCAmelCase__ )
return parser.parse_args()
def lowerCamelCase ( ) -> Optional[int]:
lowercase_ : Any = parse_args()
# Import training_script as a module.
lowercase_ : int = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowercase_ : List[Any] = script_fpath.stem
lowercase_ : str = importlib.import_module(UpperCAmelCase__ )
# Patch sys.argv
lowercase_ : int = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 368 | '''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
if isinstance(UpperCAmelCase__ , np.ndarray ):
return list(tensor.shape )
lowercase_ : Tuple = tf.shape(UpperCAmelCase__ )
if tensor.shape == tf.TensorShape(UpperCAmelCase__ ):
return dynamic
lowercase_ : Dict = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase__ )]
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase__ , name=UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=1e-5 , UpperCAmelCase__ : List[str]=-1 ) -> List[str]:
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
lowercase_ , lowercase_ : List[str] = tf.nn.moments(UpperCAmelCase__ , axes=[axis] , keepdims=UpperCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
lowercase_ : List[Any] = [1] * inputs.shape.rank
lowercase_ : List[str] = shape_list(UpperCAmelCase__ )[axis]
lowercase_ : List[str] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase_ : List[Any] = tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
lowercase_ : str = tf.nn.batch_normalization(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , offset=UpperCAmelCase__ , scale=UpperCAmelCase__ , variance_epsilon=UpperCAmelCase__ , )
return outputs
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : Any=-1 ) -> Dict:
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
lowercase_ : List[Any] = tf.shape(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
lowercase_ : Dict = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor ) -> tf.Tensor:
if not isinstance(UpperCAmelCase__ , tf.Tensor ):
lowercase_ : List[Any] = tf.convert_to_tensor(UpperCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
lowercase_ : Any = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
lowercase_ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
lowercase_ : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowerCamelCase ( UpperCAmelCase__ : tf.Tensor , UpperCAmelCase__ : int , UpperCAmelCase__ : str = "input_ids" ) -> None:
tf.debugging.assert_less(
UpperCAmelCase__ , tf.cast(UpperCAmelCase__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def lowerCamelCase ( UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ) -> Any:
lowercase_ : int = 64512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
lowercase_ : Optional[Any] = [x for x in data if len(UpperCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
lowercase_ : Any = np.asarray(UpperCAmelCase__ )
lowercase_ : Union[str, Any] = 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
lowercase_ : Optional[Any] = np.array_split(UpperCAmelCase__ , UpperCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = chunk_data
else:
lowercase_ : Any = data
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : Union[str, Any] ) -> str:
if name in group.attrs:
lowercase_ : Optional[Any] = [n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
lowercase_ : int = []
lowercase_ : Optional[int] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(UpperCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] ) -> Any:
def _expand_single_ad_tensor(UpperCAmelCase__ : Optional[Any] ):
if isinstance(UpperCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase__ )
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : List[Any] , A : List[str]=3 , A : List[str]=32 , A : Optional[int]=3 , A : str=10 , A : Any=[10, 20, 30, 40] , A : int=[1, 1, 2, 1] , A : int=True , A : Dict=True , A : Optional[int]="relu" , A : Optional[int]=3 , A : Tuple=None , ):
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : List[Any] = image_size
_UpperCAmelCase : Dict = num_channels
_UpperCAmelCase : List[str] = embeddings_size
_UpperCAmelCase : Tuple = hidden_sizes
_UpperCAmelCase : Optional[int] = depths
_UpperCAmelCase : Tuple = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : Dict = scope
_UpperCAmelCase : List[Any] = len(A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : Optional[Any] = None
if self.use_labels:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase : Tuple = self.get_config()
return config, pixel_values, labels
def _A ( self : str ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _A ( self : Optional[int] , A : Any , A : int , A : str ):
_UpperCAmelCase : int = TFRegNetModel(config=A )
_UpperCAmelCase : Optional[int] = model(A , training=A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _A ( self : Union[str, Any] , A : str , A : Optional[Any] , A : Optional[int] ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : str = TFRegNetForImageClassification(A )
_UpperCAmelCase : Tuple = model(A , labels=A , training=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : int = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = config_and_inputs
_UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
__UpperCamelCase: Union[str, Any] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
__UpperCamelCase: Dict = False
__UpperCamelCase: Optional[int] = False
__UpperCamelCase: Any = False
__UpperCamelCase: Optional[Any] = False
__UpperCamelCase: Tuple = False
def _A ( self : Any ):
_UpperCAmelCase : Any = TFRegNetModelTester(self )
_UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , has_text_modality=A )
def _A ( self : List[Any] ):
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def _A ( self : List[Any] ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def _A ( self : Optional[Any] ):
super().test_keras_fit()
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def _A ( self : str ):
pass
def _A ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(A )
_UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
_UpperCAmelCase : Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : List[Any] ):
def check_hidden_states_output(A : Any , A : Any , A : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = model_class(A )
_UpperCAmelCase : Optional[int] = model(**self._prepare_for_class(A , A ) , training=A )
_UpperCAmelCase : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[str] = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCAmelCase : List[str] = layer_type
_UpperCAmelCase : Dict = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : int = True
check_hidden_states_output(A , A , A )
def _A ( self : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(A : Optional[int] , A : Union[str, Any] , A : Optional[Any] , A : str={} ):
_UpperCAmelCase : Tuple = model(A , return_dict=A , **A )
_UpperCAmelCase : Any = model(A , return_dict=A , **A ).to_tuple()
def recursive_check(A : Tuple , A : Tuple ):
if isinstance(A , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(A , A ):
recursive_check(A , A )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(A , A ) ) , msg=(
"Tuple and dict output are not equal. Difference:"
F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(A , A )
for model_class in self.all_model_classes:
_UpperCAmelCase : Tuple = model_class(A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : str = self._prepare_for_class(A , A )
check_equivalence(A , A , A )
_UpperCAmelCase : Optional[int] = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Optional[int] = self._prepare_for_class(A , A , return_labels=A )
check_equivalence(A , A , A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : Tuple = self._prepare_for_class(A , A )
check_equivalence(A , A , A , {"output_hidden_states": True} )
_UpperCAmelCase : int = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Union[str, Any] = self._prepare_for_class(A , A , return_labels=A )
check_equivalence(A , A , A , {"output_hidden_states": True} )
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def _A ( self : List[str] ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Any = TFRegNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCamelCase_ ( ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def _A ( self : Optional[int] ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Dict = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_UpperCAmelCase : Tuple = self.default_image_processor
_UpperCAmelCase : int = prepare_img()
_UpperCAmelCase : Optional[Any] = image_processor(images=A , return_tensors="tf" )
# forward pass
_UpperCAmelCase : Optional[Any] = model(**A , training=A )
# verify the logits
_UpperCAmelCase : Union[str, Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , A )
_UpperCAmelCase : Optional[Any] = tf.constant([-0.4_180, -1.5_051, -3.4_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , A , atol=1E-4 )
| 31 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase__ : Optional[int] = []
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
F"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
F"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
F"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
F"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Tuple = []
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Tuple:
lowerCamelCase__ : Union[str, Any] = []
token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
return token
def SCREAMING_SNAKE_CASE ( ) -> str:
lowerCamelCase__ : str = []
head.append(('layernorm.weight', 'norm.weight') )
head.append(('layernorm.bias', 'norm.bias') )
head.append(('classifier.weight', 'head.weight') )
head.append(('classifier.bias', 'head.bias') )
return head
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
lowerCamelCase__ : Tuple = 'imagenet-1k-id2label.json'
lowerCamelCase__ : Union[str, Any] = 1000
lowerCamelCase__ : Optional[Any] = 'huggingface/label-files'
lowerCamelCase__ : Any = num_labels
lowerCamelCase__ : Dict = json.load(open(cached_download(hf_hub_url(_UpperCAmelCase , _UpperCAmelCase , repo_type='dataset' ) ) , 'r' ) )
lowerCamelCase__ : int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase__ : Tuple = idalabel
lowerCamelCase__ : List[Any] = {v: k for k, v in idalabel.items()}
lowerCamelCase__ : List[str] = CvtConfig(num_labels=_UpperCAmelCase , idalabel=_UpperCAmelCase , labelaid=_UpperCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('/' , 1 )[-1][4:6] == "13":
lowerCamelCase__ : List[Any] = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('/' , 1 )[-1][4:6] == "21":
lowerCamelCase__ : Dict = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowerCamelCase__ : Optional[Any] = [2, 2, 20]
lowerCamelCase__ : Optional[int] = [3, 12, 16]
lowerCamelCase__ : str = [192, 768, 1024]
lowerCamelCase__ : Any = CvtForImageClassification(_UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
lowerCamelCase__ : Tuple = image_size
lowerCamelCase__ : List[str] = torch.load(_UpperCAmelCase , map_location=torch.device('cpu' ) )
lowerCamelCase__ : Optional[int] = OrderedDict()
lowerCamelCase__ : Tuple = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowerCamelCase__ : Optional[Any] = list_of_state_dict + cls_token(_UpperCAmelCase )
lowerCamelCase__ : str = list_of_state_dict + embeddings(_UpperCAmelCase )
for cnt in range(config.depth[idx] ):
lowerCamelCase__ : str = list_of_state_dict + attention(_UpperCAmelCase , _UpperCAmelCase )
lowerCamelCase__ : int = list_of_state_dict + final()
for gg in list_of_state_dict:
print(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
lowerCamelCase__ : str = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_UpperCAmelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--cvt_model""",
default="""cvt-w24""",
type=str,
help="""Name of the cvt model you'd like to convert.""",
)
parser.add_argument(
"""--image_size""",
default=3_84,
type=int,
help="""Input Image Size""",
)
parser.add_argument(
"""--cvt_file_name""",
default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
type=str,
help="""Input Image Size""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
_UpperCAmelCase : List[str] = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 50 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase__ :
def __init__( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : int=13 , UpperCamelCase__ : List[Any]=7 , UpperCamelCase__ : int=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : int=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Optional[int]=99 , UpperCamelCase__ : List[Any]=16 , UpperCamelCase__ : Union[str, Any]=36 , UpperCamelCase__ : List[Any]=6 , UpperCamelCase__ : List[str]=6 , UpperCamelCase__ : Optional[Any]=6 , UpperCamelCase__ : List[Any]=37 , UpperCamelCase__ : List[Any]="gelu" , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : str=512 , UpperCamelCase__ : int=16 , UpperCamelCase__ : Any=2 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : Any=3 , UpperCamelCase__ : Dict=4 , UpperCamelCase__ : Dict=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = parent
SCREAMING_SNAKE_CASE : str = batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = seq_length
SCREAMING_SNAKE_CASE : Dict = is_training
SCREAMING_SNAKE_CASE : Dict = use_input_mask
SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE : List[str] = embedding_size
SCREAMING_SNAKE_CASE : Dict = hidden_size
SCREAMING_SNAKE_CASE : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE : List[str] = num_hidden_groups
SCREAMING_SNAKE_CASE : Dict = num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : int = max_position_embeddings
SCREAMING_SNAKE_CASE : Optional[Any] = type_vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = num_labels
SCREAMING_SNAKE_CASE : List[str] = num_choices
SCREAMING_SNAKE_CASE : Dict = scope
def __A ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : int = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : str = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : str = None
SCREAMING_SNAKE_CASE : Dict = None
if self.use_labels:
SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A ( self : Optional[Any] ):
'''simple docstring'''
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def __A ( self : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AlbertModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Dict = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Tuple = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = model(UpperCamelCase__ )
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 __A ( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AlbertForPreTraining(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Optional[Any] = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , sentence_order_label=UpperCamelCase__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def __A ( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = AlbertForMaskedLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self : Union[str, Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = AlbertForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Any = model(
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 __A ( self : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE : Optional[int] = AlbertForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : str = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = AlbertForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Dict = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A ( self : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.num_choices
SCREAMING_SNAKE_CASE : Any = AlbertForMultipleChoice(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE : str = model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.prepare_config_and_inputs()
(
SCREAMING_SNAKE_CASE
) : Tuple = config_and_inputs
SCREAMING_SNAKE_CASE : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase__ ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
UpperCamelCase_ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCamelCase_ = (
{
"""feature-extraction""": AlbertModel,
"""fill-mask""": AlbertForMaskedLM,
"""question-answering""": AlbertForQuestionAnswering,
"""text-classification""": AlbertForSequenceClassification,
"""token-classification""": AlbertForTokenClassification,
"""zero-shot""": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase_ = True
def __A ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Any=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = super()._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if return_labels:
if model_class in get_values(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase__ )
return inputs_dict
def __A ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AlbertModelTester(self )
SCREAMING_SNAKE_CASE : Optional[int] = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def __A ( self : Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __A ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def __A ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__ )
def __A ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def __A ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )
def __A ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def __A ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def __A ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE : List[Any] = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
@slow
def __A ( self : Any ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : List[Any] = AlbertModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
class lowercase__ ( unittest.TestCase):
@slow
def __A ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = AlbertModel.from_pretrained('''albert-base-v2''' )
SCREAMING_SNAKE_CASE : Dict = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )[0]
SCREAMING_SNAKE_CASE : Dict = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCamelCase__ , atol=1E-4 ) )
| 356 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 lowercase__ ( UpperCamelCase_ , unittest.TestCase):
UpperCamelCase_ = KandinskyInpaintPipeline
UpperCamelCase_ = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
UpperCamelCase_ = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
UpperCamelCase_ = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase_ = False
@property
def __A ( self : Tuple ):
'''simple docstring'''
return 32
@property
def __A ( self : List[str] ):
'''simple docstring'''
return 32
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def __A ( self : List[Any] ):
'''simple docstring'''
return 100
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __A ( self : int ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
SCREAMING_SNAKE_CASE : Any = MultilingualCLIP(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = text_encoder.eval()
return text_encoder
@property
def __A ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE : str = UNetaDConditionModel(**UpperCamelCase__ )
return model
@property
def __A ( self : int ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __A ( self : Any ):
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __A ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_text_encoder
SCREAMING_SNAKE_CASE : Dict = self.dummy_tokenizer
SCREAMING_SNAKE_CASE : List[str] = self.dummy_unet
SCREAMING_SNAKE_CASE : int = self.dummy_movq
SCREAMING_SNAKE_CASE : List[str] = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=UpperCamelCase__ , )
SCREAMING_SNAKE_CASE : Any = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __A ( self : str , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any]=0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : int = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(UpperCamelCase__ )
# create init_image
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE : Optional[int] = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert('''RGB''' ).resize((256, 256) )
# create mask
SCREAMING_SNAKE_CASE : Tuple = np.ones((64, 64) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : List[Any] = 0
if str(UpperCamelCase__ ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE : Dict = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Dict = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __A ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = '''cpu'''
SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
SCREAMING_SNAKE_CASE : str = self.pipeline_class(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**self.get_dummy_inputs(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE : Union[str, Any] = output.images
SCREAMING_SNAKE_CASE : Any = pipe(
**self.get_dummy_inputs(UpperCamelCase__ ) , return_dict=UpperCamelCase__ , )[0]
SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : int = image_from_tuple[0, -3:, -3:, -1]
print(f"""image.shape {image.shape}""" )
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
def __A ( self : str ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase):
def __A ( self : str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE : int = np.ones((768, 768) , dtype=np.floataa )
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Optional[Any] = '''a hat'''
SCREAMING_SNAKE_CASE : Dict = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline.to(UpperCamelCase__ )
pipeline.set_progress_bar_config(disable=UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = pipe_prior(
UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE : Optional[Any] = pipeline(
UpperCamelCase__ , image=UpperCamelCase__ , mask_image=UpperCamelCase__ , image_embeds=UpperCamelCase__ , negative_image_embeds=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=100 , height=768 , width=768 , output_type='''np''' , )
SCREAMING_SNAKE_CASE : Any = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
| 258 | 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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ = logging.get_logger(__name__)
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : str = original_name.split('.' )[0]
UpperCAmelCase : Optional[Any] = key.split('.' )
UpperCAmelCase : Any = int(key_list[key_list.index(UpperCAmelCase_ ) - 2] )
UpperCAmelCase : List[Any] = int(key_list[key_list.index(UpperCAmelCase_ ) - 1] )
UpperCAmelCase : Dict = orig_block_num - offset
UpperCAmelCase : Tuple = key.replace(F"""{orig_block_num}.{layer_num}.{original_name}""" , F"""block.{new_block_num}.{layer_num}.{new_name}""" )
return key
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = OrderedDict()
UpperCAmelCase , UpperCAmelCase : Dict = 0, 0
for key, value in state_dict.items():
if key.startswith('network' ):
UpperCAmelCase : Union[str, Any] = key.replace('network' , 'poolformer.encoder' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('bias' ) and "patch_embed" not in key:
patch_emb_offset += 1
UpperCAmelCase : List[str] = key[: key.find('proj' )]
UpperCAmelCase : Any = key.replace(UpperCAmelCase_ , F"""patch_embeddings.{total_embed_found}.""" )
UpperCAmelCase : str = key.replace('proj' , 'projection' )
if key.endswith('bias' ):
total_embed_found += 1
if "patch_embeddings" in key:
UpperCAmelCase : Dict = 'poolformer.encoder.' + key
if "mlp.fc1" in key:
UpperCAmelCase : Tuple = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'mlp.fc1' , 'output.conv1' )
if "mlp.fc2" in key:
UpperCAmelCase : List[str] = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'mlp.fc2' , 'output.conv2' )
if "norm1" in key:
UpperCAmelCase : List[str] = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'norm1' , 'before_norm' )
if "norm2" in key:
UpperCAmelCase : int = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'norm2' , 'after_norm' )
if "layer_scale_1" in key:
UpperCAmelCase : List[str] = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'layer_scale_1' , 'layer_scale_1' )
if "layer_scale_2" in key:
UpperCAmelCase : Optional[Any] = replace_key_with_offset(UpperCAmelCase_ , UpperCAmelCase_ , 'layer_scale_2' , 'layer_scale_2' )
if "head" in key:
UpperCAmelCase : List[str] = key.replace('head' , 'classifier' )
UpperCAmelCase : Optional[int] = value
return new_state_dict
def UpperCamelCase( ):
UpperCAmelCase : Any = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCAmelCase : int = Image.open(requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_ ).raw )
return image
@torch.no_grad()
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Dict = PoolFormerConfig()
# set attributes based on model_name
UpperCAmelCase : List[Any] = 'huggingface/label-files'
UpperCAmelCase : Dict = model_name[-3:]
UpperCAmelCase : List[str] = 10_00
UpperCAmelCase : Any = 'imagenet-1k-id2label.json'
UpperCAmelCase : Optional[int] = (1, 10_00)
# set config attributes
UpperCAmelCase : List[str] = json.load(open(hf_hub_download(UpperCAmelCase_ , UpperCAmelCase_ , repo_type='dataset' ) , 'r' ) )
UpperCAmelCase : Dict = {int(UpperCAmelCase_ ): v for k, v in idalabel.items()}
UpperCAmelCase : Optional[Any] = idalabel
UpperCAmelCase : Optional[Any] = {v: k for k, v in idalabel.items()}
if size == "s12":
UpperCAmelCase : str = [2, 2, 6, 2]
UpperCAmelCase : List[str] = [64, 1_28, 3_20, 5_12]
UpperCAmelCase : Dict = 4.0
UpperCAmelCase : Dict = 0.9
elif size == "s24":
UpperCAmelCase : Optional[int] = [4, 4, 12, 4]
UpperCAmelCase : Tuple = [64, 1_28, 3_20, 5_12]
UpperCAmelCase : Optional[Any] = 4.0
UpperCAmelCase : Tuple = 0.9
elif size == "s36":
UpperCAmelCase : List[str] = [6, 6, 18, 6]
UpperCAmelCase : List[Any] = [64, 1_28, 3_20, 5_12]
UpperCAmelCase : Optional[Any] = 4.0
UpperCAmelCase : Any = 1E-6
UpperCAmelCase : Optional[Any] = 0.9
elif size == "m36":
UpperCAmelCase : str = [6, 6, 18, 6]
UpperCAmelCase : Dict = [96, 1_92, 3_84, 7_68]
UpperCAmelCase : str = 4.0
UpperCAmelCase : int = 1E-6
UpperCAmelCase : Any = 0.95
elif size == "m48":
UpperCAmelCase : Optional[int] = [8, 8, 24, 8]
UpperCAmelCase : int = [96, 1_92, 3_84, 7_68]
UpperCAmelCase : List[Any] = 4.0
UpperCAmelCase : Union[str, Any] = 1E-6
UpperCAmelCase : Optional[Any] = 0.95
else:
raise ValueError(F"""Size {size} not supported""" )
# load image processor
UpperCAmelCase : Union[str, Any] = PoolFormerImageProcessor(crop_pct=UpperCAmelCase_ )
# Prepare image
UpperCAmelCase : Any = prepare_img()
UpperCAmelCase : List[Any] = image_processor(images=UpperCAmelCase_ , return_tensors='pt' ).pixel_values
logger.info(F"""Converting model {model_name}...""" )
# load original state dict
UpperCAmelCase : int = torch.load(UpperCAmelCase_ , map_location=torch.device('cpu' ) )
# rename keys
UpperCAmelCase : int = rename_keys(UpperCAmelCase_ )
# create HuggingFace model and load state dict
UpperCAmelCase : List[str] = PoolFormerForImageClassification(UpperCAmelCase_ )
model.load_state_dict(UpperCAmelCase_ )
model.eval()
# Define image processor
UpperCAmelCase : Optional[int] = PoolFormerImageProcessor(crop_pct=UpperCAmelCase_ )
UpperCAmelCase : int = image_processor(images=prepare_img() , return_tensors='pt' ).pixel_values
# forward pass
UpperCAmelCase : Any = model(UpperCAmelCase_ )
UpperCAmelCase : List[Any] = outputs.logits
# define expected logit slices for different models
if size == "s12":
UpperCAmelCase : Any = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
UpperCAmelCase : int = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
UpperCAmelCase : Any = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
UpperCAmelCase : str = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
UpperCAmelCase : str = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F"""Size {size} not supported""" )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :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_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="poolformer_s12",
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."
)
lowercase__ = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 151 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase( UpperCAmelCase_ ):
warnings.warn(
'The preprocess method is deprecated and will be removed in a future version. Please'
' use VaeImageProcessor.preprocess instead' , UpperCAmelCase_ , )
if isinstance(UpperCAmelCase_ , torch.Tensor ):
return image
elif isinstance(UpperCAmelCase_ , PIL.Image.Image ):
UpperCAmelCase : List[str] = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : List[str] = image[0].size
UpperCAmelCase , UpperCAmelCase : str = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
UpperCAmelCase : str = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
UpperCAmelCase : Optional[int] = np.concatenate(UpperCAmelCase_ , axis=0 )
UpperCAmelCase : List[Any] = np.array(UpperCAmelCase_ ).astype(np.floataa ) / 255.0
UpperCAmelCase : Any = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase : Any = 2.0 * image - 1.0
UpperCAmelCase : List[str] = torch.from_numpy(UpperCAmelCase_ )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : List[Any] = torch.cat(UpperCAmelCase_ , dim=0 )
return image
def UpperCamelCase( UpperCAmelCase_ ):
if isinstance(UpperCAmelCase_ , torch.Tensor ):
return mask
elif isinstance(UpperCAmelCase_ , PIL.Image.Image ):
UpperCAmelCase : List[str] = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : List[Any] = mask[0].size
UpperCAmelCase , UpperCAmelCase : Dict = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCAmelCase : Tuple = [np.array(m.convert('L' ).resize((w, h) , resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
UpperCAmelCase : Optional[int] = np.concatenate(UpperCAmelCase_ , axis=0 )
UpperCAmelCase : Any = mask.astype(np.floataa ) / 255.0
UpperCAmelCase : str = 0
UpperCAmelCase : Dict = 1
UpperCAmelCase : Optional[Any] = torch.from_numpy(UpperCAmelCase_ )
elif isinstance(mask[0] , torch.Tensor ):
UpperCAmelCase : List[str] = torch.cat(UpperCAmelCase_ , dim=0 )
return mask
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : UNetaDModel
UpperCAmelCase_ : RePaintScheduler
def __init__( self : List[str] , lowercase_ : List[str] , lowercase_ : Tuple ) -> Tuple:
super().__init__()
self.register_modules(unet=lowercase_ , scheduler=lowercase_ )
@torch.no_grad()
def __call__( self : List[str] , lowercase_ : Union[torch.Tensor, PIL.Image.Image] , lowercase_ : Union[torch.Tensor, PIL.Image.Image] , lowercase_ : int = 250 , lowercase_ : float = 0.0 , lowercase_ : int = 10 , lowercase_ : int = 10 , lowercase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
UpperCAmelCase : Dict = image
UpperCAmelCase : Optional[int] = _preprocess_image(lowercase_ )
UpperCAmelCase : Optional[Any] = original_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : Optional[Any] = _preprocess_mask(lowercase_ )
UpperCAmelCase : List[str] = mask_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : Any = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(lowercase_ )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
UpperCAmelCase : List[str] = original_image.shape
UpperCAmelCase : str = randn_tensor(lowercase_ , generator=lowercase_ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowercase_ , lowercase_ , lowercase_ , self.device )
UpperCAmelCase : Tuple = eta
UpperCAmelCase : Optional[int] = self.scheduler.timesteps[0] + 1
UpperCAmelCase : List[Any] = generator[0] if isinstance(lowercase_ , lowercase_ ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
UpperCAmelCase : str = self.unet(lowercase_ , lowercase_ ).sample
# compute previous image: x_t -> x_t-1
UpperCAmelCase : Dict = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
UpperCAmelCase : int = self.scheduler.undo_step(lowercase_ , lowercase_ , lowercase_ )
UpperCAmelCase : Union[str, Any] = t
UpperCAmelCase : List[Any] = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : List[str] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase_ )
| 151 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
lowerCamelCase__ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
lowerCamelCase__ = {
"""vocab_file""": {
"""google/electra-small-generator""": (
"""https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
),
"""google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
"""google/electra-large-generator""": (
"""https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
),
"""google/electra-small-discriminator""": (
"""https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
),
"""google/electra-base-discriminator""": (
"""https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
),
"""google/electra-large-discriminator""": (
"""https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""google/electra-small-generator""": (
"""https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
),
"""google/electra-base-generator""": (
"""https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
),
"""google/electra-large-generator""": (
"""https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
),
"""google/electra-small-discriminator""": (
"""https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
),
"""google/electra-base-discriminator""": (
"""https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
),
"""google/electra-large-discriminator""": (
"""https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
),
},
}
lowerCamelCase__ = {
"""google/electra-small-generator""": 512,
"""google/electra-base-generator""": 512,
"""google/electra-large-generator""": 512,
"""google/electra-small-discriminator""": 512,
"""google/electra-base-discriminator""": 512,
"""google/electra-large-discriminator""": 512,
}
lowerCamelCase__ = {
"""google/electra-small-generator""": {"""do_lower_case""": True},
"""google/electra-base-generator""": {"""do_lower_case""": True},
"""google/electra-large-generator""": {"""do_lower_case""": True},
"""google/electra-small-discriminator""": {"""do_lower_case""": True},
"""google/electra-base-discriminator""": {"""do_lower_case""": True},
"""google/electra-large-discriminator""": {"""do_lower_case""": True},
}
class A__ ( __magic_name__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_INIT_CONFIGURATION
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ElectraTokenizer
def __init__( self : int , a : str=None , a : int=None , a : List[Any]=True , a : Union[str, Any]="[UNK]" , a : Dict="[SEP]" , a : Union[str, Any]="[PAD]" , a : str="[CLS]" , a : Optional[int]="[MASK]" , a : Union[str, Any]=True , a : Any=None , **a : List[str] , ):
'''simple docstring'''
super().__init__(
a , tokenizer_file=a , do_lower_case=a , unk_token=a , sep_token=a , pad_token=a , cls_token=a , mask_token=a , tokenize_chinese_chars=a , strip_accents=a , **a , )
lowerCAmelCase__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , a ) != do_lower_case
or normalizer_state.get('strip_accents' , a ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , a ) != tokenize_chinese_chars
):
lowerCAmelCase__ : str = getattr(a , normalizer_state.pop('type' ) )
lowerCAmelCase__ : Optional[int] = do_lower_case
lowerCAmelCase__ : List[str] = strip_accents
lowerCAmelCase__ : Dict = tokenize_chinese_chars
lowerCAmelCase__ : List[Any] = normalizer_class(**a )
lowerCAmelCase__ : List[Any] = do_lower_case
def _lowerCamelCase ( self : Any , a : Tuple , a : Tuple=None ):
'''simple docstring'''
lowerCAmelCase__ : Dict = [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 : int , a : List[int] , a : Optional[List[int]] = None ):
'''simple docstring'''
lowerCAmelCase__ : Any = [self.sep_token_id]
lowerCAmelCase__ : Dict = [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 : Optional[Any] , a : str , a : Optional[str] = None ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self._tokenizer.model.save(a , name=a )
return tuple(a )
| 356 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCamelCase__ = logging.getLogger(__name__)
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
lowerCAmelCase__ : Dict = np.argmax(SCREAMING_SNAKE_CASE_ , axis=1 )
return np.sum(outputs == labels )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
with open(SCREAMING_SNAKE_CASE_ , encoding='utf_8' ) as f:
lowerCAmelCase__ : Dict = csv.reader(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = []
next(SCREAMING_SNAKE_CASE_ ) # skip the first line
for line in tqdm(SCREAMING_SNAKE_CASE_ ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
lowerCAmelCase__ : Dict = []
for dataset in encoded_datasets:
lowerCAmelCase__ : List[str] = len(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
lowerCAmelCase__ : Tuple = np.zeros((n_batch, 2) , dtype=np.intaa )
lowerCAmelCase__ : List[Any] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
lowerCAmelCase__ : Tuple = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Tuple = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCAmelCase__ : Any = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
lowerCAmelCase__ : Optional[Any] = with_conta
lowerCAmelCase__ : List[str] = with_conta
lowerCAmelCase__ : List[Any] = len(SCREAMING_SNAKE_CASE_ ) - 1
lowerCAmelCase__ : Tuple = len(SCREAMING_SNAKE_CASE_ ) - 1
lowerCAmelCase__ : Tuple = with_conta
lowerCAmelCase__ : Optional[int] = with_conta
lowerCAmelCase__ : Optional[int] = mc_label
lowerCAmelCase__ : Dict = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_ ) for t in all_inputs ) )
return tensor_datasets
def lowerCAmelCase__ ( ) -> int:
lowerCAmelCase__ : int = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=SCREAMING_SNAKE_CASE_ , default='openai-gpt' , help='pretrained model name' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir' , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=SCREAMING_SNAKE_CASE_ , default='' )
parser.add_argument('--eval_dataset' , type=SCREAMING_SNAKE_CASE_ , default='' )
parser.add_argument('--seed' , type=SCREAMING_SNAKE_CASE_ , default=42 )
parser.add_argument('--num_train_epochs' , type=SCREAMING_SNAKE_CASE_ , default=3 )
parser.add_argument('--train_batch_size' , type=SCREAMING_SNAKE_CASE_ , default=8 )
parser.add_argument('--eval_batch_size' , type=SCREAMING_SNAKE_CASE_ , default=16 )
parser.add_argument('--adam_epsilon' , default=1e-8 , type=SCREAMING_SNAKE_CASE_ , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , type=SCREAMING_SNAKE_CASE_ , default=1 )
parser.add_argument(
'--max_steps' , default=-1 , type=SCREAMING_SNAKE_CASE_ , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=SCREAMING_SNAKE_CASE_ , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=SCREAMING_SNAKE_CASE_ , default=6.25e-5 )
parser.add_argument('--warmup_steps' , default=0 , type=SCREAMING_SNAKE_CASE_ , help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule' , type=SCREAMING_SNAKE_CASE_ , default='warmup_linear' )
parser.add_argument('--weight_decay' , type=SCREAMING_SNAKE_CASE_ , default=0.01 )
parser.add_argument('--lm_coef' , type=SCREAMING_SNAKE_CASE_ , default=0.9 )
parser.add_argument('--n_valid' , type=SCREAMING_SNAKE_CASE_ , default=374 )
parser.add_argument('--server_ip' , type=SCREAMING_SNAKE_CASE_ , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=SCREAMING_SNAKE_CASE_ , default='' , help='Can be used for distant debugging.' )
lowerCAmelCase__ : List[str] = parser.parse_args()
print(SCREAMING_SNAKE_CASE_ )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=SCREAMING_SNAKE_CASE_ )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
lowerCAmelCase__ : str = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
lowerCAmelCase__ : Dict = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
lowerCAmelCase__ : Union[str, Any] = ['_start_', '_delimiter_', '_classify_']
lowerCAmelCase__ : Dict = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Any = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_ ) )
model.to(SCREAMING_SNAKE_CASE_ )
# Load and encode the datasets
def tokenize_and_encode(SCREAMING_SNAKE_CASE_ ):
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return obj
return [tokenize_and_encode(SCREAMING_SNAKE_CASE_ ) for o in obj]
logger.info('Encoding dataset...' )
lowerCAmelCase__ : List[Any] = load_rocstories_dataset(args.train_dataset )
lowerCAmelCase__ : str = load_rocstories_dataset(args.eval_dataset )
lowerCAmelCase__ : Union[str, Any] = (train_dataset, eval_dataset)
lowerCAmelCase__ : List[str] = tokenize_and_encode(SCREAMING_SNAKE_CASE_ )
# Compute the max input length for the Transformer
lowerCAmelCase__ : Union[str, Any] = model.config.n_positions // 2 - 2
lowerCAmelCase__ : Tuple = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
lowerCAmelCase__ : Dict = min(SCREAMING_SNAKE_CASE_ , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
lowerCAmelCase__ : int = pre_process_datasets(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = tensor_datasets[0], tensor_datasets[1]
lowerCAmelCase__ : str = TensorDataset(*SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Tuple = RandomSampler(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Tuple = DataLoader(SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.train_batch_size )
lowerCAmelCase__ : Optional[Any] = TensorDataset(*SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = SequentialSampler(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Any = DataLoader(SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
lowerCAmelCase__ : Union[str, Any] = args.max_steps
lowerCAmelCase__ : int = args.max_steps // (len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps) + 1
else:
lowerCAmelCase__ : Optional[Any] = len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps * args.num_train_epochs
lowerCAmelCase__ : Optional[int] = list(model.named_parameters() )
lowerCAmelCase__ : Tuple = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
lowerCAmelCase__ : str = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
lowerCAmelCase__ : Union[str, Any] = AdamW(SCREAMING_SNAKE_CASE_ , lr=args.learning_rate , eps=args.adam_epsilon )
lowerCAmelCase__ : int = get_linear_schedule_with_warmup(
SCREAMING_SNAKE_CASE_ , num_warmup_steps=args.warmup_steps , num_training_steps=SCREAMING_SNAKE_CASE_ )
if args.do_train:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='Epoch' ):
lowerCAmelCase__ : str = 0
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : str = tqdm(SCREAMING_SNAKE_CASE_ , desc='Training' )
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase__ : Union[str, Any] = tuple(t.to(SCREAMING_SNAKE_CASE_ ) for t in batch )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Any = batch
lowerCAmelCase__ : Tuple = model(SCREAMING_SNAKE_CASE_ , mc_token_ids=SCREAMING_SNAKE_CASE_ , lm_labels=SCREAMING_SNAKE_CASE_ , mc_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : str = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
lowerCAmelCase__ : Optional[int] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
lowerCAmelCase__ : List[str] = 'Training loss: {:.2e} lr: {:.2e}'.format(SCREAMING_SNAKE_CASE_ , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
lowerCAmelCase__ : Optional[int] = model.module if hasattr(SCREAMING_SNAKE_CASE_ , 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
lowerCAmelCase__ : List[str] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : List[str] = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ )
torch.save(model_to_save.state_dict() , SCREAMING_SNAKE_CASE_ )
model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE_ )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
lowerCAmelCase__ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
lowerCAmelCase__ : List[Any] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(SCREAMING_SNAKE_CASE_ )
if args.do_eval:
model.eval()
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = 0, 0
lowerCAmelCase__ , lowerCAmelCase__ : Any = 0, 0
for batch in tqdm(SCREAMING_SNAKE_CASE_ , desc='Evaluating' ):
lowerCAmelCase__ : str = tuple(t.to(SCREAMING_SNAKE_CASE_ ) for t in batch )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Tuple = batch
with torch.no_grad():
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[str] = model(
SCREAMING_SNAKE_CASE_ , mc_token_ids=SCREAMING_SNAKE_CASE_ , lm_labels=SCREAMING_SNAKE_CASE_ , mc_labels=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Any = mc_logits.detach().cpu().numpy()
lowerCAmelCase__ : List[Any] = mc_labels.to('cpu' ).numpy()
lowerCAmelCase__ : str = accuracy(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
lowerCAmelCase__ : Optional[int] = eval_loss / nb_eval_steps
lowerCAmelCase__ : Any = eval_accuracy / nb_eval_examples
lowerCAmelCase__ : Union[str, Any] = tr_loss / nb_tr_steps if args.do_train else None
lowerCAmelCase__ : Tuple = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
lowerCAmelCase__ : Dict = os.path.join(args.output_dir , 'eval_results.txt' )
with open(SCREAMING_SNAKE_CASE_ , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , SCREAMING_SNAKE_CASE_ , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main() | 307 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
_a = logging.get_logger(__name__)
@add_end_docstrings(snake_case__)
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __init__( self , **UpperCAmelCase ):
"""simple docstring"""
super().__init__(**UpperCAmelCase )
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , 'vision' )
self.check_model_type(UpperCAmelCase )
def __call__( self , UpperCAmelCase , UpperCAmelCase = None , **UpperCAmelCase , ):
"""simple docstring"""
if "text_queries" in kwargs:
_UpperCAmelCase = kwargs.pop('text_queries' )
if isinstance(UpperCAmelCase , (str, Image.Image) ):
_UpperCAmelCase = {'image': image, 'candidate_labels': candidate_labels}
else:
_UpperCAmelCase = image
_UpperCAmelCase = super().__call__(UpperCAmelCase , **UpperCAmelCase )
return results
def UpperCamelCase ( self , **UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = {}
if "threshold" in kwargs:
_UpperCAmelCase = kwargs['threshold']
if "top_k" in kwargs:
_UpperCAmelCase = kwargs['top_k']
return {}, {}, postprocess_params
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = load_image(inputs['image'] )
_UpperCAmelCase = inputs['candidate_labels']
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_UpperCAmelCase = candidate_labels.split(',' )
_UpperCAmelCase = torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(UpperCAmelCase ):
_UpperCAmelCase = self.tokenizer(UpperCAmelCase , return_tensors=self.framework )
_UpperCAmelCase = self.image_processor(UpperCAmelCase , return_tensors=self.framework )
yield {
"is_last": i == len(UpperCAmelCase ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = model_inputs.pop('target_size' )
_UpperCAmelCase = model_inputs.pop('candidate_label' )
_UpperCAmelCase = model_inputs.pop('is_last' )
_UpperCAmelCase = self.model(**UpperCAmelCase )
_UpperCAmelCase = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=0.1 , UpperCAmelCase=None ):
"""simple docstring"""
_UpperCAmelCase = []
for model_output in model_outputs:
_UpperCAmelCase = model_output['candidate_label']
_UpperCAmelCase = BaseModelOutput(UpperCAmelCase )
_UpperCAmelCase = self.image_processor.post_process_object_detection(
outputs=UpperCAmelCase , threshold=UpperCAmelCase , target_sizes=model_output['target_size'] )[0]
for index in outputs["scores"].nonzero():
_UpperCAmelCase = outputs['scores'][index].item()
_UpperCAmelCase = self._get_bounding_box(outputs['boxes'][index][0] )
_UpperCAmelCase = {'score': score, 'label': label, 'box': box}
results.append(UpperCAmelCase )
_UpperCAmelCase = sorted(UpperCAmelCase , key=lambda UpperCAmelCase : x["score"] , reverse=UpperCAmelCase )
if top_k:
_UpperCAmelCase = results[:top_k]
return results
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
if self.framework != "pt":
raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = box.int().tolist()
_UpperCAmelCase = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 39 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __A ( __lowerCAmelCase )-> str:
"""simple docstring"""
return "".join(sorted(__lowerCAmelCase ) )
def __A ( __lowerCAmelCase )-> list[str]:
"""simple docstring"""
return word_by_signature[signature(__lowerCAmelCase )]
_a = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_a = sorted({word.strip().lower() for word in data.splitlines()})
_a = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
_a = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 39 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def __get__( self ,a_ ,a_=None ) -> Optional[Any]:
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError("""unreadable attribute""" )
_UpperCAmelCase : Dict = """__cached_""" + self.fget.__name__
_UpperCAmelCase : str = getattr(a_ ,a_ ,a_ )
if cached is None:
_UpperCAmelCase : Tuple = self.fget(a_ )
setattr(a_ ,a_ ,a_ )
return cached
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F'''invalid truth value {val!r}''' )
def snake_case_ ( lowerCAmelCase_ )-> Tuple:
'''simple docstring'''
if is_torch_fx_proxy(lowerCAmelCase_ ):
return True
if is_torch_available():
import torch
if isinstance(lowerCAmelCase_ , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(lowerCAmelCase_ , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(lowerCAmelCase_ , (jnp.ndarray, Tracer) ):
return True
return isinstance(lowerCAmelCase_ , np.ndarray )
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
return isinstance(lowerCAmelCase_ , np.ndarray )
def snake_case_ ( lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
return _is_numpy(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
import torch
return isinstance(lowerCAmelCase_ , torch.Tensor )
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
return False if not is_torch_available() else _is_torch(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
import torch
return isinstance(lowerCAmelCase_ , torch.device )
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
import torch
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
if hasattr(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : Any = getattr(lowerCAmelCase_ , lowerCAmelCase_ )
else:
return False
return isinstance(lowerCAmelCase_ , torch.dtype )
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
import tensorflow as tf
return isinstance(lowerCAmelCase_ , tf.Tensor )
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(lowerCAmelCase_ , """is_symbolic_tensor""" ):
return tf.is_symbolic_tensor(lowerCAmelCase_ )
return type(lowerCAmelCase_ ) == tf.Tensor
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(lowerCAmelCase_ , jnp.ndarray )
def snake_case_ ( lowerCAmelCase_ )-> List[Any]:
'''simple docstring'''
return False if not is_flax_available() else _is_jax(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , (dict, UserDict) ):
return {k: to_py_obj(lowerCAmelCase_ ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase_ , (list, tuple) ):
return [to_py_obj(lowerCAmelCase_ ) for o in obj]
elif is_tf_tensor(lowerCAmelCase_ ):
return obj.numpy().tolist()
elif is_torch_tensor(lowerCAmelCase_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(lowerCAmelCase_ ):
return np.asarray(lowerCAmelCase_ ).tolist()
elif isinstance(lowerCAmelCase_ , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def snake_case_ ( lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , (dict, UserDict) ):
return {k: to_numpy(lowerCAmelCase_ ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase_ , (list, tuple) ):
return np.array(lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
return obj.numpy()
elif is_torch_tensor(lowerCAmelCase_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(lowerCAmelCase_ ):
return np.asarray(lowerCAmelCase_ )
else:
return obj
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Union[str, Any] = fields(self )
# Safety and consistency checks
if not len(a_ ):
raise ValueError(f'''{self.__class__.__name__} has no fields.''' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.''' )
_UpperCAmelCase : Tuple = getattr(self ,class_fields[0].name )
_UpperCAmelCase : Tuple = all(getattr(self ,field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(a_ ):
if isinstance(a_ ,a_ ):
_UpperCAmelCase : Union[str, Any] = first_field.items()
_UpperCAmelCase : int = True
else:
try:
_UpperCAmelCase : Optional[int] = iter(a_ )
_UpperCAmelCase : Tuple = True
except TypeError:
_UpperCAmelCase : int = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(a_ ):
if (
not isinstance(a_ ,(list, tuple) )
or not len(a_ ) == 2
or not isinstance(element[0] ,a_ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_UpperCAmelCase : int = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).''' )
break
setattr(self ,element[0] ,element[1] )
if element[1] is not None:
_UpperCAmelCase : str = element[1]
elif first_field is not None:
_UpperCAmelCase : List[str] = first_field
else:
for field in class_fields:
_UpperCAmelCase : Optional[Any] = getattr(self ,field.name )
if v is not None:
_UpperCAmelCase : Any = v
def __delitem__( self ,*a_ ,**a_ ) -> int:
raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.''' )
def _snake_case ( self ,*a_ ,**a_ ) -> Optional[Any]:
raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.''' )
def _snake_case ( self ,*a_ ,**a_ ) -> Union[str, Any]:
raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.''' )
def _snake_case ( self ,*a_ ,**a_ ) -> str:
raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.''' )
def __getitem__( self ,a_ ) -> int:
if isinstance(a_ ,a_ ):
_UpperCAmelCase : Any = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self ,a_ ,a_ ) -> Union[str, Any]:
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(a_ ,a_ )
super().__setattr__(a_ ,a_ )
def __setitem__( self ,a_ ,a_ ) -> str:
# Will raise a KeyException if needed
super().__setitem__(a_ ,a_ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(a_ ,a_ )
def _snake_case ( self ) -> Tuple[Any]:
return tuple(self[k] for k in self.keys() )
class lowercase ( _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
@classmethod
def _snake_case ( cls ,a_ ) -> Union[str, Any]:
raise ValueError(
f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}''' )
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """longest"""
UpperCAmelCase = """max_length"""
UpperCAmelCase = """do_not_pad"""
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """pt"""
UpperCAmelCase = """tf"""
UpperCAmelCase = """np"""
UpperCAmelCase = """jax"""
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> Union[str, Any]:
_UpperCAmelCase : List[str] = context_managers
_UpperCAmelCase : Union[str, Any] = ExitStack()
def __enter__( self ) -> List[Any]:
for context_manager in self.context_managers:
self.stack.enter_context(a_ )
def __exit__( self ,*a_ ,**a_ ) -> List[str]:
self.stack.__exit__(*a_ ,**a_ )
def snake_case_ ( lowerCAmelCase_ )-> Any:
'''simple docstring'''
_UpperCAmelCase : Dict = infer_framework(lowerCAmelCase_ )
if framework == "tf":
_UpperCAmelCase : int = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : Any = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : Optional[int] = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def snake_case_ ( lowerCAmelCase_ )-> str:
'''simple docstring'''
_UpperCAmelCase : List[Any] = model_class.__name__
_UpperCAmelCase : Dict = infer_framework(lowerCAmelCase_ )
if framework == "tf":
_UpperCAmelCase : Any = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : int = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = "" , lowerCAmelCase_ = "." )-> Tuple:
'''simple docstring'''
def _flatten_dict(lowerCAmelCase_ , lowerCAmelCase_="" , lowerCAmelCase_="." ):
for k, v in d.items():
_UpperCAmelCase : List[Any] = str(lowerCAmelCase_ ) + delimiter + str(lowerCAmelCase_ ) if parent_key else k
if v and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
yield from flatten_dict(lowerCAmelCase_ , lowerCAmelCase_ , delimiter=lowerCAmelCase_ ).items()
else:
yield key, v
return dict(_flatten_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) )
@contextmanager
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ = False )-> Tuple:
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=None )-> Union[str, Any]:
'''simple docstring'''
if is_numpy_array(lowerCAmelCase_ ):
return np.transpose(lowerCAmelCase_ , axes=lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.T if axes is None else array.permute(*lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.transpose(lowerCAmelCase_ , perm=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.transpose(lowerCAmelCase_ , axes=lowerCAmelCase_ )
else:
raise ValueError(F'''Type not supported for transpose: {type(lowerCAmelCase_ )}.''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> str:
'''simple docstring'''
if is_numpy_array(lowerCAmelCase_ ):
return np.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.reshape(*lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
else:
raise ValueError(F'''Type not supported for reshape: {type(lowerCAmelCase_ )}.''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_=None )-> Dict:
'''simple docstring'''
if is_numpy_array(lowerCAmelCase_ ):
return np.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.squeeze() if axis is None else array.squeeze(dim=lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
else:
raise ValueError(F'''Type not supported for squeeze: {type(lowerCAmelCase_ )}.''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> List[str]:
'''simple docstring'''
if is_numpy_array(lowerCAmelCase_ ):
return np.expand_dims(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.unsqueeze(dim=lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.expand_dims(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.expand_dims(lowerCAmelCase_ , axis=lowerCAmelCase_ )
else:
raise ValueError(F'''Type not supported for expand_dims: {type(lowerCAmelCase_ )}.''' )
def snake_case_ ( lowerCAmelCase_ )-> Union[str, Any]:
'''simple docstring'''
if is_numpy_array(lowerCAmelCase_ ):
return np.size(lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.numel()
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.size(lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return array.size
else:
raise ValueError(F'''Type not supported for expand_dims: {type(lowerCAmelCase_ )}.''' )
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[Any]:
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(lowerCAmelCase_ , (tuple, list) ):
_UpperCAmelCase : Optional[Any] = [F'''{repo_id}--{v}''' if (v is not None and """--""" not in v) else v for v in value]
elif value is not None and "--" not in value:
_UpperCAmelCase : List[Any] = F'''{repo_id}--{value}'''
return auto_map
def snake_case_ ( lowerCAmelCase_ )-> Dict:
'''simple docstring'''
for base_class in inspect.getmro(lowerCAmelCase_ ):
_UpperCAmelCase : Union[str, Any] = base_class.__module__
_UpperCAmelCase : List[str] = base_class.__name__
if module.startswith("""tensorflow""" ) or module.startswith("""keras""" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("""torch""" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("""flax""" ) or module.startswith("""jax""" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F'''Could not infer framework from class {model_class}.''' )
| 349 |
'''simple docstring'''
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ )-> bytes:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
_UpperCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(lowerCAmelCase_ ).content
if __name__ == "__main__":
A_ : Union[str, Any] = input("""Enter Video/IGTV url: """).strip()
A_ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, """wb""") as fp:
fp.write(download_video(url))
print(f"""Done. Video saved to disk as {file_name}.""")
| 349 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : Union[str, Any]=False ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = "backbone." if is_semantic else ""
_UpperCAmelCase : Tuple = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
(F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
(F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
(F"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
_UpperCAmelCase : List[str] = "backbone." if is_semantic else ""
# queries, keys and values
_UpperCAmelCase : int = state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias""" )
_UpperCAmelCase : Dict = in_proj_weight[
: config.hidden_size, :
]
_UpperCAmelCase : Optional[int] = q_bias
_UpperCAmelCase : Any = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_UpperCAmelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_UpperCAmelCase : Dict = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
_UpperCAmelCase : Any = state_dict.pop(F"""{prefix}blocks.{i}.gamma_1""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""{prefix}blocks.{i}.gamma_2""" )
_UpperCAmelCase : List[Any] = gamma_a
_UpperCAmelCase : Any = gamma_a
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = val
def UpperCamelCase_ ( ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[str] = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any]=False ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = False if "rvlcdip" in checkpoint_url else True
_UpperCAmelCase : Tuple = BeitConfig(use_absolute_position_embeddings=_UpperCAmelCase , use_mask_token=_UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
_UpperCAmelCase : Optional[int] = 1_024
_UpperCAmelCase : Union[str, Any] = 4_096
_UpperCAmelCase : Tuple = 24
_UpperCAmelCase : int = 16
# labels
if "rvlcdip" in checkpoint_url:
_UpperCAmelCase : int = 16
_UpperCAmelCase : Optional[int] = "huggingface/label-files"
_UpperCAmelCase : Dict = "rvlcdip-id2label.json"
_UpperCAmelCase : Any = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : Optional[Any] = idalabel
_UpperCAmelCase : int = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
_UpperCAmelCase : Optional[int] = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location="cpu" )["model"]
_UpperCAmelCase : Tuple = create_rename_keys(_UpperCAmelCase , has_lm_head=_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_q_k_v(_UpperCAmelCase , _UpperCAmelCase , has_lm_head=_UpperCAmelCase )
# load HuggingFace model
_UpperCAmelCase : Union[str, Any] = BeitForMaskedImageModeling(_UpperCAmelCase ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase )
model.eval()
model.load_state_dict(_UpperCAmelCase )
# Check outputs on an image
_UpperCAmelCase : int = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=_UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = prepare_img()
_UpperCAmelCase : Any = image_processor(images=_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : Optional[int] = encoding["pixel_values"]
_UpperCAmelCase : List[str] = model(_UpperCAmelCase )
_UpperCAmelCase : str = outputs.logits
# verify logits
_UpperCAmelCase : Any = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(_UpperCAmelCase ), "Shape of logits not as expected"
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
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 push_to_hub:
if has_lm_head:
_UpperCAmelCase : Optional[int] = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
_UpperCAmelCase : List[str] = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_UpperCAmelCase , )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
type=str,
help="""URL 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."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 31 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__snake_case : Any =2_0_4_8
__snake_case : Union[str, Any] =4_0_9_6
__snake_case : Optional[Any] =4_2
__snake_case : Dict =os.environ.pop('PROCESS_TRAIN', 'false')
__snake_case : List[str] ={'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any]):
'''simple docstring'''
def choose_first(lowerCamelCase_ : List[str] ,lowerCamelCase_ : Any=False):
assert isinstance(lowerCamelCase_ ,lowerCamelCase_)
if len(lowerCamelCase_) == 1:
lowerCAmelCase__ : Optional[int] = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
lowerCAmelCase__ : Any = {k: [a[k]] for k in a}
if len(a['''start_token''']) > 0:
break
return a
lowerCAmelCase__ : Optional[Any] = {'''id''': example['''id''']}
lowerCAmelCase__ : int = example['''annotations''']
lowerCAmelCase__ : str = annotation['''yes_no_answer''']
if 0 in yes_no_answer or 1 in yes_no_answer:
lowerCAmelCase__ : Union[str, Any] = ['''yes'''] if 1 in yes_no_answer else ['''no''']
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : int = ['''<cls>''']
else:
lowerCAmelCase__ : Tuple = ['''short''']
lowerCAmelCase__ : int = choose_first(annotation['''short_answers'''])
if len(out['''start_token''']) == 0:
# answer will be long if short is not available
lowerCAmelCase__ : Optional[Any] = ['''long''']
lowerCAmelCase__ : str = choose_first(annotation['''long_answer'''] ,is_long_answer=lowerCamelCase_)
lowerCAmelCase__ : Optional[int] = []
answer.update(lowerCamelCase_)
# disregard some samples
if len(answer['''start_token''']) > 1 or answer["start_token"] == answer["end_token"]:
lowerCAmelCase__ : Optional[Any] = True
else:
lowerCAmelCase__ : Union[str, Any] = False
lowerCAmelCase__ : Tuple = ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
if not all(isinstance(answer[k] ,lowerCamelCase_) for k in cols):
raise ValueError('''Issue in ID''' ,example['''id'''])
return answer
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any] ,lowerCamelCase_ : Union[str, Any]=False):
'''simple docstring'''
lowerCAmelCase__ : Any = _get_single_answer(lowerCamelCase_)
# 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
lowerCAmelCase__ : List[Any] = example['''document''']['''tokens''']
lowerCAmelCase__ : Any = []
for i in range(len(doc['''token'''])):
if not doc["is_html"][i]:
context.append(doc['''token'''][i])
return {
"context": " ".join(lowerCamelCase_),
"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
lowerCAmelCase__ : Union[str, Any] = ['''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
lowerCAmelCase__ : List[Any] = example['''document''']['''tokens''']
lowerCAmelCase__ : Optional[Any] = answer['''start_token''']
lowerCAmelCase__ : Union[str, Any] = answer['''end_token''']
lowerCAmelCase__ : 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
lowerCAmelCase__ : List[Any] = ''' '''.join(context[start_token:end_token])
# checking above code
if assertion:
lowerCAmelCase__ : str = doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
lowerCAmelCase__ : List[Any] = doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
lowerCAmelCase__ : Optional[int] = ''' '''.join([old[i] for i in range(len(lowerCamelCase_)) if not is_html[i]])
if new != old:
print('''ID:''' ,example['''id'''])
print('''New:''' ,lowerCamelCase_ ,end='''\n''')
print('''Old:''' ,lowerCamelCase_ ,end='''\n\n''')
return {
"context": " ".join(lowerCamelCase_),
"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 lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : str ,lowerCamelCase_ : Tuple=2048 ,lowerCamelCase_ : Dict=4096 ,lowerCamelCase_ : Optional[Any]=True):
'''simple docstring'''
lowerCAmelCase__ : int = get_context_and_ans(lowerCamelCase_ ,assertion=lowerCamelCase_)
lowerCAmelCase__ : Union[str, 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"],
},
}
lowerCAmelCase__ : Union[str, Any] = tokenizer(example['''question''']['''text'''] ,out['''context''']).input_ids
lowerCAmelCase__ : List[str] = input_ids.index(tokenizer.sep_token_id) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ : List[Any] = input_ids[:q_len]
lowerCAmelCase__ : List[Any] = range(lowerCamelCase_ ,len(lowerCamelCase_) ,max_length - doc_stride)
for i in doc_start_indices:
lowerCAmelCase__ : Union[str, Any] = i + max_length - q_len
lowerCAmelCase__ : 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(lowerCamelCase_),
"end_token": [-100] * len(lowerCamelCase_),
"category": category,
},
}
lowerCAmelCase__ : Optional[Any] = out['''context'''].split()
lowerCAmelCase__ : Union[str, Any] = splitted_context[answer['''end_token''']]
lowerCAmelCase__ : Optional[int] = len(
tokenizer(
''' '''.join(splitted_context[: answer['''start_token''']]) ,add_special_tokens=lowerCamelCase_ ,).input_ids)
lowerCAmelCase__ : Dict = len(
tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]) ,add_special_tokens=lowerCamelCase_).input_ids)
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
lowerCAmelCase__ : int = len(tokenizer(lowerCamelCase_ ,add_special_tokens=lowerCamelCase_).input_ids)
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
lowerCAmelCase__ : Union[str, Any] = input_ids[answer['''start_token'''] : answer['''end_token'''] + 1] # right & left are inclusive
lowerCAmelCase__ : List[str] = answer['''start_token''']
lowerCAmelCase__ : Union[str, Any] = answer['''end_token''']
if assertion:
lowerCAmelCase__ : int = tokenizer.decode(lowerCamelCase_)
if answer["span"] != new:
print('''ISSUE IN TOKENIZATION''')
print('''OLD:''' ,answer['''span'''])
print('''NEW:''' ,lowerCamelCase_ ,end='''\n\n''')
if len(lowerCamelCase_) <= 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"],
},
}
lowerCAmelCase__ : int = input_ids[:q_len]
lowerCAmelCase__ : Optional[Any] = range(lowerCamelCase_ ,len(lowerCamelCase_) ,max_length - doc_stride)
lowerCAmelCase__ : Tuple = []
lowerCAmelCase__ : Optional[Any] = []
lowerCAmelCase__ : Union[str, Any] = []
lowerCAmelCase__ : Any = [] # null, yes, no, long, short
for i in doc_start_indices:
lowerCAmelCase__ : str = i + max_length - q_len
lowerCAmelCase__ : List[str] = 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:
lowerCAmelCase__ : int = start_token - i + q_len
lowerCAmelCase__ : str = end_token - i + q_len
answers_category.append(answer['''category'''][0]) # ["short"] -> "short"
else:
lowerCAmelCase__ : Tuple = -100
lowerCAmelCase__ : List[str] = -100
answers_category.append('''null''')
lowerCAmelCase__ : int = inputs[-1][start_token : end_token + 1]
answers_start_token.append(lowerCamelCase_)
answers_end_token.append(lowerCamelCase_)
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('''ISSUE in strided for ID:''' ,example['''id'''])
print('''New:''' ,tokenizer.decode(lowerCamelCase_))
print('''Old:''' ,tokenizer.decode(lowerCamelCase_) ,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 lowerCAmelCase__ ( lowerCamelCase_ : Any ,lowerCamelCase_ : List[Any] ,lowerCamelCase_ : int=2048 ,lowerCamelCase_ : Tuple=4096 ,lowerCamelCase_ : Optional[int]=False):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = get_strided_contexts_and_ans(
lowerCamelCase_ ,lowerCamelCase_ ,doc_stride=lowerCamelCase_ ,max_length=lowerCamelCase_ ,assertion=lowerCamelCase_ ,)
return example
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : int):
'''simple docstring'''
with jsonlines.open(lowerCamelCase_ ,'''a''') as writer:
for example in tqdm(lowerCamelCase_ ,total=len(lowerCamelCase_) ,desc='''Saving samples ... '''):
lowerCAmelCase__ : Optional[Any] = 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
__snake_case : Optional[int] =load_dataset('natural_questions')
__snake_case : Union[str, Any] =BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
__snake_case : Tuple =data['train' if PROCESS_TRAIN == 'true' else 'validation']
__snake_case : Optional[int] ={
'tokenizer': tokenizer,
'doc_stride': DOC_STRIDE,
'max_length': MAX_LENGTH,
'assertion': False,
}
__snake_case : Dict =data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__snake_case : Dict =data.remove_columns(['annotations', 'document', 'id', 'question'])
print(data)
np.random.seed(SEED)
__snake_case : int ='nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
save_to_disk(data, file_name=cache_file_name)
| 129 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _lowerCAmelCase ( lowerCamelCase__ , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
@property
def _a (self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a (self ):
A_ : Tuple = ort.SessionOptions()
A_ : Tuple = False
return options
def _a (self ):
A_ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
A_ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
A_ : Tuple = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
A_ : Tuple = """A red cat sitting on a park bench"""
A_ : Any = np.random.RandomState(0 )
A_ : Union[str, Any] = pipe(
prompt=lowercase , image=lowercase , mask_image=lowercase , guidance_scale=7.5 , num_inference_steps=10 , generator=lowercase , output_type="""np""" , )
A_ : Optional[Any] = output.images
A_ : List[Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
A_ : List[Any] = np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def _a (self ):
A_ : Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
A_ : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
A_ : List[Any] = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , subfolder="""scheduler""" , revision="""onnx""" )
A_ : Dict = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" , revision="""onnx""" , scheduler=lowercase , safety_checker=lowercase , feature_extractor=lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=lowercase )
A_ : Any = """A red cat sitting on a park bench"""
A_ : Dict = np.random.RandomState(0 )
A_ : Tuple = pipe(
prompt=lowercase , image=lowercase , mask_image=lowercase , guidance_scale=7.5 , num_inference_steps=20 , generator=lowercase , output_type="""np""" , )
A_ : Optional[int] = output.images
A_ : Any = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
A_ : Any = np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 356 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=128 , lowercase=32 , lowercase=16 , lowercase=2 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , ):
A_ : Union[str, Any] = parent
A_ : Optional[int] = batch_size
A_ : Any = seq_length
A_ : int = is_training
A_ : List[str] = use_input_mask
A_ : Any = use_token_type_ids
A_ : List[Any] = use_labels
A_ : Dict = vocab_size
A_ : Optional[int] = hidden_size
A_ : int = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : Dict = intermediate_size
A_ : List[str] = hidden_act
A_ : List[str] = hidden_dropout_prob
A_ : Union[str, Any] = attention_probs_dropout_prob
A_ : Optional[Any] = max_position_embeddings
A_ : Optional[Any] = type_vocab_size
A_ : List[Any] = type_sequence_label_size
A_ : Tuple = initializer_range
A_ : List[Any] = num_labels
A_ : str = num_choices
A_ : Tuple = scope
def _a (self ):
A_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A_ : Tuple = None
if self.use_input_mask:
A_ : str = random_attention_mask([self.batch_size, self.seq_length] )
A_ : Any = None
if self.use_token_type_ids:
A_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A_ : Dict = None
A_ : Any = None
A_ : List[Any] = None
if self.use_labels:
A_ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A_ : int = ids_tensor([self.batch_size] , self.num_choices )
A_ : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a (self ):
return NezhaConfig(
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=lowercase , initializer_range=self.initializer_range , )
def _a (self ):
(
(
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
),
) : Union[str, Any] = self.prepare_config_and_inputs()
A_ : Union[str, Any] = True
A_ : List[Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A_ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : Union[str, Any] = NezhaModel(config=lowercase )
model.to(lowercase )
model.eval()
A_ : int = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase )
A_ : Optional[Any] = model(lowercase , token_type_ids=lowercase )
A_ : str = model(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 _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ):
A_ : Optional[int] = True
A_ : Optional[Any] = NezhaModel(lowercase )
model.to(lowercase )
model.eval()
A_ : Optional[int] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , encoder_hidden_states=lowercase , encoder_attention_mask=lowercase , )
A_ : str = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , encoder_hidden_states=lowercase , )
A_ : Tuple = model(lowercase , attention_mask=lowercase , token_type_ids=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 _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : Optional[Any] = NezhaForMaskedLM(config=lowercase )
model.to(lowercase )
model.eval()
A_ : List[str] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : Tuple = NezhaForNextSentencePrediction(config=lowercase )
model.to(lowercase )
model.eval()
A_ : Union[str, Any] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : int = NezhaForPreTraining(config=lowercase )
model.to(lowercase )
model.eval()
A_ : str = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , next_sentence_label=lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : Any = NezhaForQuestionAnswering(config=lowercase )
model.to(lowercase )
model.eval()
A_ : Optional[int] = model(
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 _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : Optional[Any] = self.num_labels
A_ : int = NezhaForSequenceClassification(lowercase )
model.to(lowercase )
model.eval()
A_ : List[Any] = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : List[str] = self.num_labels
A_ : Optional[int] = NezhaForTokenClassification(config=lowercase )
model.to(lowercase )
model.eval()
A_ : int = model(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 _a (self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ):
A_ : int = self.num_choices
A_ : int = NezhaForMultipleChoice(config=lowercase )
model.to(lowercase )
model.eval()
A_ : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : str = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
A_ : Optional[int] = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a (self ):
A_ : Tuple = self.prepare_config_and_inputs()
(
(
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
),
) : int = config_and_inputs
A_ : Any = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
__SCREAMING_SNAKE_CASE : str = (
{
'feature-extraction': NezhaModel,
'fill-mask': NezhaForMaskedLM,
'question-answering': NezhaForQuestionAnswering,
'text-classification': NezhaForSequenceClassification,
'token-classification': NezhaForTokenClassification,
'zero-shot': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : List[Any] = True
def _a (self , lowercase , lowercase , lowercase=False ):
A_ : Optional[Any] = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
if return_labels:
if model_class in get_values(lowercase ):
A_ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=lowercase )
A_ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase )
return inputs_dict
def _a (self ):
A_ : Optional[int] = NezhaModelTester(self )
A_ : Any = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def _a (self ):
self.config_tester.run_common_tests()
def _a (self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def _a (self ):
A_ : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*lowercase )
def _a (self ):
# This regression test was failing with PyTorch < 1.3
(
(
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
), (
A_
),
) : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
A_ : str = None
self.model_tester.create_and_check_model_as_decoder(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , )
def _a (self ):
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def _a (self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase )
def _a (self ):
A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase )
def _a (self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase )
def _a (self ):
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
def _a (self ):
A_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase )
def _a (self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
@slow
def _a (self ):
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Any = NezhaModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@slow
@require_torch_gpu
def _a (self ):
A_, A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
A_ : Optional[int] = True
A_ : str = model_class(config=lowercase )
A_ : str = self._prepare_for_class(lowercase , lowercase )
A_ : Tuple = torch.jit.trace(
lowercase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(lowercase , os.path.join(lowercase , """bert.pt""" ) )
A_ : List[str] = torch.jit.load(os.path.join(lowercase , """bert.pt""" ) , map_location=lowercase )
loaded(inputs_dict["""input_ids"""].to(lowercase ) , inputs_dict["""attention_mask"""].to(lowercase ) )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def _a (self ):
A_ : Dict = NezhaModel.from_pretrained("""sijunhe/nezha-cn-base""" )
A_ : List[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
A_ : List[str] = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A_ : Optional[int] = model(lowercase , attention_mask=lowercase )[0]
A_ : Optional[int] = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , lowercase )
A_ : List[Any] = torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowercase , atol=1E-4 ) )
@slow
def _a (self ):
A_ : str = NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base""" )
A_ : Union[str, Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
A_ : str = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A_ : Tuple = model(lowercase , attention_mask=lowercase )[0]
A_ : str = torch.Size((1, 6, 21128) )
self.assertEqual(output.shape , lowercase )
A_ : List[Any] = torch.tensor(
[[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowercase , atol=1E-4 ) ) | 135 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class _A ( lowerCamelCase__ ):
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=1_000 ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = p_stop
__UpperCAmelCase : int = max_length
def __iter__( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = 0
__UpperCAmelCase : List[str] = False
while not stop and count < self.max_length:
yield count
count += 1
__UpperCAmelCase : List[str] = random.random() < self.p_stop
class _A ( unittest.TestCase ):
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : int = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
__UpperCAmelCase : str = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Optional[int] = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCAmelCase : int = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : List[str] = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCAmelCase : Any = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Any = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCAmelCase : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
__UpperCAmelCase : Optional[int] = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Tuple = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
__UpperCAmelCase : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : List[Any] = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__UpperCAmelCase : Optional[int] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCAmelCase : List[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__UpperCAmelCase : str = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCAmelCase : Tuple = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Tuple = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__UpperCAmelCase : Union[str, Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
__UpperCAmelCase : Tuple = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : str = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : List[str] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
__UpperCAmelCase : Any = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
__UpperCAmelCase : Any = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : str = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
__UpperCAmelCase : Optional[int] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Tuple = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Any = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : int = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : Any = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Optional[int] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
__UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : List[str] = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
__UpperCAmelCase : Optional[int] = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : Dict = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : List[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
__UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Optional[int] = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
__UpperCAmelCase : str = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : Any = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
__UpperCAmelCase : Optional[int] = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ) -> List[Any]:
'''simple docstring'''
random.seed(__UpperCAmelCase )
__UpperCAmelCase : Any = list(__UpperCAmelCase )
__UpperCAmelCase : str = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
__UpperCAmelCase : Any = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
__UpperCAmelCase : Union[str, Any] = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
__UpperCAmelCase : List[str] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
__UpperCAmelCase : Optional[Any] = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = 42
__UpperCAmelCase : Dict = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
__UpperCAmelCase : Tuple = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def __A ( self ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
__UpperCAmelCase : int = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __A ( self ) -> str:
'''simple docstring'''
__UpperCAmelCase : Dict = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = DataLoader(list(range(16 ) ) , batch_size=4 )
__UpperCAmelCase : Tuple = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def __A ( self ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def __A ( self ) -> List[str]:
'''simple docstring'''
Accelerator()
__UpperCAmelCase : str = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 254 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
_lowerCAmelCase : int = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
and Jeffrey Dean},
year={2016},
eprint={1609.08144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
'''
_lowerCAmelCase : Tuple = '''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''
_lowerCAmelCase : int = '''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.
Args:
predictions (list of str): list of translations to score.
Each translation should be tokenized into a list of tokens.
references (list of list of str): list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.
Returns:
\'google_bleu\': google_bleu score
Examples:
Example 1:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.44
Example 2:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
>>> print(round(results["google_bleu"], 2))
0.61
Example 3:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
>>> print(round(results["google_bleu"], 2))
0.53
Example 4:
>>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
>>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
>>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
... \'heed\', \'the\', \'cat\', \'commands\']
>>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
... \'of\', \'the\', \'cat\']
>>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
... \'interested\', \'in\', \'world\', \'history\']
>>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
... \'because\', \'he\', \'read\', \'the\', \'book\']
>>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
>>> hypotheses = [hyp1, hyp2]
>>> google_bleu = datasets.load_metric("google_bleu")
>>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
>>> print(round(results["google_bleu"], 2))
0.4
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self :List[str] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def SCREAMING_SNAKE_CASE ( self :int , snake_case :List[List[List[str]]] , snake_case :List[List[str]] , snake_case :int = 1 , snake_case :int = 4 , ):
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=snake_case , hypotheses=snake_case , min_len=snake_case , max_len=snake_case )
}
| 300 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=[10, 20, 30, 40] , _SCREAMING_SNAKE_CASE=[1, 1, 2, 1] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=None , )->Optional[int]:
'''simple docstring'''
A_ : Optional[Any] = parent
A_ : List[Any] = batch_size
A_ : Tuple = image_size
A_ : Tuple = num_channels
A_ : Optional[Any] = embeddings_size
A_ : Dict = hidden_sizes
A_ : int = depths
A_ : Union[str, Any] = is_training
A_ : Union[str, Any] = use_labels
A_ : List[Any] = hidden_act
A_ : Dict = num_labels
A_ : Tuple = scope
A_ : Optional[int] = len(a__ )
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : str = self.get_config()
return config, pixel_values
def _snake_case ( self )->Any:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->int:
'''simple docstring'''
A_ : Any = FlaxRegNetModel(config=a__ )
A_ : Optional[int] = model(a__ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->str:
'''simple docstring'''
A_ : int = self.num_labels
A_ : List[str] = FlaxRegNetForImageClassification(config=a__ )
A_ : List[str] = model(a__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self )->str:
'''simple docstring'''
A_ : Dict = self.prepare_config_and_inputs()
A_ , A_ : List[Any] = config_and_inputs
A_ : List[str] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class _lowerCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
snake_case = False
snake_case = False
snake_case = False
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
A_ : int = FlaxRegNetModelTester(self )
A_ : Tuple = ConfigTester(self , config_class=a__ , has_text_modality=a__ )
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
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 _snake_case ( self )->Any:
'''simple docstring'''
return
def _snake_case ( self )->int:
'''simple docstring'''
A_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a__ )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _snake_case ( self )->Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _snake_case ( self )->int:
'''simple docstring'''
pass
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ , A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Any = model_class(a__ )
A_ : Dict = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : List[str] = [*signature.parameters.keys()]
A_ : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , a__ )
def _snake_case ( self )->List[Any]:
'''simple docstring'''
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : Any = model_class(a__ )
A_ : int = model(**self._prepare_for_class(a__ , a__ ) )
A_ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : str = self.model_tester.num_stages
self.assertEqual(len(a__ ) , expected_num_stages + 1 )
A_ , A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[int] = True
check_hidden_states_output(a__ , a__ , a__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : Dict = True
check_hidden_states_output(a__ , a__ , a__ )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
A_ , A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
A_ : List[Any] = self._prepare_for_class(a__ , a__ )
A_ : List[str] = model_class(a__ )
@jax.jit
def model_jitted(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
return model(pixel_values=a__ , **a__ )
with self.subTest('''JIT Enabled''' ):
A_ : Optional[Any] = model_jitted(**a__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
A_ : Optional[int] = model_jitted(**a__ ).to_tuple()
self.assertEqual(len(a__ ) , len(a__ ) )
for jitted_output, output in zip(a__ , a__ ):
self.assertEqual(jitted_output.shape , output.shape )
def _SCREAMING_SNAKE_CASE ( ):
A_ : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : Union[str, Any] = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
A_ : int = self.default_image_processor
A_ : Optional[Any] = prepare_img()
A_ : str = image_processor(images=a__ , return_tensors='''np''' )
A_ : Optional[int] = model(**a__ )
# verify the logits
A_ : Dict = (1, 1000)
self.assertEqual(outputs.logits.shape , a__ )
A_ : str = jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , a__ , atol=1e-4 ) )
| 363 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->None:
'''simple docstring'''
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , _SCREAMING_SNAKE_CASE , )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 65 | 0 |
'''simple docstring'''
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--original_config_file',
type=str,
required=True,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--image_size',
default=512,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"could not parse string as bool {string}" )
parser.add_argument(
'--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
)
parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)
a : List[str] = parser.parse_args()
a : str = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 56 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCamelCase : Optional[Any] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
lowerCamelCase : Tuple = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
lowerCamelCase : Dict = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
lowerCamelCase : Any = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
lowerCamelCase : Tuple = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
lowerCamelCase : Optional[int] = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
lowerCamelCase : Dict = (
('JH AH TH KH QH', 23),
('JH 9H TH KH QH', 22),
('JC KH JS JD JH', 21),
('KH KC 3S 3H 3D', 20),
('8C 9C 5C 3C TC', 19),
('JS QS 9H TS KH', 18),
('7C 7S KH 2H 7H', 17),
('3C KH 5D 5S KH', 16),
('QH 8H KD JH 8S', 15),
('2D 6D 9D TH 7D', 14),
)
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
"""simple docstring"""
lowercase__ ,lowercase__ = randrange(len(A ) ), randrange(len(A ) )
lowercase__ = ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
lowercase__ ,lowercase__ = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def _SCREAMING_SNAKE_CASE (A = 100 ) -> str:
"""simple docstring"""
return (generate_random_hand() for _ in range(A ))
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> List[str]:
"""simple docstring"""
assert PokerHand(A )._is_flush() == expected
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(A )._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Any:
"""simple docstring"""
lowercase__ = PokerHand(A )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Tuple:
"""simple docstring"""
assert PokerHand(A )._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(A )._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected''' , A )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Union[str, Any]:
"""simple docstring"""
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
@pytest.mark.parametrize('''hand, other, expected''' , generate_random_hands() )
def _SCREAMING_SNAKE_CASE (A , A , A ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
def _SCREAMING_SNAKE_CASE () -> Tuple:
"""simple docstring"""
lowercase__ = [PokerHand(A ) for hand in SORTED_HANDS]
lowercase__ = poker_hands.copy()
shuffle(A )
lowercase__ = chain(sorted(A ) )
for index, hand in enumerate(A ):
assert hand == poker_hands[index]
def _SCREAMING_SNAKE_CASE () -> List[Any]:
"""simple docstring"""
lowercase__ = [PokerHand('''2D AC 3H 4H 5S''' ), PokerHand('''2S 3H 4H 5S 6C''' )]
pokerhands.sort(reverse=A )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def _SCREAMING_SNAKE_CASE () -> int:
"""simple docstring"""
lowercase__ = PokerHand('''2C 4S AS 3D 5C''' )
lowercase__ = True
lowercase__ = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def _SCREAMING_SNAKE_CASE () -> Union[str, Any]:
"""simple docstring"""
lowercase__ = 0
lowercase__ = os.path.abspath(os.path.dirname(A ) )
lowercase__ = os.path.join(A , '''poker_hands.txt''' )
with open(A ) as file_hand:
for line in file_hand:
lowercase__ = line[:14].strip()
lowercase__ = line[15:].strip()
lowercase__ ,lowercase__ = PokerHand(A ), PokerHand(A )
lowercase__ = player.compare_with(A )
if output == "Win":
answer += 1
assert answer == 376
| 2 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def _lowerCamelCase ( lowercase : Tuple ) -> str:
_a = image.size
_a = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_a = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_a = np.array(snake_case__ ).astype(np.floataa ) / 2_55.0
_a = image[None].transpose(0 , 3 , 1 , 2 )
_a = torch.from_numpy(snake_case__ )
return 2.0 * image - 1.0
class __SCREAMING_SNAKE_CASE (a__ ):
"""simple docstring"""
def __init__( self : Optional[int] , __a : Tuple , __a : Tuple , __a : Optional[Any] , ):
super().__init__()
self.register_modules(vqvae=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self : Tuple , __a : List[Any] = None , __a : List[str] = 1 , __a : Any = 1_00 , __a : Optional[Any] = 0.0 , __a : List[Any] = None , __a : Union[str, Any] = "pil" , __a : Dict = True , ):
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
_a = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
_a = image.shape[0]
else:
raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_ )}' )
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
_a = preprocess(SCREAMING_SNAKE_CASE_ )
_a = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_a = (batch_size, self.unet.config.in_channels // 2, height, width)
_a = next(self.unet.parameters() ).dtype
_a = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
_a = image.to(device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , device=self.device )
_a = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
for t in self.progress_bar(SCREAMING_SNAKE_CASE_ ):
# concat latents and low resolution image in the channel dimension.
_a = torch.cat([latents, image] , dim=1 )
_a = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
_a = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# decode the image latents with the VQVAE
_a = self.vqvae.decode(SCREAMING_SNAKE_CASE_ ).sample
_a = torch.clamp(SCREAMING_SNAKE_CASE_ , -1.0 , 1.0 )
_a = image / 2 + 0.5
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 365 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 10 ) -> str:
if not isinstance(lowercase , lowercase ) or n < 0:
raise ValueError("Invalid input" )
_a = 10**n
_a = 2_8433 * (pow(2 , 783_0457 , lowercase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(10) = }""")
| 346 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Optional[Any] = ["""image_processor""", """tokenizer"""]
SCREAMING_SNAKE_CASE_ : List[Any] = """AutoImageProcessor"""
SCREAMING_SNAKE_CASE_ : int = """AutoTokenizer"""
def __init__( self : Dict , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : List[Any]=None , **UpperCamelCase__ : int)-> Optional[Any]:
'''simple docstring'''
__lowerCAmelCase: 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." , UpperCamelCase__ , )
__lowerCAmelCase: str = kwargs.pop("feature_extractor")
__lowerCAmelCase: Optional[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__(UpperCamelCase__ , UpperCamelCase__)
__lowerCAmelCase: List[str] = self.image_processor
__lowerCAmelCase: Dict = False
def __call__( self : Tuple , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Any)-> str:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*UpperCamelCase__ , **UpperCamelCase__)
__lowerCAmelCase: List[Any] = kwargs.pop("images" , UpperCamelCase__)
__lowerCAmelCase: str = kwargs.pop("text" , UpperCamelCase__)
if len(UpperCamelCase__) > 0:
__lowerCAmelCase: Optional[Any] = args[0]
__lowerCAmelCase: Tuple = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process.")
if images is not None:
__lowerCAmelCase: Tuple = self.image_processor(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__)
if text is not None:
__lowerCAmelCase: int = self.tokenizer(UpperCamelCase__ , **UpperCamelCase__)
if text is None:
return inputs
elif images is None:
return encodings
else:
__lowerCAmelCase: List[Any] = encodings["input_ids"]
return inputs
def lowercase_ ( self : Optional[Any] , *UpperCamelCase__ : Union[str, Any] , **UpperCamelCase__ : int)-> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__)
def lowercase_ ( self : Dict , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : Optional[Any])-> Optional[int]:
'''simple docstring'''
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__)
@contextmanager
def lowercase_ ( self : Optional[int])-> List[Any]:
'''simple docstring'''
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your images inputs, or in a separate call.")
__lowerCAmelCase: List[str] = True
__lowerCAmelCase: int = self.tokenizer
yield
__lowerCAmelCase: Union[str, Any] = self.image_processor
__lowerCAmelCase: int = False
def lowercase_ ( self : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any=False , UpperCamelCase__ : Optional[Any]=None)-> List[Any]:
'''simple docstring'''
if added_vocab is None:
__lowerCAmelCase: Tuple = self.tokenizer.get_added_vocab()
__lowerCAmelCase: int = {}
while tokens:
__lowerCAmelCase: Optional[int] = re.search(R"<s_(.*?)>" , UpperCamelCase__ , re.IGNORECASE)
if start_token is None:
break
__lowerCAmelCase: Any = start_token.group(1)
__lowerCAmelCase: Dict = re.search(Rf"</s_{key}>" , UpperCamelCase__ , re.IGNORECASE)
__lowerCAmelCase: List[str] = start_token.group()
if end_token is None:
__lowerCAmelCase: Optional[Any] = tokens.replace(UpperCamelCase__ , "")
else:
__lowerCAmelCase: int = end_token.group()
__lowerCAmelCase: List[str] = re.escape(UpperCamelCase__)
__lowerCAmelCase: List[Any] = re.escape(UpperCamelCase__)
__lowerCAmelCase: Tuple = re.search(f"{start_token_escaped}(.*?){end_token_escaped}" , UpperCamelCase__ , re.IGNORECASE)
if content is not None:
__lowerCAmelCase: Optional[int] = content.group(1).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
__lowerCAmelCase: Tuple = self.tokenajson(UpperCamelCase__ , is_inner_value=UpperCamelCase__ , added_vocab=UpperCamelCase__)
if value:
if len(UpperCamelCase__) == 1:
__lowerCAmelCase: Any = value[0]
__lowerCAmelCase: int = value
else: # leaf nodes
__lowerCAmelCase: Dict = []
for leaf in content.split(R"<sep/>"):
__lowerCAmelCase: str = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
__lowerCAmelCase: Tuple = leaf[1:-2] # for categorical special tokens
output[key].append(UpperCamelCase__)
if len(output[key]) == 1:
__lowerCAmelCase: List[str] = output[key][0]
__lowerCAmelCase: List[str] = tokens[tokens.find(UpperCamelCase__) + len(UpperCamelCase__) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=UpperCamelCase__ , added_vocab=UpperCamelCase__)
if len(UpperCamelCase__):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def lowercase_ ( self : List[str])-> Optional[int]:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , UpperCamelCase__ , )
return self.image_processor_class
@property
def lowercase_ ( self : Tuple)-> int:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , UpperCamelCase__ , )
return self.image_processor
| 217 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __SCREAMING_SNAKE_CASE ) -> bool:
__lowerCAmelCase: Tuple = str(__SCREAMING_SNAKE_CASE )
return len(__SCREAMING_SNAKE_CASE ) == 9 and set(__SCREAMING_SNAKE_CASE ) == set("123456789" )
def a__ ( ) -> int | None:
for base_num in range(9_9_9_9 , 4_9_9_9 , -1 ):
__lowerCAmelCase: Tuple = 1_0_0_0_0_2 * base_num
if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
return candidate
for base_num in range(3_3_3 , 9_9 , -1 ):
__lowerCAmelCase: int = 1_0_0_2_0_0_3 * base_num
if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
return candidate
return None
if __name__ == "__main__":
print(F'''{solution() = }''')
| 217 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCAmelCase_ ( _snake_case : str ) -> int:
'''simple docstring'''
__magic_name__ : Any = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def lowerCAmelCase_ ( _snake_case : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ : Optional[int] = emb.weight.shape
__magic_name__ : Optional[Any] = nn.Linear(_snake_case , _snake_case , bias=_snake_case )
__magic_name__ : Optional[Any] = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : int=None ) -> Any:
'''simple docstring'''
__magic_name__ : Union[str, Any] = {}
for old_key in state_dict.keys():
__magic_name__ : Any = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
__magic_name__ : int = key.replace("moe_layer.experts.0" , F'''ffn.experts.expert_{expert_idx}''' )
else:
__magic_name__ : str = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
__magic_name__ : int = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
__magic_name__ : Any = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
__magic_name__ : List[str] = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
__magic_name__ : List[str] = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
__magic_name__ : Dict = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
__magic_name__ : str = key.replace("final_layer_norm" , "ff_layer_norm" )
__magic_name__ : List[str] = state_dict[old_key]
return new_dict
def lowerCAmelCase_ ( _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Tuple , _snake_case : str , _snake_case : str = WEIGHTS_NAME ) -> Tuple:
'''simple docstring'''
__magic_name__ : Optional[Any] = []
__magic_name__ : Tuple = 0
os.makedirs(_snake_case , exist_ok=_snake_case )
for expert in range(_snake_case ):
__magic_name__ : Dict = switch_checkpoint_path + F'''-rank-{expert}.pt'''
if os.path.isfile(_snake_case ):
__magic_name__ : Optional[int] = torch.load(_snake_case )["model"]
remove_ignore_keys_(_snake_case )
__magic_name__ : Dict = rename_fairseq_keys(_snake_case , _snake_case )
__magic_name__ : Any = os.path.join(
_snake_case , weights_name.replace(".bin" , F'''-{len(_snake_case )+1:05d}-of-???.bin''' ) )
torch.save(_snake_case , _snake_case )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(_snake_case )[0]].dtype )
# Add the last block
__magic_name__ : int = os.path.join(_snake_case , weights_name.replace(".bin" , F'''-{len(_snake_case )+1:05d}-of-???.bin''' ) )
__magic_name__ : Tuple = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(_snake_case )
__magic_name__ : List[Any] = rename_fairseq_keys(_snake_case , _snake_case )
__magic_name__ : Tuple = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(_snake_case ) == 1:
__magic_name__ : List[Any] = os.path.join(_snake_case , _snake_case )
torch.save(_snake_case , _snake_case )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(_snake_case , _snake_case )
# Otherwise, let's build the index
__magic_name__ : List[str] = {}
for idx, shard in enumerate(_snake_case ):
__magic_name__ : Dict = weights_name.replace(".bin" , F'''-{idx+1:05d}-of-{len(_snake_case ):05d}.bin''' )
__magic_name__ : Any = os.path.join(_snake_case , weights_name.replace(".bin" , F'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(_snake_case , os.path.join(_snake_case , _snake_case ) )
for key in shard:
__magic_name__ : Tuple = shard_file
# Add the metadata
__magic_name__ : str = {"total_size": total_size}
__magic_name__ : str = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(_snake_case , _snake_case ) , "w" , encoding="utf-8" ) as f:
__magic_name__ : Optional[int] = json.dumps(_snake_case , indent=2 , sort_keys=_snake_case ) + "\n"
f.write(_snake_case )
return metadata, index
if __name__ == "__main__":
snake_case : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--nllb_moe_checkpoint_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
type=str,
required=False,
help="Path to the output pytorch model.",
)
snake_case : Optional[int] = parser.parse_args()
snake_case ,snake_case : Dict = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
snake_case : Union[str, Any] = NllbMoeConfig.from_pretrained(
"facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
snake_case : int = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print("Done")
model.save_pretrained(args.pytorch_dump_folder_path)
| 41 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
class _snake_case :
UpperCamelCase__ = 42
UpperCamelCase__ = None
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self , _a ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self ):
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def SCREAMING_SNAKE_CASE ( cls ):
return f'''`pip install {cls.pip_package or cls.name}`'''
class _snake_case ( snake_case ):
UpperCamelCase__ = 'optuna'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_optuna_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_optuna(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_optuna(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'ray'
UpperCamelCase__ = '\'ray[tune]\''
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_ray_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_ray(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_ray(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'sigopt'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_sigopt_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_sigopt(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_sigopt(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'wandb'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_wandb_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_wandb(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_wandb(_a )
snake_case : int = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_snake_case ) > 0:
__magic_name__ : Dict = available_backends[0].name
if len(_snake_case ) > 1:
logger.info(
F'''{len(_snake_case )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
F''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 41 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> Dict:
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
lowerCamelCase__: Any =F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
lowerCamelCase__: Any =datasets.load_dataset(__a , __a )
if save_dir is None:
lowerCamelCase__: str =F"""{dataset}-{pair}"""
lowerCamelCase__: Union[str, Any] =Path(__a )
save_dir.mkdir(exist_ok=__a )
for split in ds.keys():
print(F"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
lowerCamelCase__: int ="""val""" if split == """validation""" else split
lowerCamelCase__: List[str] =save_dir.joinpath(F"""{fn}.source""" )
lowerCamelCase__: Optional[int] =save_dir.joinpath(F"""{fn}.target""" )
lowerCamelCase__: Any =src_path.open("w+" )
lowerCamelCase__: Optional[int] =tgt_path.open("w+" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
lowerCamelCase__: Any =x["""translation"""]
src_fp.write(ex[src_lang] + "\n" )
tgt_fp.write(ex[tgt_lang] + "\n" )
print(F"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 10 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
A: str = logging.get_logger(__name__)
A: List[Any] = {"vocab_file": "vocab.txt"}
A: List[str] = {
"vocab_file": {
"facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
"facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
},
}
A: Dict = {
"facebook/esm2_t6_8M_UR50D": 1_0_2_4,
"facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}
def _snake_case ( UpperCamelCase : int ):
with open(UpperCamelCase , """r""" ) as f:
UpperCAmelCase : int = f.read().splitlines()
return [l.strip() for l in lines]
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : str = ['input_ids', 'attention_mask']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<cls>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE="<eos>" , **_SCREAMING_SNAKE_CASE , ) -> Any:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[Any] = load_vocab_file(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[Any] = dict(enumerate(self.all_tokens ) )
UpperCAmelCase : List[str] = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase : Any = unk_token
UpperCAmelCase : str = cls_token
UpperCAmelCase : int = pad_token
UpperCAmelCase : Tuple = mask_token
UpperCAmelCase : str = eos_token
UpperCAmelCase : List[str] = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
return self._id_to_token.get(_SCREAMING_SNAKE_CASE , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return self._token_to_id.get(_SCREAMING_SNAKE_CASE , self._token_to_id.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
return text.split()
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE=False ) -> Union[str, Any]:
'''simple docstring'''
return len(self._id_to_token )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
return {token: i for i, token in enumerate(self.all_tokens )}
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return self._token_to_id.get(_SCREAMING_SNAKE_CASE , self._token_to_id.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
return self._id_to_token.get(_SCREAMING_SNAKE_CASE , self.unk_token )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = [self.cls_token_id]
UpperCAmelCase : Tuple = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!""" )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
'''simple docstring'''
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 token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase : str = [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
if token_ids_a is not None:
mask += [0] * len(_SCREAMING_SNAKE_CASE ) + [1]
return mask
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt""" )
with open(_SCREAMING_SNAKE_CASE , """w""" ) as f:
f.write("""\n""".join(self.all_tokens ) )
return (vocab_file,)
@property
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) -> int:
'''simple docstring'''
return super()._add_tokens(_SCREAMING_SNAKE_CASE , special_tokens=_SCREAMING_SNAKE_CASE )
| 109 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def a__ ( A__, A__, A__, A__, A__=True, A__="pt" ):
SCREAMING_SNAKE_CASE_ : List[str] = {'add_prefix_space': True} if isinstance(A__, A__ ) and not line.startswith(' ' ) else {}
SCREAMING_SNAKE_CASE_ : int = padding_side
return tokenizer(
[line], max_length=A__, padding='max_length' if pad_to_max_length else None, truncation=A__, return_tensors=A__, add_special_tokens=A__, **A__, )
def a__ ( A__, A__, A__=None, ):
SCREAMING_SNAKE_CASE_ : str = input_ids.ne(A__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="train" , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="" , ):
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE_ : Dict = Path(lowerCAmelCase__ ).joinpath(type_path + '.source' )
SCREAMING_SNAKE_CASE_ : Optional[int] = Path(lowerCAmelCase__ ).joinpath(type_path + '.target' )
SCREAMING_SNAKE_CASE_ : Tuple = self.get_char_lens(self.src_file )
SCREAMING_SNAKE_CASE_ : int = max_source_length
SCREAMING_SNAKE_CASE_ : Dict = max_target_length
assert min(self.src_lens ) > 0, F'''found empty line in {self.src_file}'''
SCREAMING_SNAKE_CASE_ : Dict = tokenizer
SCREAMING_SNAKE_CASE_ : Union[str, Any] = prefix
if n_obs is not None:
SCREAMING_SNAKE_CASE_ : Tuple = self.src_lens[:n_obs]
SCREAMING_SNAKE_CASE_ : List[str] = src_lang
SCREAMING_SNAKE_CASE_ : int = tgt_lang
def __len__( self ):
"""simple docstring"""
return len(self.src_lens )
def __getitem__( self , lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = index + 1 # linecache starts at 1
SCREAMING_SNAKE_CASE_ : str = self.prefix + linecache.getline(str(self.src_file ) , lowerCAmelCase__ ).rstrip('\n' )
SCREAMING_SNAKE_CASE_ : Any = linecache.getline(str(self.tgt_file ) , lowerCAmelCase__ ).rstrip('\n' )
assert source_line, F'''empty source line for index {index}'''
assert tgt_line, F'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , lowerCAmelCase__ ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
SCREAMING_SNAKE_CASE_ : str = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , lowerCAmelCase__ ) else self.tokenizer
)
SCREAMING_SNAKE_CASE_ : Tuple = self.tokenizer.generator if isinstance(self.tokenizer , lowerCAmelCase__ ) else self.tokenizer
SCREAMING_SNAKE_CASE_ : Optional[int] = encode_line(lowerCAmelCase__ , lowerCAmelCase__ , self.max_source_length , 'right' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = encode_line(lowerCAmelCase__ , lowerCAmelCase__ , self.max_target_length , 'right' )
SCREAMING_SNAKE_CASE_ : int = source_inputs['input_ids'].squeeze()
SCREAMING_SNAKE_CASE_ : Optional[Any] = target_inputs['input_ids'].squeeze()
SCREAMING_SNAKE_CASE_ : str = source_inputs['attention_mask'].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCamelCase__ ( lowerCAmelCase__ ):
"""simple docstring"""
return [len(lowerCAmelCase__ ) for x in Path(lowerCAmelCase__ ).open().readlines()]
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = torch.stack([x['input_ids'] for x in batch] )
SCREAMING_SNAKE_CASE_ : int = torch.stack([x['attention_mask'] for x in batch] )
SCREAMING_SNAKE_CASE_ : int = torch.stack([x['decoder_input_ids'] for x in batch] )
SCREAMING_SNAKE_CASE_ : Tuple = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , lowerCAmelCase__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , lowerCAmelCase__ )
else self.tokenizer.pad_token_id
)
SCREAMING_SNAKE_CASE_ : Tuple = trim_batch(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = trim_batch(lowerCAmelCase__ , lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = {
'input_ids': source_ids,
'attention_mask': source_mask,
'decoder_input_ids': y,
}
return batch
lowerCAmelCase__ : List[str] =getLogger(__name__)
def a__ ( A__ ):
return list(itertools.chain.from_iterable(A__ ) )
def a__ ( A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = get_git_info()
save_json(A__, os.path.join(A__, 'git_log.json' ) )
def a__ ( A__, A__, A__=4, **A__ ):
with open(A__, 'w' ) as f:
json.dump(A__, A__, indent=A__, **A__ )
def a__ ( A__ ):
with open(A__ ) as f:
return json.load(A__ )
def a__ ( ):
SCREAMING_SNAKE_CASE_ : str = git.Repo(search_parent_directories=A__ )
SCREAMING_SNAKE_CASE_ : int = {
'repo_id': str(A__ ),
'repo_sha': str(repo.head.object.hexsha ),
'repo_branch': str(repo.active_branch ),
'hostname': str(socket.gethostname() ),
}
return repo_infos
def a__ ( A__, A__ ):
return list(map(A__, A__ ) )
def a__ ( A__, A__ ):
with open(A__, 'wb' ) as f:
return pickle.dump(A__, A__ )
def a__ ( A__ ):
def remove_articles(A__ ):
return re.sub(r'\b(a|an|the)\b', ' ', A__ )
def white_space_fix(A__ ):
return " ".join(text.split() )
def remove_punc(A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(A__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(A__ ) ) ) )
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = normalize_answer(A__ ).split()
SCREAMING_SNAKE_CASE_ : Any = normalize_answer(A__ ).split()
SCREAMING_SNAKE_CASE_ : Optional[Any] = Counter(A__ ) & Counter(A__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = sum(common.values() )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE_ : Optional[Any] = 1.0 * num_same / len(A__ )
SCREAMING_SNAKE_CASE_ : Dict = 1.0 * num_same / len(A__ )
SCREAMING_SNAKE_CASE_ : Any = (2 * precision * recall) / (precision + recall)
return fa
def a__ ( A__, A__ ):
return normalize_answer(A__ ) == normalize_answer(A__ )
def a__ ( A__, A__ ):
assert len(A__ ) == len(A__ )
SCREAMING_SNAKE_CASE_ : List[str] = 0
for hypo, pred in zip(A__, A__ ):
em += exact_match_score(A__, A__ )
if len(A__ ) > 0:
em /= len(A__ )
return {"em": em}
def a__ ( A__ ):
return model_prefix.startswith('rag' )
def a__ ( A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
SCREAMING_SNAKE_CASE_ : Optional[int] = 'dropout_rate'
for p in extra_params:
if getattr(A__, A__, A__ ):
if not hasattr(A__, A__ ) and not hasattr(A__, equivalent_param[p] ):
logger.info('config doesn\'t have a `{}` attribute'.format(A__ ) )
delattr(A__, A__ )
continue
SCREAMING_SNAKE_CASE_ : Tuple = p if hasattr(A__, A__ ) else equivalent_param[p]
setattr(A__, A__, getattr(A__, A__ ) )
delattr(A__, A__ )
return hparams, config
| 162 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase__ : Optional[Any] =logging.getLogger(__name__)
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Any = np.argmax(A__, axis=1 )
return np.sum(outputs == labels )
def a__ ( A__ ):
with open(A__, encoding='utf_8' ) as f:
SCREAMING_SNAKE_CASE_ : int = csv.reader(A__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
next(A__ ) # skip the first line
for line in tqdm(A__ ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def a__ ( A__, A__, A__, A__, A__, A__ ):
SCREAMING_SNAKE_CASE_ : str = []
for dataset in encoded_datasets:
SCREAMING_SNAKE_CASE_ : str = len(A__ )
SCREAMING_SNAKE_CASE_ : List[Any] = np.zeros((n_batch, 2, input_len), dtype=np.intaa )
SCREAMING_SNAKE_CASE_ : str = np.zeros((n_batch, 2), dtype=np.intaa )
SCREAMING_SNAKE_CASE_ : Optional[int] = np.full((n_batch, 2, input_len), fill_value=-1_0_0, dtype=np.intaa )
SCREAMING_SNAKE_CASE_ : Optional[Any] = np.zeros((n_batch,), dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(A__ ):
SCREAMING_SNAKE_CASE_ : List[str] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
SCREAMING_SNAKE_CASE_ : Optional[Any] = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
SCREAMING_SNAKE_CASE_ : Any = with_conta
SCREAMING_SNAKE_CASE_ : Union[str, Any] = with_conta
SCREAMING_SNAKE_CASE_ : Dict = len(A__ ) - 1
SCREAMING_SNAKE_CASE_ : str = len(A__ ) - 1
SCREAMING_SNAKE_CASE_ : Any = with_conta
SCREAMING_SNAKE_CASE_ : str = with_conta
SCREAMING_SNAKE_CASE_ : List[str] = mc_label
SCREAMING_SNAKE_CASE_ : Any = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(A__ ) for t in all_inputs ) )
return tensor_datasets
def a__ ( ):
SCREAMING_SNAKE_CASE_ : Any = argparse.ArgumentParser()
parser.add_argument('--model_name', type=A__, default='openai-gpt', help='pretrained model name' )
parser.add_argument('--do_train', action='store_true', help='Whether to run training.' )
parser.add_argument('--do_eval', action='store_true', help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir', default=A__, type=A__, required=A__, help='The output directory where the model predictions and checkpoints will be written.', )
parser.add_argument('--train_dataset', type=A__, default='' )
parser.add_argument('--eval_dataset', type=A__, default='' )
parser.add_argument('--seed', type=A__, default=4_2 )
parser.add_argument('--num_train_epochs', type=A__, default=3 )
parser.add_argument('--train_batch_size', type=A__, default=8 )
parser.add_argument('--eval_batch_size', type=A__, default=1_6 )
parser.add_argument('--adam_epsilon', default=1E-8, type=A__, help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm', type=A__, default=1 )
parser.add_argument(
'--max_steps', default=-1, type=A__, help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
), )
parser.add_argument(
'--gradient_accumulation_steps', type=A__, default=1, help='Number of updates steps to accumulate before performing a backward/update pass.', )
parser.add_argument('--learning_rate', type=A__, default=6.25E-5 )
parser.add_argument('--warmup_steps', default=0, type=A__, help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule', type=A__, default='warmup_linear' )
parser.add_argument('--weight_decay', type=A__, default=0.01 )
parser.add_argument('--lm_coef', type=A__, default=0.9 )
parser.add_argument('--n_valid', type=A__, default=3_7_4 )
parser.add_argument('--server_ip', type=A__, default='', help='Can be used for distant debugging.' )
parser.add_argument('--server_port', type=A__, default='', help='Can be used for distant debugging.' )
SCREAMING_SNAKE_CASE_ : Any = parser.parse_args()
print(A__ )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=A__ )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
SCREAMING_SNAKE_CASE_ : Optional[int] = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
SCREAMING_SNAKE_CASE_ : str = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(A__, A__ ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
SCREAMING_SNAKE_CASE_ : List[Any] = ['_start_', '_delimiter_', '_classify_']
SCREAMING_SNAKE_CASE_ : Dict = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(A__ )
SCREAMING_SNAKE_CASE_ : int = tokenizer.convert_tokens_to_ids(A__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(A__ ) )
model.to(A__ )
# Load and encode the datasets
def tokenize_and_encode(A__ ):
if isinstance(A__, A__ ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(A__ ) )
elif isinstance(A__, A__ ):
return obj
return [tokenize_and_encode(A__ ) for o in obj]
logger.info('Encoding dataset...' )
SCREAMING_SNAKE_CASE_ : int = load_rocstories_dataset(args.train_dataset )
SCREAMING_SNAKE_CASE_ : int = load_rocstories_dataset(args.eval_dataset )
SCREAMING_SNAKE_CASE_ : Optional[Any] = (train_dataset, eval_dataset)
SCREAMING_SNAKE_CASE_ : List[str] = tokenize_and_encode(A__ )
# Compute the max input length for the Transformer
SCREAMING_SNAKE_CASE_ : Tuple = model.config.n_positions // 2 - 2
SCREAMING_SNAKE_CASE_ : Optional[int] = max(
len(story[:max_length] ) + max(len(conta[:max_length] ), len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
SCREAMING_SNAKE_CASE_ : str = min(A__, model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
SCREAMING_SNAKE_CASE_ : Tuple = pre_process_datasets(A__, A__, A__, *A__ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = tensor_datasets[0], tensor_datasets[1]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = TensorDataset(*A__ )
SCREAMING_SNAKE_CASE_ : str = RandomSampler(A__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = DataLoader(A__, sampler=A__, batch_size=args.train_batch_size )
SCREAMING_SNAKE_CASE_ : List[Any] = TensorDataset(*A__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = SequentialSampler(A__ )
SCREAMING_SNAKE_CASE_ : str = DataLoader(A__, sampler=A__, batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
SCREAMING_SNAKE_CASE_ : int = args.max_steps
SCREAMING_SNAKE_CASE_ : Any = args.max_steps // (len(A__ ) // args.gradient_accumulation_steps) + 1
else:
SCREAMING_SNAKE_CASE_ : List[Any] = len(A__ ) // args.gradient_accumulation_steps * args.num_train_epochs
SCREAMING_SNAKE_CASE_ : Optional[Any] = list(model.named_parameters() )
SCREAMING_SNAKE_CASE_ : Optional[int] = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
SCREAMING_SNAKE_CASE_ : Optional[Any] = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
SCREAMING_SNAKE_CASE_ : Optional[Any] = AdamW(A__, lr=args.learning_rate, eps=args.adam_epsilon )
SCREAMING_SNAKE_CASE_ : List[Any] = get_linear_schedule_with_warmup(
A__, num_warmup_steps=args.warmup_steps, num_training_steps=A__ )
if args.do_train:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ), desc='Epoch' ):
SCREAMING_SNAKE_CASE_ : int = 0
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CASE_ : List[Any] = tqdm(A__, desc='Training' )
for step, batch in enumerate(A__ ):
SCREAMING_SNAKE_CASE_ : List[Any] = tuple(t.to(A__ ) for t in batch )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] = batch
SCREAMING_SNAKE_CASE_ : Tuple = model(A__, mc_token_ids=A__, lm_labels=A__, mc_labels=A__ )
SCREAMING_SNAKE_CASE_ : str = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
SCREAMING_SNAKE_CASE_ : Tuple = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
SCREAMING_SNAKE_CASE_ : List[str] = 'Training loss: {:.2e} lr: {:.2e}'.format(A__, scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
SCREAMING_SNAKE_CASE_ : List[str] = model.module if hasattr(A__, 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
SCREAMING_SNAKE_CASE_ : Optional[Any] = os.path.join(args.output_dir, A__ )
SCREAMING_SNAKE_CASE_ : Optional[Any] = os.path.join(args.output_dir, A__ )
torch.save(model_to_save.state_dict(), A__ )
model_to_save.config.to_json_file(A__ )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
SCREAMING_SNAKE_CASE_ : Dict = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
SCREAMING_SNAKE_CASE_ : int = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(A__ )
if args.do_eval:
model.eval()
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[str] = 0, 0
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Tuple = 0, 0
for batch in tqdm(A__, desc='Evaluating' ):
SCREAMING_SNAKE_CASE_ : int = tuple(t.to(A__ ) for t in batch )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = batch
with torch.no_grad():
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = model(
A__, mc_token_ids=A__, lm_labels=A__, mc_labels=A__ )
SCREAMING_SNAKE_CASE_ : List[Any] = mc_logits.detach().cpu().numpy()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = mc_labels.to('cpu' ).numpy()
SCREAMING_SNAKE_CASE_ : Dict = accuracy(A__, A__ )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
SCREAMING_SNAKE_CASE_ : List[str] = eval_loss / nb_eval_steps
SCREAMING_SNAKE_CASE_ : List[Any] = eval_accuracy / nb_eval_examples
SCREAMING_SNAKE_CASE_ : List[Any] = tr_loss / nb_tr_steps if args.do_train else None
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
SCREAMING_SNAKE_CASE_ : int = os.path.join(args.output_dir, 'eval_results.txt' )
with open(A__, 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s', A__, str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 162 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
__a = 0
@slow
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(__lowercase ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(__lowercase ) , 0 )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
__a = AutoConfig.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
# Check that tokenizer_type ≠ model_type
__a = AutoTokenizer.from_pretrained(__lowercase , config=__lowercase )
self.assertIsInstance(__lowercase , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.txt""" , os.path.join(__lowercase , """vocab.txt""" ) )
__a = AutoTokenizer.from_pretrained(__lowercase , tokenizer_type="""bert""" , use_fast=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.json""" , os.path.join(__lowercase , """vocab.json""" ) )
shutil.copy("""./tests/fixtures/merges.txt""" , os.path.join(__lowercase , """merges.txt""" ) )
__a = AutoTokenizer.from_pretrained(__lowercase , tokenizer_type="""gpt2""" , use_fast=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@require_tokenizers
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.txt""" , os.path.join(__lowercase , """vocab.txt""" ) )
__a = AutoTokenizer.from_pretrained(__lowercase , tokenizer_type="""bert""" )
self.assertIsInstance(__lowercase , __lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("""./tests/fixtures/vocab.json""" , os.path.join(__lowercase , """vocab.json""" ) )
shutil.copy("""./tests/fixtures/merges.txt""" , os.path.join(__lowercase , """merges.txt""" ) )
__a = AutoTokenizer.from_pretrained(__lowercase , tokenizer_type="""gpt2""" )
self.assertIsInstance(__lowercase , __lowercase )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
with pytest.raises(__lowercase ):
AutoTokenizer.from_pretrained("""./""" , tokenizer_type="""xxx""" )
@require_tokenizers
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
__a = tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased""" )
self.assertIsInstance(__lowercase , (BertTokenizer, BertTokenizerFast) )
if isinstance(__lowercase , __lowercase ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , __lowercase )
else:
self.assertEqual(tokenizer.do_lower_case , __lowercase )
self.assertEqual(tokenizer.model_max_length , 512 )
@require_tokenizers
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
__lowercase , """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier""" , ):
__a = tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists""" )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
# tests: https://github.com/huggingface/transformers/pull/13251
# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
__a = TOKENIZER_MAPPING.values()
__a = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(__lowercase )
@require_tokenizers
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=__lowercase ) , __lowercase )
self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased""" ) , __lowercase )
@require_tokenizers
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained("""distilbert-base-uncased""" , do_lower_case=__lowercase )
__a = """Hello, world. How are you?"""
__a = tokenizer.tokenize(__lowercase )
self.assertEqual("""[UNK]""" , tokens[0] )
__a = AutoTokenizer.from_pretrained("""microsoft/mpnet-base""" , do_lower_case=__lowercase )
__a = tokenizer.tokenize(__lowercase )
self.assertEqual("""[UNK]""" , tokens[0] )
@require_tokenizers
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config""" )
self.assertEqual(type(__lowercase ) , __lowercase )
self.assertEqual(tokenizer.model_max_length , 512 )
self.assertEqual(tokenizer.vocab_size , 30000 )
self.assertEqual(tokenizer.unk_token , """[UNK]""" )
self.assertEqual(tokenizer.padding_side , """right""" )
self.assertEqual(tokenizer.truncation_side , """right""" )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained("""ctrl""" )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(__lowercase , __lowercase )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
# Check we can load the tokenizer config of an online model.
__a = get_tokenizer_config("""bert-base-cased""" )
__a = config.pop("""_commit_hash""" , __lowercase )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(__lowercase , {"""do_lower_case""": False} )
# This model does not have a tokenizer_config so we get back an empty dict.
__a = get_tokenizer_config(__lowercase )
self.assertDictEqual(__lowercase , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
__a = AutoTokenizer.from_pretrained(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = get_tokenizer_config(__lowercase )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config["""tokenizer_class"""] , """BertTokenizer""" )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
try:
AutoConfig.register("""custom""" , __lowercase )
AutoTokenizer.register(__lowercase , slow_tokenizer_class=__lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__lowercase ):
AutoTokenizer.register(__lowercase , slow_tokenizer_class=__lowercase )
__a = CustomTokenizer.from_pretrained(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
try:
AutoConfig.register("""custom""" , __lowercase )
# Can register in two steps
AutoTokenizer.register(__lowercase , slow_tokenizer_class=__lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(__lowercase , fast_tokenizer_class=__lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
__lowercase , slow_tokenizer_class=__lowercase , fast_tokenizer_class=__lowercase )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__lowercase ):
AutoTokenizer.register(__lowercase , fast_tokenizer_class=__lowercase )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
__a = BertTokenizerFast.from_pretrained(__lowercase )
bert_tokenizer.save_pretrained(__lowercase )
__a = CustomTokenizerFast.from_pretrained(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase , use_fast=__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__lowercase ):
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__lowercase ):
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase )
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase , trust_remote_code=__lowercase )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase , use_fast=__lowercase )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__lowercase )
__a = AutoTokenizer.from_pretrained(__lowercase , trust_remote_code=__lowercase , use_fast=__lowercase )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , """NewTokenizer""" )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , """NewTokenizer""" )
@require_tokenizers
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : List[str] =False
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : Tuple =NewTokenizer
__lowerCamelCase : Tuple =False
try:
AutoConfig.register("""custom""" , __lowercase )
AutoTokenizer.register(__lowercase , slow_tokenizer_class=__lowercase )
AutoTokenizer.register(__lowercase , fast_tokenizer_class=__lowercase )
# If remote code is not set, the default is to use local
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
self.assertFalse(tokenizer.special_attribute_present )
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer""" , use_fast=__lowercase )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
self.assertFalse(tokenizer.special_attribute_present )
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase , use_fast=__lowercase )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
self.assertTrue(tokenizer.special_attribute_present )
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer""" , trust_remote_code=__lowercase , use_fast=__lowercase )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer_legacy""" , trust_remote_code=__lowercase )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
__a = AutoTokenizer.from_pretrained(
"""hf-internal-testing/test_dynamic_tokenizer_legacy""" , trust_remote_code=__lowercase , use_fast=__lowercase )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
__lowercase , """bert-base is not a local folder and is not a valid model identifier""" ):
__a = AutoTokenizer.from_pretrained("""bert-base""" )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
with self.assertRaisesRegex(
__lowercase , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
__a = AutoTokenizer.from_pretrained(__lowercase , revision="""aaaaaa""" )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
# Make sure we have cached the tokenizer.
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
with RequestCounter() as counter:
__a = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 302 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = Dict[str, Any]
lowerCamelCase__ = List[Prediction]
@add_end_docstrings(lowerCamelCase__ )
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def __init__( self : Tuple , *__lowercase : Tuple , **__lowercase : Optional[int] ):
'''simple docstring'''
super().__init__(*__lowercase , **__lowercase )
if self.framework == "tf":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
requires_backends(self , """vision""" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def UpperCamelCase_ ( self : Optional[int] , **__lowercase : List[str] ):
'''simple docstring'''
__a = {}
if "threshold" in kwargs:
__a = kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self : List[Any] , *__lowercase : Any , **__lowercase : Tuple ):
'''simple docstring'''
return super().__call__(*__lowercase , **__lowercase )
def UpperCamelCase_ ( self : str , __lowercase : Tuple ):
'''simple docstring'''
__a = load_image(__lowercase )
__a = torch.IntTensor([[image.height, image.width]] )
__a = self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
__a = self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
__a = target_size
return inputs
def UpperCamelCase_ ( self : Dict , __lowercase : List[str] ):
'''simple docstring'''
__a = model_inputs.pop("""target_size""" )
__a = self.model(**__lowercase )
__a = outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
__a = model_inputs["""bbox"""]
return model_outputs
def UpperCamelCase_ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any]=0.9 ):
'''simple docstring'''
__a = model_outputs["""target_size"""]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__a , __a = target_size[0].tolist()
def unnormalize(__lowercase : Optional[Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
__a , __a = model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__a = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__a = [unnormalize(__lowercase ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
__a = ["""score""", """label""", """box"""]
__a = [dict(zip(__lowercase , __lowercase ) ) for vals in zip(scores.tolist() , __lowercase , __lowercase ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__a = self.image_processor.post_process_object_detection(__lowercase , __lowercase , __lowercase )
__a = raw_annotations[0]
__a = raw_annotation["""scores"""]
__a = raw_annotation["""labels"""]
__a = raw_annotation["""boxes"""]
__a = scores.tolist()
__a = [self.model.config.idalabel[label.item()] for label in labels]
__a = [self._get_bounding_box(__lowercase ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__a = ["""score""", """label""", """box"""]
__a = [
dict(zip(__lowercase , __lowercase ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def UpperCamelCase_ ( self : Optional[int] , __lowercase : "torch.Tensor" ):
'''simple docstring'''
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
__a , __a , __a , __a = box.int().tolist()
__a = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 302 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
__snake_case : Tuple = '0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def _lowercase ( __snake_case ,__snake_case ,__snake_case=None ) -> str:
if rng is None:
__lowerCAmelCase : str = random.Random()
__lowerCAmelCase : List[Any] = 1
for dim in shape:
total_dims *= dim
__lowerCAmelCase : int = []
for _ in range(__snake_case ):
values.append(rng.randint(0 ,vocab_size - 1 ) )
__lowerCAmelCase : Dict = np.array(__snake_case ,dtype=jnp.intaa ).reshape(__snake_case )
return output
def _lowercase ( __snake_case ,__snake_case=None ) -> Optional[Any]:
__lowerCAmelCase : List[str] = ids_tensor(__snake_case ,vocab_size=2 ,rng=__snake_case )
# make sure that at least one token is attended to for each batch
__lowerCAmelCase : str = 1
return attn_mask
@require_flax
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = ()
def _SCREAMING_SNAKE_CASE ( self: int) -> Tuple:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
__lowerCAmelCase : Tuple = 2
__lowerCAmelCase : Dict = inputs["input_ids"].shape[-1] // 2
__lowerCAmelCase : Union[str, Any] = inputs["input_ids"][:max_batch_size, :sequence_length]
__lowerCAmelCase : str = jnp.ones_like(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
__lowerCAmelCase : Dict = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
__lowerCAmelCase : int = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def _SCREAMING_SNAKE_CASE ( self: int) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : str = self._get_input_ids_and_config()
__lowerCAmelCase : Dict = False
__lowerCAmelCase : Dict = max_length
__lowerCAmelCase : Any = 0
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Union[str, Any] = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCAmelCase : int = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = pt_model_class(_SCREAMING_SNAKE_CASE).eval()
__lowerCAmelCase : Optional[int] = load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE , flax_model.params)
__lowerCAmelCase : int = flax_model.generate(_SCREAMING_SNAKE_CASE).sequences
__lowerCAmelCase : Any = pt_model.generate(torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.long))
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
__lowerCAmelCase : Any = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[Any] = self._get_input_ids_and_config()
__lowerCAmelCase : List[str] = False
__lowerCAmelCase : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : List[str] = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = jit(model.generate)
__lowerCAmelCase : List[str] = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[Any] = self._get_input_ids_and_config()
__lowerCAmelCase : Dict = True
__lowerCAmelCase : List[str] = max_length
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Tuple = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = jit(model.generate)
__lowerCAmelCase : Optional[Any] = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Dict:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Any = self._get_input_ids_and_config()
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Tuple = max_length
__lowerCAmelCase : Any = 2
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Optional[int] = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = jit(model.generate)
__lowerCAmelCase : Dict = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: str) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = self._get_input_ids_and_config()
__lowerCAmelCase : List[Any] = False
__lowerCAmelCase : Any = max_length
__lowerCAmelCase : Dict = 2
__lowerCAmelCase : int = 2
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : int = self._get_input_ids_and_config()
__lowerCAmelCase : str = True
__lowerCAmelCase : Tuple = max_length
__lowerCAmelCase : Tuple = 0.8
__lowerCAmelCase : Any = 10
__lowerCAmelCase : Any = 0.3
__lowerCAmelCase : List[Any] = 1
__lowerCAmelCase : int = 8
__lowerCAmelCase : Optional[int] = 9
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Union[str, Any] = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = jit(model.generate)
__lowerCAmelCase : str = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[str] = self._get_input_ids_and_config()
__lowerCAmelCase : int = max_length
__lowerCAmelCase : Tuple = 1
__lowerCAmelCase : List[str] = 8
__lowerCAmelCase : str = 9
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Dict = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = jit(model.generate)
__lowerCAmelCase : str = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Dict:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : int = self._get_input_ids_and_config()
__lowerCAmelCase : Union[str, Any] = max_length
__lowerCAmelCase : Dict = 2
__lowerCAmelCase : Tuple = 1
__lowerCAmelCase : int = 8
__lowerCAmelCase : str = 9
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = model.generate(_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[Any] = jit(model.generate)
__lowerCAmelCase : Union[str, Any] = jit_generate(_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: str) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = self._get_input_ids_and_config()
# pad attention mask on the left
__lowerCAmelCase : Tuple = attention_mask.at[(0, 0)].set(0)
__lowerCAmelCase : Optional[Any] = False
__lowerCAmelCase : int = max_length
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = jit(model.generate)
__lowerCAmelCase : Dict = jit_generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Any = self._get_input_ids_and_config()
# pad attention mask on the left
__lowerCAmelCase : int = attention_mask.at[(0, 0)].set(0)
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[int] = model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = jit(model.generate)
__lowerCAmelCase : Any = jit_generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = self._get_input_ids_and_config()
# pad attention mask on the left
__lowerCAmelCase : int = attention_mask.at[(0, 0)].set(0)
__lowerCAmelCase : Tuple = 2
__lowerCAmelCase : Dict = max_length
for model_class in self.all_generative_model_classes:
__lowerCAmelCase : Tuple = model_class(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Optional[Any] = model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertEqual(generation_outputs.shape[-1] , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = jit(model.generate)
__lowerCAmelCase : int = jit_generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist())
@require_flax
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Any:
"""simple docstring"""
__lowerCAmelCase : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert")
__lowerCAmelCase : Optional[int] = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
__lowerCAmelCase : Optional[Any] = "Hello world"
__lowerCAmelCase : str = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors="np").input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE , "do_samples"):
model.generate(_SCREAMING_SNAKE_CASE , do_samples=_SCREAMING_SNAKE_CASE)
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE , "foo"):
__lowerCAmelCase : int = {"foo": "bar"}
model.generate(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE) | 58 |
"""simple docstring"""
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
__snake_case : int = logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['input_features']
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: Optional[Any]=80 , _SCREAMING_SNAKE_CASE: Union[str, Any]=1_6000 , _SCREAMING_SNAKE_CASE: Optional[int]=160 , _SCREAMING_SNAKE_CASE: Dict=30 , _SCREAMING_SNAKE_CASE: str=400 , _SCREAMING_SNAKE_CASE: str=0.0 , _SCREAMING_SNAKE_CASE: Optional[Any]=False , **_SCREAMING_SNAKE_CASE: Tuple , ) -> List[Any]:
"""simple docstring"""
super().__init__(
feature_size=_SCREAMING_SNAKE_CASE , sampling_rate=_SCREAMING_SNAKE_CASE , padding_value=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Dict = n_fft
__lowerCAmelCase : Any = hop_length
__lowerCAmelCase : List[Any] = chunk_length
__lowerCAmelCase : Dict = chunk_length * sampling_rate
__lowerCAmelCase : Optional[int] = self.n_samples // hop_length
__lowerCAmelCase : Tuple = sampling_rate
__lowerCAmelCase : int = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=_SCREAMING_SNAKE_CASE , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=_SCREAMING_SNAKE_CASE , norm="slaney" , mel_scale="slaney" , )
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: np.array) -> np.ndarray:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = spectrogram(
_SCREAMING_SNAKE_CASE , 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" , )
__lowerCAmelCase : Dict = log_spec[:, :-1]
__lowerCAmelCase : List[str] = np.maximum(_SCREAMING_SNAKE_CASE , log_spec.max() - 8.0)
__lowerCAmelCase : Union[str, Any] = (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 _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE: List[np.ndarray] , _SCREAMING_SNAKE_CASE: List[np.ndarray] , _SCREAMING_SNAKE_CASE: float = 0.0) -> List[np.ndarray]:
"""simple docstring"""
if attention_mask is not None:
__lowerCAmelCase : List[str] = np.array(_SCREAMING_SNAKE_CASE , np.intaa)
__lowerCAmelCase : int = []
for vector, length in zip(_SCREAMING_SNAKE_CASE , attention_mask.sum(-1)):
__lowerCAmelCase : Optional[int] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
if length < normed_slice.shape[0]:
__lowerCAmelCase : List[str] = padding_value
normed_input_values.append(_SCREAMING_SNAKE_CASE)
else:
__lowerCAmelCase : int = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]
return normed_input_values
def __call__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _SCREAMING_SNAKE_CASE: bool = True , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , _SCREAMING_SNAKE_CASE: Optional[str] = "max_length" , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[int] = None , _SCREAMING_SNAKE_CASE: Optional[bool] = None , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> BatchFeature:
"""simple docstring"""
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.")
__lowerCAmelCase : str = isinstance(_SCREAMING_SNAKE_CASE , 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}""")
__lowerCAmelCase : Optional[int] = is_batched_numpy or (
isinstance(_SCREAMING_SNAKE_CASE , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
__lowerCAmelCase : str = [np.asarray([speech] , dtype=np.floataa).T for speech in raw_speech]
elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray):
__lowerCAmelCase : Union[str, Any] = np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa)
elif isinstance(_SCREAMING_SNAKE_CASE , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
__lowerCAmelCase : List[Any] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
__lowerCAmelCase : str = [np.asarray([raw_speech]).T]
__lowerCAmelCase : str = BatchFeature({"input_features": raw_speech})
# convert into correct format for padding
__lowerCAmelCase : Optional[int] = self.pad(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , max_length=max_length if max_length else self.n_samples , truncation=_SCREAMING_SNAKE_CASE , pad_to_multiple_of=_SCREAMING_SNAKE_CASE , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
__lowerCAmelCase : List[str] = self.zero_mean_unit_var_norm(
padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , )
__lowerCAmelCase : Dict = np.stack(padded_inputs["input_features"] , axis=0)
# make sure list is in array format
__lowerCAmelCase : Union[str, Any] = padded_inputs.get("input_features").transpose(2 , 0 , 1)
__lowerCAmelCase : Dict = [self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE) for waveform in input_features[0]]
if isinstance(input_features[0] , _SCREAMING_SNAKE_CASE):
__lowerCAmelCase : Dict = [np.asarray(_SCREAMING_SNAKE_CASE , dtype=np.floataa) for feature in input_features]
else:
__lowerCAmelCase : Dict = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
__lowerCAmelCase : Optional[Any] = padded_inputs["attention_mask"][:, :: self.hop_length]
if return_tensors is not None:
__lowerCAmelCase : List[str] = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE)
return padded_inputs
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> Dict[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Any = copy.deepcopy(self.__dict__)
__lowerCAmelCase : str = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output | 58 | 1 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
__a = logging.get_logger(__name__)
class A__ ( _A ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : int=None , **lowerCAmelCase__ : Dict ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead." , UpperCamelCase__ , )
super().__init__(args=UpperCamelCase__ , **UpperCamelCase__ ) | 145 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = [
"""decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(A_, A_ )
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ , __magic_name__ = emb.weight.shape
__magic_name__ = nn.Linear(A_, A_, bias=A_ )
__magic_name__ = emb.weight.data
return lin_layer
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = torch.load(A_, map_location="""cpu""" )
__magic_name__ = Namespace(**checkpoint["""cfg"""]["""model"""] )
__magic_name__ = checkpoint["""model"""]
remove_ignore_keys_(A_ )
__magic_name__ = state_dict["""decoder.embed_tokens.weight"""].shape[0]
__magic_name__ = {key.replace("""decoder""", """model""" ): val for key, val in state_dict.items()}
__magic_name__ = XGLMConfig(
vocab_size=A_, max_position_embeddings=args.max_target_positions, num_layers=args.decoder_layers, attention_heads=args.decoder_attention_heads, ffn_dim=args.decoder_ffn_embed_dim, d_model=args.decoder_embed_dim, layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function="""gelu""", scale_embedding=not args.no_scale_embedding, tie_word_embeddings=args.share_decoder_input_output_embed, )
__magic_name__ = XGLMForCausalLM(A_ )
__magic_name__ = model.load_state_dict(A_, strict=A_ )
print(A_ )
__magic_name__ = make_linear_from_emb(model.model.embed_tokens )
return model
if __name__ == "__main__":
__lowerCAmelCase : Dict = 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.')
__lowerCAmelCase : List[str] = parser.parse_args()
__lowerCAmelCase : str = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
model.save_pretrained(args.pytorch_dump_folder_path)
| 88 | 0 |
# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
__snake_case = min(snake_case_ ) # min() finds the minimum value
__snake_case = max(snake_case_ ) # max() finds the maximum value
__snake_case = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__snake_case = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(snake_case_ , snake_case_ ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__snake_case = 0
for count in range(snake_case_ ):
while holes[count] > 0:
holes[count] -= 1
__snake_case = count + min_val
i += 1
def lowerCamelCase__ ( ) -> Union[str, Any]:
__snake_case = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(snake_case_ )
print('''Sorted order is:''' , ''' '''.join(snake_case_ ) )
if __name__ == "__main__":
main()
| 368 |
from __future__ import annotations
snake_case_ = 'Muhammad Umer Farooq'
snake_case_ = 'MIT'
snake_case_ = '1.0.0'
snake_case_ = 'Muhammad Umer Farooq'
snake_case_ = 'contact@muhammadumerfarooq.me'
snake_case_ = 'Alpha'
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__(self : Dict , a__ : str ):
"""simple docstring"""
super().__init__()
__snake_case = []
__snake_case = domain
def a (self : Tuple , a__ : str , a__ : list[tuple[str, str | None]] ):
"""simple docstring"""
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__snake_case = parse.urljoin(self.domain , a__ )
self.urls.append(a__ )
def lowerCamelCase__ ( snake_case_ : str ) -> str:
return ".".join(get_sub_domain_name(snake_case_ ).split('''.''' )[-2:] )
def lowerCamelCase__ ( snake_case_ : str ) -> str:
return parse.urlparse(snake_case_ ).netloc
def lowerCamelCase__ ( snake_case_ : str = "https://github.com" ) -> list[str]:
__snake_case = get_domain_name(snake_case_ )
# Initialize the parser
__snake_case = Parser(snake_case_ )
try:
# Open URL
__snake_case = requests.get(snake_case_ )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__snake_case = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__snake_case = requests.get(snake_case_ )
# Get the valid email.
__snake_case = re.findall('''[a-zA-Z0-9]+@''' + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(snake_case_ )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(snake_case_ )
if __name__ == "__main__":
snake_case_ = emails_from_url('https://github.com')
print(F'{len(emails)} emails found:')
print('\n'.join(sorted(emails)))
| 238 | 0 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
elif hole_conc < 0:
raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
elif intrinsic_conc < 0:
raise ValueError(
'''Intrinsic concentration cannot be negative in a semiconductor''' )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 259 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None ):
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
__snake_case : List[Any] = nn.Parameter(__lowerCamelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
__snake_case : Any = nn.Parameter(__lowerCamelCase )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# set torch weights for 1-to-1 comparison
__snake_case : List[Any] = np.asarray(weights[0] )
__snake_case : Optional[int] = np.asarray(weights[1] )
__snake_case : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCamelCase ).view(-1 , __lowerCamelCase ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# set torch weights for 1-to-1 comparison
__snake_case : int = np.asarray(weights[0] )
__snake_case : Any = np.asarray(weights[1] )
__snake_case : List[Any] = np.asarray(weights[2] )
__snake_case : int = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCamelCase ).view(-1 , __lowerCamelCase ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# layernorm 1
__snake_case : List[Any] = weights[0][0][0]
__snake_case : Any = np.asarray(layer_norm_a[0] )
__snake_case : int = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# lsh weights + output
__snake_case : Union[str, Any] = weights[0][1]
if len(__lowerCamelCase ) < 4:
set_layer_weights_in_torch_lsh(__lowerCamelCase , torch_block.attention , __lowerCamelCase )
else:
set_layer_weights_in_torch_local(__lowerCamelCase , torch_block.attention , __lowerCamelCase )
# intermediate weighs
__snake_case : Optional[int] = weights[2][0][1][2]
# Chunked Feed Forward
if len(__lowerCamelCase ) == 4:
__snake_case : Dict = intermediate_weights[2]
# layernorm 2
__snake_case : Tuple = np.asarray(intermediate_weights[0][0] )
__snake_case : Optional[int] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# intermediate dense
__snake_case : Optional[int] = np.asarray(intermediate_weights[1][0] )
__snake_case : List[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
# intermediate out
__snake_case : Union[str, Any] = np.asarray(intermediate_weights[4][0] )
__snake_case : List[str] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# reformer model
__snake_case : Tuple = torch_model.reformer
# word embeds
__snake_case : Union[str, Any] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__lowerCamelCase ) , )
if isinstance(weights[3] , __lowerCamelCase ):
__snake_case : List[Any] = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__snake_case : Union[str, Any] = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'{position_embeddings[emb_idx]} emb does not match'
__snake_case : Optional[int] = nn.Parameter(torch.tensor(__lowerCamelCase ) )
__snake_case : Tuple = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__lowerCamelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__snake_case : Optional[Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# output layer norm
__snake_case : Optional[int] = np.asarray(weights[7][0] )
__snake_case : List[str] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# output embeddings
__snake_case : List[str] = np.asarray(weights[9][0] )
__snake_case : str = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# Initialise PyTorch model
__snake_case : List[Any] = ReformerConfig.from_json_file(__lowerCamelCase )
print(F'Building PyTorch model from configuration: {config}' )
__snake_case : Optional[Any] = ReformerModelWithLMHead(__lowerCamelCase )
with open(__lowerCamelCase , "rb" ) as f:
__snake_case : int = pickle.load(__lowerCamelCase )["weights"]
set_model_weights_in_torch(__lowerCamelCase , __lowerCamelCase , config.hidden_size )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , __lowerCamelCase )
if __name__ == "__main__":
_snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_snake_case : Union[str, Any] = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 134 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_snake_case : int = "scheduler_config.json"
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = 1
__UpperCAmelCase : Tuple = 2
__UpperCAmelCase : Union[str, Any] = 3
__UpperCAmelCase : List[Any] = 4
__UpperCAmelCase : Tuple = 5
@dataclass
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : jnp.ndarray
class a :
"""simple docstring"""
__UpperCAmelCase : Dict = SCHEDULER_CONFIG_NAME
__UpperCAmelCase : Union[str, Any] = ["dtype"]
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : int = True
@classmethod
def __snake_case ( cls : List[str] , lowerCamelCase : Dict[str, Any] = None , lowerCamelCase : Optional[str] = None , lowerCamelCase : List[str]=False , **lowerCamelCase : Union[str, Any] , ) -> List[str]:
__snake_case , __snake_case : List[str] = cls.load_config(
pretrained_model_name_or_path=lowerCamelCase , subfolder=lowerCamelCase , return_unused_kwargs=lowerCamelCase , **lowerCamelCase , )
__snake_case , __snake_case : Dict = cls.from_config(lowerCamelCase , return_unused_kwargs=lowerCamelCase , **lowerCamelCase )
if hasattr(lowerCamelCase , "create_state" ) and getattr(lowerCamelCase , "has_state" , lowerCamelCase ):
__snake_case : Tuple = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def __snake_case ( self : Any , lowerCamelCase : Union[str, os.PathLike] , lowerCamelCase : bool = False , **lowerCamelCase : List[Any] ) -> int:
self.save_config(save_directory=lowerCamelCase , push_to_hub=lowerCamelCase , **lowerCamelCase )
@property
def __snake_case ( self : Tuple ) -> List[Any]:
return self._get_compatibles()
@classmethod
def __snake_case ( cls : int ) -> Dict:
__snake_case : Tuple = list(set([cls.__name__] + cls._compatibles ) )
__snake_case : int = importlib.import_module(__name__.split("." )[0] )
__snake_case : Tuple = [
getattr(lowerCamelCase , lowerCamelCase ) for c in compatible_classes_str if hasattr(lowerCamelCase , lowerCamelCase )
]
return compatible_classes
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
assert len(__lowerCamelCase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase ) - x.ndim) ) , __lowerCamelCase )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase=0.9_9_9 , __lowerCamelCase=jnp.floataa ):
def alpha_bar(__lowerCamelCase ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__snake_case : List[Any] = []
for i in range(__lowerCamelCase ):
__snake_case : Dict = i / num_diffusion_timesteps
__snake_case : str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(__lowerCamelCase ) / alpha_bar(__lowerCamelCase ) , __lowerCamelCase ) )
return jnp.array(__lowerCamelCase , dtype=__lowerCamelCase )
@flax.struct.dataclass
class a :
"""simple docstring"""
__UpperCAmelCase : jnp.ndarray
__UpperCAmelCase : jnp.ndarray
__UpperCAmelCase : jnp.ndarray
@classmethod
def __snake_case ( cls : Union[str, Any] , lowerCamelCase : int ) -> List[Any]:
__snake_case : Dict = scheduler.config
if config.trained_betas is not None:
__snake_case : Dict = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__snake_case : Optional[int] = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__snake_case : str = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__snake_case : Optional[Any] = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
__snake_case : Any = 1.0 - betas
__snake_case : int = jnp.cumprod(lowerCamelCase , axis=0 )
return cls(
alphas=lowerCamelCase , betas=lowerCamelCase , alphas_cumprod=lowerCamelCase , )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : List[Any] = state.alphas_cumprod
__snake_case : str = alphas_cumprod[timesteps] ** 0.5
__snake_case : Dict = sqrt_alpha_prod.flatten()
__snake_case : str = broadcast_to_shape_from_left(__lowerCamelCase , original_samples.shape )
__snake_case : Tuple = (1 - alphas_cumprod[timesteps]) ** 0.5
__snake_case : str = sqrt_one_minus_alpha_prod.flatten()
__snake_case : Tuple = broadcast_to_shape_from_left(__lowerCamelCase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case , __snake_case : Union[str, Any] = get_sqrt_alpha_prod(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
__snake_case : List[str] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case , __snake_case : Dict = get_sqrt_alpha_prod(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
__snake_case : Optional[int] = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 134 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_a = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
_a = logging.WARNING
def __a ( ):
UpperCAmelCase_ : Dict = os.getenv("DATASETS_VERBOSITY", __lowerCamelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def __a ( ):
return __name__.split("." )[0]
def __a ( ):
return logging.getLogger(_get_library_name() )
def __a ( ):
# Apply our default configuration to the library root logger.
UpperCAmelCase_ : int = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def __a ( ):
UpperCAmelCase_ : int = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def __a ( __lowerCamelCase = None ):
if name is None:
UpperCAmelCase_ : str = _get_library_name()
return logging.getLogger(__lowerCamelCase )
def __a ( ):
return _get_library_root_logger().getEffectiveLevel()
def __a ( __lowerCamelCase ):
_get_library_root_logger().setLevel(__lowerCamelCase )
def __a ( ):
return set_verbosity(__lowerCamelCase )
def __a ( ):
return set_verbosity(__lowerCamelCase )
def __a ( ):
return set_verbosity(__lowerCamelCase )
def __a ( ):
return set_verbosity(__lowerCamelCase )
def __a ( ):
UpperCAmelCase_ : Tuple = False
def __a ( ):
UpperCAmelCase_ : List[Any] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class A_ :
'''simple docstring'''
def __init__( self , *lowercase_ , **lowercase_ ): # pylint: disable=unused-argument
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = args[0] if args else None
def __iter__( self ):
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self , lowercase_ ):
"""simple docstring"""
def empty_fn(*lowercase_ , **lowercase_ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
"""simple docstring"""
return self
def __exit__( self , lowercase_ , lowercase_ , lowercase_ ):
"""simple docstring"""
return
_a = True
class A_ :
'''simple docstring'''
def __call__( self , *lowercase_ , lowercase_=False , **lowercase_ ):
"""simple docstring"""
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*lowercase_ , **lowercase_ )
else:
return EmptyTqdm(*lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self , *lowercase_ , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowercase_ , **lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
_a = _tqdm_cls()
def __a ( ):
global _tqdm_active
return bool(_tqdm_active )
def __a ( ):
global _tqdm_active
UpperCAmelCase_ : Tuple = True
def __a ( ):
global _tqdm_active
UpperCAmelCase_ : int = False
| 61 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__a = logging.get_logger(__name__)
__a = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__a = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__a = {'''facebook/blenderbot-3B''': 1_28}
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Dict = VOCAB_FILES_NAMES
A : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
A : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Optional[int] = ['input_ids', 'attention_mask']
A : str = BlenderbotTokenizer
def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="replace" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=True , **SCREAMING_SNAKE_CASE__ , ):
super().__init__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , tokenizer_file=SCREAMING_SNAKE_CASE__ , errors=SCREAMING_SNAKE_CASE__ , bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , add_prefix_space=SCREAMING_SNAKE_CASE__ , trim_offsets=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowercase : str = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : List[Any] = getattr(SCREAMING_SNAKE_CASE__ , pre_tok_state.pop('''type''' ) )
lowercase : str = add_prefix_space
lowercase : List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = add_prefix_space
lowercase : str = '''post_processor'''
lowercase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if tokenizer_component_instance:
lowercase : Optional[int] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowercase : Tuple = tuple(state['''sep'''] )
if "cls" in state:
lowercase : Union[str, Any] = tuple(state['''cls'''] )
lowercase : Optional[int] = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
lowercase : Any = add_prefix_space
lowercase : Tuple = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
lowercase : List[str] = trim_offsets
lowercase : Optional[int] = True
if changes_to_apply:
lowercase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
lowercase : Union[str, Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __lowerCamelCase ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
lowercase : Any = value
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ):
lowercase : Any = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : int = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : 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 + sep + token_ids_a + sep ) * [0]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
return token_ids_a + [self.eos_token_id]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Any = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = ''' '''.join(SCREAMING_SNAKE_CASE__ )
lowercase : Any = self.encode(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) > self.model_max_length:
lowercase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 337 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
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 lowercase ( A__ , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = KandinskyVaaImgaImgPipeline
__SCREAMING_SNAKE_CASE = ["""image_embeds""", """negative_image_embeds""", """image"""]
__SCREAMING_SNAKE_CASE = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
__SCREAMING_SNAKE_CASE = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
__SCREAMING_SNAKE_CASE = False
@property
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
return 32
@property
def snake_case_ ( self ) -> Dict:
"""simple docstring"""
return 32
@property
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
return self.time_input_dim
@property
def snake_case_ ( self ) -> int:
"""simple docstring"""
return self.time_input_dim * 4
@property
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
return 100
@property
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
UpperCAmelCase = UNetaDConditionModel(**_snake_case )
return model
@property
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def snake_case_ ( self ) -> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.dummy_unet
UpperCAmelCase = self.dummy_movq
UpperCAmelCase = {
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
UpperCAmelCase = DDIMScheduler(**_snake_case )
UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def snake_case_ ( self , _snake_case , _snake_case=0 ) -> Dict:
"""simple docstring"""
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_snake_case ) ).to(_snake_case )
UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_snake_case )
# create init_image
UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(_snake_case ) ).to(_snake_case )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase = Image.fromarray(np.uinta(_snake_case ) ).convert('''RGB''' ).resize((256, 256) )
if str(_snake_case ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(_snake_case )
else:
UpperCAmelCase = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
UpperCAmelCase = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def snake_case_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase = '''cpu'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = self.pipeline_class(**_snake_case )
UpperCAmelCase = pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
UpperCAmelCase = pipe(**self.get_dummy_inputs(_snake_case ) )
UpperCAmelCase = output.images
UpperCAmelCase = pipe(
**self.get_dummy_inputs(_snake_case ) , return_dict=_snake_case , )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCAmelCase = np.array(
[0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
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 lowercase ( unittest.TestCase ):
'''simple docstring'''
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
UpperCAmelCase = '''A red cartoon frog, 4k'''
UpperCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_snake_case )
UpperCAmelCase = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
UpperCAmelCase = pipeline.to(_snake_case )
pipeline.set_progress_bar_config(disable=_snake_case )
UpperCAmelCase = torch.Generator(device='''cpu''' ).manual_seed(0 )
UpperCAmelCase , UpperCAmelCase = pipe_prior(
_snake_case , generator=_snake_case , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
UpperCAmelCase = pipeline(
image=_snake_case , image_embeds=_snake_case , negative_image_embeds=_snake_case , generator=_snake_case , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_snake_case , _snake_case )
| 152 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["BeitFeatureExtractor"]
__magic_name__ = ["BeitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BeitForImageClassification",
"BeitForMaskedImageModeling",
"BeitForSemanticSegmentation",
"BeitModel",
"BeitPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
"FlaxBeitModel",
"FlaxBeitPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 152 | 1 |
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