


'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


_lowerCAmelCase  :		Dict		     =	logging.getLogger(__name__)

_lowerCAmelCase  :		Optional[Any]		     =	tf.data.AUTOTUNE

def 			__UpperCamelCase    (    )    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= argparse.ArgumentParser(description='Train a masked language model on TPU.'				)
	parser.add_argument(
	    '--pretrained_model_config'					,   type=_A					,   default='roberta-base'					,   help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'					,   )
	parser.add_argument(
	    '--tokenizer'					,   type=_A					,   default='unigram-tokenizer-wikitext'					,   help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'					,   )

	parser.add_argument(
	    '--per_replica_batch_size'					,   type=_A					,   default=8					,   help='Batch size per TPU core.'					,   )

	parser.add_argument(
	    '--no_tpu'					,   action='store_true'					,   help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'					,   )

	parser.add_argument(
	    '--tpu_name'					,   type=_A					,   help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'					,   default='local'					,   )

	parser.add_argument(
	    '--tpu_zone'					,   type=_A					,   help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'					,   )

	parser.add_argument(
	    '--gcp_project'					,   type=_A					,   help='Google cloud project name. Only used for non-Colab TPU nodes.'				)

	parser.add_argument(
	    '--bfloat16'					,   action='store_true'					,   help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'					,   )

	parser.add_argument(
	    '--train_dataset'					,   type=_A					,   help='Path to training dataset to load. If the path begins with `gs://`'
	    ' then the dataset will be loaded from a Google Cloud Storage bucket.'					,   )

	parser.add_argument(
	    '--shuffle_buffer_size'					,   type=_A					,   default=2**18					,   help='Size of the shuffle buffer (in samples)'					,   )

	parser.add_argument(
	    '--eval_dataset'					,   type=_A					,   help='Path to evaluation dataset to load. If the path begins with `gs://`'
	    ' then the dataset will be loaded from a Google Cloud Storage bucket.'					,   )

	parser.add_argument(
	    '--num_epochs'					,   type=_A					,   default=1					,   help='Number of epochs to train for.'					,   )

	parser.add_argument(
	    '--learning_rate'					,   type=_A					,   default=1e-4					,   help='Learning rate to use for training.'					,   )

	parser.add_argument(
	    '--weight_decay_rate'					,   type=_A					,   default=1e-3					,   help='Weight decay rate to use for training.'					,   )

	parser.add_argument(
	    '--max_length'					,   type=_A					,   default=5_12					,   help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'					,   )

	parser.add_argument(
	    '--mlm_probability'					,   type=_A					,   default=0.15					,   help='Fraction of tokens to mask during training.'					,   )

	parser.add_argument('--output_dir'					,   type=_A					,   required=_A					,   help='Path to save model checkpoints to.'				)
	parser.add_argument('--hub_model_id'					,   type=_A					,   help='Model ID to upload to on the Hugging Face Hub.'				)

	lowerCAmelCase					:    int      							= parser.parse_args()
	return args

def 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Union[str, Any]:




	"""simple docstring"""





	try:
		if args.tpu_name:
			lowerCAmelCase					:    int      							= tf.distribute.cluster_resolver.TPUClusterResolver(
			    args.tpu_name					,   zone=args.tpu_zone					,   project=args.gcp_project				)
		else:
			lowerCAmelCase					:    Tuple      							= tf.distribute.cluster_resolver.TPUClusterResolver()
	except ValueError:
		raise RuntimeError(
		    'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
		    '--gcp_project. When running on a TPU VM, use --tpu_name local.'				)

	tf.config.experimental_connect_to_cluster(_A				)
	tf.tpu.experimental.initialize_tpu_system(_A				)

	return tpu

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      str:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 0
	for file in file_list:
		lowerCAmelCase					:    int      							= file.split('/'				)[-1]
		lowerCAmelCase					:    List[str]      							= re.search(r'-\d+-(\d+)\.tfrecord'					,   _A				).group(1				)
		lowerCAmelCase					:    Any      							= int(_A				)
		num_samples += sample_count

	return num_samples

def 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				Union[str, Any]					,   _A    :				Union[str, Any]					,   _A    :				List[str]					,   _A    :				Tuple					,   _A    :				int=None				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= count_samples(_A				)
	lowerCAmelCase					:    List[str]      							= tf.data.Dataset.from_tensor_slices(_A				)
	if shuffle:
		lowerCAmelCase					:    List[str]      							= dataset.shuffle(len(_A				)				)
	lowerCAmelCase					:    int      							= tf.data.TFRecordDataset(_A					,   num_parallel_reads=_A				)
	# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
	lowerCAmelCase					:    List[str]      							= dataset.apply(tf.data.experimental.assert_cardinality(_A				)				)
	lowerCAmelCase					:    Union[str, Any]      							= dataset.map(_A					,   num_parallel_calls=_A				)
	if shuffle:
		assert shuffle_buffer_size is not None
		lowerCAmelCase					:    Optional[Any]      							= dataset.shuffle(args.shuffle_buffer_size				)
	lowerCAmelCase					:    List[str]      							= dataset.batch(_A					,   drop_remainder=_A				)
	lowerCAmelCase					:    Union[str, Any]      							= dataset.map(_A					,   num_parallel_calls=_A				)
	lowerCAmelCase					:    Union[str, Any]      							= dataset.prefetch(_A				)
	return dataset

def 			__UpperCamelCase    (    _A    :				str				)    ->      Any:




	"""simple docstring"""





	if not args.no_tpu:
		lowerCAmelCase					:    int      							= initialize_tpu(_A				)
		lowerCAmelCase					:    Optional[Any]      							= tf.distribute.TPUStrategy(_A				)
	else:
		lowerCAmelCase					:    Any      							= tf.distribute.OneDeviceStrategy(device='/gpu:0'				)

	if args.bfloataa:
		tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'				)

	lowerCAmelCase					:    Tuple      							= AutoTokenizer.from_pretrained(args.tokenizer				)
	lowerCAmelCase					:    str      							= AutoConfig.from_pretrained(args.pretrained_model_config				)
	lowerCAmelCase					:    Tuple      							= tokenizer.vocab_size

	lowerCAmelCase					:    Tuple      							= tf.io.gfile.glob(os.path.join(args.train_dataset					,   '*.tfrecord'				)				)
	if not training_records:
		raise ValueError(F"No .tfrecord files found in {args.train_dataset}."				)
	lowerCAmelCase					:    Tuple      							= tf.io.gfile.glob(os.path.join(args.eval_dataset					,   '*.tfrecord'				)				)
	if not eval_records:
		raise ValueError(F"No .tfrecord files found in {args.eval_dataset}."				)

	lowerCAmelCase					:    Tuple      							= count_samples(_A				)

	lowerCAmelCase					:    Dict      							= num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
	lowerCAmelCase					:    Optional[int]      							= steps_per_epoch * args.num_epochs

	with strategy.scope():
		lowerCAmelCase					:    Union[str, Any]      							= TFAutoModelForMaskedLM.from_config(_A				)
		model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
		lowerCAmelCase							,   lowerCAmelCase					:    Tuple      							= create_optimizer(
		    num_train_steps=_A					,   num_warmup_steps=total_train_steps // 20					,   init_lr=args.learning_rate					,   weight_decay_rate=args.weight_decay_rate					,   )

		# Transformers models compute the right loss for their task by default when labels are passed, and will
		# use this for training unless you specify your own loss function in compile().
		model.compile(optimizer=_A					,   metrics=['accuracy']				)

	def decode_fn(_A    :				Optional[int]				):
		lowerCAmelCase					:    Optional[int]      							= {
		    'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa					,   shape=(args.max_length,)				),
		    'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa					,   shape=(args.max_length,)				),
		}
		return tf.io.parse_single_example(_A					,   _A				)

	# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
	# use their methods in our data pipeline.
	lowerCAmelCase					:    Any      							= DataCollatorForLanguageModeling(
	    tokenizer=_A					,   mlm_probability=args.mlm_probability					,   mlm=_A					,   return_tensors='tf'				)

	def mask_with_collator(_A    :				Any				):
		# TF really needs an isin() function
		lowerCAmelCase					:    List[str]      							= (
		    ~tf.cast(batch['attention_mask']					,   tf.bool				)
		    | (batch['input_ids'] == tokenizer.cls_token_id)
		    | (batch['input_ids'] == tokenizer.sep_token_id)
		)
		lowerCAmelCase							,   lowerCAmelCase					:    Any      							= data_collator.tf_mask_tokens(
		    batch['input_ids']					,   vocab_size=len(_A				)					,   mask_token_id=tokenizer.mask_token_id					,   special_tokens_mask=_A					,   )
		return batch

	lowerCAmelCase					:    List[Any]      							= args.per_replica_batch_size * strategy.num_replicas_in_sync

	lowerCAmelCase					:    Any      							= prepare_dataset(
	    _A					,   decode_fn=_A					,   mask_fn=_A					,   batch_size=_A					,   shuffle=_A					,   shuffle_buffer_size=args.shuffle_buffer_size					,   )

	lowerCAmelCase					:    Tuple      							= prepare_dataset(
	    _A					,   decode_fn=_A					,   mask_fn=_A					,   batch_size=_A					,   shuffle=_A					,   )

	lowerCAmelCase					:    List[Any]      							= []
	if args.hub_model_id:
		callbacks.append(
		    PushToHubCallback(output_dir=args.output_dir					,   hub_model_id=args.hub_model_id					,   tokenizer=_A				)				)

	model.fit(
	    _A					,   validation_data=_A					,   epochs=args.num_epochs					,   callbacks=_A					,   )

	model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
				_lowerCAmelCase  :		Tuple		     =	parse_args()
				main(args)




'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = PegasusTokenizer
	_lowerCamelCase   :					Union[str, Any]			      = PegasusTokenizerFast
	_lowerCamelCase   :					Optional[Any]			      = True
	_lowerCamelCase   :					Optional[Any]			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    List[Any]      							= PegasusTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/pegasus-large'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= '</s>'
		lowerCAmelCase					:    int      							= 1

		self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__    ) ,					snake_case__    )
		self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<pad>'    )
		self.assertEqual(vocab_keys[1] ,					'</s>'    )
		self.assertEqual(vocab_keys[-1] ,					'v'    )
		self.assertEqual(len(snake_case__    ) ,					1103    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					1103    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Optional[Any]      							= (
		    'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
		    ' </s> <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Optional[Any]      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Optional[int]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= self._large_tokenizer
		# <mask_1> masks whole sentence while <mask_2> masks single word
		lowerCAmelCase					:    List[str]      							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
		lowerCAmelCase					:    Optional[Any]      							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Optional[Any]      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[Any]      							= self._large_tokenizer
		# The tracebacks for the following asserts are **better** without messages or self.assertEqual
		assert tokenizer.vocab_size == 9_6103
		assert tokenizer.pad_token_id == 0
		assert tokenizer.eos_token_id == 1
		assert tokenizer.offset == 103
		assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
		assert tokenizer.unk_token == "<unk>"
		assert tokenizer.model_max_length == 1024
		lowerCAmelCase					:    List[Any]      							= 'To ensure a smooth flow of bank resolutions.'
		lowerCAmelCase					:    Optional[int]      							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
		lowerCAmelCase					:    Any      							= tokenizer([raw_input_str] ,					return_tensors=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )
		assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= ['This is going to be way too long.' * 150, 'short example']
		lowerCAmelCase					:    int      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    Dict      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 1024)
		assert batch.attention_mask.shape == (2, 1024)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	@slow
	def 				lowercase     (					self    ):
		# fmt: off
		lowerCAmelCase					:    Tuple      							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
		# fmt: on

		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='google/bigbird-pegasus-large-arxiv' ,					revision='ba85d0851d708441f91440d509690f1ab6353415' ,					)



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Optional[Any]			      = PegasusTokenizer
	_lowerCamelCase   :					str			      = PegasusTokenizerFast
	_lowerCamelCase   :					Tuple			      = True
	_lowerCamelCase   :					int			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    int      							= PegasusTokenizer(snake_case__ ,					offset=0 ,					mask_token_sent=snake_case__ ,					mask_token='[MASK]'    )
		tokenizer.save_pretrained(self.tmpdirname    )



	@cached_property
	def 				lowercase     (					self    ):
		return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'    )



	def 				lowercase     (					self ,					**snake_case__    ):
		return PegasusTokenizer.from_pretrained(self.tmpdirname ,					**snake_case__    )



	def 				lowercase     (					self ,					snake_case__    ):
		return ("This is a test", "This is a test")



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Union[str, Any]      							= self.tokenizer_class.from_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    List[str]      							= (
		    'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
		    ' <pad> <pad> <pad>'
		)
		lowerCAmelCase					:    Dict      							= rust_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		lowerCAmelCase					:    Union[str, Any]      							= py_tokenizer([raw_input_str] ,					return_tensors=snake_case__ ,					add_special_tokens=snake_case__    ).input_ids[0]
		self.assertListEqual(snake_case__ ,					snake_case__    )



	@require_torch
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= ['This is going to be way too long.' * 1000, 'short example']
		lowerCAmelCase					:    Union[str, Any]      							= ['not super long but more than 5 tokens', 'tiny']
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(snake_case__ ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )
		lowerCAmelCase					:    List[str]      							= self._large_tokenizer(
		    text_target=snake_case__ ,					max_length=5 ,					padding=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt'    )

		assert batch.input_ids.shape == (2, 4096)
		assert batch.attention_mask.shape == (2, 4096)
		assert targets["input_ids"].shape == (2, 5)
		assert len(snake_case__    ) == 2  # input_ids, attention_mask.



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= (
		    'This is an example string that is used to test the original TF implementation against the HF'
		    ' implementation'
		)

		lowerCAmelCase					:    Tuple      							= self._large_tokenizer(snake_case__    ).input_ids

		self.assertListEqual(
		    snake_case__ ,					[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] ,					)
