






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A        =			'▁'

A        =			get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class 							__a							(  __UpperCAmelCase ,	unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				Union[str, Any]				    =						BigBirdTokenizer
		UpperCAmelCase__      :				Any				    =						BigBirdTokenizerFast
		UpperCAmelCase__      :				str				    =						True
		UpperCAmelCase__      :				Optional[Any]				    =						True


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

						SCREAMING_SNAKE_CASE_   :	Any													=   self.tokenizer_class(UpperCAmelCase_ ,    keep_accents=UpperCAmelCase_       )
						tokenizer.save_pretrained(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   '<s>'
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   1

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   list(self.get_tokenizer().get_vocab().keys()       )

						self.assertEqual(vocab_keys[0] ,    '<unk>'       )
						self.assertEqual(vocab_keys[1] ,    '<s>'       )
						self.assertEqual(vocab_keys[-1] ,    '[MASK]'       )
						self.assertEqual(len(UpperCAmelCase_       ) ,    1004       )


		def   __snake_case			(						self       ):
						self.assertEqual(self.get_tokenizer().vocab_size ,    1000       )


		def   __snake_case			(						self       ):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer.tokenize(UpperCAmelCase_       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   rust_tokenizer.tokenize(UpperCAmelCase_       )
						self.assertListEqual(UpperCAmelCase_ ,    UpperCAmelCase_       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.encode(UpperCAmelCase_ ,    add_special_tokens=UpperCAmelCase_       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   rust_tokenizer.encode(UpperCAmelCase_ ,    add_special_tokens=UpperCAmelCase_       )
						self.assertListEqual(UpperCAmelCase_ ,    UpperCAmelCase_       )

						SCREAMING_SNAKE_CASE_   :	int													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	str													=   tokenizer.encode(UpperCAmelCase_       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   rust_tokenizer.encode(UpperCAmelCase_       )
						self.assertListEqual(UpperCAmelCase_ ,    UpperCAmelCase_       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   BigBirdTokenizer(UpperCAmelCase_ ,    keep_accents=UpperCAmelCase_       )

						SCREAMING_SNAKE_CASE_   :	List[str]													=   tokenizer.tokenize('This is a test'       )
						self.assertListEqual(UpperCAmelCase_ ,    ['▁This', '▁is', '▁a', '▁t', 'est']       )

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCAmelCase_       ) ,    [285, 46, 10, 170, 382] ,    )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
						self.assertListEqual(
						    UpperCAmelCase_ ,    [
						        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',
						        '.',
						    ] ,    )
						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer.convert_tokens_to_ids(UpperCAmelCase_       )
						self.assertListEqual(
						    UpperCAmelCase_ ,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,    )

						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.convert_ids_to_tokens(UpperCAmelCase_       )
						self.assertListEqual(
						    UpperCAmelCase_ ,    [
						        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   __snake_case			(						self       ):
						return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )


		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   'Hello World!'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   [65, 18536, 2260, 101, 66]

						self.assertListEqual(UpperCAmelCase_ ,    self.big_tokenizer.encode(UpperCAmelCase_       )       )


		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (
						    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
						    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
						)
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	List[str]													=   [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
						# fmt: on
						self.assertListEqual(UpperCAmelCase_ ,    self.big_tokenizer.encode(UpperCAmelCase_       )       )


		@require_torch
		@slow
		def   __snake_case			(						self       ):
						import torch

						from transformers import BigBirdConfig, BigBirdModel

						# Build sequence
						SCREAMING_SNAKE_CASE_   :	int													=   list(self.big_tokenizer.get_vocab().keys()       )[:10]
						SCREAMING_SNAKE_CASE_   :	Any													=   ' '.join(UpperCAmelCase_       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.big_tokenizer.encode_plus(UpperCAmelCase_ ,    return_tensors='pt' ,    return_token_type_ids=UpperCAmelCase_       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.big_tokenizer.batch_encode_plus(
						    [sequence + ' ' + sequence] ,    return_tensors='pt' ,    return_token_type_ids=UpperCAmelCase_       )

						SCREAMING_SNAKE_CASE_   :	int													=   BigBirdConfig(attention_type='original_full'       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   BigBirdModel(UpperCAmelCase_       )

						assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

						with torch.no_grad():
										model(**UpperCAmelCase_       )
										model(**UpperCAmelCase_       )


		@slow
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )
						SCREAMING_SNAKE_CASE_   :	str													=   tokenizer.decode(tokenizer('Paris is the [MASK].'       ).input_ids       )

						self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'       )






		@slow
		def   __snake_case			(						self       ):
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCAmelCase_ ,    model_name='google/bigbird-roberta-base' ,    revision='215c99f1600e06f83acce68422f2035b2b5c3510' ,    )







import argparse
import logging
import os
import re

import tensorflow as tf

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


A        =			logging.getLogger(__name__)

A        =			tf.data.AUTOTUNE



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[Any]													=   argparse.ArgumentParser(description='Train a masked language model on TPU.'      )
				parser.add_argument(
				    '--pretrained_model_config'    ,    type=lowerCAmelCase__    ,    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=lowerCAmelCase__    ,    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=lowerCAmelCase__    ,    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=lowerCAmelCase__    ,    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=lowerCAmelCase__    ,    help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'    ,    )

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

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

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

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

				parser.add_argument(
				    '--max_length'    ,    type=lowerCAmelCase__    ,    default=512    ,    help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'    ,    )

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

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

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   parser.parse_args()
				return args



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				try:
								if args.tpu_name:
												SCREAMING_SNAKE_CASE_   :	Dict													=   tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name    ,    zone=args.tpu_zone    ,    project=args.gcp_project      )
								else:
												SCREAMING_SNAKE_CASE_   :	Any													=   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(lowerCAmelCase__      )
				tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase__      )

				return tpu



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0
				for file in file_list:
								SCREAMING_SNAKE_CASE_   :	Dict													=   file.split('/'      )[-1]
								SCREAMING_SNAKE_CASE_   :	Any													=   re.search(R'-\d+-(\d+)\.tfrecord'    ,    lowerCAmelCase__      ).group(1      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   int(lowerCAmelCase__      )
								num_samples += sample_count

				return num_samples



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[str]=None      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]      ):





				'''simple docstring'''







				if not args.no_tpu:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   initialize_tpu(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	str													=   tf.distribute.TPUStrategy(lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   tf.distribute.OneDeviceStrategy(device='/gpu:0'      )

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

				SCREAMING_SNAKE_CASE_   :	str													=   AutoTokenizer.from_pretrained(args.tokenizer      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   AutoConfig.from_pretrained(args.pretrained_model_config      )
				SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.vocab_size

				SCREAMING_SNAKE_CASE_   :	str													=   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}.'''      )
				SCREAMING_SNAKE_CASE_   :	str													=   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}.'''      )

				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   count_samples(lowerCAmelCase__      )

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

				with strategy.scope():
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   TFAutoModelForMaskedLM.from_config(lowerCAmelCase__      )
								model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   create_optimizer(
								    num_train_steps=lowerCAmelCase__    ,    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=lowerCAmelCase__    ,    metrics=['accuracy']      )

				def decode_fn(lowerCAmelCase__				:		Optional[int]      ):
								SCREAMING_SNAKE_CASE_   :	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(lowerCAmelCase__    ,    lowerCAmelCase__      )

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

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

				SCREAMING_SNAKE_CASE_   :	Any													=   args.per_replica_batch_size * strategy.num_replicas_in_sync

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    shuffle_buffer_size=args.shuffle_buffer_size    ,    )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   prepare_dataset(
				    lowerCAmelCase__    ,    decode_fn=lowerCAmelCase__    ,    mask_fn=lowerCAmelCase__    ,    batch_size=lowerCAmelCase__    ,    shuffle=lowerCAmelCase__    ,    )

				SCREAMING_SNAKE_CASE_   :	str													=   []
				if args.hub_model_id:
								callbacks.append(
								    PushToHubCallback(output_dir=args.output_dir    ,    hub_model_id=args.hub_model_id    ,    tokenizer=lowerCAmelCase__      )      )

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

				model.save_pretrained(args.output_dir      )


if __name__ == "__main__":
				A        =			parse_args()
				main(args)