






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

A        =			25_6047
A        =			25_6145


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





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[int]				    =						NllbTokenizer
		UpperCAmelCase__      :				Optional[Any]				    =						NllbTokenizerFast
		UpperCAmelCase__      :				Dict				    =						True
		UpperCAmelCase__      :				int				    =						True
		UpperCAmelCase__      :				Dict				    =						{}


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

						# We have a SentencePiece fixture for testing
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   NllbTokenizer(UpperCamelCase__ ,    keep_accents=UpperCamelCase__       )
						tokenizer.save_pretrained(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   NllbTokenizer(UpperCamelCase__ ,    keep_accents=UpperCamelCase__       )

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

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCamelCase__       ) ,    [value + tokenizer.fairseq_offset for value in [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_   :	Union[str, Any]													=   tokenizer.convert_tokens_to_ids(UpperCamelCase__       )
						self.assertListEqual(
						    UpperCamelCase__ ,    [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ] ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[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>',
						        '.',
						    ] ,    )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
														SCREAMING_SNAKE_CASE_   :	str													=   self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ ,    **UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.tokenizer_class.from_pretrained(UpperCamelCase__ ,    **UpperCamelCase__       )

														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tokenizer_r.save_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   tokenizer_p.save_pretrained(UpperCamelCase__       )

														# 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       )       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f       )
														self.assertSequenceEqual(UpperCamelCase__ ,    UpperCamelCase__       )

														# Checks everything loads correctly in the same way
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   tokenizer_r.from_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer_p.from_pretrained(UpperCamelCase__       )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(UpperCamelCase__ ,    UpperCamelCase__       )       )

														shutil.rmtree(UpperCamelCase__       )

														# Save tokenizer rust, legacy_format=True
														SCREAMING_SNAKE_CASE_   :	int													=   tempfile.mkdtemp()

														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer_r.save_pretrained(UpperCamelCase__ ,    legacy_format=UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   tokenizer_p.save_pretrained(UpperCamelCase__       )

														# Checks it save with the same files
														self.assertSequenceEqual(UpperCamelCase__ ,    UpperCamelCase__       )

														# Checks everything loads correctly in the same way
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   tokenizer_r.from_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer_p.from_pretrained(UpperCamelCase__       )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(UpperCamelCase__ ,    UpperCamelCase__       )       )

														shutil.rmtree(UpperCamelCase__       )

														# Save tokenizer rust, legacy_format=False
														SCREAMING_SNAKE_CASE_   :	Dict													=   tempfile.mkdtemp()

														SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer_r.save_pretrained(UpperCamelCase__ ,    legacy_format=UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	str													=   tokenizer_p.save_pretrained(UpperCamelCase__       )

														# 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
														SCREAMING_SNAKE_CASE_   :	str													=   tokenizer_r.from_pretrained(UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   tokenizer_p.from_pretrained(UpperCamelCase__       )

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(UpperCamelCase__ ,    UpperCamelCase__       )       )

														shutil.rmtree(UpperCamelCase__       )


		@require_torch
		def   __snake_case			(						self       ):
						if not self.test_seqaseq:
										return

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.get_tokenizers()
						for tokenizer in tokenizers:
										with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
														# Longer text that will definitely require truncation.
														SCREAMING_SNAKE_CASE_   :	List[str]													=   [
														    ' 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.',
														]
														SCREAMING_SNAKE_CASE_   :	int													=   [
														    '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.',
														]
														try:
																		SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   tokenizer.prepare_seqaseq_batch(
																		    src_texts=UpperCamelCase__ ,    tgt_texts=UpperCamelCase__ ,    max_length=3 ,    max_target_length=10 ,    return_tensors='pt' ,    src_lang='eng_Latn' ,    tgt_lang='ron_Latn' ,    )
														except NotImplementedError:
																		return
														self.assertEqual(batch.input_ids.shape[1] ,    3       )
														self.assertEqual(batch.labels.shape[1] ,    10       )
														# max_target_length will default to max_length if not specified
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tokenizer.prepare_seqaseq_batch(
														    UpperCamelCase__ ,    tgt_texts=UpperCamelCase__ ,    max_length=3 ,    return_tensors='pt'       )
														self.assertEqual(batch.input_ids.shape[1] ,    3       )
														self.assertEqual(batch.labels.shape[1] ,    3       )

														SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.prepare_seqaseq_batch(
														    src_texts=UpperCamelCase__ ,    max_length=3 ,    max_target_length=10 ,    return_tensors='pt'       )
														self.assertEqual(batch_encoder_only.input_ids.shape[1] ,    3       )
														self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,    3       )
														self.assertNotIn('decoder_input_ids' ,    UpperCamelCase__       )


		@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'       )
		def   __snake_case			(						self       ):
						pass






		def   __snake_case			(						self       ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
														SCREAMING_SNAKE_CASE_   :	str													=   [AddedToken('<special>' ,    lstrip=UpperCamelCase__       )]

														SCREAMING_SNAKE_CASE_   :	Tuple													=   self.rust_tokenizer_class.from_pretrained(
														    UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    **UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer_r.encode('Hey this is a <special> token'       )

														SCREAMING_SNAKE_CASE_   :	int													=   tokenizer_r.encode('<special>' ,    add_special_tokens=UpperCamelCase__       )[0]

														self.assertTrue(special_token_id in r_output       )

														if self.test_slow_tokenizer:
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.rust_tokenizer_class.from_pretrained(
																		    UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    **UpperCamelCase__ ,    )
																		SCREAMING_SNAKE_CASE_   :	int													=   self.tokenizer_class.from_pretrained(
																		    UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    **UpperCamelCase__       )

																		SCREAMING_SNAKE_CASE_   :	Dict													=   tokenizer_p.encode('Hey this is a <special> token'       )

																		SCREAMING_SNAKE_CASE_   :	str													=   tokenizer_cr.encode('Hey this is a <special> token'       )

																		self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )
																		self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )
																		self.assertTrue(special_token_id in p_output       )
																		self.assertTrue(special_token_id in cr_output       )






@require_torch
@require_sentencepiece
@require_tokenizers
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		UpperCAmelCase__      :				List[Any]				    =						"""facebook/nllb-200-distilled-600M"""
		UpperCAmelCase__      :				Optional[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.""",
		]
		UpperCAmelCase__      :				str				    =						[
		    """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.""",
		]
		UpperCAmelCase__      :				Dict				    =						[
		    256047,
		    16297,
		    134408,
		    8165,
		    248066,
		    14734,
		    950,
		    1135,
		    105721,
		    3573,
		    83,
		    27352,
		    108,
		    49486,
		    2,
		]


		@classmethod
		def   __snake_case			(						cls       ):
						SCREAMING_SNAKE_CASE_   :	NllbTokenizer													=   NllbTokenizer.from_pretrained(
						    cls.checkpoint_name ,    src_lang='eng_Latn' ,    tgt_lang='ron_Latn'       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   1
						return cls


		def   __snake_case			(						self       ):
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] ,    256001       )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] ,    256002       )
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] ,    256057       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
						self.assertListEqual(self.expected_src_tokens ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						self.assertIn(UpperCamelCase__ ,    self.tokenizer.all_special_ids       )
						# fmt: off
						SCREAMING_SNAKE_CASE_   :	str													=   [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
						# fmt: on

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer.decode(UpperCamelCase__ ,    skip_special_tokens=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   self.tokenizer.decode(generated_ids[1:] ,    skip_special_tokens=UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )
						self.assertNotIn(self.tokenizer.eos_token ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   ['this is gunna be a long sentence ' * 20]
						assert isinstance(src_text[0] ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   10
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer(UpperCamelCase__ ,    max_length=UpperCamelCase__ ,    truncation=UpperCamelCase__       ).input_ids[0]
						self.assertEqual(ids[-1] ,    2       )
						self.assertEqual(ids[0] ,    UpperCamelCase__       )
						self.assertEqual(len(UpperCamelCase__       ) ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']       ) ,    [256203, 3]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   tempfile.mkdtemp()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   NllbTokenizer.from_pretrained(UpperCamelCase__       )
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,    UpperCamelCase__       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.tokenizer(
						    self.src_text ,    text_target=self.tgt_text ,    padding=UpperCamelCase__ ,    truncation=UpperCamelCase__ ,    max_length=len(self.expected_src_tokens       ) ,    return_tensors='pt' ,    )
						SCREAMING_SNAKE_CASE_   :	int													=   shift_tokens_right(
						    batch['labels'] ,    self.tokenizer.pad_token_id ,    self.tokenizer.lang_code_to_id['ron_Latn']       )

						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )

						self.assertEqual((2, 15) ,    batch.input_ids.shape       )
						self.assertEqual((2, 15) ,    batch.attention_mask.shape       )
						SCREAMING_SNAKE_CASE_   :	int													=   batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens ,    UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    batch.decoder_input_ids[0, 0]       )  # EOS
						# 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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.tokenizer(self.src_text ,    padding=UpperCamelCase__ ,    truncation=UpperCamelCase__ ,    max_length=3 ,    return_tensors='pt'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer(
						    text_target=self.tgt_text ,    padding=UpperCamelCase__ ,    truncation=UpperCamelCase__ ,    max_length=10 ,    return_tensors='pt'       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   targets['input_ids']
						SCREAMING_SNAKE_CASE_   :	int													=   shift_tokens_right(
						    UpperCamelCase__ ,    self.tokenizer.pad_token_id ,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,    )

						self.assertEqual(batch.input_ids.shape[1] ,    3       )
						self.assertEqual(batch.decoder_input_ids.shape[1] ,    10       )


		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.tokenizer._build_translation_inputs(
						    'A test' ,    return_tensors='pt' ,    src_lang='eng_Latn' ,    tgt_lang='fra_Latn'       )

						self.assertEqual(
						    nested_simplify(UpperCamelCase__       ) ,    {
						        # A, test, EOS, en_XX
						        'input_ids': [[256047, 70, 7356, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 256057,
						    } ,    )






		@require_torch
		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   True
						SCREAMING_SNAKE_CASE_   :	Any													=   self.tokenizer(
						    'UN Chief says there is no military solution in Syria' ,    src_lang='eng_Latn' ,    tgt_lang='fra_Latn'       )
						self.assertEqual(
						    inputs.input_ids ,    [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]       )

						SCREAMING_SNAKE_CASE_   :	Any													=   False
						SCREAMING_SNAKE_CASE_   :	Any													=   self.tokenizer(
						    'UN Chief says there is no military solution in Syria' ,    src_lang='eng_Latn' ,    tgt_lang='fra_Latn'       )
						self.assertEqual(
						    inputs.input_ids ,    [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]       )







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)