



'''simple docstring'''




import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
						from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCamelCase							:Tuple       = logging.get_logger(__name__)

lowerCamelCase							:Optional[int]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:int       = {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase							:Tuple       = {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

lowerCamelCase							:str       = '''▁'''







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		str		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ['input_ids', 'attention_mask']


	def __init__(self	,				lowercase	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase=100	,				lowercase=None	,				lowercase = None	,				lowercase=True	,				**lowercase	,				):
				# Add extra_ids to the special token list
				if extra_ids > 0 and additional_special_tokens is None:
							A_     :						Any     =					[F'<extra_id_{i}>' for i in range(lowercase							)]
				elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							A_     :						Tuple     =					len(set(filter(lambda lowercase							: bool("""extra_id""" in str(lowercase							)							)	,				lowercase							)							)							)
							if extra_tokens != extra_ids:
										raise ValueError(
										    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
										    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
										    """ tokens"""							)
				if legacy:
							logger.warning_once(
							    F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
							    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""							)

				A_     :						str     =					legacy
				A_     :						Dict     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=lowercase	,				unk_token=lowercase	,				pad_token=lowercase	,				extra_ids=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				legacy=lowercase	,				**lowercase	,				)

				A_     :						List[str]     =					vocab_file
				A_     :						Tuple     =					extra_ids

				A_     :						Tuple     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)


	@staticmethod
	def    _a     (lowercase	,				lowercase	,				lowercase							):
				if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
							A_     :						Union[str, Any]     =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
							if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
							elif init_max_model_length is None:
										warnings.warn(
										    """This tokenizer was incorrectly instantiated with a model max length of"""
										    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
										    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
										    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
										    F' {pretrained_model_name_or_path} automatically truncating your input to'
										    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
										    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
										    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
										    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""	,				lowercase	,				)

				return max_model_length


	@property
	def    _a     (self							):
				return self.sp_model.get_piece_size() + self._extra_ids


	def    _a     (self							):
				A_     :						Union[str, Any]     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				# normal case: some special tokens
				if token_ids_a is None:
							return ([0] * len(lowercase							)) + [1]
				return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1]


	def    _a     (self							):
				return list(
				    set(filter(lambda lowercase							: bool(re.search(R"""<extra_id_\d+>"""	,				lowercase							)							) is not None	,				self.additional_special_tokens							)							)							)


	def    _a     (self							):
				return [self._convert_token_to_id(lowercase							) for token in self.get_sentinel_tokens()]


	def    _a     (self	,				lowercase							):
				if len(lowercase							) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
							    """ eos tokens being added."""							)
							return token_ids
				else:
							return token_ids + [self.eos_token_id]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Dict     =					[self.eos_token_id]

				if token_ids_a is None:
							return len(token_ids_a + eos							) * [0]
				return len(token_ids_a + eos + token_ids_a + eos							) * [0]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					self._add_eos_if_not_present(lowercase							)
				if token_ids_a is None:
							return token_ids_a
				else:
							A_     :						List[Any]     =					self._add_eos_if_not_present(lowercase							)
							return token_ids_a + token_ids_a


	def __getstate__(self							):
				A_     :						int     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						Dict     =					{}

				A_     :						Any     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
				# the beginning of the text
				if not self.legacy:
							A_     :						Tuple     =					SPIECE_UNDERLINE + text.replace(lowercase	,				""" """							)
				return super().tokenize(lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				**lowercase							):
				if not self.legacy:
							A_     :						Dict     =					text.startswith(lowercase							)
							if is_first:
										A_     :						str     =					text[1:]

				A_     :						Optional[int]     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)

				if not self.legacy and not is_first and not text.startswith(""" """							) and tokens[0].startswith(lowercase							):
							A_     :						Optional[int]     =					([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
				return tokens


	def    _a     (self	,				lowercase							):
				if token.startswith("""<extra_id_"""							):
							A_     :						Union[str, Any]     =					re.match(R"""<extra_id_(\d+)>"""	,				lowercase							)
							A_     :						str     =					int(match.group(1							)							)
							return self.vocab_size - num - 1
				return self.sp_model.piece_to_id(lowercase							)


	def    _a     (self	,				lowercase							):
				if index < self.sp_model.get_piece_size():
							A_     :						List[Any]     =					self.sp_model.IdToPiece(lowercase							)
				else:
							A_     :						Dict     =					F'<extra_id_{self.vocab_size - 1 - index}>'
				return token


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					[]
				A_     :						int     =					""""""
				A_     :						Any     =					False
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										if not prev_is_special:
													out_string += " "
										out_string += self.sp_model.decode(lowercase							) + token
										A_     :						Dict     =					True
										A_     :						Union[str, Any]     =					[]
							else:
										current_sub_tokens.append(lowercase							)
										A_     :						Optional[Any]     =					False
				out_string += self.sp_model.decode(lowercase							)
				return out_string.strip()


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Optional[Any]     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file	,				lowercase							)
				elif not os.path.isfile(self.vocab_file							):
							with open(lowercase	,				"""wb"""							) as fi:
										A_     :						Union[str, Any]     =					self.sp_model.serialized_model_proto()
										fi.write(lowercase							)

				return (out_vocab_file,)




'''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							:Optional[int]       = logging.getLogger(__name__)

lowerCamelCase							:Optional[int]       = tf.data.AUTOTUNE



def      a      (			):
			'''simple docstring'''





			A_     :						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=5_12						,				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."""   )

			A_     :						Dict     =					parser.parse_args()
			return args



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			try:
						if args.tpu_name:
									A_     :						Optional[Any]     =					tf.distribute.cluster_resolver.TPUClusterResolver(
									    args.tpu_name						,				zone=args.tpu_zone						,				project=args.gcp_project   )
						else:
									A_     :						Union[str, 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      a      (			lowerCamelCase__   ):
			'''simple docstring'''





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

			return num_samples



def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__=None   ):
			'''simple docstring'''





			A_     :						List[Any]     =					count_samples(lowerCamelCase__   )
			A_     :						List[str]     =					tf.data.Dataset.from_tensor_slices(lowerCamelCase__   )
			if shuffle:
						A_     :						Optional[int]     =					dataset.shuffle(len(lowerCamelCase__   )   )
			A_     :						Tuple     =					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
			A_     :						Union[str, Any]     =					dataset.apply(tf.data.experimental.assert_cardinality(lowerCamelCase__   )   )
			A_     :						Any     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			if shuffle:
						assert shuffle_buffer_size is not None
						A_     :						List[str]     =					dataset.shuffle(args.shuffle_buffer_size   )
			A_     :						str     =					dataset.batch(lowerCamelCase__						,				drop_remainder=lowerCamelCase__   )
			A_     :						str     =					dataset.map(lowerCamelCase__						,				num_parallel_calls=lowerCamelCase__   )
			A_     :						Optional[int]     =					dataset.prefetch(lowerCamelCase__   )
			return dataset



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if not args.no_tpu:
						A_     :						Dict     =					initialize_tpu(lowerCamelCase__   )
						A_     :						str     =					tf.distribute.TPUStrategy(lowerCamelCase__   )
			else:
						A_     :						Union[str, Any]     =					tf.distribute.OneDeviceStrategy(device="""/gpu:0"""   )

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

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

			A_     :						Dict     =					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}.'   )
			A_     :						Optional[int]     =					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}.'   )

			A_     :						Dict     =					count_samples(lowerCamelCase__   )

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

			with strategy.scope():
						A_     :						Optional[int]     =					TFAutoModelForMaskedLM.from_config(lowerCamelCase__   )
						model(model.dummy_inputs   )  # Pass some dummy inputs through the model to ensure all the weights are built
						A_,   A_     :						Any     =					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__   ):
						A_     :						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(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.
			A_     :						Optional[int]     =					DataCollatorForLanguageModeling(
			    tokenizer=lowerCamelCase__						,				mlm_probability=args.mlm_probability						,				mlm=lowerCamelCase__						,				return_tensors="""tf"""   )

			def mask_with_collator(lowerCamelCase__   ):
						# TF really needs an isin() function
						A_     :						str     =					(
						    ~tf.cast(batch["""attention_mask"""]						,				tf.bool   )
						    | (batch["""input_ids"""] == tokenizer.cls_token_id)
						    | (batch["""input_ids"""] == tokenizer.sep_token_id)
						)
						A_,   A_     :						Union[str, Any]     =					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

			A_     :						str     =					args.per_replica_batch_size * strategy.num_replicas_in_sync

			A_     :						Tuple     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				shuffle_buffer_size=args.shuffle_buffer_size						,				)

			A_     :						Optional[Any]     =					prepare_dataset(
			    lowerCamelCase__						,				decode_fn=lowerCamelCase__						,				mask_fn=lowerCamelCase__						,				batch_size=lowerCamelCase__						,				shuffle=lowerCamelCase__						,				)

			A_     :						Union[str, Any]     =					[]
			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__":
						lowerCamelCase							:Tuple       = parse_args()
						main(args)