



import argparse
import logging
import os
import re

import tensorflow as tf

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


A__  :  Dict								=					logging.getLogger(__name__)

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


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     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=0.1_5     ,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.''' )

		lowerCAmelCase_			:     Any									= parser.parse_args()
		return args


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any] ):
		try:
				if args.tpu_name:
						lowerCAmelCase_			:     List[str]									= tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name     ,zone=args.tpu_zone     ,project=args.gcp_project )
				else:
						lowerCAmelCase_			:     List[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 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Union[str, Any]									= 0
		for file in file_list:
				lowerCAmelCase_			:     Union[str, Any]									= file.split('''/''' )[-1]
				lowerCAmelCase_			:     int									= re.search(R'''-\d+-(\d+)\.tfrecord'''     ,__lowerCAmelCase ).group(1 )
				lowerCAmelCase_			:     Any									= int(__lowerCAmelCase )
				num_samples += sample_count

		return num_samples


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


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		if not args.no_tpu:
				lowerCAmelCase_			:     Dict									= initialize_tpu(__lowerCAmelCase )
				lowerCAmelCase_			:     List[str]									= tf.distribute.TPUStrategy(__lowerCAmelCase )
		else:
				lowerCAmelCase_			:     Tuple									= tf.distribute.OneDeviceStrategy(device='''/gpu:0''' )

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

		lowerCAmelCase_			:     str									= AutoTokenizer.from_pretrained(args.tokenizer )
		lowerCAmelCase_			:     List[str]									= AutoConfig.from_pretrained(args.pretrained_model_config )
		lowerCAmelCase_			:     Union[str, Any]									= tokenizer.vocab_size

		lowerCAmelCase_			:     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}.""" )
		lowerCAmelCase_			:     List[Any]									= 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_			:     int									= count_samples(__lowerCAmelCase )

		lowerCAmelCase_			:     List[Any]									= num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		lowerCAmelCase_			:     Tuple									= steps_per_epoch * args.num_epochs

		with strategy.scope():
				lowerCAmelCase_			:     Dict									= TFAutoModelForMaskedLM.from_config(__lowerCAmelCase )
				model(model.dummy_inputs )  # Pass some dummy inputs through the model to ensure all the weights are built
				lowerCAmelCase_			:     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(__UpperCamelCase							:		Union[str, Any] ):
				lowerCAmelCase_			:     Any									= {
				    "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.
		lowerCAmelCase_			:     List[str]									= DataCollatorForLanguageModeling(
		    tokenizer=__lowerCAmelCase     ,mlm_probability=args.mlm_probability     ,mlm=__lowerCAmelCase     ,return_tensors='''tf''' )

		def mask_with_collator(__UpperCamelCase							:		Optional[int] ):
				# TF really needs an isin() function
				lowerCAmelCase_			:     int									= (
				    ~tf.cast(batch['''attention_mask''']     ,tf.bool )
				    | (batch["input_ids"] == tokenizer.cls_token_id)
				    | (batch["input_ids"] == tokenizer.sep_token_id)
				)
				lowerCAmelCase_			:     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

		lowerCAmelCase_			:     Optional[int]									= args.per_replica_batch_size * strategy.num_replicas_in_sync

		lowerCAmelCase_			:     List[str]									= prepare_dataset(
		    __lowerCAmelCase     ,decode_fn=__lowerCAmelCase     ,mask_fn=__lowerCAmelCase     ,batch_size=__lowerCAmelCase     ,shuffle=__lowerCAmelCase     ,shuffle_buffer_size=args.shuffle_buffer_size     ,)

		lowerCAmelCase_			:     List[Any]									= prepare_dataset(
		    __lowerCAmelCase     ,decode_fn=__lowerCAmelCase     ,mask_fn=__lowerCAmelCase     ,batch_size=__lowerCAmelCase     ,shuffle=__lowerCAmelCase     ,)

		lowerCAmelCase_			:     Tuple									= []
		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__  :  str								=					parse_args()
						main(args)









'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase__					=   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCamelCase__					=   {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Any				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Union[str, Any]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		int				      =   ["input_ids", "attention_mask"]




			def __init__(						self     :   int		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   List[Any]="<s>"		,     lowerCamelCase__     :   Dict="</s>"		,     lowerCamelCase__     :   Union[str, Any]="</s>"		,     lowerCamelCase__     :   List[Any]="<s>"		,     lowerCamelCase__     :   List[str]="<unk>"		,     lowerCamelCase__     :   str="<pad>"		,     lowerCamelCase__     :   Tuple="<mask>"		,     lowerCamelCase__     :   Optional[Dict[str, Any]] = None		,     **lowerCamelCase__     :   Dict		,     )      ->None:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__		,     lowerCamelCase__       ) else mask_token

					_UpperCAmelCase						:				Any	    =					{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=lowerCamelCase__		,     eos_token=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Union[str, Any]	    =					vocab_file
					_UpperCAmelCase						:				Dict	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(str(lowerCamelCase__       )       )

					_UpperCAmelCase						:				Optional[int]	    =					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					_UpperCAmelCase						:				List[str]	    =					len(self.sp_model       ) - 1
					_UpperCAmelCase						:				List[str]	    =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCAmelCase						:				Union[str, Any]	    =					[self.cls_token_id]
					_UpperCAmelCase						:				str	    =					[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None		,     lowerCamelCase__     :   bool = False       )      ->List[int]:



					'''simple docstring'''







					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCamelCase__		,     token_ids_a=lowerCamelCase__		,     already_has_special_tokens=lowerCamelCase__       )

					if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase__       )) + [1]
					return [1] + ([0] * len(lowerCamelCase__       )) + [1, 1] + ([0] * len(lowerCamelCase__       )) + [1]




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[int]	    =					[self.cls_token_id]

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




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					return len(self.sp_model       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					{self.convert_ids_to_tokens(lowerCamelCase__       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   str       )      ->List[str]:



					'''simple docstring'''







					return self.sp_model.encode(lowerCamelCase__		,     out_type=lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[Any]:



					'''simple docstring'''







					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					_UpperCAmelCase						:				Optional[Any]	    =					self.sp_model.PieceToId(lowerCamelCase__       )

					return spm_id if spm_id else self.unk_token_id




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Any       )      ->Optional[Any]:



					'''simple docstring'''







					if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				Optional[Any]	    =					""
					_UpperCAmelCase						:				Optional[int]	    =					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(lowerCamelCase__       ) + token
									_UpperCAmelCase						:				Union[str, Any]	    =					True
									_UpperCAmelCase						:				Union[str, Any]	    =					[]
							else:
									current_sub_tokens.append(lowerCamelCase__       )
									_UpperCAmelCase						:				int	    =					False
					out_string += self.sp_model.decode(lowerCamelCase__       )
					return out_string.strip()




			def __getstate__(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.__dict__.copy()
					_UpperCAmelCase						:				Optional[int]	    =					None
					return state




			def __setstate__(						self     :   Any		,     lowerCamelCase__     :   Dict       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					d

					# for backward compatibility
					if not hasattr(self		,     "sp_model_kwargs"       ):
							_UpperCAmelCase						:				List[Any]	    =					{}

					_UpperCAmelCase						:				Optional[int]	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.vocab_file       )






			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				Union[str, Any]	    =					os.path.join(
					    lowerCamelCase__		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ) and os.path.isfile(self.vocab_file       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )
					elif not os.path.isfile(self.vocab_file       ):
							with open(lowerCamelCase__		,     "wb"       ) as fi:
									_UpperCAmelCase						:				Dict	    =					self.sp_model.serialized_model_proto()
									fi.write(lowerCamelCase__       )

					return (out_vocab_file,)



