



'''simple docstring'''






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__:   Union[str, Any]								=			16
UpperCamelCase__:   Any								=			32


def  snake_case_     (					_lowerCAmelCase  :						Accelerator   ,      _lowerCAmelCase  :						int = 16  )      ->   Optional[int]:
							UpperCAmelCase :       Tuple 							=		AutoTokenizer.from_pretrained('''bert-base-cased'''  )
							UpperCAmelCase :       str 							=		load_dataset('''glue'''   ,      '''mrpc'''  )

							def tokenize_function(_lowerCAmelCase  :						str  ):
														# max_length=None => use the model max length (it's actually the default)
														UpperCAmelCase :       Optional[int] 							=		tokenizer(examples['''sentence1''']   ,      examples['''sentence2''']   ,      truncation=_lowerCAmelCase   ,      max_length=_lowerCAmelCase  )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														UpperCAmelCase :       Tuple 							=		datasets.map(
														    _lowerCAmelCase   ,      batched=_lowerCAmelCase   ,      remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,      )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							UpperCAmelCase :       Dict 							=		tokenized_datasets.rename_column('''label'''   ,      '''labels'''  )

							def collate_fn(_lowerCAmelCase  :						List[str]  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														UpperCAmelCase :       Tuple 							=		128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					UpperCAmelCase :       Any 							=		16
														elif accelerator.mixed_precision != "no":
																					UpperCAmelCase :       int 							=		8
														else:
																					UpperCAmelCase :       Union[str, Any] 							=		None

														return tokenizer.pad(
														    _lowerCAmelCase   ,      padding='''longest'''   ,      max_length=_lowerCAmelCase   ,      pad_to_multiple_of=_lowerCAmelCase   ,      return_tensors='''pt'''   ,      )

							# Instantiate dataloaders.
							UpperCAmelCase :       Optional[int] 							=		DataLoader(
							    tokenized_datasets['''train''']   ,      shuffle=_lowerCAmelCase   ,      collate_fn=_lowerCAmelCase   ,      batch_size=_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		DataLoader(
							    tokenized_datasets['''validation''']   ,      shuffle=_lowerCAmelCase   ,      collate_fn=_lowerCAmelCase   ,      batch_size=_lowerCAmelCase  )

							return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						UpperCamelCase__:   Dict								=			mocked_dataloaders  # noqa: F811


def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						int  )      ->   Any:
							# For testing only
							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,      _lowerCAmelCase  ) == "1":
														UpperCAmelCase :       Tuple 							=		2
							# New Code #
							UpperCAmelCase :       Optional[int] 							=		int(args.gradient_accumulation_steps  )
							# Initialize accelerator
							UpperCAmelCase :       Tuple 							=		Accelerator(
							    cpu=args.cpu   ,      mixed_precision=args.mixed_precision   ,      gradient_accumulation_steps=_lowerCAmelCase  )
							if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
														raise NotImplementedError(
														    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCAmelCase :       List[Any] 							=		config['''lr''']
							UpperCAmelCase :       Optional[int] 							=		int(config['''num_epochs''']  )
							UpperCAmelCase :       List[str] 							=		int(config['''seed''']  )
							UpperCAmelCase :       Union[str, Any] 							=		int(config['''batch_size''']  )

							UpperCAmelCase :       Dict 							=		evaluate.load('''glue'''   ,      '''mrpc'''  )

							set_seed(_lowerCAmelCase  )
							UpperCAmelCase							,		UpperCAmelCase :       int 							=		get_dataloaders(_lowerCAmelCase   ,      _lowerCAmelCase  )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							UpperCAmelCase :       List[Any] 							=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,      return_dict=_lowerCAmelCase  )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							UpperCAmelCase :       Optional[Any] 							=		model.to(accelerator.device  )

							# Instantiate optimizer
							UpperCAmelCase :       List[Any] 							=		AdamW(params=model.parameters()   ,      lr=_lowerCAmelCase  )

							# Instantiate scheduler
							UpperCAmelCase :       int 							=		get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase   ,      num_warmup_steps=100   ,      num_training_steps=(len(_lowerCAmelCase  ) * num_epochs)   ,      )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		accelerator.prepare(
							    _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							# Now we train the model
							for epoch in range(_lowerCAmelCase  ):
														model.train()
														for step, batch in enumerate(_lowerCAmelCase  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					# New code #
																					# We use the new `accumulate` context manager to perform gradient accumulation
																					# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																					with accelerator.accumulate(_lowerCAmelCase  ):
																												UpperCAmelCase :       Any 							=		model(**_lowerCAmelCase  )
																												UpperCAmelCase :       Any 							=		output.loss
																												accelerator.backward(_lowerCAmelCase  )
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														for step, batch in enumerate(_lowerCAmelCase  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					with torch.no_grad():
																												UpperCAmelCase :       List[str] 							=		model(**_lowerCAmelCase  )
																					UpperCAmelCase :       int 							=		outputs.logits.argmax(dim=-1  )
																					UpperCAmelCase							,		UpperCAmelCase :       int 							=		accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
																					metric.add_batch(
																					    predictions=_lowerCAmelCase   ,      references=_lowerCAmelCase   ,      )

														UpperCAmelCase :       str 							=		metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""   ,      _lowerCAmelCase  )


def  snake_case_     (					)      ->   Union[str, Any]:
							UpperCAmelCase :       Any 							=		argparse.ArgumentParser(description='''Simple example of training script.'''  )
							parser.add_argument(
							    '''--mixed_precision'''   ,      type=_lowerCAmelCase   ,      default=_lowerCAmelCase   ,      choices=['''no''', '''fp16''', '''bf16''', '''fp8''']   ,      help='''Whether to use mixed precision. Choose'''
							    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
							    '''and an Nvidia Ampere GPU.'''   ,      )
							# New Code #
							parser.add_argument(
							    '''--gradient_accumulation_steps'''   ,      type=_lowerCAmelCase   ,      default=1   ,      help='''The number of minibatches to be ran before gradients are accumulated.'''   ,      )
							parser.add_argument('''--cpu'''   ,      action='''store_true'''   ,      help='''If passed, will train on the CPU.'''  )
							UpperCAmelCase :       Optional[int] 							=		parser.parse_args()
							UpperCAmelCase :       Dict 							=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(_lowerCAmelCase   ,      _lowerCAmelCase  )


if __name__ == "__main__":
						main()










'''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


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "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"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

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

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

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								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 :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

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







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[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      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, 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(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

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







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





