



'''simple docstring'''






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						str  )      ->   Union[str, Any]:
							for param, grad_param in zip(model_a.parameters()   ,      model_b.parameters()  ):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad   ,      grad_param.grad  ) is False
																					), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad   ,      grad_param.grad  ) is True
																					), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict=True  )      ->   Any:
							model.train()
							UpperCAmelCase :       List[Any] 							=		model(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		F.mse_loss(_lowerCAmelCase   ,      target.to(output.device  )  )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[str]=False  )      ->   str:
							set_seed(42  )
							UpperCAmelCase :       Union[str, Any] 							=		RegressionModel()
							UpperCAmelCase :       Dict 							=		deepcopy(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		RegressionDataset(length=80  )
							UpperCAmelCase :       Tuple 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							model.to(accelerator.device  )
							if sched:
														UpperCAmelCase :       Optional[int] 							=		AdamW(params=model.parameters()   ,      lr=1e-3  )
														UpperCAmelCase :       str 							=		AdamW(params=ddp_model.parameters()   ,      lr=1e-3  )
														UpperCAmelCase :       Union[str, Any] 							=		LambdaLR(_lowerCAmelCase   ,      lr_lambda=lambda _lowerCAmelCase  : epoch**0.6_5  )
														UpperCAmelCase :       int 							=		LambdaLR(_lowerCAmelCase   ,      lr_lambda=lambda _lowerCAmelCase  : epoch**0.6_5  )
							# Make a copy of `model`
							if sched:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		accelerator.prepare(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							else:
														UpperCAmelCase							,		UpperCAmelCase :       int 							=		accelerator.prepare(_lowerCAmelCase   ,      _lowerCAmelCase  )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							# Test when on a single CPU or GPU that the context manager does nothing
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase  )
							# Use a single batch
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		next(iter(_lowerCAmelCase  )  ).values()
							for iteration in range(3  ):
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		input.to(accelerator.device  ), target.to(accelerator.device  )
														# Perform our initial ground truth step in non "DDP"
														step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(_lowerCAmelCase  ):
																												step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														else:
																					# Sync grads
																					step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters()  ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad   ,      ddp_param.grad  ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration  )
														UpperCAmelCase :       str 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   int:
							# Test on distributed setup that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase  )
							# Use a single batch
							UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		next(iter(_lowerCAmelCase  )  ).values()
							for iteration in range(3  ):
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		input.to(accelerator.device  ), target.to(accelerator.device  )
														# Perform our initial ground truth step in non "DDP"
														step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(_lowerCAmelCase  ):
																												step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														else:
																					# Sync grads
																					step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters()  ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad   ,      ddp_param.grad  ) is False
																												), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad   ,      ddp_param.grad  ) is True
																												), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration  )
														UpperCAmelCase :       Optional[Any] 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]


def  snake_case_     (					_lowerCAmelCase  :						List[Any]=False   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   Dict:
							UpperCAmelCase :       Union[str, Any] 							=		Accelerator(
							    split_batches=_lowerCAmelCase   ,      dispatch_batches=_lowerCAmelCase   ,      gradient_accumulation_steps=2  )
							# Test that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		get_training_setup(_lowerCAmelCase  )
							for iteration, batch in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		batch.values()
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		input.to(accelerator.device  ), target.to(accelerator.device  )
														# Perform our initial ground truth step in non "DDP"
														step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(_lowerCAmelCase  ):
																					step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()   ,      ddp_model.parameters()  ):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(_lowerCAmelCase  ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad   ,      ddp_param.grad  ) is True
																												), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad   ,      ddp_param.grad  ) is False
																												), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration  )
														UpperCAmelCase :       List[Any] 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]
							GradientState._reset_state()


def  snake_case_     (					_lowerCAmelCase  :						List[str]=False   ,      _lowerCAmelCase  :						str=False  )      ->   Optional[int]:
							UpperCAmelCase :       Dict 							=		Accelerator(
							    split_batches=_lowerCAmelCase   ,      dispatch_batches=_lowerCAmelCase   ,      gradient_accumulation_steps=2  )
							# Test that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for iteration, batch in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		batch.values()
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       str 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		input.to(accelerator.device  ), target.to(accelerator.device  )
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCAmelCase  )):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes  ):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(_lowerCAmelCase  ):
																					step_model(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
														UpperCAmelCase :       str 							=		(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCAmelCase  ))
														if accelerator.num_processes > 1:
																					check_model_parameters(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration  )
							GradientState._reset_state()


def  snake_case_     (					)      ->   Tuple:
							UpperCAmelCase :       Optional[Any] 							=		Accelerator()

							UpperCAmelCase :       List[str] 							=		RegressionDataset(length=80  )
							UpperCAmelCase :       str 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							UpperCAmelCase :       List[Any] 							=		RegressionDataset(length=96  )
							UpperCAmelCase :       List[Any] 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		accelerator.prepare(_lowerCAmelCase   ,      _lowerCAmelCase  )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(_lowerCAmelCase  ):
														assert id(accelerator.gradient_state.active_dataloader  ) == id(_lowerCAmelCase  )
														if iteration < len(_lowerCAmelCase  ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(_lowerCAmelCase  ):
																																			assert id(accelerator.gradient_state.active_dataloader  ) == id(_lowerCAmelCase  )
																																			if batch_num < len(_lowerCAmelCase  ) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None


def  snake_case_     (					)      ->   Optional[int]:
							UpperCAmelCase :       int 							=		Accelerator()
							UpperCAmelCase :       Any 							=		accelerator.state
							if state.local_process_index == 0:
														print('''**Test `accumulate` gradient accumulation with dataloader break**'''  )
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print('''**Test NOOP `no_sync` context manager**'''  )
														test_noop_sync(_lowerCAmelCase  )
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print('''**Test Distributed `no_sync` context manager**'''  )
														test_distributed_sync(_lowerCAmelCase  )
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    '''**Test `accumulate` gradient accumulation, '''   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
																												test_gradient_accumulation(_lowerCAmelCase   ,      _lowerCAmelCase  )

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version('''<'''   ,      '''2.0'''  ) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''   ,      '''`split_batches=False`, `dispatch_batches=False`**'''   ,      )
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, '''   ,      f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""   ,      )
																																			test_gradient_accumulation_with_opt_and_scheduler(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Tuple:
							# For xla_spawn (TPUs)
							main()


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,)





