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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					:   Dict							=		logging.get_logger(__name__)

_lowerCamelCase					:   Tuple							=		{'''vocab_file''': '''sentencepiece.model'''}

_lowerCamelCase					:   Any							=		{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

_lowerCamelCase					:   List[str]							=		{
    '''google/rembert''': 2_56,
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							VOCAB_FILES_NAMES
							lowercase__						:       Tuple								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       int								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(						self   :					Any	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					List[Any]=False	,							_UpperCamelCase   :					Optional[int]=True	,							_UpperCamelCase   :					List[Any]=True	,							_UpperCamelCase   :					Union[str, Any]="[CLS]"	,							_UpperCamelCase   :					List[Any]="[SEP]"	,							_UpperCamelCase   :					Dict="[UNK]"	,							_UpperCamelCase   :					Dict="[SEP]"	,							_UpperCamelCase   :					Union[str, Any]="[PAD]"	,							_UpperCamelCase   :					Union[str, Any]="[CLS]"	,							_UpperCamelCase   :					Optional[int]="[MASK]"	,							**_UpperCamelCase   :					int	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(
												    do_lower_case=_UpperCamelCase	,							remove_space=_UpperCamelCase	,							keep_accents=_UpperCamelCase	,							bos_token=_UpperCamelCase	,							eos_token=_UpperCamelCase	,							unk_token=_UpperCamelCase	,							sep_token=_UpperCamelCase	,							pad_token=_UpperCamelCase	,							cls_token=_UpperCamelCase	,							mask_token=_UpperCamelCase	,							**_UpperCamelCase	,							)

												SCREAMING_SNAKE_CASE    			=       do_lower_case
												SCREAMING_SNAKE_CASE    			=       remove_space
												SCREAMING_SNAKE_CASE    			=       keep_accents
												SCREAMING_SNAKE_CASE    			=       vocab_file

												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor()
												self.sp_model.Load(_UpperCamelCase      )




							@property
							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												return len(self.sp_model      )




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {self.convert_ids_to_tokens(_UpperCamelCase      ): i for i in range(self.vocab_size      )}
												vocab.update(self.added_tokens_encoder      )
												return vocab




							def __getstate__(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					Optional[Any]	,							_UpperCamelCase   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d
												SCREAMING_SNAKE_CASE    			=       spm.SentencePieceProcessor()
												self.sp_model.Load(self.vocab_file      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Optional[int]=False      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.sp_model.EncodeAsPieces(_UpperCamelCase      )
												return pieces




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[str]      )					->				Any:



												'''simple docstring'''

												return self.sp_model.PieceToId(_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												return self.sp_model.IdToPiece(_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					List[str]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.sp_model.decode_pieces(_UpperCamelCase      )
												return out_string




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [self.cls_token_id]
												if token_ids_a is None:
																	return cls + token_ids_a + sep
												return cls + token_ids_a + sep + token_ids_a + sep




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					List[int]	,							_UpperCamelCase   :					Optional[List[int]] = None	,							_UpperCamelCase   :					bool = False      )					->				List[int]:



												'''simple docstring'''

												if already_has_special_tokens:
																	if token_ids_a is not None:
																						raise ValueError(
																						    "You should not supply a second sequence if the provided sequence of "
																						    "ids is already formatted with special tokens for the model."      )
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

												if token_ids_a is not None:
																	return [1] + ([0] * len(_UpperCamelCase      )) + [1] + ([0] * len(_UpperCamelCase      )) + [1]
												return [1] + ([0] * len(_UpperCamelCase      )) + [1]




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.sep_token_id]
												SCREAMING_SNAKE_CASE    			=       [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      ) * [0] + len(token_ids_a + sep      ) * [1]





							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					str	,							_UpperCamelCase   :					Optional[str] = None      )					->				Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase      ):
																	logger.error("Vocabulary path ({}) should be a directory".format(_UpperCamelCase      )      )
																	return
												SCREAMING_SNAKE_CASE    			=       os.path.join(
												    _UpperCamelCase	,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

												if os.path.abspath(self.vocab_file      ) != os.path.abspath(_UpperCamelCase      ):
																	copyfile(self.vocab_file	,							_UpperCamelCase      )

												return (out_vocab_file,)





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Union[str, Any]      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       inspect.getfile(accelerate.test_utils      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_script.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(
												    mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_distributed_data_loop.py"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "test_ops.py"]      )




							@require_multi_gpu
							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices."      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
												print(F"Command: {cmd}"      )
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )




							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
												with patch_environment(omp_num_threads=1      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )





							@require_multi_gpu
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"      )
												SCREAMING_SNAKE_CASE    			=       ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
												with patch_environment(omp_num_threads=1	,							cuda_visible_devices="0,1"      ):
																	execute_subprocess_async(_UpperCamelCase	,							env=os.environ.copy()      )


if __name__ == "__main__":
							_lowerCamelCase					:   str							=		Accelerator()
							_lowerCamelCase					:   List[str]							=		(accelerator.state.process_index + 2, 10)
							_lowerCamelCase					:   str							=		torch.randint(0, 10, shape).to(accelerator.device)

							_lowerCamelCase					:   Optional[Any]							=		''''''

							_lowerCamelCase					:   str							=		accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							_lowerCamelCase					:   Any							=		accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							_lowerCamelCase					:   int							=		accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)




