


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

import sentencepiece as spm

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


lowerCAmelCase       =							logging.get_logger(__name__)

lowerCAmelCase       =							{'vocab_file': 'spiece.model'}

lowerCAmelCase       =							{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCAmelCase       =							{'bert_for_seq_generation': 512}


class        _a							(			UpperCamelCase__			):
							_lowercase	:			List[Any]        = VOCAB_FILES_NAMES
							_lowercase	:			Dict        = PRETRAINED_VOCAB_FILES_MAP
							_lowercase	:			Optional[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase	:			List[int]        = []
							_lowercase	:			str        = ['''input_ids''', '''attention_mask''']

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





									"""simple docstring"""







									lowercase__            =      {} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=UpperCamelCase_    ,						eos_token=UpperCamelCase_    ,						unk_token=UpperCamelCase_    ,						pad_token=UpperCamelCase_    ,						sep_token=UpperCamelCase_    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**UpperCamelCase_    ,						)

									lowercase__            =      vocab_file

									lowercase__            =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(UpperCamelCase_							)

							@property
							def       lowerCamelCase_ (							self:	Any							)			->   str:





									"""simple docstring"""







									return self.sp_model.get_piece_size()

							def       lowerCamelCase_ (							self:	Any							)			->   int:





									"""simple docstring"""







									lowercase__            =      {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:	Any							)			->   Union[str, Any]:





									"""simple docstring"""







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

							def __setstate__(							self:	Dict    ,						UpperCamelCase_:	Union[str, Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      d

									# for backward compatibility
									if not hasattr(self    ,						'''sp_model_kwargs'''							):
											lowercase__            =      {}

									lowercase__            =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)

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





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Tuple							)			->   Dict:





									"""simple docstring"""







									return self.sp_model.piece_to_id(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.sp_model.IdToPiece(UpperCamelCase_							)
									return token

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      []
									lowercase__            =      ''''''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(UpperCamelCase_							) + token
													lowercase__            =      []
											else:
													current_sub_tokens.append(UpperCamelCase_							)
									out_string += self.sp_model.decode(UpperCamelCase_							)
									return out_string.strip()


							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	Optional[str] = None							)			->   Tuple[str]:





									"""simple docstring"""







									if not os.path.isdir(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
											return
									lowercase__            =      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_							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file    ,						UpperCamelCase_							)
									elif not os.path.isfile(self.vocab_file							):
											with open(UpperCamelCase_    ,						'''wb'''							) as fi:
													lowercase__            =      self.sp_model.serialized_model_proto()
													fi.write(UpperCamelCase_							)

									return (out_vocab_file,)




'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'    ,   return_dict=lowerCAmelCase_  ).to(lowerCAmelCase_  )
								_snake_case   						=		AutoTokenizer.from_pretrained('google/mt5-small'  )

								_snake_case   						=		tokenizer('Hello there'    ,   return_tensors='pt'  ).input_ids
								_snake_case   						=		tokenizer('Hi I am'    ,   return_tensors='pt'  ).input_ids

								_snake_case   						=		model(input_ids.to(lowerCAmelCase_  )    ,   labels=labels.to(lowerCAmelCase_  )  ).loss
								_snake_case   						=		-(labels.shape[-1] * loss.item())

								_snake_case   						=		-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 1E-4  )

