





"""simple docstring"""





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        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			VOCAB_FILES_NAMES
						UpperCAmelCase_ =			PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase_ =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase_ =			[]
						UpperCAmelCase_ =			["input_ids", "attention_mask"]


						def __init__(self   ,							__a   ,							__a="<s>"   ,							__a="</s>"   ,							__a="<unk>"   ,							__a="<pad>"   ,							__a="<::::>"   ,							__a = None   ,							**__a   ,							) ->   None:
								UpperCamelCase   =	{} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCamelCase   =	vocab_file

								UpperCamelCase   =	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(__a		)


						@property
						def     snake_case_   (self		) ->   int:
								return self.sp_model.get_piece_size()


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	{self.convert_ids_to_tokens(__a		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab


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


						def __setstate__(self   ,							__a		) ->   List[str]:
								UpperCamelCase   =	d

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

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


						def     snake_case_   (self   ,							__a		) ->   List[str]:
								return self.sp_model.encode(__a   ,							out_type=__a		)


						def     snake_case_   (self   ,							__a		) ->   int:
								return self.sp_model.piece_to_id(__a		)


						def     snake_case_   (self   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	self.sp_model.IdToPiece(__a		)
								return token


						def     snake_case_   (self   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	[]
								UpperCamelCase   =	""
								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(__a		) + token
												UpperCamelCase   =	[]
										else:
												current_sub_tokens.append(__a		)
								out_string += self.sp_model.decode(__a		)
								return out_string.strip()




						def     snake_case_   (self   ,							__a   ,							__a = None		) ->   Tuple[str]:
								if not os.path.isdir(__a		):
										logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
										return
								UpperCamelCase   =	os.path.join(
								    __a   ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

								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        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

								UpperCamelCase   =	tokenizer("Hello there"   ,							return_tensors="pt"		).input_ids
								UpperCamelCase   =	tokenizer("Hi I am"   ,							return_tensors="pt"		).input_ids

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

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






