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


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[int]									=     {'vocab_file': 'sentencepiece.model'}

__UpperCamelCase		:       Optional[Any]									=     {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

__UpperCamelCase		:       Optional[int]									=     {
    'google/rembert': 256,
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


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





												'''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							:    Optional[Any]							=							do_lower_case
												SCREAMING_SNAKE_CASE							:    Any							=							remove_space
												SCREAMING_SNAKE_CASE							:    Dict							=							keep_accents
												SCREAMING_SNAKE_CASE							:    Dict							=							vocab_file

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							spm.SentencePieceProcessor()
												self.sp_model.Load(UpperCamelCase__						)


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												return len(self.sp_model						)


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							{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	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.__dict__.copy()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None
												return state


						def __setstate__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							d
												SCREAMING_SNAKE_CASE							:    str							=							spm.SentencePieceProcessor()
												self.sp_model.Load(self.vocab_file						)


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				Union[str, Any]=False						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.sp_model.EncodeAsPieces(UpperCamelCase__						)
												return pieces


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												return self.sp_model.PieceToId(UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												return self.sp_model.IdToPiece(UpperCamelCase__						)


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.sp_model.decode_pieces(UpperCamelCase__						)
												return out_string


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:    List[str]							=							[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        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None    ,		UpperCamelCase__	:				bool = False						):





												'''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        __A       (       self	:				int    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None						):





												'''simple docstring'''





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



						def        __A       (       self	:				List[str]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[str] = None						):





												'''simple docstring'''





												if not os.path.isdir(UpperCamelCase__						):
																		logger.error('''Vocabulary path ({}) should be a directory'''.format(UpperCamelCase__						)						)
																		return
												SCREAMING_SNAKE_CASE							:    Tuple							=							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 pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





