



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


SCREAMING_SNAKE_CASE_:List[str] 			=		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:List[Any] 			=		"""▁"""

SCREAMING_SNAKE_CASE_:List[str] 			=		{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_:Dict 			=		{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE_:List[str] 			=		{
    """google/reformer-crime-and-punishment""": 524_288,
}






class SCREAMING_SNAKE_CASE__	(						SCREAMING_SNAKE_CASE_       ):




    '''simple docstring'''






    __lowerCamelCase     :					Tuple   = VOCAB_FILES_NAMES
    __lowerCamelCase     :					Optional[Any]   = PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase     :					Dict   = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase     :					List[str]   = ['input_ids', 'attention_mask']






    def __init__(			self,						lowerCamelCase__,						lowerCamelCase__="</s>",						lowerCamelCase__="<unk>",						lowerCamelCase__=[],						lowerCamelCase__ = None,						**lowerCamelCase__,						):
        A					:			Union[str, Any]		=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=lowerCamelCase__,						unk_token=lowerCamelCase__,						additional_special_tokens=lowerCamelCase__,						sp_model_kwargs=self.sp_model_kwargs,						**lowerCamelCase__,						)

        A					:			Tuple		=  vocab_file
        A					:			Dict		=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(lowerCamelCase__						)






    @property
    def      _lowerCAmelCase   (			self						):
        return self.sp_model.get_piece_size()






    def      _lowerCAmelCase   (			self						):
        A					:			List[Any]		=  {self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab






    def __getstate__(			self						):
        A					:			Any		=  self.__dict__.copy()
        A					:			int		=  None
        return state






    def __setstate__(			self,						lowerCamelCase__						):
        A					:			Union[str, Any]		=  d

        # for backward compatibility
        if not hasattr(self,						"""sp_model_kwargs"""						):
            A					:			List[Any]		=  {}

        A					:			Optional[int]		=  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(self.vocab_file						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        return self.sp_model.encode(lowerCamelCase__,						out_type=lowerCamelCase__						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        return self.sp_model.piece_to_id(lowerCamelCase__						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):


        if index < self.sp_model.get_piece_size():
            A					:			Dict		=  self.sp_model.IdToPiece(lowerCamelCase__						)
        return token






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        A					:			Any		=  []
        A					:			List[str]		=  ''
        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(lowerCamelCase__						) + token
                A					:			Dict		=  []
            else:
                current_sub_tokens.append(lowerCamelCase__						)
        out_string += self.sp_model.decode(lowerCamelCase__						)
        return out_string.strip()






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__ = None						):


        if not os.path.isdir(lowerCamelCase__						):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
            return
        A					:			int		=  os.path.join(
            lowerCamelCase__,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						) and os.path.isfile(self.vocab_file						):
            copyfile(self.vocab_file,						lowerCamelCase__						)
        elif not os.path.isfile(self.vocab_file						):
            with open(lowerCamelCase__,						"""wb"""						) as fi:
                A					:			List[str]		=  self.sp_model.serialized_model_proto()
                fi.write(lowerCamelCase__						)

        return (out_vocab_file,)







def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data