



def 					__snake_case     (      _UpperCAmelCase    ):

    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''    )

    for cell_n in range(1       ,			len(grid[0]    )    ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __a							=    grid[0]

    for row_n in range(1       ,			len(_UpperCAmelCase    )    ):
        __a							=    grid[row_n]
        __a							=    fill_row(_UpperCAmelCase       ,			_UpperCAmelCase    )
        __a							=    grid[row_n]

    return grid[-1][-1]





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    current_row[0] += row_above[0]
    for cell_n in range(1       ,			len(_UpperCAmelCase    )    ):
        current_row[cell_n] += min(current_row[cell_n - 1]       ,			row_above[cell_n]    )

    return current_row


if __name__ == "__main__":
 import doctest

 doctest.testmod()










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


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


__snake_case						:List[Any]						=					'''▁'''

__snake_case						:List[Any]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Tuple						=					{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case						:List[Any]						=					{
    '''google/reformer-crime-and-punishment''': 52_4288,
}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Dict						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   Optional[int]						 =						['''input_ids''', '''attention_mask''']



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Any=[]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]					,		):


           '''simple docstring'''

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

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

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



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return self.sp_model.get_piece_size()



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

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



       def __getstate__(		self							:					Dict):


           '''simple docstring'''

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



       def __setstate__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    d

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

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



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           if index < self.sp_model.get_piece_size():
               __a							=    self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)
           return token



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    []
           __a							=    ''''''
           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(__SCREAMING_SNAKE_CASE) + token
                   __a							=    []
               else:
                   current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE)
           return out_string.strip()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

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

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

           return (out_vocab_file,)





