

"""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


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

SCREAMING_SNAKE_CASE_					:	int							  =						{"""vocab_file""": """spiece.model"""}

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

SCREAMING_SNAKE_CASE_					:	int							  =						{"""bert_for_seq_generation""": 5_1_2}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase   =					VOCAB_FILES_NAMES
    UpperCAmelCase   =					PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase   =					[]
    UpperCAmelCase   =					["input_ids", "attention_mask"]

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






         """simple docstring"""


         A__										=  {} 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__    ,						)

         A__										=  vocab_file

         A__										=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(UpperCamelCase__       )

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return self.sp_model.get_piece_size()

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






         """simple docstring"""


         A__										=  {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: int       ):






         """simple docstring"""


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

    def __setstate__(     self: Dict    ,						UpperCamelCase: 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 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: Optional[int]       ):






         """simple docstring"""


         return self.sp_model.piece_to_id(UpperCamelCase__       )

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__										=  self.sp_model.IdToPiece(UpperCamelCase__       )
         return token

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: 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(UpperCamelCase__       ) + token
                   A__										=  []
              else:
                   current_sub_tokens.append(UpperCamelCase__       )
         out_string += self.sp_model.decode(UpperCamelCase__       )
         return out_string.strip()





    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: str    ,						UpperCamelCase: Tuple = None       ):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__       ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
              return
         A__										=  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:
                   A__										=  self.sp_model.serialized_model_proto()
                   fi.write(UpperCamelCase__       )

         return (out_vocab_file,)









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  generate_pascal_triangle(UpperCAmelCase_			)
     for row_idx in range(UpperCAmelCase_			):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1			):
               print(end=""" """			)
          # Print row values
          for col_idx in range(row_idx + 1			):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx]					,   end=""" """			)
               else:
                    print(triangle[row_idx][col_idx]					,   end=""""""			)
          print()





def  _snake_case     (    UpperCAmelCase_  :					int			):

     if not isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
          raise TypeError("""The input value of 'num_rows' should be 'int'"""			)

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              """The input value of 'num_rows' should be greater than or equal to 0"""			)

     A__										=  []
     for current_row_idx in range(UpperCAmelCase_			):
          A__										=  populate_current_row(UpperCAmelCase_					,   UpperCAmelCase_			)
          triangle.append(UpperCAmelCase_			)
     return triangle





def  _snake_case     (    UpperCAmelCase_  :					list[list[int]]					,   UpperCAmelCase_  :					int			):
     A__										=  [-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     A__			,				A__										=  1, 1
     for current_col_idx in range(1					,   UpperCAmelCase_			):
          calculate_current_element(
              UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)
     return current_row





def  _snake_case     (    UpperCAmelCase_  :					list[list[int]]					,   UpperCAmelCase_  :					list[int]					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					int					,   ):
     A__										=  triangle[current_row_idx - 1][current_col_idx - 1]
     A__										=  triangle[current_row_idx - 1][current_col_idx]
     A__										=  above_to_left_elt + above_to_right_elt





def  _snake_case     (    UpperCAmelCase_  :					int			):

     if not isinstance(UpperCAmelCase_					,   UpperCAmelCase_			):
          raise TypeError("""The input value of 'num_rows' should be 'int'"""			)

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              """The input value of 'num_rows' should be greater than or equal to 0"""			)

     A__										=  [[1]]

     for row_index in range(1					,   UpperCAmelCase_			):
          A__										=  [0] + result[-1] + [0]
          A__										=  row_index + 1
          # Calculate the number of distinct elements in a row
          A__										=  sum(divmod(UpperCAmelCase_					,   2			)			)
          A__										=  [
              temp_row[i - 1] + temp_row[i] for i in range(1					,   distinct_elements + 1			)
          ]
          A__										=  row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          A__										=  row_first_half + row_second_half
          result.append(UpperCAmelCase_			)

     return result





def  _snake_case     (    ):
     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(UpperCAmelCase_  :					Callable					,   UpperCAmelCase_  :					int			) -> None:
          A__										=  F"""{func.__name__}({value})"""
          A__										=  timeit(F"""__main__.{call}"""					,   setup="""import __main__"""			)
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F"""{call:38} -- {timing:.4f} seconds"""			)

     for value in range(15			):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(UpperCAmelCase_					,   UpperCAmelCase_			)
          print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()






