
'''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


A__   :		Union[str, Any] 		=logging.get_logger(__name__)

A__   :		List[str] 		={'vocab_file': 'spiece.model'}

A__   :		Optional[Any] 		={
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

A__   :		List[str] 		={'bert_for_seq_generation': 512}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =VOCAB_FILES_NAMES
 lowerCamelCase				       =PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase				       =[]
 lowerCamelCase				       =['''input_ids''', '''attention_mask''']
 def __init__(   self			:    Union[str, Any]				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Dict="<s>"				,   lowerCamelCase			:    Tuple="</s>"				,   lowerCamelCase			:    Optional[int]="<unk>"				,   lowerCamelCase			:    Tuple="<pad>"				,   lowerCamelCase			:    List[Any]="<::::>"				,   lowerCamelCase			:    Optional[Dict[str, Any]] = None				,   **lowerCamelCase			:    Union[str, Any]				,   ):



   """simple docstring"""





   __A		:     Optional[Any]												=		{} if sp_model_kwargs is None else sp_model_kwargs

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

   __A		:     int												=		vocab_file

   __A		:     int												=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.Load(lowerCamelCase       )
 @property
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   return self.sp_model.get_piece_size()
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     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			:    List[str]       ):



   """simple docstring"""





   __A		:     str												=		self.__dict__.copy()
   __A		:     Optional[Any]												=		None
   return state
 def __setstate__(   self			:    Any				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     Dict												=		d

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

   __A		:     Union[str, Any]												=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.Load(self.vocab_file       )
 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    str       ):



   """simple docstring"""





   return self.sp_model.encode(lowerCamelCase				,   out_type=lowerCamelCase       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   return self.sp_model.piece_to_id(lowerCamelCase       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    str       ):



   """simple docstring"""





   __A		:     int												=		self.sp_model.IdToPiece(lowerCamelCase       )
   return token
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     str												=		[]
   __A		:     Union[str, Any]												=		""""""
   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		:     Tuple												=		[]
     else:
       current_sub_tokens.append(lowerCamelCase       )
   out_string += self.sp_model.decode(lowerCamelCase       )
   return out_string.strip()






 def 		lowercase_(   self			:    Optional[int]				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[str] = None       ):



   """simple docstring"""





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

   return (out_vocab_file,)





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # 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        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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		:     list[list[int]]												=		[[1]]

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

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



