
'''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


snake_case_ :  Optional[int]     =					logging.get_logger(__name__)


snake_case_ :  Union[str, Any]     =					'▁'

snake_case_ :  Optional[int]     =					{'vocab_file': 'spiece.model'}

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

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


class 						lowercase__	(  SCREAMING_SNAKE_CASE_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__=[] ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

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

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







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.sp_model.get_piece_size()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {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   ):

      '''simple docstring'''







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







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

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

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.piece_to_id(UpperCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if index < self.sp_model.get_piece_size():
         UpperCamelCase       =  self.sp_model.IdToPiece(UpperCamelCase__   )
      return token







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      UpperCamelCase       =  ''''''
      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
            UpperCamelCase       =  []
         else:
            current_sub_tokens.append(UpperCamelCase__   )
      out_string += self.sp_model.decode(UpperCamelCase__   )
      return out_string.strip()







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

      '''simple docstring'''







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

      return (out_vocab_file,)







'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   if start < end:
      UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
      UpperCamelCase       =  a[end]
      UpperCamelCase       =  a[pivot]
      UpperCamelCase       =  temp

      UpperCamelCase     , UpperCamelCase       =  _in_place_partition(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
      count += _in_place_quick_sort(_UpperCAmelCase,      _UpperCAmelCase,      p - 1)
      count += _in_place_quick_sort(_UpperCAmelCase,      p + 1,      _UpperCAmelCase)
   return count



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  a[pivot]
   UpperCamelCase       =  temp
   UpperCamelCase       =  start - 1
   for index in range(_UpperCAmelCase,      _UpperCAmelCase):
      count += 1
      if a[index] < a[end]:  # check if current val is less than pivot value
         UpperCamelCase       =  new_pivot_index + 1
         UpperCamelCase       =  a[new_pivot_index]
         UpperCamelCase       =  a[index]
         UpperCamelCase       =  temp

   UpperCamelCase       =  a[new_pivot_index + 1]
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  temp
   return new_pivot_index + 1, count


snake_case_ :  List[Any]     =					TemporaryFile()
snake_case_ :  Optional[Any]     =					100  # 1000 elements are to be sorted


snake_case_  , snake_case_ :  List[str]     =					0, 1  # mean and standard deviation
snake_case_ :  Any     =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
snake_case_ :  Optional[int]     =					np.load(outfile)
snake_case_ :  Optional[int]     =					len(M) - 1
snake_case_ :  List[str]     =					_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)





