





'''simple docstring'''




from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt
def 	UpperCAmelCase_							(					__lowercase							:      Sequence[float]			,     __lowercase							:      int			,     __lowercase							:      int       )		->       tuple[int | None, int | None, float]:


     '''simple docstring'''



     if not arr:
          return None, None, 0
     if low == high:
          return low, high, arr[low]

     _UpperCAmelCase     							=   (low + high) // 2
     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   max_subarray(__lowercase			,     __lowercase			,     __lowercase       )
     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   max_subarray(__lowercase			,     mid + 1			,     __lowercase       )
     _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   max_cross_sum(__lowercase			,     __lowercase			,     __lowercase			,     __lowercase       )
     if left_sum >= right_sum and left_sum >= cross_sum:
          return left_low, left_high, left_sum
     elif right_sum >= left_sum and right_sum >= cross_sum:
          return right_low, right_high, right_sum
     return cross_left, cross_right, cross_sum
def 	UpperCAmelCase_							(					__lowercase							:      Sequence[float]			,     __lowercase							:      int			,     __lowercase							:      int			,     __lowercase							:      int       )		->       tuple[int, int, float]:


     '''simple docstring'''



     _UpperCAmelCase							,  _UpperCAmelCase     							=   float("-inf"       ), -1
     _UpperCAmelCase							,  _UpperCAmelCase     							=   float("-inf"       ), -1

     _UpperCAmelCase     							=   0
     for i in range(__lowercase			,     low - 1			,     -1       ):
          summ += arr[i]
          if summ > left_sum:
               _UpperCAmelCase     							=   summ
               _UpperCAmelCase     							=   i

     _UpperCAmelCase     							=   0
     for i in range(mid + 1			,     high + 1       ):
          summ += arr[i]
          if summ > right_sum:
               _UpperCAmelCase     							=   summ
               _UpperCAmelCase     							=   i

     return max_left, max_right, (left_sum + right_sum)
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       float:


     '''simple docstring'''



     _UpperCAmelCase     							=   [randint(1			,     __lowercase       ) for _ in range(__lowercase       )]
     _UpperCAmelCase     							=   time.time()
     max_subarray(__lowercase			,     0			,     input_size - 1       )
     _UpperCAmelCase     							=   time.time()
     return end - start
def 	UpperCAmelCase_							(					)		->       None:


     '''simple docstring'''



     _UpperCAmelCase     							=   [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
     _UpperCAmelCase     							=   [time_max_subarray(__lowercase       ) for input_size in input_sizes]
     print("No of Inputs\t\tTime Taken"       )
     for input_size, runtime in zip(__lowercase			,     __lowercase       ):
          print(__lowercase			,     "\t\t"			,     __lowercase       )
     plt.plot(__lowercase			,     __lowercase       )
     plt.xlabel("Number of Inputs"       )
     plt.ylabel("Time taken in seconds"       )
     plt.show()


if __name__ == "__main__":
  from doctest import testmod

  testmod()











'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  __SCREAMING_SNAKE_CASE     :List[Any]       =				None

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE     :List[str]       =				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE     :List[Any]       =				{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__SCREAMING_SNAKE_CASE     :Optional[Any]       =				{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

__SCREAMING_SNAKE_CASE     :Optional[int]       =				'''▁'''







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : Any           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Tuple           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : int           =							AlbertTokenizer




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    str=True	,			snake_case_       :    Tuple=False	,			snake_case_       :    List[Any]="[CLS]"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    str="<unk>"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    List[Any]="<pad>"	,			snake_case_       :    List[str]="[CLS]"	,			snake_case_       :    int="[MASK]"	,			**snake_case_       :    Any	,			):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      _UpperCAmelCase     							=   (
          AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_	,			normalized=snake_case_    )
          if isinstance(snake_case_	,			snake_case_    )
          else mask_token
      )

      super().__init__(
          snake_case_	,			tokenizer_file=snake_case_	,			do_lower_case=snake_case_	,			remove_space=snake_case_	,			keep_accents=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			unk_token=snake_case_	,			sep_token=snake_case_	,			pad_token=snake_case_	,			cls_token=snake_case_	,			mask_token=snake_case_	,			**snake_case_	,			)

      _UpperCAmelCase     							=   do_lower_case
      _UpperCAmelCase     							=   remove_space
      _UpperCAmelCase     							=   keep_accents
      _UpperCAmelCase     							=   vocab_file
      _UpperCAmelCase     							=   False if not self.vocab_file else True




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




 def      lowercase	(					self       :    Dict	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
      return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      if not self.can_save_slow_tokenizer:
           raise ValueError(
               "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
               "tokenizer."    )

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

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
           copyfile(self.vocab_file	,			snake_case_    )

      return (out_vocab_file,)




