





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_UpperCAmelCase             =							logging.get_logger(__name__)


_UpperCAmelCase             =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_UpperCAmelCase             =							{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_UpperCAmelCase             =							{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}






class        snake_case_    (		__lowercase						):
  A_      							=   VOCAB_FILES_NAMES
  A_      							=   PRETRAINED_VOCAB_FILES_MAP
  A_      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_      							=   ['input_ids', 'attention_mask']
  A_      							=   BartTokenizer






  def __init__(							self			:					Dict       ,  _snake_case			:					Dict=None       ,  _snake_case			:					Optional[Any]=None       ,  _snake_case			:					int=None       ,  _snake_case			:					Optional[int]="replace"       ,  _snake_case			:					Optional[int]="<s>"       ,  _snake_case			:					str="</s>"       ,  _snake_case			:					Optional[Any]="</s>"       ,  _snake_case			:					List[Any]="<s>"       ,  _snake_case			:					Tuple="<unk>"       ,  _snake_case			:					Any="<pad>"       ,  _snake_case			:					int="<mask>"       ,  _snake_case			:					List[str]=False       ,  _snake_case			:					List[str]=True       ,  **_snake_case			:					Union[str, Any]       ,  )->Tuple:






   '''simple docstring'''

   super().__init__(
       _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE       ,  tokenizer_file=_SCREAMING_SNAKE_CASE       ,  errors=_SCREAMING_SNAKE_CASE       ,  bos_token=_SCREAMING_SNAKE_CASE       ,  eos_token=_SCREAMING_SNAKE_CASE       ,  sep_token=_SCREAMING_SNAKE_CASE       ,  cls_token=_SCREAMING_SNAKE_CASE       ,  unk_token=_SCREAMING_SNAKE_CASE       ,  pad_token=_SCREAMING_SNAKE_CASE       ,  mask_token=_SCREAMING_SNAKE_CASE       ,  add_prefix_space=_SCREAMING_SNAKE_CASE       ,  trim_offsets=_SCREAMING_SNAKE_CASE       ,  **_SCREAMING_SNAKE_CASE       ,  )

   __lowerCAmelCase					:	List[str]      =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""       ,  _SCREAMING_SNAKE_CASE      ) != add_prefix_space:
    __lowerCAmelCase					:	int      =       getattr(_SCREAMING_SNAKE_CASE       ,  pre_tok_state.pop("""type"""      )      )
    __lowerCAmelCase					:	Dict      =       add_prefix_space
    __lowerCAmelCase					:	List[Any]      =       pre_tok_class(**_SCREAMING_SNAKE_CASE      )

   __lowerCAmelCase					:	List[Any]      =       add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   __lowerCAmelCase					:	Optional[Any]      =       '''post_processor'''
   __lowerCAmelCase					:	Tuple      =       getattr(self.backend_tokenizer       ,  _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE      )
   if tokenizer_component_instance:
    __lowerCAmelCase					:	Optional[Any]      =       json.loads(tokenizer_component_instance.__getstate__()      )

    # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
    if "sep" in state:
     __lowerCAmelCase					:	Tuple      =       tuple(state["""sep"""]      )
    if "cls" in state:
     __lowerCAmelCase					:	List[str]      =       tuple(state["""cls"""]      )

    __lowerCAmelCase					:	int      =       False

    if state.get("""add_prefix_space"""       ,  _SCREAMING_SNAKE_CASE      ) != add_prefix_space:
     __lowerCAmelCase					:	Any      =       add_prefix_space
     __lowerCAmelCase					:	Any      =       True

    if state.get("""trim_offsets"""       ,  _SCREAMING_SNAKE_CASE      ) != trim_offsets:
     __lowerCAmelCase					:	Dict      =       trim_offsets
     __lowerCAmelCase					:	Any      =       True

    if changes_to_apply:
     __lowerCAmelCase					:	List[Any]      =       getattr(_SCREAMING_SNAKE_CASE       ,  state.pop("""type"""      )      )
     __lowerCAmelCase					:	Optional[int]      =       component_class(**_SCREAMING_SNAKE_CASE      )
     setattr(self.backend_tokenizer       ,  _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE      )






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->str:






   '''simple docstring'''

   if self._mask_token is None:
    if self.verbose:
     logger.error("""Using mask_token, but it is not set yet."""      )
    return None
   return str(self._mask_token      )






  @mask_token.setter
  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Optional[Any]      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       AddedToken(_SCREAMING_SNAKE_CASE       ,  lstrip=_SCREAMING_SNAKE_CASE       ,  rstrip=_SCREAMING_SNAKE_CASE      ) if isinstance(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE      ) else value
   __lowerCAmelCase					:	List[str]      =       value






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  *_snake_case			:					Optional[int]       ,  **_snake_case			:					Optional[int]      )->BatchEncoding:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       kwargs.get("""is_split_into_words"""       ,  _SCREAMING_SNAKE_CASE      )

   if is_split_into_words and not self.add_prefix_space:
    raise ValueError(
        F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
        """to use it with pretokenized inputs."""      )

   return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE       ,  **_SCREAMING_SNAKE_CASE      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  *_snake_case			:					Dict       ,  **_snake_case			:					Tuple      )->BatchEncoding:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       kwargs.get("""is_split_into_words"""       ,  _SCREAMING_SNAKE_CASE      )

   if is_split_into_words and not self.add_prefix_space:
    raise ValueError(
        F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
        """to use it with pretokenized inputs."""      )

   return super()._encode_plus(*_SCREAMING_SNAKE_CASE       ,  **_SCREAMING_SNAKE_CASE      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					Optional[Any] = None      )->Tuple[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,  name=_SCREAMING_SNAKE_CASE      )
   return tuple(_SCREAMING_SNAKE_CASE      )






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					List[str]       ,  _snake_case			:					List[str]=None      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
    return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Tuple       ,  _snake_case			:					Any = None      )->List[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       [self.sep_token_id]
   __lowerCAmelCase					:	Optional[int]      =       [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 + sep + token_ids_a + sep      ) * [0]






def 	_SCREAMING_SNAKE_CASE		(      )      ->   list[list[int]]:
 return [list(range(1_000 - i   ,   -1_000 - i   ,   -1   )   ) for i in range(1_000   )]


_UpperCAmelCase             =							generate_large_matrix()
_UpperCAmelCase             =							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   None:
 assert all(row == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for row in grid   )
 assert all(list(SCREAMING_SNAKE_CASE   ) == sorted(SCREAMING_SNAKE_CASE   ,   reverse=SCREAMING_SNAKE_CASE   ) for col in zip(*SCREAMING_SNAKE_CASE   )   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[int]   )      ->   int:
 __lowerCAmelCase					:	Optional[int]      =       0
 __lowerCAmelCase					:	Tuple      =       len(SCREAMING_SNAKE_CASE   ) - 1

 # Edge cases such as no values or all numbers are negative.
 if not array or array[0] < 0:
  return 0

 while right + 1 > left:
  __lowerCAmelCase					:	Any      =       (left + right) // 2
  __lowerCAmelCase					:	Optional[int]      =       array[mid]

  # Num must be negative and the index must be greater than or equal to 0.
  if num < 0 and array[mid - 1] >= 0:
   return mid

  if num >= 0:
   __lowerCAmelCase					:	Dict      =       mid + 1
  else:
   __lowerCAmelCase					:	str      =       mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
 return len(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	Any      =       0
 __lowerCAmelCase					:	Union[str, Any]      =       len(grid[0]   )

 for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  __lowerCAmelCase					:	str      =       find_negative_index(grid[i][:bound]   )
  total += bound
 return (len(SCREAMING_SNAKE_CASE   ) * len(grid[0]   )) - total






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 return len([number for row in grid for number in row if number < 0]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int]]   )      ->   int:
 __lowerCAmelCase					:	List[str]      =       0
 for row in grid:
  for i, number in enumerate(SCREAMING_SNAKE_CASE   ):
   if number < 0:
    total += len(SCREAMING_SNAKE_CASE   ) - i
    break
 return total






def 	_SCREAMING_SNAKE_CASE		(      )      ->   None:
 from timeit import timeit

 print("""Running benchmarks"""   )
 __lowerCAmelCase					:	Tuple      =       (
     """from __main__ import count_negatives_binary_search, """
     """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
 )
 for func in (
     "count_negatives_binary_search",  # took 0.7727 seconds
     "count_negatives_brute_force_with_break",  # took 4.6505 seconds
     "count_negatives_brute_force",  # took 12.8160 seconds
 ):
  __lowerCAmelCase					:	Optional[int]      =       timeit(F'''{func}(grid=grid)'''   ,   setup=SCREAMING_SNAKE_CASE   ,   number=500   )
  print(F'''{func}() took {time:0.4f} seconds'''   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()