





def       __UpperCamelCase  ()    ->       list[list[int]]:
  return [list(range(1000 - i		,						-1000 - i		,						-1		)		) for i in range(1000		)]


lowercase_          =		generate_large_matrix()
lowercase_          =		(
    [[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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       int:
  lowercase__           =  0
  lowercase__           =  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:
    lowercase__           =  (left + right) // 2
    lowercase__           =  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:
      lowercase__           =  mid + 1
    else:
      lowercase__           =  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
  return len(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       int:
  lowercase__           =  0
  lowercase__           =  len(grid[0]		)

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




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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       int:
  lowercase__           =  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       __UpperCamelCase  ()    ->       None:
  from timeit import timeit

  print('Running benchmarks'		)
  lowercase__           =  (
      '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
  ):
    lowercase__           =  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()









import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase_          =		logging.get_logger(__name__)
lowercase_          =		{"""vocab_file""": """spiece.model"""}

lowercase_          =		{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

lowercase_          =		{
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		List[str]			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		List[Any]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Union[str, Any]			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Any			 =	['input_ids', 'attention_mask']


   def __init__(   self      :    Optional[Any]      ,					a      :    Tuple      ,					a      :    Optional[int]=False      ,					a      :    str=False      ,					a      :    str=False      ,					a      :    Tuple=None      ,					a      :    Any=None      ,					a      :    Union[str, Any]=None      ,					a      :    Union[str, Any]=None      ,					a      :    Optional[Dict[str, Any]] = None      ,					**a      :    Optional[int]      ,					)->       None:





     """simple docstring"""







     lowercase__           =  {} if sp_model_kwargs is None else sp_model_kwargs

     lowercase__           =  kwargs.get('name_or_path'	)
     if name_or_path is None:
       logger.warning(
           'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
           ' you are testing the model, this can safely be ignored'	)
       lowercase__           =  'None'

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     lowercase__           =  '<|endoftext|>' if eos_token is None else eos_token
     lowercase__           =  '<unk>' if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
       lowercase__           =  unk_token if pad_token is None else pad_token
       lowercase__           =  eos_token if bos_token is None else bos_token
     else:
       lowercase__           =  '<pad>' if pad_token is None else pad_token
       lowercase__           =  '<s>' if bos_token is None else bos_token

     super().__init__(
         do_lower_case=a      ,					remove_space=a      ,					keep_accents=a      ,					bos_token=a      ,					eos_token=a      ,					unk_token=a      ,					pad_token=a      ,					sp_model_kwargs=self.sp_model_kwargs      ,					**a      ,					)

     lowercase__           =  do_lower_case
     lowercase__           =  remove_space
     lowercase__           =  keep_accents
     lowercase__           =  vocab_file

     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(a	)

     # Used for whitespace normalization in input texts
     # fmt : off
     lowercase__           =  {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     lowercase__           =  re.compile(
         f"""[{"".join(map(a      ,					list(range(0      ,					9	)	) + list(range(11      ,					32	)	) + list(range(127      ,					160	)	) + [160, 173, 8_203]	)	)}]"""	)


   def __getstate__(   self      :    Any	)->       str:





     """simple docstring"""







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


   def __setstate__(   self      :    int      ,					a      :    Optional[Any]	)->       int:





     """simple docstring"""







     lowercase__           =  d

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

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


   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       int:





     """simple docstring"""







     return len(self.sp_model	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    str	)->       str:





     """simple docstring"""







     lowercase__           =  self.non_printing_characters_re.sub(''      ,					a	)

     # Normalize whitespaces
     lowercase__           =  ''.join([char if char not in self.whitespaces else ' ' for char in text]	)

     # NFC Unicode normalization
     lowercase__           =  unicodedata.normalize('NFC'      ,					a	)
     return text


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					**a      :    Tuple	)->       List[str]:





     """simple docstring"""







     lowercase__           =  self.preprocess_text(a	)
     return self.sp_model.encode(a      ,					out_type=a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str	)->       int:





     """simple docstring"""







     return self.sp_model.PieceToId(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       str:





     """simple docstring"""







     return self.sp_model.IdToPiece(a	)


   @staticmethod
   def 	SCREAMING_SNAKE_CASE_							(   a      :    str	)->       str:





     """simple docstring"""







     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]	)->       str:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ''
     lowercase__           =  False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
         if not prev_is_special:
           out_string += " "

         out_string += self.sp_model.decode(a	) + token
         lowercase__           =  True
         lowercase__           =  []
       else:
         current_sub_tokens.append(a	)
         lowercase__           =  False
     out_string += self.sp_model.decode(a	)

     return out_string


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, int]:





     """simple docstring"""







     lowercase__           =  {self.convert_ids_to_tokens(a	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







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

     if os.path.abspath(self.vocab_file	) != os.path.abspath(a	) and os.path.isfile(self.vocab_file	):
       copyfile(self.vocab_file      ,					a	)
     elif not os.path.isfile(self.vocab_file	):
       with open(a      ,					'wb'	) as fi:
         lowercase__           =  self.sp_model.serialized_model_proto()
         fi.write(a	)

     return (out_vocab_file,)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Union[str, List[str]]      ,					a      :    Union[str, bool] = False	)->       Union[List[int], List[List[int]], "torch.Tensor"]:





     """simple docstring"""







     if isinstance(a      ,					a	):
       lowercase__           =  self.preprocess_text(a	)
       lowercase__           =  self.sp_model.encode(a	)
     else:
       lowercase__           =  [self.preprocess_text(a	) for t in text]
       lowercase__           =  self.sp_model.encode(a	)

     if return_tensors is True or return_tensors == "pt":
       lowercase__           =  torch.tensor(a	)

     return token_ids


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[int, List[int]]	)->       str:





     """simple docstring"""







     return self.sp_model.decode(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    "Conversation"	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
     lowercase__           =  (
         f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a	) + f"""{self.bos_token}Bot:"""
     )
     return self.encode(text=a	)


