


'''simple docstring'''







def 			lowercase				():







       """simple docstring"""







       return [list(range(1_0_0_0 - i   ,						-1_0_0_0 - i   ,						-1					)					) for i in range(1_0_0_0					)]


lowerCAmelCase    :     Union[str, Any]					=						generate_large_matrix()
lowerCAmelCase    :     Any					=						(
    [[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 			lowercase				(_A					):







       """simple docstring"""







       assert all(row == sorted(_A   ,						reverse=_A					) for row in grid					)
       assert all(list(_A					) == sorted(_A   ,						reverse=_A					) for col in zip(*_A					)					)






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       0
       _lowerCAmelCase    :							Dict   			=       len(_A					) - 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    :							int   			=       (left + right) // 2
              _lowerCAmelCase    :							Any   			=       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    :							int   			=       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(_A					)






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       0
       _lowerCAmelCase    :							List[str]   			=       len(grid[0]					)

       for i in range(len(_A					)					):
              _lowerCAmelCase    :							Union[str, Any]   			=       find_negative_index(grid[i][:bound]					)
              total += bound
       return (len(_A					) * len(grid[0]					)) - total






def 			lowercase				(_A					):







       """simple docstring"""







       return len([number for row in grid for number in row if number < 0]					)






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       0
       for row in grid:
              for i, number in enumerate(_A					):
                     if number < 0:
                            total += len(_A					) - i
                            break
       return total






def 			lowercase				():







       """simple docstring"""







       from timeit import timeit

       print('Running benchmarks'					)
       _lowerCAmelCase    :							Optional[Any]   			=       (
           '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    :							Any   			=       timeit(f'{func}(grid=grid)'   ,						setup=_A   ,						number=5_0_0					)
              print(f'{func}() took {time:0.4f} seconds'					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

