'''simple docstring'''

def __a (	)	->							list[list[int]]:
      '''simple docstring'''


      return [list(range(1_000 - i ,		-1_000 - i ,		-1				)				) for i in range(1_000				)]


lowerCAmelCase_ : Optional[Any]						  =      generate_large_matrix()
lowerCAmelCase_ : List[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 __a (	__lowerCamelCase			:						list[list[int]]				)	->							None:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						list[int]				)	->							int:
      '''simple docstring'''


      lowercase_     =      0
      lowercase_     =      len(__lowerCamelCase				) - 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(__lowerCamelCase				)


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


      lowercase_     =      0
      lowercase_     =      len(grid[0]				)

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


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							int:
      '''simple docstring'''


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


def __a (	)	->							None:
      '''simple docstring'''


      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=__lowerCamelCase ,		number=500				)
            print(f'{func}() took {time:0.4f} seconds'				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






