


"""simple docstring"""





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


snake_case__						:   Optional[Any]			 = generate_large_matrix()
snake_case__						:   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    _snake_case  (  _snake_case :	list[list[int]]	):
      assert all(row == sorted(_snake_case       ,      reverse=_snake_case	) for row in grid	)
      assert all(list(_snake_case	) == sorted(_snake_case       ,      reverse=_snake_case	) for col in zip(*_snake_case	)	)


def    _snake_case  (  _snake_case :	list[int]	):
      lowerCAmelCase    :		Any				=      0
      lowerCAmelCase    :		Dict				=      len(_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    :		str				=      (left + right) // 2
            lowerCAmelCase    :		Optional[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    :		List[Any]				=      mid + 1
            else:
                  lowerCAmelCase    :		int				=      mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
      return len(_snake_case	)


def    _snake_case  (  _snake_case :	list[list[int]]	):
      lowerCAmelCase    :		Any				=      0
      lowerCAmelCase    :		Tuple				=      len(grid[0]	)

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


def    _snake_case  (  _snake_case :	list[list[int]]	):
      return len([number for row in grid for number in row if number < 0]	)


def    _snake_case  (  _snake_case :	list[list[int]]	):
      lowerCAmelCase    :		Optional[int]				=      0
      for row in grid:
            for i, number in enumerate(_snake_case	):
                  if number < 0:
                        total += len(_snake_case	) - i
                        break
      return total


def    _snake_case  (  ):
      from timeit import timeit

      print('''Running benchmarks'''	)
      lowerCAmelCase    :		Optional[int]				=      (
          '''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=_snake_case       ,      number=500	)
            print(f'''{func}() took {time:0.4f} seconds'''	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()







"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





