



def 					snake_case__       (		):
			"""simple docstring"""
			return [list(range(1000 - i							,		-1000 - i							,		-1     )     ) for i in range(1000     )]


_lowercase			:			Optional[int]										=						generate_large_matrix()
_lowercase			:			int										=						(
    [[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__       (		__lowerCamelCase    :   Tuple     ):
			"""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 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =0
			lowerCamelCase__		:		str		     =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:
						lowerCamelCase__		:		List[Any]		     =(left + right) // 2
						lowerCamelCase__		:		Tuple		     =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__		:		Tuple		     =mid + 1
						else:
									lowerCamelCase__		:		Tuple		     =mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
			return len(_lowerCAmelCase     )






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]		     =0
			lowerCamelCase__		:		Optional[int]		     =len(grid[0]     )

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






def 					snake_case__       (		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			return len([number for row in grid for number in row if number < 0]     )






def 					snake_case__       (		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =0
			for row in grid:
						for i, number in enumerate(_lowerCAmelCase     ):
									if number < 0:
												total += len(_lowerCAmelCase     ) - i
												break
			return total






def 					snake_case__       (		):
			"""simple docstring"""
			from timeit import timeit

			print('''Running benchmarks'''     )
			lowerCamelCase__		:		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__		:		Tuple		     =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 argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if "model" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''model.'''							,		''''''     )
			if "norm1" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''norm1'''							,		'''attention.output.LayerNorm'''     )
			if "norm2" in orig_key:
						lowerCamelCase__		:		List[Any]		     =orig_key.replace('''norm2'''							,		'''output.LayerNorm'''     )
			if "norm" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''norm'''							,		'''LayerNorm'''     )
			if "transformer" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.split('''.'''     )[0].split('''_'''     )[-1]
						lowerCamelCase__		:		Dict		     =orig_key.replace(f'''transformer_{layer_num}'''							,		f'''encoder.layer.{layer_num}'''     )
			if "mha.attn" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''mha.attn'''							,		'''attention.self'''     )
			if "mha" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''mha'''							,		'''attention'''     )
			if "W_q" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''W_q'''							,		'''self.query'''     )
			if "W_k" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''W_k'''							,		'''self.key'''     )
			if "W_v" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''W_v'''							,		'''self.value'''     )
			if "ff1" in orig_key:
						lowerCamelCase__		:		Dict		     =orig_key.replace('''ff1'''							,		'''intermediate.dense'''     )
			if "ff2" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''ff2'''							,		'''output.dense'''     )
			if "ff" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''ff'''							,		'''output.dense'''     )
			if "mlm_class" in orig_key:
						lowerCamelCase__		:		Tuple		     =orig_key.replace('''mlm.mlm_class'''							,		'''cls.predictions.decoder'''     )
			if "mlm" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''mlm'''							,		'''cls.predictions.transform'''     )
			if "cls" not in orig_key:
						lowerCamelCase__		:		Optional[int]		     ='''yoso.''' + orig_key

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =convert_checkpoint_helper(config.max_position_embeddings							,		__lowerCamelCase     )

			print(model.load_state_dict(__lowerCamelCase     )     )
			model.eval()
			model.save_pretrained(__lowerCamelCase     )

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''     )


if __name__ == "__main__":
		_lowercase			:			int										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




