



class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Optional[Any]					)-> List[str]:
								lowerCamelCase__		:		int		     =""""""
								lowerCamelCase__		:		str		     =""""""
								lowerCamelCase__		:		Dict		     =[]



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  int,  lowerCamelCase							:  int					)-> Optional[int]:
								if m == -1:
											return n + 1
								elif n == -1:
											return m + 1
								elif self.dp[m][n] > -1:
											return self.dp[m][n]
								else:
											if self.worda[m] == self.worda[n]:
														lowerCamelCase__		:		int		     =self.__min_dist_top_down_dp(m - 1,  n - 1					)
											else:
														lowerCamelCase__		:		Union[str, Any]		     =self.__min_dist_top_down_dp(lowercase_,  n - 1					)
														lowerCamelCase__		:		List[Any]		     =self.__min_dist_top_down_dp(m - 1,  lowercase_					)
														lowerCamelCase__		:		List[str]		     =self.__min_dist_top_down_dp(m - 1,  n - 1					)
														lowerCamelCase__		:		Optional[Any]		     =1 + min(lowercase_,  lowercase_,  lowercase_					)

											return self.dp[m][n]



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  str					)-> Tuple:
								lowerCamelCase__		:		Any		     =worda
								lowerCamelCase__		:		Union[str, Any]		     =worda
								lowerCamelCase__		:		Optional[int]		     =[[-1 for _ in range(len(lowercase_					)					)] for _ in range(len(lowercase_					)					)]

								return self.__min_dist_top_down_dp(len(lowercase_					) - 1,  len(lowercase_					) - 1					)




					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  str,  lowerCamelCase							:  str					)-> Union[str, Any]:
								lowerCamelCase__		:		Any		     =worda
								lowerCamelCase__		:		int		     =worda
								lowerCamelCase__		:		str		     =len(lowercase_					)
								lowerCamelCase__		:		int		     =len(lowercase_					)
								lowerCamelCase__		:		Tuple		     =[[0 for _ in range(n + 1					)] for _ in range(m + 1					)]

								for i in range(m + 1					):
											for j in range(n + 1					):
														if i == 0:  # first string is empty
																	lowerCamelCase__		:		Tuple		     =j
														elif j == 0:  # second string is empty
																	lowerCamelCase__		:		str		     =i
														elif worda[i - 1] == worda[j - 1]:  # last characters are equal
																	lowerCamelCase__		:		Any		     =self.dp[i - 1][j - 1]
														else:
																	lowerCamelCase__		:		str		     =self.dp[i][j - 1]
																	lowerCamelCase__		:		Union[str, Any]		     =self.dp[i - 1][j]
																	lowerCamelCase__		:		List[Any]		     =self.dp[i - 1][j - 1]
																	lowerCamelCase__		:		int		     =1 + min(lowercase_,  lowercase_,  lowercase_					)
								return self.dp[m][n]


if __name__ == "__main__":
		_lowercase			:			int										=						EditDistance()

		print("****************** Testing Edit Distance DP Algorithm ******************")
		print()

		_lowercase			:			List[str]										=						input("Enter the first string: ").strip()
		_lowercase			:			Union[str, Any]										=						input("Enter the second string: ").strip()

		print()
		print(f'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
		print(f'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
		print()
		print("*************** End of Testing Edit Distance DP Algorithm ***************")









"""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)




