
'''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 	A  (   UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  int   )       ->							List[Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				torch.load(UpperCamelCase_    ,						map_location="cpu"   )

			lowerCAmelCase__										=				chkpt["model"]

			# We have the base model one level deeper than the original XLM repository
			lowerCAmelCase__										=				{}
			for k, v in state_dict.items():
						if "pred_layer" in k:
									lowerCAmelCase__										=				v
						else:
									lowerCAmelCase__										=				v

			lowerCAmelCase__										=				chkpt["params"]
			lowerCAmelCase__										=				{n: v for n, v in config.items() if not isinstance(UpperCamelCase_    ,						(torch.FloatTensor, numpy.ndarray)   )}

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

			# Save pytorch-model
			lowerCAmelCase__										=				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
			lowerCAmelCase__										=				pytorch_dump_folder_path + "/" + CONFIG_NAME
			lowerCAmelCase__										=				pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

			print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
			torch.save(UpperCamelCase_    ,						UpperCamelCase_   )

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

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


if __name__ == "__main__":
		UpperCAmelCase__ :			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."
		)
		UpperCAmelCase__ :			Any											=		parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    bool:
		__lowercase											=     len(snake_case							) + 1
		__lowercase											=     len(snake_case							) + 1

		# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
		# length i of input_string matches with prefix string of length j of
		# given pattern.
		# "dp" stands for dynamic programming.
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		# since string of zero length match pattern of zero length
		__lowercase											=     1

		# since pattern of zero length will never match with string of non-zero length
		for i in range(1						,   snake_case							):
				__lowercase											=     0

		# since string of zero length will match with pattern where there
		# is at least one * alternatively
		for j in range(1						,   snake_case							):
				__lowercase											=     dp[0][j - 2] if pattern[j - 1] == '*' else 0

		# now using bottom-up approach to find for all remaining lengths
		for i in range(1						,   snake_case							):
				for j in range(1						,   snake_case							):
						if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
								__lowercase											=     dp[i - 1][j - 1]

						elif pattern[j - 1] == "*":
								if dp[i][j - 2] == 1:
										__lowercase											=     1
								elif pattern[j - 2] in (input_string[i - 1], "."):
										__lowercase											=     dp[i - 1][j]
								else:
										__lowercase											=     0
						else:
								__lowercase											=     0

		return bool(dp[-1][-1]							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		SCREAMING_SNAKE_CASE_   :      Any			=   '''aab'''
		SCREAMING_SNAKE_CASE_   :      str			=   '''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(F"""{input_string} matches the given pattern {pattern}""")
		else:
				print(F"""{input_string} does not match with the given pattern {pattern}""")




