


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)

	A								=				chkpt["""model"""]

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

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

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

	# Save pytorch-model
	A								=				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A								=				pytorch_dump_folder_path + """/""" + CONFIG_NAME
	A								=				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__":
				_lowercase       :      List[str]	=	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       :      str	=	parser.parse_args()
				convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


if __name__ == "__main__":
				print(f'''{solution() = }''')



