



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_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       int:
						# Load checkpoint
						UpperCAmelCase         =     torch.load(lowerCamelCase_    ,   map_location="cpu"    )

						UpperCAmelCase         =     chkpt["model"]

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

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

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

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

						print(F'Save PyTorch model to {pytorch_weights_dump_path}'    )
						torch.save(lowerCamelCase_    ,   lowerCamelCase_    )

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

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


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








def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     int(lowerCamelCase_    )

						# Initialize Result
						UpperCAmelCase         =     []

						# Traverse through all denomination
						for denomination in reversed(lowerCamelCase_    ):
												# Find denominations
												while int(lowerCamelCase_    ) >= int(lowerCamelCase_    ):
																		total_value -= int(lowerCamelCase_    )
																		answer.append(lowerCamelCase_    )  # Append the "answers" array

						return answer


# Driver Code
if __name__ == "__main__":
		__lowerCamelCase     :							Optional[int]  							=				[]
		__lowerCamelCase     :							Dict  							=				"0"

		if (
		    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
		    == "y"
		):
				__lowerCamelCase     :							List[Any]  							=				int(input("Enter the number of denominations you want to add: ").strip())

				for i in range(0, n):
						denominations.append(int(input(F'''Denomination {i}: ''').strip()))
				__lowerCamelCase     :							str  							=				input("Enter the change you want to make in Indian Currency: ").strip()
		else:
				# All denominations of Indian Currency if user does not enter
				__lowerCamelCase     :							Optional[int]  							=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
				__lowerCamelCase     :							Optional[int]  							=				input("Enter the change you want to make: ").strip()

		if int(value) == 0 or int(value) < 0:
				print("The total value cannot be zero or negative.")

		else:
				print(F'''Following is minimal change for {value}: ''')
				__lowerCamelCase     :							Union[str, Any]  							=				find_minimum_change(denominations, value)
				# Print result
				for i in range(len(answer)):
						print(answer[i], end=" ")



