
"""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    lowerCAmelCase_   (		snake_case_		:			List[str]							,    snake_case_		:			List[str]			)   ->int:
			lowerCamelCase__	:     Dict              =torch.load(snake_case_							,    map_location='cpu'			)

			lowerCamelCase__	:     List[Any]              =chkpt['model']

			# We have the base model one level deeper than the original XLM repository
			lowerCamelCase__	:     Optional[Any]              ={}
			for k, v in state_dict.items():
						if "pred_layer" in k:
									lowerCamelCase__	:     List[str]              =v
						else:
									lowerCamelCase__	:     Optional[Any]              =v

			lowerCamelCase__	:     Dict              =chkpt['params']
			lowerCamelCase__	:     Optional[Any]              ={n: v for n, v in config.items() if not isinstance(snake_case_							,    (torch.FloatTensor, numpy.ndarray)			)}

			lowerCamelCase__	:     List[str]              =chkpt['dico_word2id']
			lowerCamelCase__	:     Optional[int]              ={s + '</w>' if s.find('@@'			) == -1 and i > 1_3 else s.replace('@@'							,    ''			): i for s, i in vocab.items()}

			# Save pytorch-model
			lowerCamelCase__	:     Optional[int]              =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCamelCase__	:     Dict              =pytorch_dump_folder_path + '/' + CONFIG_NAME
			lowerCamelCase__	:     Dict              =pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

			print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""			)
			torch.save(snake_case_							,    snake_case_			)

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

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


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





def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       float	,							lowercase	:       list[float]     ):


      '''simple docstring'''



      if discount_rate < 0:
            raise ValueError('Discount rate cannot be negative'     )
      if not cash_flows:
            raise ValueError('Cash flows list cannot be empty'     )
      lowerCamelCase_		     =    sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowercase     )     )
      return round(lowercase	,							ndigits=2     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



