


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 __UpperCAmelCase       (		__A ,						__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     torch.load(UpperCamelCase__ ,						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(UpperCamelCase__ ,						(torch.FloatTensor, numpy.ndarray)			)}

       UpperCAmelCase__										=     chkpt["dico_word2id"]
       UpperCAmelCase__										=     {s + "</w>" if s.find("@@"			) == -1 and i > 1_3 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(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__":
    A      =   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."
    )
    A      =   parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









def __UpperCAmelCase       (		__A			)						->					list[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    A      = input("Enter numbers separated by a comma:\n").strip()
    A      = [int(item) for item in user_input.split(",")]
    print(exchange_sort(unsorted))





