





'''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(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      torch.load(SCREAMING_SNAKE_CASE_		,       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(SCREAMING_SNAKE_CASE_		,       (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(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )

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

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :   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."
       )
       _SCREAMING_SNAKE_CASE     :   Union[str, Any]	 =							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


