





'''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	 =							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	 =							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE	 =							{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


