





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__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Optional[Any]    )->Union[str, Any]:
       # Load checkpoint
       A__		   =		torch.load(UpperCamelCase__					,	map_location='''cpu'''    )

       A__		   =		chkpt['''model''']

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

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

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

       # Save pytorch-model
       A__		   =		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
       A__		   =		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
       A__		   =		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__: Any							   =						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__: List[str]							   =						parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





