
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 						__magic_name__			(   __a      :	Dict	,		__a      :	Tuple						):



     '''simple docstring'''




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

     UpperCamelCase__				=    chkpt["""dico_word2id"""]
     UpperCamelCase__				=    {s + """</w>""" if s.find("""@@"""						) == -1 and i > 13 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(__a	,		__a						)

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

     print(f"Save vocab file to {pytorch_config_dump_path}"						)
     with open(__a	,		"""w"""	,		encoding="""utf-8"""						) as f:
          f.write(json.dumps(__a	,		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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





