



'''simple docstring'''





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def   a_   (		_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      Any ,_UpperCAmelCase    :      Optional[int]				)    ->  int:
      # Construct model
      if openai_config_file == "":
            __snake_case :   str													=    OpenAIGPTConfig()
      else:
            __snake_case :   List[Any]													=    OpenAIGPTConfig.from_json_file(_UpperCAmelCase				)
      __snake_case :   Optional[int]													=    OpenAIGPTModel(_UpperCAmelCase				)

      # Load weights from numpy
      load_tf_weights_in_openai_gpt(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)

      # Save pytorch-model
      __snake_case :   List[str]													=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
      __snake_case :   List[Any]													=    pytorch_dump_folder_path + '/' + CONFIG_NAME
      print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
      torch.save(model.state_dict() ,_UpperCAmelCase				)
      print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
      with open(_UpperCAmelCase ,'w' ,encoding='utf-8'				) as f:
            f.write(config.to_json_string()				)


if __name__ == "__main__":
    A__    :   Any           =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--openai_checkpoint_folder_path''',
        default=None,
        type=str,
        required=True,
        help='''Path to the TensorFlow checkpoint path.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--openai_config_file''',
        default='''''',
        type=str,
        help=(
            '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    A__    :   Any           =					parser.parse_args()
    convert_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )








'''simple docstring'''





from typing import TYPE_CHECKING

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


A__    :   Optional[Any]           =					{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Tuple           =					['''SqueezeBertTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   str           =					[
        '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SqueezeBertForMaskedLM''',
        '''SqueezeBertForMultipleChoice''',
        '''SqueezeBertForQuestionAnswering''',
        '''SqueezeBertForSequenceClassification''',
        '''SqueezeBertForTokenClassification''',
        '''SqueezeBertModel''',
        '''SqueezeBertModule''',
        '''SqueezeBertPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_squeezebert import (
        SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SqueezeBertConfig,
        SqueezeBertOnnxConfig,
    )
    from .tokenization_squeezebert import SqueezeBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_squeezebert import (
            SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
            SqueezeBertModel,
            SqueezeBertModule,
            SqueezeBertPreTrainedModel,
        )

else:
    import sys

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



