






"""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     __lowercase					(    a :				Union[str, Any]       ,		a :				Any       ,		a :				Optional[Any] )					->					Union[str, Any]:

      if openai_config_file == "":
            __snake_case      :    int  			=OpenAIGPTConfig()
      else:
            __snake_case      :    str  			=OpenAIGPTConfig.from_json_file(snake_case__ )
      __snake_case      :    int  			=OpenAIGPTModel(snake_case__ )

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

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


if __name__ == "__main__":
    UpperCamelCase_	:				Tuple										= 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."""
        ),
    )
    UpperCamelCase_	:				List[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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_	:				Dict										= logging.get_logger(__name__)

UpperCamelCase_	:				Dict										= {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     Union[str, Any]								=   '''xmod'''


      def __init__(    self				:	Optional[int]							,   a				:	Dict=3_0_5_2_2							,   a				:	Any=7_6_8							,   a				:	str=1_2							,   a				:	Optional[int]=1_2							,   a				:	Any=3_0_7_2							,   a				:	int="gelu"							,   a				:	List[Any]=0.1							,   a				:	Union[str, Any]=0.1							,   a				:	Tuple=5_1_2							,   a				:	str=2							,   a				:	Any=0.0_2							,   a				:	Tuple=1e-12							,   a				:	Optional[int]=1							,   a				:	Any=0							,   a				:	str=2							,   a				:	str="absolute"							,   a				:	List[Any]=True							,   a				:	Optional[Any]=None							,   a				:	Tuple=False							,   a				:	Union[str, Any]=2							,   a				:	Any=False							,   a				:	Dict=True							,   a				:	int=True							,   a				:	str=("en_XX",)							,   a				:	Dict=None							,   **a				:	Tuple							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   bos_token_id=a							,   eos_token_id=a							,   **a						)

            __snake_case      :    Union[str, Any]  			=vocab_size
            __snake_case      :    Union[str, Any]  			=hidden_size
            __snake_case      :    int  			=num_hidden_layers
            __snake_case      :    Union[str, Any]  			=num_attention_heads
            __snake_case      :    Any  			=hidden_act
            __snake_case      :    Tuple  			=intermediate_size
            __snake_case      :    int  			=hidden_dropout_prob
            __snake_case      :    str  			=attention_probs_dropout_prob
            __snake_case      :    int  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Union[str, Any]  			=initializer_range
            __snake_case      :    Dict  			=layer_norm_eps
            __snake_case      :    Optional[int]  			=position_embedding_type
            __snake_case      :    int  			=use_cache
            __snake_case      :    int  			=classifier_dropout
            __snake_case      :    int  			=pre_norm
            __snake_case      :    Any  			=adapter_reduction_factor
            __snake_case      :    str  			=adapter_layer_norm
            __snake_case      :    Union[str, Any]  			=adapter_reuse_layer_norm
            __snake_case      :    List[str]  			=ln_before_adapter
            __snake_case      :    Optional[int]  			=list(a						)
            __snake_case      :    int  			=default_language



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            if self.task == "multiple-choice":
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''sequence'''}
            return OrderedDict(
                [
                    ('''input_ids''', dynamic_axis),
                    ('''attention_mask''', dynamic_axis),
                ]						)


