






"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_   : int   			= logging.get_logger(__name__)

UpperCAmelCase_   : Union[str, Any]   			= {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "roberta"
      def __init__(						self     :  List[Any]						,	lowercase_     :  Optional[Any]=50265						,	lowercase_     :  List[Any]=768						,	lowercase_     :  Tuple=12						,	lowercase_     :  Optional[Any]=12						,	lowercase_     :  Any=3072						,	lowercase_     :  Tuple="gelu"						,	lowercase_     :  Tuple=0.1						,	lowercase_     :  List[Any]=0.1						,	lowercase_     :  Tuple=512						,	lowercase_     :  Optional[int]=2						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  Tuple=1e-12						,	lowercase_     :  Any=1						,	lowercase_     :  Any=0						,	lowercase_     :  Optional[int]=2						,	lowercase_     :  Optional[int]="absolute"						,	lowercase_     :  str=True						,	lowercase_     :  str=None						,	**lowercase_     :  int						,	):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  int    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  str    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  Any    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  str    						=		hidden_dropout_prob
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_      :  Dict    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		type_vocab_size
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  str    						=		position_embedding_type
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		use_cache
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		classifier_dropout




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


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










"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_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()    ,				__a							)
    print(f'Save configuration file to {pytorch_config_dump_path}'							)
    with open(__a    ,				'''w'''    ,				encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_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(
      """--gpt2_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_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


