

'''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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Optional[int]				)      ->			Optional[Any]:
     """simple docstring"""




     if openai_config_file == "":
          _SCREAMING_SNAKE_CASE							    =OpenAIGPTConfig()
     else:
          _SCREAMING_SNAKE_CASE							    =OpenAIGPTConfig.from_json_file(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =OpenAIGPTModel(_UpperCamelCase				)

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

     # Save pytorch-model
     _SCREAMING_SNAKE_CASE							    =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     _SCREAMING_SNAKE_CASE							    =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__":
     lowerCamelCase :      Dict					 =     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."
         ),
     )
     lowerCamelCase :      str					 =     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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



