



"""simple docstring"""




import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


_lowerCAmelCase       :      Union[str, Any]							=							logging.get_logger(__name__)






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     Any				  =				'hybrid-clip'
      __SCREAMING_SNAKE_CASE		:     List[Any]				  =				True



      def __init__(  self  :			int  ,			A  :			int=5_1_2  ,			**A  :			Union[str, Any]		):
           super().__init__(**A		)

           if "text_config" not in kwargs:
                raise ValueError("`text_config` can not be `None`."		)

           if "vision_config" not in kwargs:
                raise ValueError("`vision_config` can not be `None`."		)

           _UpperCAmelCase    :			Tuple              =					kwargs.pop("text_config"		)
           _UpperCAmelCase    :			Optional[int]              =					kwargs.pop("vision_config"		)

           _UpperCAmelCase    :			List[str]              =					text_config.pop("model_type"		)
           _UpperCAmelCase    :			Union[str, Any]              =					vision_config.pop("model_type"		)

           from transformers import AutoConfig

           _UpperCAmelCase    :			str              =					AutoConfig.for_model(A  ,			**A		)

           if vision_model_type == "clip":
                _UpperCAmelCase    :			List[str]              =					AutoConfig.for_model(A  ,			**A		).vision_config
           elif vision_model_type == "clip_vision_model":
                from transformers import CLIPVisionConfig

                _UpperCAmelCase    :			int              =					CLIPVisionConfig(**A		)
           else:
                _UpperCAmelCase    :			Tuple              =					AutoConfig.for_model(A  ,			**A		)

           _UpperCAmelCase    :			Union[str, Any]              =					projection_dim
           _UpperCAmelCase    :			Dict              =					1.0



      @classmethod
      def 		snake_case_      (  cls  :			Dict  ,			A  :			PretrainedConfig  ,			A  :			PretrainedConfig  ,			**A  :			Dict		):
           return cls(text_config=text_config.to_dict()  ,			vision_config=vision_config.to_dict()  ,			**A		)



      def 		snake_case_      (  self  :			int		):
           _UpperCAmelCase    :			Any              =					copy.deepcopy(self.__dict__		)
           _UpperCAmelCase    :			List[str]              =					self.text_config.to_dict()
           _UpperCAmelCase    :			str              =					self.vision_config.to_dict()
           _UpperCAmelCase    :			Optional[Any]              =					self.__class__.model_type
           return output





"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
     _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
     _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )

     # Load weights from numpy
     load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )

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


if __name__ == "__main__":
 _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--imagegpt_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Path to the TensorFlow checkpoint path.",
 )
 parser.add_argument(
     "--model_size",
     default=None,
     type=str,
     required=True,
     help="Size of the model (can be either 'small', 'medium' or 'large').",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )
