





import copy

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


SCREAMING_SNAKE_CASE_		  =			logging.get_logger(__name__)

class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      str						      =     "hybrid-clip"
     __snake_case       :      Dict						      =     True






     def __init__(				self		:       str		,lowerCamelCase__		:       str=512		,**lowerCamelCase__		:       Union[str, Any]			)	->						Tuple:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__			)

           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`."""			)

           SCREAMING_SNAKE_CASE						=		kwargs.pop("""text_config"""			)
           SCREAMING_SNAKE_CASE						=		kwargs.pop("""vision_config"""			)

           SCREAMING_SNAKE_CASE						=		text_config.pop("""model_type"""			)
           SCREAMING_SNAKE_CASE						=		vision_config.pop("""model_type"""			)

           from transformers import AutoConfig

           SCREAMING_SNAKE_CASE						=		AutoConfig.for_model(lowerCamelCase__		,**lowerCamelCase__			)

           if vision_model_type == "clip":
                 SCREAMING_SNAKE_CASE						=		AutoConfig.for_model(lowerCamelCase__		,**lowerCamelCase__			).vision_config
           elif vision_model_type == "clip_vision_model":
                 from transformers import CLIPVisionConfig

                 SCREAMING_SNAKE_CASE						=		CLIPVisionConfig(**lowerCamelCase__			)
           else:
                 SCREAMING_SNAKE_CASE						=		AutoConfig.for_model(lowerCamelCase__		,**lowerCamelCase__			)

           SCREAMING_SNAKE_CASE						=		projection_dim
           SCREAMING_SNAKE_CASE						=		1.0






     @classmethod
     def 							SCREAMING_SNAKE_CASE__    (				cls		:       Union[str, Any]		,lowerCamelCase__		:       PretrainedConfig		,lowerCamelCase__		:       PretrainedConfig		,**lowerCamelCase__		:       Union[str, Any]			)	->						Union[str, Any]:




           '''simple docstring'''


           return cls(text_config=text_config.to_dict()		,vision_config=vision_config.to_dict()		,**lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		copy.deepcopy(self.__dict__			)
           SCREAMING_SNAKE_CASE						=		self.text_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE						=		self.__class__.model_type
           return output









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


