
"""simple docstring"""




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['image_processor', 'tokenizer']
     _A								=							'BlipImageProcessor'
     _A								=							'AutoTokenizer'



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: List[Any] 							=						False
          super().__init__(__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.image_processor



     def __call__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = False ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = 0 ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = False ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					BatchEncoding:
          if images is None and text is None:
               raise ValueError("You have to specify either images or text."  )

          # Get only text
          if images is None:
               UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer
               UpperCAmelCase__							: str 							=						self.tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
               return text_encoding

          # add pixel_values
          UpperCAmelCase__							: List[str] 							=						self.image_processor(__UpperCamelCase ,			return_tensors=__UpperCamelCase  )

          if text is not None:
               UpperCAmelCase__							: Any 							=						self.tokenizer(
                   text=__UpperCamelCase ,			add_special_tokens=__UpperCamelCase ,			padding=__UpperCamelCase ,			truncation=__UpperCamelCase ,			max_length=__UpperCamelCase ,			stride=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			return_overflowing_tokens=__UpperCamelCase ,			return_special_tokens_mask=__UpperCamelCase ,			return_offsets_mapping=__UpperCamelCase ,			return_token_type_ids=__UpperCamelCase ,			return_length=__UpperCamelCase ,			verbose=__UpperCamelCase ,			return_tensors=__UpperCamelCase ,			**__UpperCamelCase ,			)
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          if text_encoding is not None:
               encoding_image_processor.update(__UpperCamelCase  )

          return encoding_image_processor



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					str:
          return self.tokenizer.batch_decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[Any]:
          return self.tokenizer.decode(*__UpperCamelCase ,			**__UpperCamelCase  )



     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def  lowerCAmelCase__       (      self  )->					int:
          UpperCAmelCase__							: int 							=						self.tokenizer.model_input_names
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







"""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__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

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

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


if __name__ == "__main__":
    A__      :	Optional[int]          =    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."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



