
"""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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					if gpta_config_file == "":
										_UpperCamelCase									=    GPTaConfig()
					else:
										_UpperCamelCase									=    GPTaConfig.from_json_file(__snake_case      )
					_UpperCamelCase									=    GPTaModel(__snake_case      )

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

					# Save pytorch-model
					_UpperCamelCase									=    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					_UpperCamelCase									=    pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
					torch.save(model.state_dict(),	__snake_case      )
					print(F'''Save configuration file to {pytorch_config_dump_path}'''      )
					with open(__snake_case,	'''w''',	encoding='''utf-8'''      ) as f:
										f.write(config.to_json_string()      )


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)








"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['image_processor', 'tokenizer']
		lowercase__         = 'ViTImageProcessor'
		lowercase__         = ('CLIPTokenizer', 'CLIPTokenizerFast')



		def __init__(   self							,						__a=None							,						__a=None							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''							,						__a							,						)
												_UpperCamelCase									=    kwargs.pop('''feature_extractor''')

							_UpperCamelCase									=    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.''')
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.''')

							super().__init__(__a							,						__a)



		def __call__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=None							,						**__a)			->			Tuple:






							'''simple docstring'''
							if text is None and visual_prompt is None and images is None:
												raise ValueError('''You have to specify either text, visual prompt or images.''')

							if text is not None and visual_prompt is not None:
												raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')

							if text is not None:
												_UpperCamelCase									=    self.tokenizer(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if images is not None:
												_UpperCamelCase									=    self.image_processor(__a							,						return_tensors=__a							,						**__a)

							if visual_prompt is not None and images is not None:
												_UpperCamelCase									=    {
												    '''pixel_values''': image_features.pixel_values,
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							elif text is not None and images is not None:
												_UpperCamelCase									=    image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							elif visual_prompt is not None:
												_UpperCamelCase									=    {
												    '''conditional_pixel_values''': prompt_features.pixel_values,
												}
												return encoding
							else:
												return BatchEncoding(data=dict(**__a)							,						tensor_type=__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			Any:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						*__a							,						**__a)			->			List[str]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,						__a							,						)
							return self.image_processor_class




		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,						__a							,						)
							return self.image_processor






