


'''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__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Dict:

    if gpta_config_file == "":
        UpperCAmelCase__			:		Dict													=				GPTaConfig()
    else:
        UpperCAmelCase__			:		List[str]													=				GPTaConfig.from_json_file(__a						)
    UpperCAmelCase__			:		str													=				GPTaModel(__a						)

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

    # Save pytorch-model
    UpperCAmelCase__			:		str													=				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()  ,					__a						)
    print(F"""Save configuration file to {pytorch_config_dump_path}"""						)
    with open(__a  ,					'''w'''  ,					encoding='''utf-8'''						) as f:
        f.write(config.to_json_string()						)


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



import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		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       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		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       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



