
import copy

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


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

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = """encoder-decoder"""
			__UpperCamelCase      :						Dict					      = True

			def __init__(       self :       Tuple						,	**snake_case_ :       Tuple      ):
							super().__init__(**snake_case_      )
							assert (
							    "encoder" in kwargs and "decoder" in kwargs
							), "Config has to be initialized with encoder and decoder config"
							UpperCamelCase_:							List[Any]	=						kwargs.pop("""encoder"""      )
							UpperCamelCase_:							Union[str, Any]	=						encoder_config.pop("""model_type"""      )
							UpperCamelCase_:							Optional[int]	=						kwargs.pop("""decoder"""      )
							UpperCamelCase_:							str	=						decoder_config.pop("""model_type"""      )

							from ..auto.configuration_auto import AutoConfig

							UpperCamelCase_:							List[Any]	=						AutoConfig.for_model(snake_case_						,	**snake_case_      )
							UpperCamelCase_:							List[Any]	=						AutoConfig.for_model(snake_case_						,	**snake_case_      )
							UpperCamelCase_:							Dict	=						True

			@classmethod
			def       lowerCAmelCase__	(       cls :       Any						,	snake_case_ :       PretrainedConfig						,	snake_case_ :       PretrainedConfig						,	**snake_case_ :       Optional[Any]      ):
							logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""      )
							UpperCamelCase_:							List[str]	=						True
							UpperCamelCase_:							str	=						True

							return cls(encoder=encoder_config.to_dict()						,	decoder=decoder_config.to_dict()						,	**snake_case_      )




			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							int	=						copy.deepcopy(self.__dict__      )
							UpperCamelCase_:							Optional[Any]	=						self.encoder.to_dict()
							UpperCamelCase_:							Optional[int]	=						self.decoder.to_dict()
							UpperCamelCase_:							List[str]	=						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        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Union[str, Any]:
				# Initialise PyTorch model
				UpperCamelCase_:							List[Any]	=						TaConfig.from_json_file(lowerCamelCase						)
				print(F'''Building PyTorch model from configuration: {config}'''						)
				UpperCamelCase_:							Any	=						TaForConditionalGeneration(lowerCamelCase						)

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

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


if __name__ == "__main__":
							lowerCamelCase_						: Tuple         =  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."""
							)
							lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






