



'''simple docstring'''






import argparse

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


logging.set_verbosity_info()





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str] ,       snake_case_    :     Optional[Any] ,       snake_case_    :     List[Any]  )		->Dict:






							'''simple docstring'''
							# Initialise PyTorch model
							_lowercase							:		int				=   TaConfig.from_json_file(snake_case_  )
							print(F"Building PyTorch model from configuration: {config}"  )
							_lowercase							:		Any				=   TaForConditionalGeneration(snake_case_  )

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

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


if __name__ == "__main__":
						lowerCamelCase__	  =		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__	  =		parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


lowerCamelCase__	  =		TypeVar('T', bound='TaskTemplate')

@dataclass(frozen=__A       )
class 	_lowerCAmelCase     (    abc.ABC       ):






		'''simple docstring'''
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= 42
		def 						__lowercase      (						self							:       T      ,		UpperCamelCase_							:       Features		)      ->			T:







									'''simple docstring'''





									return copy.deepcopy(self		)
		@property
		def 						__lowercase      (						self							:       int		)      ->			Features:







									'''simple docstring'''





									return Features(**self.input_schema      ,		**self.label_schema		)
		@property
		@abc.abstractmethod
		def 						__lowercase      (						self							:       List[str]		)      ->			Dict[str, str]:







									'''simple docstring'''





									raise NotImplementedError





		@classmethod
		def 						__lowercase      (						cls							:       Type[T]      ,		UpperCamelCase_							:       dict		)      ->			T:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {f.name for f in dataclasses.fields(cls		)}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)



