


'''simple docstring'''
import argparse

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


logging.set_verbosity_info()


def      a (     __a					,   __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Tuple				   =					TaConfig.from_json_file(__a  )
					print(f'''Building PyTorch model from configuration: {config}'''  )
					UpperCamelCase__  :str				   =					TaForConditionalGeneration(__a  )

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

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


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



'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels