"""simple docstring"""





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands


def    lowercase_   (   ) ->      str:



       '''simple docstring'''

       __lowerCamelCase					:		Optional[int]		   =     ArgumentParser("Transformers CLI tool"						,   usage="transformers-cli <command> [<args>]"      )
       __lowerCamelCase					:		Dict		   =     parser.add_subparsers(help="transformers-cli command helpers"      )

       # Register commands
       ConvertCommand.register_subcommand(_lowerCamelCase      )
       DownloadCommand.register_subcommand(_lowerCamelCase      )
       EnvironmentCommand.register_subcommand(_lowerCamelCase      )
       RunCommand.register_subcommand(_lowerCamelCase      )
       ServeCommand.register_subcommand(_lowerCamelCase      )
       UserCommands.register_subcommand(_lowerCamelCase      )
       AddNewModelCommand.register_subcommand(_lowerCamelCase      )
       AddNewModelLikeCommand.register_subcommand(_lowerCamelCase      )
       LfsCommands.register_subcommand(_lowerCamelCase      )
       PTtoTFCommand.register_subcommand(_lowerCamelCase      )

       # Let's go
       __lowerCamelCase					:		Optional[Any]		   =     parser.parse_args()

       if not hasattr(_lowerCamelCase						,   "func"      ):
              parser.print_help()
              exit(1      )

       # Run
       __lowerCamelCase					:		Dict		   =     args.func(_lowerCamelCase      )
       service.run()


if __name__ == "__main__":
    main()
"""simple docstring"""





import argparse

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


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     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__":
				__A  =				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.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)