
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Dict:
    return ConvertCommand(
        args.model_type		,    args.tf_checkpoint		,    args.pytorch_dump_output		,    args.config		,    args.finetuning_task_name		)


__lowerCAmelCase    :	Optional[int]						=						"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      @staticmethod
      def 						snake_case_	(					_snake_case						:				Optional[Any] ):
          __lowercase						:	int					=				parser.add_parser(
              '''convert''' ,				help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.''' ,				)
          train_parser.add_argument('''--model_type''' ,				type=lowerCAmelCase__ ,				required=lowerCAmelCase__ ,				help='''Model\'s type.''' )
          train_parser.add_argument(
              '''--tf_checkpoint''' ,				type=lowerCAmelCase__ ,				required=lowerCAmelCase__ ,				help='''TensorFlow checkpoint path or folder.''' )
          train_parser.add_argument(
              '''--pytorch_dump_output''' ,				type=lowerCAmelCase__ ,				required=lowerCAmelCase__ ,				help='''Path to the PyTorch saved model output.''' )
          train_parser.add_argument('''--config''' ,				type=lowerCAmelCase__ ,				default='''''' ,				help='''Configuration file path or folder.''' )
          train_parser.add_argument(
              '''--finetuning_task_name''' ,				type=lowerCAmelCase__ ,				default=lowerCAmelCase__ ,				help='''Optional fine-tuning task name if the TF model was a finetuned model.''' ,				)
          train_parser.set_defaults(func=lowerCAmelCase__ )




      def __init__(					self						:				str ,				_snake_case						:				str ,				_snake_case						:				int ,				_snake_case						:				Dict ,				_snake_case						:				Dict ,				_snake_case						:				Optional[Any] ,				*_snake_case						:				List[Any] ,				):
          __lowercase						:	Optional[int]					=				logging.get_logger('''transformers-cli/converting''' )

          self._logger.info(F'Loading model {model_type}' )
          __lowercase						:	Optional[int]					=				model_type
          __lowercase						:	Union[str, Any]					=				tf_checkpoint
          __lowercase						:	Optional[Any]					=				pytorch_dump_output
          __lowercase						:	Dict					=				config
          __lowercase						:	Tuple					=				finetuning_task_name




      def 						snake_case_	(					self						:				str ):




          if self._model_type == "albert":
              try:
                  from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "bert":
              try:
                  from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "funnel":
              try:
                  from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                      convert_tf_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "t5":
              try:
                  from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "gpt":
              from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                  convert_openai_checkpoint_to_pytorch,
              )

              convert_openai_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "transfo_xl":
              try:
                  from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                      convert_transfo_xl_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              if "ckpt" in self._tf_checkpoint.lower():
                  __lowercase						:	Tuple					=				self._tf_checkpoint
                  __lowercase						:	str					=				""
              else:
                  __lowercase						:	Union[str, Any]					=				self._tf_checkpoint
                  __lowercase						:	List[Any]					=				""
              convert_transfo_xl_checkpoint_to_pytorch(
                  lowerCAmelCase__ ,				self._config ,				self._pytorch_dump_output ,				lowerCAmelCase__ )
          elif self._model_type == "gpt2":
              try:
                  from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                      convert_gpta_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          elif self._model_type == "xlnet":
              try:
                  from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                      convert_xlnet_checkpoint_to_pytorch,
                  )
              except ImportError:
                  raise ImportError(lowerCAmelCase__ )

              convert_xlnet_checkpoint_to_pytorch(
                  self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output ,				self._finetuning_task_name )
          elif self._model_type == "xlm":
              from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                  convert_xlm_checkpoint_to_pytorch,
              )

              convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint ,				self._pytorch_dump_output )
          elif self._model_type == "lxmert":
              from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                  convert_lxmert_checkpoint_to_pytorch,
              )

              convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint ,				self._pytorch_dump_output )
          elif self._model_type == "rembert":
              from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                  convert_rembert_tf_checkpoint_to_pytorch,
              )

              convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,				self._config ,				self._pytorch_dump_output )
          else:
              raise ValueError(
                  '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]''' )









from math import pi
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))






