





"""simple docstring"""






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def       __A (     a_  :Union[str, Any])						->						Any:
    return ConvertCommand(
        args.model_type				,    args.tf_checkpoint				,    args.pytorch_dump_output				,    args.config				,    args.finetuning_task_name)


A	     =					'''\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'''






class 			__lowercase     (     __a							):







     '''simple docstring'''




     @staticmethod
     def 	_lowerCamelCase						(  _UpperCAmelCase       ):
         __a	:  Dict         =					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=a__		,     required=a__		,     help='''Model\'s type.'''       )
         train_parser.add_argument(
             '''--tf_checkpoint'''		,     type=a__		,     required=a__		,     help='''TensorFlow checkpoint path or folder.'''       )
         train_parser.add_argument(
             '''--pytorch_dump_output'''		,     type=a__		,     required=a__		,     help='''Path to the PyTorch saved model output.'''       )
         train_parser.add_argument('''--config'''		,     type=a__		,     default=''''''		,     help='''Configuration file path or folder.'''       )
         train_parser.add_argument(
             '''--finetuning_task_name'''		,     type=a__		,     default=a__		,     help='''Optional fine-tuning task name if the TF model was a finetuned model.'''		,     )
         train_parser.set_defaults(func=a__       )







     def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     *_UpperCAmelCase		,     ):
         __a	:  int         =					logging.get_logger('''transformers-cli/converting'''       )

         self._logger.info(f"""Loading model {model_type}"""       )
         __a	:  Tuple         =					model_type
         __a	:  Optional[Any]         =					tf_checkpoint
         __a	:  Optional[Any]         =					pytorch_dump_output
         __a	:  Dict         =					config
         __a	:  Tuple         =					finetuning_task_name







     def 	_lowerCamelCase						(  self       ):
         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(a__       )

             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(a__       )

             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(a__       )

             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(a__       )

             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(a__       )

             if "ckpt" in self._tf_checkpoint.lower():
                 __a	:  List[str]         =					self._tf_checkpoint
                 __a	:  Optional[int]         =					''''''
             else:
                 __a	:  Optional[int]         =					self._tf_checkpoint
                 __a	:  Tuple         =					''''''
             convert_transfo_xl_checkpoint_to_pytorch(
                 a__		,     self._config		,     self._pytorch_dump_output		,     a__       )
         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(a__       )

             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(a__       )

             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]'''       )






"""simple docstring"""






def       __A (     a_  :int = 1_00_00_00)						->						int:
				__a	:  Tuple         =					[i - 1 for i in range(limit + 1)]

				for i in range(2				,    limit + 1):
								if phi[i] == i - 1:
												for j in range(2 * i				,    limit + 1				,    a_):
																phi[j] -= phi[j] // i

				return sum(phi[2 : limit + 1])


if __name__ == "__main__":
		print(solution())