


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__			):
 return ConvertCommand(
     args.model_type      ,	args.tf_checkpoint      ,	args.pytorch_dump_output      ,	args.config      ,	args.finetuning_task_name			)


lowercase_	   =     '\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 	A_ (	lowercase__   ):




      '''simple docstring'''


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




      def __init__(    self:	str						,				a:	str						,				a:	str						,				a:	str						,				a:	str						,				a:	str						,				*a:	Any						,				):
       __lowerCamelCase		:							Dict		 =						logging.get_logger('transformers-cli/converting'    )

       self._logger.info(F'Loading model {model_type}'    )
       __lowerCamelCase		:							str		 =						model_type
       __lowerCamelCase		:							Dict		 =						tf_checkpoint
       __lowerCamelCase		:							List[Any]		 =						pytorch_dump_output
       __lowerCamelCase		:							int		 =						config
       __lowerCamelCase		:							int		 =						finetuning_task_name




      def 					_snake_case     (    self:	Tuple    ):
       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(_UpperCamelCase    )

        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(_UpperCamelCase    )

        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(_UpperCamelCase    )

        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(_UpperCamelCase    )

        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(_UpperCamelCase    )

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

        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(_UpperCamelCase    )

        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 __future__ import annotations
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						0.00
 __lowerCamelCase		:							Tuple		 =						0
 for resistor in resistors:
  if resistor <= 0:
   __lowerCamelCase		:							Union[str, Any]		 =						f'Resistor at index {index} has a negative or zero value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  first_sum += 1 / float(SCREAMING_SNAKE_CASE__						)
  index += 1
 return 1 / first_sum
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						0.00
 __lowerCamelCase		:							str		 =						0
 for resistor in resistors:
  sum_r += resistor
  if resistor < 0:
   __lowerCamelCase		:							Any		 =						f'Resistor at index {index} has a negative value!'
   raise ValueError(SCREAMING_SNAKE_CASE__						)
  index += 1
 return sum_r


if __name__ == "__main__":
       import doctest

       doctest.testmod()

