
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return ConvertCommand(
        args.model_type    ,		args.tf_checkpoint    ,		args.pytorch_dump_output    ,		args.config    ,		args.finetuning_task_name							)


__magic_name__            =  '''
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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      @staticmethod
      def A_			(			lowerCamelCase ):
          snake_case__						=				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		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	*lowerCamelCase		,	):
          snake_case__						=				logging.get_logger("transformers-cli/converting" )

          self._logger.info(F"""Loading model {model_type}""" )
          snake_case__						=				model_type
          snake_case__						=				tf_checkpoint
          snake_case__						=				pytorch_dump_output
          snake_case__						=				config
          snake_case__						=				finetuning_task_name



      def A_			(			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(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():
                  snake_case__						=				self._tf_checkpoint
                  snake_case__						=				""
              else:
                  snake_case__						=				self._tf_checkpoint
                  snake_case__						=				""
              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 __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



