"""simple docstring"""



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					_lowerCamelCase(					a							):
       return ConvertCommand(
           args.model_type ,  args.tf_checkpoint ,  args.pytorch_dump_output ,  args.config ,  args.finetuning_task_name							)


SCREAMING_SNAKE_CASE__: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 				snake_case__     (   snake_case_   ):


 @staticmethod
 def 		a__ (				lowerCamelCase		):
        __a     				=  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  ,						):
        __a     				=  logging.get_logger("transformers-cli/converting"		)

        self._logger.info(F"Loading model {model_type}"		)
        __a     				=  model_type
        __a     				=  tf_checkpoint
        __a     				=  pytorch_dump_output
        __a     				=  config
        __a     				=  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():
                      __a     				=  self._tf_checkpoint
                      __a     				=  ""
               else:
                      __a     				=  self._tf_checkpoint
                      __a     				=  ""
               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]"		)

"""simple docstring"""



from ..utils import DummyObject, requires_backends






class 				snake_case__     (   metaclass=snake_case_   ):
 _snake_case   :			Union[str, Any]           =		["""onnx"""]


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(self  ,						["onnx"]		)


 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)






 @classmethod
 def 		a__ (				cls  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        requires_backends(cls  ,						["onnx"]		)
