


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Namespace    ):
     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    UpperCAmelCase__	( A_		):



    """simple docstring"""






    @staticmethod
    def _a (      A_   )  ->	Dict:
         __UpperCamelCase   							=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 ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       *A_ ,       )  ->	Tuple:
         __UpperCamelCase   							=logging.get_logger('transformers-cli/converting'   )

         self._logger.info(f'Loading model {model_type}'   )
         __UpperCamelCase   							=model_type
         __UpperCamelCase   							=tf_checkpoint
         __UpperCamelCase   							=pytorch_dump_output
         __UpperCamelCase   							=config
         __UpperCamelCase   							=finetuning_task_name






    def _a (      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(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():
                   __UpperCamelCase   							=self._tf_checkpoint
                   __UpperCamelCase   							=''
              else:
                   __UpperCamelCase   							=self._tf_checkpoint
                   __UpperCamelCase   							=''
              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]'   )









from ....utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=None ,       A_=2048   )  ->	Any:
         __UpperCamelCase   							=config.__dict__
         __UpperCamelCase   							=modal_hidden_size
         if num_labels:
              __UpperCamelCase   							=num_labels





