
"""simple docstring"""

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






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


UpperCamelCase	:				str  	= '''
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 __lowerCAmelCase		(      UpperCAmelCase__			):





     @staticmethod
     def 						UpperCAmelCase					(	__UpperCAmelCase      ):

      '''simple docstring'''




      __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=__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	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     *__UpperCAmelCase	,     ):

      '''simple docstring'''




      __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 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      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():
        __UpperCamelCase				  =       self._tf_checkpoint
        __UpperCamelCase				  =       """"""
       else:
        __UpperCamelCase				  =       self._tf_checkpoint
        __UpperCamelCase				  =       """"""
       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]'      )







'''simple docstring'''







A__:    Tuple					    =       '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

A__:    Tuple					    =       [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A__:    Any					    =       {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}


