



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Namespace					):






 '''simple docstring'''

 return ConvertCommand(
     args.model_type ,					args.tf_checkpoint ,					args.pytorch_dump_output ,					args.config ,					args.finetuning_task_name					)


A_		:     List[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 		_lowercase		(       UpperCAmelCase__	):

   @staticmethod
   def 		A							(      __lowerCAmelCase			:       ArgumentParser							)       ->				Optional[Any]:


    """simple docstring"""






    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			:       Optional[Any]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       str							,   *__lowerCAmelCase			:       Dict							,   )       ->				Tuple:


    """simple docstring"""






    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			:       int							)       ->				Dict:


    """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(__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]"							)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

