
"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def    _lowerCAmelCase    (     lowerCamelCase__							:							Namespace				)  ->    int:
  return ConvertCommand(
      args.model_type,						args.tf_checkpoint,						args.pytorch_dump_output,						args.config,						args.finetuning_task_name				)


lowercase_       :						Any     					=	"""
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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = 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=snake_case__  ,     required=snake_case__  ,     help="Model\'s type."				)
       train_parser.add_argument(
           "--tf_checkpoint"  ,     type=snake_case__  ,     required=snake_case__  ,     help="TensorFlow checkpoint path or folder."				)
       train_parser.add_argument(
           "--pytorch_dump_output"  ,     type=snake_case__  ,     required=snake_case__  ,     help="Path to the PyTorch saved model output."				)
       train_parser.add_argument("--config"  ,     type=snake_case__  ,     default=""  ,     help="Configuration file path or folder."				)
       train_parser.add_argument(
           "--finetuning_task_name"  ,     type=snake_case__  ,     default=snake_case__  ,     help="Optional fine-tuning task name if the TF model was a finetuned model."  ,     )
       train_parser.set_defaults(func=snake_case__				)
     def __init__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     *snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = logging.get_logger("transformers-cli/converting"				)

       self._logger.info(F'''Loading model {model_type}'''				)
       _SCREAMING_SNAKE_CASE   :				Any							    = model_type
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf_checkpoint
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = pytorch_dump_output
       _SCREAMING_SNAKE_CASE   :				int							    = config
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = finetuning_task_name




     def 				__SCREAMING_SNAKE_CASE							(     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(snake_case__				)

         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(snake_case__				)

         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(snake_case__				)

         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(snake_case__				)

         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(snake_case__				)

         if "ckpt" in self._tf_checkpoint.lower():
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self._tf_checkpoint
           _SCREAMING_SNAKE_CASE   :				int							    = ""
         else:
           _SCREAMING_SNAKE_CASE   :				Dict							    = self._tf_checkpoint
           _SCREAMING_SNAKE_CASE   :				int							    = ""
         convert_transfo_xl_checkpoint_to_pytorch(
             snake_case__  ,     self._config  ,     self._pytorch_dump_output  ,     snake_case__				)
       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(snake_case__				)

         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(snake_case__				)

         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"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



