





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Namespace     ):
  return ConvertCommand(
      args.model_type			,							args.tf_checkpoint			,							args.pytorch_dump_output			,							args.config			,							args.finetuning_task_name     )


snake_case 				=       """
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 				A_				(      _snake_case	):







       """simple docstring"""





       @staticmethod
       def    __UpperCAmelCase (    __A	:	str  )						->					int:
         _lowercase							= 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	:	str						,__A	:	Tuple						,__A	:	List[str]						,__A	:	Optional[int]						,__A	:	Union[str, Any]						,__A	:	str						,*__A	:	Union[str, Any]						,)						->					List[Any]:
         _lowercase							= logging.get_logger('transformers-cli/converting'  )

         self._logger.info(F"""Loading model {model_type}"""  )
         _lowercase							= model_type
         _lowercase							= tf_checkpoint
         _lowercase							= pytorch_dump_output
         _lowercase							= config
         _lowercase							= finetuning_task_name







       def    __UpperCAmelCase (    self	:	List[str]  )						->					List[Any]:
         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():
             _lowercase							= self._tf_checkpoint
             _lowercase							= ""
           else:
             _lowercase							= self._tf_checkpoint
             _lowercase							= ""
           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]'  )






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )