





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def  __UpperCAmelCase(   lowercase_   ):
       return ConvertCommand(
           args.model_type			,     args.tf_checkpoint			,     args.pytorch_dump_output			,     args.config			,     args.finetuning_task_name   )


_lowerCamelCase      							=   '\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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      @staticmethod
      def 					__snake_case      (		a__):



             """simple docstring"""





             _lowerCamelCase				:  int						=		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__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		logging.get_logger('''transformers-cli/converting''')

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



      def 					__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(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():
                           _lowerCamelCase				:  int						=		self._tf_checkpoint
                           _lowerCamelCase				:  int						=		''''''
                    else:
                           _lowerCamelCase				:  Tuple						=		self._tf_checkpoint
                           _lowerCamelCase				:  List[Any]						=		''''''
                    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]''')








import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

