'''simple docstring'''

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def snake_case_      (					__snake_case  :							List[Any])			->					Optional[int]:
   return ConvertCommand(
       args.model_type	,   args.tf_checkpoint	,   args.pytorch_dump_output	,   args.config	,   args.finetuning_task_name)


A_     :						List[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       ( __a		):





  @staticmethod
  def 						UpperCAmelCase_	(	_lowerCamelCase ):
     lowerCAmelCase_			=      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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      logging.get_logger('''transformers-cli/converting''' )

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







  def 						UpperCAmelCase_	(	self ):




     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():
           lowerCAmelCase_			=      self._tf_checkpoint
           lowerCAmelCase_			=      ""
        else:
           lowerCAmelCase_			=      self._tf_checkpoint
           lowerCAmelCase_			=      ""
        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'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE     =       {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""DeiTFeatureExtractor"""]
      SCREAMING_SNAKE_CASE     =       ["""DeiTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DeiTForImageClassification""",
          """DeiTForImageClassificationWithTeacher""",
          """DeiTForMaskedImageModeling""",
          """DeiTModel""",
          """DeiTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFDeiTForImageClassification""",
          """TFDeiTForImageClassificationWithTeacher""",
          """TFDeiTForMaskedImageModeling""",
          """TFDeiTModel""",
          """TFDeiTPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deit import DeiTFeatureExtractor
            from .image_processing_deit import DeiTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deit import (
                DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeiTForImageClassification,
                DeiTForImageClassificationWithTeacher,
                DeiTForMaskedImageModeling,
                DeiTModel,
                DeiTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deit import (
                TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDeiTForImageClassification,
                TFDeiTForImageClassificationWithTeacher,
                TFDeiTForMaskedImageModeling,
                TFDeiTModel,
                TFDeiTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



