

'''simple docstring'''




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 						UpperCamelCase	(			a			)				->     Any:






     '''simple docstring'''
     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   _SCREAMING_SNAKE_CASE   (   __a     ):



 @staticmethod
 def 			snake_case__  (   a__		:   ArgumentParser      ):
      __magic_name__						       =    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		:   int			,				a__		:   str			,				a__		:   str			,				a__		:   str			,				a__		:   str			,				a__		:   str			,				*a__		:   Optional[Any]			,				):
      __magic_name__						       =    logging.get_logger('''transformers-cli/converting'''      )

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



 def 			snake_case__  (   self		:   List[str]      ):
      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():
                __magic_name__						       =    self._tf_checkpoint
                __magic_name__						       =    ''''''
           else:
                __magic_name__						       =    self._tf_checkpoint
                __magic_name__						       =    ''''''
           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]'''      )









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =						{
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      _lowerCAmelCase         =						["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

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






