


"""simple docstring"""







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  return ConvertCommand(
      args.model_type  ,       args.tf_checkpoint  ,       args.pytorch_dump_output  ,       args.config  ,       args.finetuning_task_name	)


__lowercase    =				"""
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  (  _a  ):







    """simple docstring"""




    @staticmethod
    def      __snake_case							( __UpperCAmelCase    :   ArgumentParser):
      a						:		List[str]											=      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=__UpperCAmelCase    ,			required=__UpperCAmelCase    ,			help="Model's type.")
      train_parser.add_argument(
          "--tf_checkpoint"    ,			type=__UpperCAmelCase    ,			required=__UpperCAmelCase    ,			help="TensorFlow checkpoint path or folder.")
      train_parser.add_argument(
          "--pytorch_dump_output"    ,			type=__UpperCAmelCase    ,			required=__UpperCAmelCase    ,			help="Path to the PyTorch saved model output.")
      train_parser.add_argument("--config"    ,			type=__UpperCAmelCase    ,			default=""    ,			help="Configuration file path or folder.")
      train_parser.add_argument(
          "--finetuning_task_name"    ,			type=__UpperCAmelCase    ,			default=__UpperCAmelCase    ,			help="Optional fine-tuning task name if the TF model was a finetuned model."    ,			)
      train_parser.set_defaults(func=__UpperCAmelCase)






    def __init__( self    :   Dict    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   str    ,			*__UpperCAmelCase    :   str    ,			):
      a						:		Union[str, Any]											=      logging.get_logger("transformers-cli/converting")

      self._logger.info(f'''Loading model {model_type}''')
      a						:		List[Any]											=      model_type
      a						:		Dict											=      tf_checkpoint
      a						:		Optional[Any]											=      pytorch_dump_output
      a						:		List[Any]											=      config
      a						:		Any											=      finetuning_task_name






    def      __snake_case							( self    :   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(__UpperCAmelCase)

        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(__UpperCAmelCase)

        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(__UpperCAmelCase)

        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(__UpperCAmelCase)

        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(__UpperCAmelCase)

        if "ckpt" in self._tf_checkpoint.lower():
          a						:		Any											=      self._tf_checkpoint
          a						:		List[Any]											=      ""
        else:
          a						:		Tuple											=      self._tf_checkpoint
          a						:		Optional[int]											=      ""
        convert_transfo_xl_checkpoint_to_pytorch(
            __UpperCAmelCase    ,			self._config    ,			self._pytorch_dump_output    ,			__UpperCAmelCase)
      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(__UpperCAmelCase)

        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(__UpperCAmelCase)

        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


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

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






