





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Tuple:
       return ConvertCommand(
           args.model_type					,		args.tf_checkpoint					,		args.pytorch_dump_output					,		args.config					,		args.finetuning_task_name  )


_UpperCAmelCase        =				'\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 							_UpperCamelCase					(	lowerCAmelCase_     ):

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: ArgumentParser		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  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=_SCREAMING_SNAKE_CASE					,   required=_SCREAMING_SNAKE_CASE					,   help="Model's type."		)
              train_parser.add_argument(
                  "--tf_checkpoint"					,   type=_SCREAMING_SNAKE_CASE					,   required=_SCREAMING_SNAKE_CASE					,   help="TensorFlow checkpoint path or folder."		)
              train_parser.add_argument(
                  "--pytorch_dump_output"					,   type=_SCREAMING_SNAKE_CASE					,   required=_SCREAMING_SNAKE_CASE					,   help="Path to the PyTorch saved model output."		)
              train_parser.add_argument("--config"					,   type=_SCREAMING_SNAKE_CASE					,   default=""					,   help="Configuration file path or folder."		)
              train_parser.add_argument(
                  "--finetuning_task_name"					,   type=_SCREAMING_SNAKE_CASE					,   default=_SCREAMING_SNAKE_CASE					,   help="Optional fine-tuning task name if the TF model was a finetuned model."					,   )
              train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE		)

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: str					,   *_SCREAMING_SNAKE_CASE: Tuple					,   )       -> str:




              """simple docstring"""


              UpperCamelCase_          =  logging.get_logger("transformers-cli/converting"		)

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



       def lowercase (		self: Any		)       -> Optional[Any]:




              """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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_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(_SCREAMING_SNAKE_CASE		)

                     if "ckpt" in self._tf_checkpoint.lower():
                            UpperCamelCase_          =  self._tf_checkpoint
                            UpperCamelCase_          =  ""
                     else:
                            UpperCamelCase_          =  self._tf_checkpoint
                            UpperCamelCase_          =  ""
                     convert_transfo_xl_checkpoint_to_pytorch(
                         _SCREAMING_SNAKE_CASE					,   self._config					,   self._pytorch_dump_output					,   _SCREAMING_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(_SCREAMING_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(_SCREAMING_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]"		)








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

