





'''simple docstring'''






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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


A__    :       int              ='''
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					(      snake_case_       ):




      @staticmethod
      def     lowercase__   (      __snake_case      :			ArgumentParser  )    ->				Optional[Any]:
            _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      :			Any      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      __snake_case      :			str      ,      *__snake_case      :			List[str]      ,      )    ->				Any:
            _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     lowercase__   (      self      :			Tuple  )    ->				List[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(__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_tokenizers_available


A__    :       int              ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Dict              =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
      from .tokenization_herbert import HerbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_herbert_fast import HerbertTokenizerFast

else:
      import sys

      A__    :       List[Any]              =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






