






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def      lowerCamelCase__       (							_lowerCamelCase       :		Any )      ->				List[Any]:
  return ConvertCommand(
      args.model_type		,				args.tf_checkpoint		,				args.pytorch_dump_output		,				args.config		,				args.finetuning_task_name )


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''\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      a						(					_UpperCAmelCase		):






    @staticmethod
    def    UpperCamelCase       (       __SCREAMING_SNAKE_CASE     :							Any					)     ->		Tuple:
      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=_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       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[Any]       ,						*__SCREAMING_SNAKE_CASE     :							List[str]       ,						)     ->		str:
      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     :							Union[str, Any]					)     ->		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():
          lowerCamelCase_         =						self._tf_checkpoint
          lowerCamelCase_         =						''''''
        else:
          lowerCamelCase_         =						self._tf_checkpoint
          lowerCamelCase_         =						''''''
        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"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      0
   lowercase__:   List[Any]					=      len(__UpperCAmelCase ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      lowercase__:   Tuple					=      left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__UpperCAmelCase ):
         return None

      lowercase__:   Optional[int]					=      sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            lowercase__:   List[Any]					=      left
            lowercase__:   int					=      point
         elif point > right:
            lowercase__:   Dict					=      right
            lowercase__:   List[str]					=      point
         else:
            if item < current_item:
               lowercase__:   int					=      point - 1
            else:
               lowercase__:   int					=      point + 1
   return None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   lowercase__:   Optional[int]					=      left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__UpperCAmelCase ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   elif point > right:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    point - 1 )
      else:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    point + 1       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if collection != sorted(__UpperCAmelCase ):
      raise ValueError('''Collection must be ascending sorted''' )
   return True


if __name__ == "__main__":
       import sys
       __A			   = 0
       if debug == 1:
              __A			   = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              __A			   = 6_7

       __A			   = interpolation_search(collection, target)
       if result is not None:
              print(f'''{target} found at positions: {result}''')
       else:
              print("Not found")
