





"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def   a_   (			lowerCAmelCase_				:      Namespace     ):
   return ConvertCommand(
       args.model_type,     args.tf_checkpoint,     args.pytorch_dump_output,     args.config,     args.finetuning_task_name     )


_snake_case  :   Optional[int]						   =  '\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   (   _UpperCamelCase   ):

  """simple docstring"""


  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		ArgumentParser					)							->						int:
     __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=lowerCAmelCase_ ,  required=lowerCAmelCase_ ,  help='Model\'s type.'					)
     train_parser.add_argument(
         '--tf_checkpoint' ,  type=lowerCAmelCase_ ,  required=lowerCAmelCase_ ,  help='TensorFlow checkpoint path or folder.'					)
     train_parser.add_argument(
         '--pytorch_dump_output' ,  type=lowerCAmelCase_ ,  required=lowerCAmelCase_ ,  help='Path to the PyTorch saved model output.'					)
     train_parser.add_argument('--config' ,  type=lowerCAmelCase_ ,  default='' ,  help='Configuration file path or folder.'					)
     train_parser.add_argument(
         '--finetuning_task_name' ,  type=lowerCAmelCase_ ,  default=lowerCAmelCase_ ,  help='Optional fine-tuning task name if the TF model was a finetuned model.' ,  )
     train_parser.set_defaults(func=lowerCAmelCase_					)






  def __init__(    self			:		List[str] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		str ,  *lowerCAmelCase_			:		List[Any] ,  )							->						Optional[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			:		str					)							->						int:
     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(lowerCAmelCase_					)

        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(lowerCAmelCase_					)

        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(lowerCAmelCase_					)

        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(lowerCAmelCase_					)

        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(lowerCAmelCase_					)

        if "ckpt" in self._tf_checkpoint.lower():
           __lowerCAmelCase							     =     self._tf_checkpoint
           __lowerCAmelCase							     =     ''
        else:
           __lowerCAmelCase							     =     self._tf_checkpoint
           __lowerCAmelCase							     =     ''
        convert_transfo_xl_checkpoint_to_pytorch(
            lowerCAmelCase_ ,  self._config ,  self._pytorch_dump_output ,  lowerCAmelCase_					)
     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(lowerCAmelCase_					)

        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(lowerCAmelCase_					)

        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]'					)








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

