'''simple docstring'''

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return ConvertCommand(
     args.model_type		,     args.tf_checkpoint		,     args.pytorch_dump_output		,     args.config		,     args.finetuning_task_name     )


__a: Optional[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  (						a__					):





       '''simple docstring'''



       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase )  ->  List[str]:
        lowercase__				:    Optional[Any]  =					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	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   *__lowerCAmelCase	,   )  ->  Dict:
        lowercase__				:    int  =					logging.get_logger('''transformers-cli/converting''' )

        self._logger.info(F"""Loading model {model_type}""" )
        lowercase__				:    Dict  =					model_type
        lowercase__				:    Any  =					tf_checkpoint
        lowercase__				:    Optional[Any]  =					pytorch_dump_output
        lowercase__				:    Optional[int]  =					config
        lowercase__				:    List[Any]  =					finetuning_task_name


       def _lowerCAmelCase(						self )  ->  List[str]:
        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():
          lowercase__				:    Optional[Any]  =					self._tf_checkpoint
          lowercase__				:    Tuple  =					''''''
         else:
          lowercase__				:    Any  =					self._tf_checkpoint
          lowercase__				:    List[Any]  =					''''''
         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]''' )


'''simple docstring'''

from __future__ import annotations

from random import random



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = None )  ->  str:
        lowercase__				:    Any  =					value
        lowercase__				:    Union[str, Any]  =					random()
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None

       def __repr__(						self )  ->  str:
        from pprint import pformat

        if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
        else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}	,   indent=1 )


       def __str__(						self )  ->  str:
        lowercase__				:    Any  =					str(self.value ) + ''' '''
        lowercase__				:    str  =					str(self.left or '''''' )
        lowercase__				:    List[str]  =					str(self.right or '''''' )
        return value + left + right


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if root is None:  # None tree is split into 2 Nones
  return None, None
 elif root.value is None:
  return None, None
 else:
  if value < root.value:
   lowercase__ ,							lowercase__				:    int  =					split(root.left		,     UpperCAmelCase     )
   return left, root
  else:
   lowercase__ ,							lowercase__				:    Optional[Any]  =					split(root.right		,     UpperCAmelCase     )
   return root, right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 if (not left) or (not right):  # If one node is None, return the other
  return left or right
 elif left.prior < right.prior:
  lowercase__				:    Any  =					merge(left.right		,     UpperCAmelCase     )
  return left
 else:
  lowercase__				:    Dict  =					merge(UpperCAmelCase		,     right.left     )
  return right
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					Node(UpperCAmelCase     )
 lowercase__ ,							lowercase__				:    Tuple  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(merge(UpperCAmelCase		,     UpperCAmelCase     )		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Dict  =					split(UpperCAmelCase		,     value - 1     )
 lowercase__ ,							lowercase__				:    int  =					split(UpperCAmelCase		,     UpperCAmelCase     )
 return merge(UpperCAmelCase		,     UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not root:  # None
  return
 else:
  inorder(root.left     )
  print(root.value		,     end=''','''     )
  inorder(root.right     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 for arg in args.split():
  if arg[0] == "+":
   lowercase__				:    Tuple  =					insert(UpperCAmelCase		,     int(arg[1:]     )     )

  elif arg[0] == "-":
   lowercase__				:    Union[str, Any]  =					erase(UpperCAmelCase		,     int(arg[1:]     )     )

  else:
   print('''Unknown command'''     )

 return root
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					None
 print(
     '''enter numbers to create a tree, + value to add value into treap, '''
     '''- value to erase all nodes with value. \'q\' to quit. '''     )

 lowercase__				:    Dict  =					input()
 while args != "q":
  lowercase__				:    Any  =					interact_treap(UpperCAmelCase		,     UpperCAmelCase     )
  print(UpperCAmelCase     )
  lowercase__				:    List[Any]  =					input()

 print('''good by!'''     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

