"""simple docstring"""






import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__    (     __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				)						->						Dict:
   # Initialise PyTorch model
   lowercase__:     Union[str, Any]      =   ElectraConfig.from_json_file(__UpperCAmelCase				)
   print(F"""Building PyTorch model from configuration: {config}"""				)

   if discriminator_or_generator == "discriminator":
      lowercase__:     Optional[Any]      =   ElectraForPreTraining(__UpperCAmelCase				)
   elif discriminator_or_generator == "generator":
      lowercase__:     List[str]      =   ElectraForMaskedLM(__UpperCAmelCase				)
   else:
      raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''				)

   # Load weights from tf checkpoint
   load_tf_weights_in_electra(
       __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					discriminator_or_generator=__UpperCAmelCase				)

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
   torch.save(model.state_dict() ,					__UpperCAmelCase				)


if __name__ == "__main__":
       __A         =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--discriminator_or_generator",
           default=None,
           type=str,
           required=True,
           help=(
               "Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or "
               "'generator'."
           ),
       )
       __A         =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
       )

"""simple docstring"""






from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A         =				TypeVar("T", bound=bool)


class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase							):
       lowercase__:     T      =   value
       lowercase__:     RandomizedHeapNode[T] | None      =   None
       lowercase__:     RandomizedHeapNode[T] | None      =   None






    @property
    def _snake_case    (     self							):
       return self._value






    @staticmethod
    def _snake_case    (     _UpperCAmelCase					,    _UpperCAmelCase							):
       if not roota:
          return roota

       if not roota:
          return roota

       if roota.value > roota.value:
          lowercase__,				lowercase__:     List[str]      =   roota, roota

       if random.choice([True, False]							):
          lowercase__,				lowercase__:     Any      =   roota.right, roota.left

       lowercase__:     Optional[int]      =   RandomizedHeapNode.merge(roota.left					,    _UpperCAmelCase							)

       return roota



class 					UpperCAmelCase		(Generic[T]						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase = ()							):
       lowercase__:     RandomizedHeapNode[T] | None      =   None

       if data:
          for item in data:
             self.insert(_UpperCAmelCase							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Any      =   RandomizedHeapNode.merge(self._root					,    RandomizedHeapNode(_UpperCAmelCase							)							)






    def _snake_case    (     self							):
       lowercase__:     Union[str, Any]      =   self.top()

       if self._root is None:
          return None

       lowercase__:     int      =   RandomizedHeapNode.merge(self._root.left					,    self._root.right							)

       return result






    def _snake_case    (     self							):
       if not self._root:
          raise IndexError('''Can\'t get top element for the empty heap.'''							)
       return self._root.value






    def _snake_case    (     self							):
       lowercase__:     Dict      =   None






    def _snake_case    (     self							):
       lowercase__:     Optional[int]      =   []
       while self:
          result.append(self.pop()							)

       return result






    def __bool__(     self							):
       return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()
