code: 
  '''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   snake_case_       (							_a     :							Tuple ,					_a     :							Optional[Any] ,					_a     :							List[Any] ,					_a     :							Optional[Any]			):
        # Initialise PyTorch model
        snake_case_						       =							ElectraConfig.from_json_file(_a			)
        print(F'''Building PyTorch model from configuration: {config}'''			)
  
        if discriminator_or_generator == "discriminator":
              snake_case_						       =							ElectraForPreTraining(_a			)
        elif discriminator_or_generator == "generator":
              snake_case_						       =							ElectraForMaskedLM(_a			)
        else:
              raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"			)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_electra(
            _a ,					_a ,					_a ,					discriminator_or_generator=_a			)
  
        # Save pytorch-model
        print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
        torch.save(model.state_dict() ,					_a			)
  
  
  if __name__ == "__main__":
       A     :   int           =   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     :   Optional[Any]           =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
       )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  A           =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : int    )   ->       None:
          snake_case_						       =							-1
          snake_case_						       =							-1
          snake_case_						       =							[-1] * length
          snake_case_						       =							None
          snake_case_						       =							None
  
  
    def __repr__(self  : List[Any]    )   ->       str:
          return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  def   snake_case_       (							_a     :							list[int]			):
        snake_case_						       =							Node(len(_a			)			)
        snake_case_       ,	snake_case_						       =							min(_a			), max(_a			)
        # Leaf node case where the node contains only one unique value
        if root.minn == root.maxx:
              return root
        snake_case_						       =							(root.minn + root.maxx) // 2
  
        snake_case_						       =							[]
        snake_case_						       =							[]
  
        for index, num in enumerate(_a			):
              if num <= pivot:
                    left_arr.append(_a			)
              else:
                    right_arr.append(_a			)
              snake_case_						       =							len(_a			)
        snake_case_						       =							build_tree(_a			)
        snake_case_						       =							build_tree(_a			)
        return root
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int			):
        if index < 0 or node is None:
              return 0
        # Leaf node cases
        if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
        snake_case_						       =							(node.minn + node.maxx) // 2
        if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left ,					_a ,					node.map_left[index] - 1			)
        else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right ,					_a ,					index - node.map_left[index]			)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if start > end:
              return 0
        snake_case_						       =							rank_till_index(_a ,					_a ,					_a			)
        snake_case_						       =							rank_till_index(_a ,					_a ,					start - 1			)
        return rank_till_end - rank_before_start
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if index > (end - start) or start > end or node is None:
              return -1
        # Leaf node case
        if node.minn == node.maxx:
              return node.minn
        # Number of elements in the left subtree in interval [start, end]
        snake_case_						       =							node.map_left[end] - (
            node.map_left[start - 1] if start else 0
        )
        if num_elements_in_left_tree > index:
              return quantile(
                  node.left ,					_a ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					)
        else:
              return quantile(
                  node.right ,					index - num_elements_in_left_tree ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if (
            start > end
            or node is None
            or start_num > end_num
            or node.minn > end_num
            or node.maxx < start_num
        ):
              return 0
        if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
        snake_case_						       =							range_counting(
            node.left ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					_a ,					_a ,					)
        snake_case_						       =							range_counting(
            node.right ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					_a ,					_a ,					)
        return left + right
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  