






from __future__ import annotations

import bisect

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							int		,		__A    :							int = 0		,		__A    :							List[Any] = -1 )      ->					int:
    if hi < 0:
        _UpperCAmelCase				:      int			  =				len(_SCREAMING_SNAKE_CASE )

    while lo < hi:
        _UpperCAmelCase				:      Any			  =				lo + (hi - lo) // 2
        if sorted_collection[mid] < item:
            _UpperCAmelCase				:      Any			  =				mid + 1
        else:
            _UpperCAmelCase				:      List[str]			  =				mid

    return lo

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							Dict		,		__A    :							str = 0		,		__A    :							Union[str, Any] = -1 )      ->					int:
    if hi < 0:
        _UpperCAmelCase				:      str			  =				len(_SCREAMING_SNAKE_CASE )

    while lo < hi:
        _UpperCAmelCase				:      str			  =				lo + (hi - lo) // 2
        if sorted_collection[mid] <= item:
            _UpperCAmelCase				:      int			  =				mid + 1
        else:
            _UpperCAmelCase				:      List[Any]			  =				mid

    return lo

def      _lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							List[str]		,		__A    :							Tuple = 0		,		__A    :							Any = -1 )      ->					None:
    sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE )		,		_SCREAMING_SNAKE_CASE )

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							Dict		,		__A    :							int = 0		,		__A    :							Union[str, Any] = -1 )      ->					None:
    sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE )		,		_SCREAMING_SNAKE_CASE )

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[int] )      ->					int | None:
    _UpperCAmelCase				:      int			  =				0
    _UpperCAmelCase				:      Tuple			  =				len(_SCREAMING_SNAKE_CASE ) - 1

    while left <= right:
        _UpperCAmelCase				:      int			  =				left + (right - left) // 2
        _UpperCAmelCase				:      Optional[int]			  =				sorted_collection[midpoint]
        if current_item == item:
            return midpoint
        elif item < current_item:
            _UpperCAmelCase				:      List[str]			  =				midpoint - 1
        else:
            _UpperCAmelCase				:      Optional[Any]			  =				midpoint + 1
    return None

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							Union[str, Any] )      ->					int | None:
    _UpperCAmelCase				:      Union[str, Any]			  =				bisect.bisect_left(_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE )
    if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
        return index
    return None

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							Optional[int]		,		__A    :							str		,		__A    :							Dict )      ->					int | None:
    if right < left:
        return None

    _UpperCAmelCase				:      Any			  =				left + (right - left) // 2

    if sorted_collection[midpoint] == item:
        return midpoint
    elif sorted_collection[midpoint] > item:
        return binary_search_by_recursion(_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		midpoint - 1 )
    else:
        return binary_search_by_recursion(_SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE		,		midpoint + 1		,		_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						input('Enter numbers separated by comma:\n').strip()
  SCREAMING_SNAKE_CASE 							=						sorted(int(item) for item in user_input.split(','))
  SCREAMING_SNAKE_CASE 							=						int(input('Enter a single number to be found in the list:\n'))
  SCREAMING_SNAKE_CASE 							=						binary_search(collection, target)
  if result is None:
    print(F'{target} was not found in {collection}.')
  else:
    print(F'{target} was found at position {result} in {collection}.')










import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




