

from __future__ import annotations

__A      		=     [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A      		=     [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 				snake_case_(_UpperCamelCase					)							->     list[float]:



  """simple docstring"""






  _snake_case						=							[]
  _snake_case						=							len(_UpperCamelCase					)

  for i in range(_UpperCamelCase					):
    _snake_case						=							-1
    for j in range(i + 1			,	_UpperCamelCase					):
      if arr[i] < arr[j]:
        _snake_case						=							arr[j]
        break
    result.append(_UpperCamelCase					)
  return result



def 				snake_case_(_UpperCamelCase					)							->     list[float]:



  """simple docstring"""






  _snake_case						=							[]
  for i, outer in enumerate(_UpperCamelCase					):
    _snake_case						=							-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        _snake_case						=							inner
        break
    result.append(_UpperCamelCase					)
  return result



def 				snake_case_(_UpperCamelCase					)							->     list[float]:



  """simple docstring"""






  _snake_case						=							len(_UpperCamelCase					)
  _snake_case						=							[]
  _snake_case						=							[-1] * arr_size

  for index in reversed(range(_UpperCamelCase					)					):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      _snake_case						=							stack[-1]
    stack.append(arr[index]					)
  return result


if __name__ == "__main__":
 from doctest import testmod
 from timeit import timeit

 testmod()
 print(next_greatest_element_slow(arr))
 print(next_greatest_element_fast(arr))
 print(next_greatest_element(arr))

 __A      		=     (
     '''from __main__ import arr, next_greatest_element_slow, '''
     '''next_greatest_element_fast, next_greatest_element'''
 )
 print(
     '''next_greatest_element_slow():''',
     timeit('''next_greatest_element_slow(arr)''', setup=setup),
 )
 print(
     '''next_greatest_element_fast():''',
     timeit('''next_greatest_element_fast(arr)''', setup=setup),
 )
 print(
     '''     next_greatest_element():''',
     timeit('''next_greatest_element(arr)''', setup=setup),
 )




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_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(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT 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.'''
 )
 __A      		=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)

