



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       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[float]				)  ->     list[float]:
 _snake_case    :  List[str]					=					[]
 _snake_case    :  List[str]					=					len(SCREAMING_SNAKE_CASE__				)

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[float]				)  ->     list[float]:
 _snake_case    :  Union[str, Any]					=					[]
 for i, outer in enumerate(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  float					=					-1
  for inner in arr[i + 1 :]:
   if outer < inner:
    _snake_case    :  Union[str, Any]					=					inner
    break
  result.append(SCREAMING_SNAKE_CASE__				)
 return result



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	list[float]				)  ->     list[float]:
 _snake_case    :  Optional[Any]					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  list[float]					=					[]
 _snake_case    :  list[float]					=					[-1] * arr_size

 for index in reversed(range(SCREAMING_SNAKE_CASE__				)				):
  if stack:
   while stack[-1] <= arr[index]:
    stack.pop()
    if not stack:
     break
  if stack:
   _snake_case    :  Union[str, Any]					=					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),
  )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
