from __future__ import annotations

a  :      List[Any]            =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
a  :      Union[str, Any]            =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Tuple       =	[]
   lowerCAmelCase_:							Union[str, Any]       =	len(lowercase					)

   for i in range(lowercase					):
      lowerCAmelCase_:							float       =	-1
      for j in range(i + 1							,				lowercase					):
         if arr[i] < arr[j]:
            lowerCAmelCase_:							str       =	arr[j]
            break
      result.append(lowercase					)
   return result
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[str]       =	[]
   for i, outer in enumerate(lowercase					):
      lowerCAmelCase_:							float       =	-1
      for inner in arr[i + 1 :]:
         if outer < inner:
            lowerCAmelCase_:							List[str]       =	inner
            break
      result.append(lowercase					)
   return result
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Dict       =	len(lowercase					)
   lowerCAmelCase_:							list[float]       =	[]
   lowerCAmelCase_:							list[float]       =	[-1] * arr_size

   for index in reversed(range(lowercase					)					):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         lowerCAmelCase_:							Optional[int]       =	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  :      Union[str, Any]            =    (
     """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_tf_available,
    is_torch_available,
    is_vision_available,
)


a  :      List[str]            =    {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[str]            =    [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 a  :      Tuple            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)