





from __future__ import annotations

snake_case_							=			[-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
snake_case_							=			[-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     list[float]:
		lowerCamelCase   : Optional[Any]								=[]
		lowerCamelCase   : Union[str, Any]								=len(SCREAMING_SNAKE_CASE_		)

		for i in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : float								=-1
				for j in range(i + 1							,       SCREAMING_SNAKE_CASE_		):
						if arr[i] < arr[j]:
								lowerCamelCase   : str								=arr[j]
								break
				result.append(SCREAMING_SNAKE_CASE_		)
		return result


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     list[float]:
		lowerCamelCase   : Optional[int]								=[]
		for i, outer in enumerate(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : float								=-1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowerCamelCase   : Union[str, Any]								=inner
								break
				result.append(SCREAMING_SNAKE_CASE_		)
		return result


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     list[float]:
		lowerCamelCase   : Dict								=len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : list[float]								=[]
		lowerCamelCase   : 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:
						lowerCamelCase   : 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))

						snake_case_							=			(
						    '''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


snake_case_							=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

				snake_case_							=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

