





'''simple docstring'''






from __future__ import annotations

A   : Union[str, Any]             =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A   : Optional[int]             =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						_snake_case:   List[Any]  					=		len(lowercase__						)

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





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						for i, outer in enumerate(lowercase__						):
												_snake_case:   float  					=		-1
												for inner in arr[i + 1 :]:
																		if outer < inner:
																								_snake_case:   List[Any]  					=		inner
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   int  					=		len(lowercase__						)
						_snake_case:   list[float]  					=		[]
						_snake_case:   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:
																		_snake_case:   Dict  					=		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),
		)












'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		A   : Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





