

"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      Union[str, Any]							      =		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		['''BeitFeatureExtractor''']
							__UpperCamelCase  :      Optional[int]							      =		['''BeitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BeitForImageClassification''',
							    '''BeitForMaskedImageModeling''',
							    '''BeitForSemanticSegmentation''',
							    '''BeitModel''',
							    '''BeitPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		[
							    '''FlaxBeitForImageClassification''',
							    '''FlaxBeitForMaskedImageModeling''',
							    '''FlaxBeitModel''',
							    '''FlaxBeitPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_beit import BeitFeatureExtractor
														from .image_processing_beit import BeitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_beit import (
														    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BeitForImageClassification,
														    BeitForMaskedImageModeling,
														    BeitForSemanticSegmentation,
														    BeitModel,
														    BeitPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_beit import (
														    FlaxBeitForImageClassification,
														    FlaxBeitForMaskedImageModeling,
														    FlaxBeitModel,
														    FlaxBeitPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      Optional[Any]							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]       ):
    create_state_space_tree(_lowerCamelCase						,       []						,       0						,       [0 for i in range(len(_lowerCamelCase       )       )]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					list[int]						,       ):

    if index == len(_lowerCamelCase       ):
        print(_lowerCamelCase       )
        return

    for i in range(len(_lowerCamelCase       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            A__				=			True
            create_state_space_tree(_lowerCamelCase						,       _lowerCamelCase						,       index + 1						,       _lowerCamelCase       )
            current_sequence.pop()
            A__				=			False
__lowerCAmelCase							:	list[int | str]			=[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCAmelCase							:	list[int | str]			=["A", "B", "C"]



generate_all_permutations(sequence_a)



