





from typing import TYPE_CHECKING

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


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

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A    :					Tuple =      ['''BeitFeatureExtractor''']
					A    :					Tuple =      ['''BeitImageProcessor''']

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


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A    :					str =      [
					    '''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

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










import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Any   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   ["""a""", """b""", """c"""]

					# Defaults to last layer if both are None
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[2]   )

					# Out indices set to match out features
					A__      ,			A__        =   get_aligned_output_features_output_indices(["""a""", """c"""]							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features set to match out indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[0, 2]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features selected from negative indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[-3, -1]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[-3, -1]   )






			def    a_       (			self   :    Optional[Any]   )	->	Optional[Any]:







					"""simple docstring"""






					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	__lowerCAmelCase   )

					# Out features must be a list
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(("""a""", """b""")							,	(0, 1)							,	["""a""", """b"""]   )

					# Out features must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	["""a"""]   )

					# Out indices must be a list or tuple
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	0							,	["""a""", """b"""]   )

					# Out indices must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	(0, 1)							,	["""a"""]   )

					# Out features and out indices must be the same length
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0,)							,	["""a""", """b""", """c"""]   )

					# Out features should match out indices
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 2)							,	["""a""", """b""", """c"""]   )

					# Out features and out indices should be in order
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""b""", """a"""]							,	(0, 1)							,	["""a""", """b"""]   )

					# Check passes with valid inputs
					verify_out_features_out_indices(["""a""", """b""", """d"""]							,	(0, 1, -1)							,	["""a""", """b""", """c""", """d"""]   )







			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   BackboneMixin()

					A__        =   ["""a""", """b""", """c"""]
					A__        =   ["""a""", """c"""]
					A__        =   [0, 2]

					# Check that the output features and indices are set correctly
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 2]   )

					# Check out features and indices are updated correctly
					A__        =   ["""a""", """b"""]
					self.assertEqual(backbone.out_features							,	["""a""", """b"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 1]   )

					A__        =   [-3, -1]
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[-3, -1]   )



