'''simple docstring'''




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_MAPPING,
						    BeitForImageClassification,
						    BeitForMaskedImageModeling,
						    BeitForSemanticSegmentation,
						    BeitModel,
						)
						from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						import PIL
						from PIL import Image

						from transformers import BeitImageProcessor






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=100			,       __lowerCAmelCase=13			,       __lowerCAmelCase=30			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       __lowerCAmelCase=True			,       __lowerCAmelCase=True			,       __lowerCAmelCase=32			,       __lowerCAmelCase=4			,       __lowerCAmelCase=4			,       __lowerCAmelCase=37			,       __lowerCAmelCase="gelu"			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase=10			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=3			,       __lowerCAmelCase=None			,       __lowerCAmelCase=[0, 1, 2, 3]			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    parent
					lowerCAmelCase     =    100
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    patch_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    is_training
					lowerCAmelCase     =    use_labels
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    type_sequence_label_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    scope
					lowerCAmelCase     =    out_indices
					lowerCAmelCase     =    num_labels

					# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					lowerCAmelCase     =    (image_size // patch_size) ** 2
					lowerCAmelCase     =    num_patches + 1



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

					lowerCAmelCase     =    None
					lowerCAmelCase     =    None
					if self.use_labels:
							lowerCAmelCase     =    ids_tensor([self.batch_size]			,       self.type_sequence_label_size)
							lowerCAmelCase     =    ids_tensor([self.batch_size, self.image_size, self.image_size]			,       self.num_labels)

					lowerCAmelCase     =    self.get_config()

					return config, pixel_values, labels, pixel_labels



			def 				a_			(  self):




					"""simple docstring"""


					return BeitConfig(
					    vocab_size=self.vocab_size			,       image_size=self.image_size			,       patch_size=self.patch_size			,       num_channels=self.num_channels			,       hidden_size=self.hidden_size			,       num_hidden_layers=self.num_hidden_layers			,       num_attention_heads=self.num_attention_heads			,       intermediate_size=self.intermediate_size			,       hidden_act=self.hidden_act			,       hidden_dropout_prob=self.hidden_dropout_prob			,       attention_probs_dropout_prob=self.attention_probs_dropout_prob			,       is_decoder=__lowerCAmelCase			,       initializer_range=self.initializer_range			,       out_indices=self.out_indices			,       )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    BeitModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase     =    model(__lowerCAmelCase)
					self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size))



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForMaskedImageModeling(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase     =    model(__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length - 1, self.vocab_size))



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    self.type_sequence_label_size
					lowerCAmelCase     =    BeitForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase     =    model(__lowerCAmelCase			,       labels=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size))

					# test greyscale images
					lowerCAmelCase     =    1
					lowerCAmelCase     =    BeitForImageClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()

					lowerCAmelCase     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
					lowerCAmelCase     =    model(__lowerCAmelCase			,       labels=__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.type_sequence_label_size))



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    self.num_labels
					lowerCAmelCase     =    BeitForSemanticSegmentation(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase     =    model(__lowerCAmelCase)
					self.parent.assertEqual(
					    result.logits.shape			,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
					lowerCAmelCase     =    model(__lowerCAmelCase			,       labels=__lowerCAmelCase)
					self.parent.assertEqual(
					    result.logits.shape			,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.prepare_config_and_inputs()
					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    config_and_inputs
					lowerCAmelCase     =    {"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			(
			    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			UpperCAmelCase_     :	Optional[int]						=			(
			    {
			        '''feature-extraction''': BeitModel,
			        '''image-classification''': BeitForImageClassification,
			        '''image-segmentation''': BeitForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCAmelCase_     :	Union[str, Any]						=			False
			UpperCAmelCase_     :	int						=			False
			UpperCAmelCase_     :	Optional[int]						=			False



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitModelTester(self)
					lowerCAmelCase     =    ConfigTester(self			,       config_class=__lowerCAmelCase			,       has_text_modality=__lowerCAmelCase			,       hidden_size=37)



			def 				a_			(  self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			@unittest.skip(reason="""BEiT does not use inputs_embeds""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@require_torch_multi_gpu
			@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							self.assertIsInstance(model.get_input_embeddings()			,       (nn.Module))
							lowerCAmelCase     =    model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowerCAmelCase			,       nn.Linear))



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							lowerCAmelCase     =    inspect.signature(model.forward)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase     =    [*signature.parameters.keys()]

							lowerCAmelCase     =    ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					if not self.model_tester.is_training:
							return

					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase     =    True

					for model_class in self.all_model_classes:
							# we don't test BeitForMaskedImageModeling
							if model_class in [*get_values(__lowerCAmelCase), BeitForMaskedImageModeling]:
									continue

							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.train()
							lowerCAmelCase     =    self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase			,       return_labels=__lowerCAmelCase)
							lowerCAmelCase     =    model(**__lowerCAmelCase).loss
							loss.backward()



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
							return

					lowerCAmelCase     =    False
					lowerCAmelCase     =    True

					for model_class in self.all_model_classes:
							# we don't test BeitForMaskedImageModeling
							if (
							    model_class in [*get_values(__lowerCAmelCase), BeitForMaskedImageModeling]
							    or not model_class.supports_gradient_checkpointing
							):
									continue

							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.gradient_checkpointing_enable()
							model.to(__lowerCAmelCase)
							model.train()
							lowerCAmelCase     =    self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase			,       return_labels=__lowerCAmelCase)
							lowerCAmelCase     =    model(**__lowerCAmelCase).loss
							loss.backward()



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					lowerCAmelCase     =    _config_zero_init(__lowerCAmelCase)
					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(config=__lowerCAmelCase)
							for name, param in model.named_parameters():
									# we skip lambda parameters as these require special initial values
									# determined by config.layer_scale_init_value
									if "lambda" in name:
											continue
									if param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()			,       [0.0, 1.0]			,       msg=f"Parameter {name} of model {model_class} seems not properly initialized"			,       )







			@slow
			def 				a_			(  self):




					"""simple docstring"""


					for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase     =    BeitModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







def 							snake_case__				(     )       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image






@require_torch
@require_vision
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			@cached_property
			def 				a_			(  self):




					"""simple docstring"""


					return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""") if is_vision_available() else None



			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""").to(__lowerCAmelCase)

					lowerCAmelCase     =    self.default_image_processor
					lowerCAmelCase     =    prepare_img()
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").pixel_values.to(__lowerCAmelCase)

					# prepare bool_masked_pos
					lowerCAmelCase     =    torch.ones((1, 196)			,       dtype=torch.bool).to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(pixel_values=__lowerCAmelCase			,       bool_masked_pos=__lowerCAmelCase)
					lowerCAmelCase     =    outputs.logits

					# verify the logits
					lowerCAmelCase     =    torch.Size((1, 196, 8192))
					self.assertEqual(logits.shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    torch.tensor(
					    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]).to(__lowerCAmelCase)

					self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]			,       __lowerCAmelCase			,       atol=1E-2))



			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""").to(__lowerCAmelCase)

					lowerCAmelCase     =    self.default_image_processor
					lowerCAmelCase     =    prepare_img()
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(**__lowerCAmelCase)
					lowerCAmelCase     =    outputs.logits

					# verify the logits
					lowerCAmelCase     =    torch.Size((1, 1000))
					self.assertEqual(logits.shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    torch.tensor([-1.2385, -1.0987, -1.0108]).to(__lowerCAmelCase)

					self.assertTrue(torch.allclose(logits[0, :3]			,       __lowerCAmelCase			,       atol=1E-4))

					lowerCAmelCase     =    281
					self.assertEqual(logits.argmax(-1).item()			,       __lowerCAmelCase)



			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""").to(
					    __lowerCAmelCase)

					lowerCAmelCase     =    self.default_image_processor
					lowerCAmelCase     =    prepare_img()
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(**__lowerCAmelCase)
					lowerCAmelCase     =    outputs.logits

					# verify the logits
					lowerCAmelCase     =    torch.Size((1, 21841))
					self.assertEqual(logits.shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    torch.tensor([1.6881, -0.2787, 0.5901]).to(__lowerCAmelCase)

					self.assertTrue(torch.allclose(logits[0, :3]			,       __lowerCAmelCase			,       atol=1E-4))

					lowerCAmelCase     =    2396
					self.assertEqual(logits.argmax(-1).item()			,       __lowerCAmelCase)



			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""")
					lowerCAmelCase     =    model.to(__lowerCAmelCase)

					lowerCAmelCase     =    BeitImageProcessor(do_resize=__lowerCAmelCase			,       size=640			,       do_center_crop=__lowerCAmelCase)

					lowerCAmelCase     =    load_dataset("""hf-internal-testing/fixtures_ade20k"""			,       split="""test""")
					lowerCAmelCase     =    Image.open(ds[0]["""file"""])
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(**__lowerCAmelCase)
					lowerCAmelCase     =    outputs.logits

					# verify the logits
					lowerCAmelCase     =    torch.Size((1, 150, 160, 160))
					self.assertEqual(logits.shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    version.parse(PIL.__version__) < version.parse("""9.0.0""")

					if is_pillow_less_than_a:
							lowerCAmelCase     =    torch.tensor(
							    [
							        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
							        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
							        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
							    ]			,       device=__lowerCAmelCase			,       )
					else:
							lowerCAmelCase     =    torch.tensor(
							    [
							        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
							        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
							        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
							    ]			,       device=__lowerCAmelCase			,       )

					self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,       __lowerCAmelCase			,       atol=1E-4))







			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""")
					lowerCAmelCase     =    model.to(__lowerCAmelCase)

					lowerCAmelCase     =    BeitImageProcessor(do_resize=__lowerCAmelCase			,       size=640			,       do_center_crop=__lowerCAmelCase)

					lowerCAmelCase     =    load_dataset("""hf-internal-testing/fixtures_ade20k"""			,       split="""test""")
					lowerCAmelCase     =    Image.open(ds[0]["""file"""])
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(**__lowerCAmelCase)

					lowerCAmelCase     =    outputs.logits.detach().cpu()

					lowerCAmelCase     =    image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase			,       target_sizes=[(500, 300)])
					lowerCAmelCase     =    torch.Size((500, 300))
					self.assertEqual(segmentation[0].shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase)
					lowerCAmelCase     =    torch.Size((160, 160))
					self.assertEqual(segmentation[0].shape			,       __lowerCAmelCase)





'''simple docstring'''




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 							snake_case__				(     _A:   Dict					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    [False] * len(_A					)
		lowerCAmelCase     =    [-1] * len(_A					)

		def dfs(_A:   Dict ,  _A:   Tuple					):
				lowerCAmelCase     =    True
				lowerCAmelCase     =    c
				for u in graph[v]:
						if not visited[u]:
								dfs(_A ,  1 - c					)

		for i in range(len(_A					)					):
				if not visited[i]:
						dfs(_A ,  0					)

		for i in range(len(_A					)					):
				for j in graph[i]:
						if color[i] == color[j]:
								return False

		return True


# Adjacency list of graph
__lowercase        =    {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}




print(check_bipartite_dfs(graph))




