


'''simple docstring'''






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

							if is_vision_available():
														from transformers import MaskFormerImageProcessor

if is_vision_available():
							from PIL import Image
class 						__UpperCamelCase	:





							def __init__(  self		:Any  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int=2  ,_UpperCamelCase		:str=True  ,_UpperCamelCase		:int=False  ,_UpperCamelCase		:int=1_0  ,_UpperCamelCase		:List[Any]=3  ,_UpperCamelCase		:Any=3_2 * 4  ,_UpperCamelCase		:int=3_2 * 6  ,_UpperCamelCase		:Optional[int]=4  ,_UpperCamelCase		:Dict=3_2  ,):
									snake_case_		:							List[Any]			       =						parent
									snake_case_		:							Dict			       =						batch_size
									snake_case_		:							Any			       =						is_training
									snake_case_		:							List[str]			       =						use_auxiliary_loss
									snake_case_		:							int			       =						num_queries
									snake_case_		:							Optional[Any]			       =						num_channels
									snake_case_		:							Any			       =						min_size
									snake_case_		:							Any			       =						max_size
									snake_case_		:							List[Any]			       =						num_labels
									snake_case_		:							Union[str, Any]			       =						mask_feature_size





							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
									    _UpperCamelCase			)

									snake_case_		:							int			       =						torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=_UpperCamelCase			)

									snake_case_		:							Tuple			       =						(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,device=_UpperCamelCase			) > 0.5
									).float()
									snake_case_		:							List[Any]			       =						(torch.rand((self.batch_size, self.num_labels)  ,device=_UpperCamelCase			) > 0.5).long()

									snake_case_		:							Optional[int]			       =						self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels





							def 		a__   (  self		:int			):
									return MaskFormerConfig.from_backbone_and_decoder_configs(
									    backbone_config=SwinConfig(
									        depths=[1, 1, 1, 1]  ,)  ,decoder_config=DetrConfig(
									        decoder_ffn_dim=1_2_8  ,num_queries=self.num_queries  ,decoder_attention_heads=2  ,d_model=self.mask_feature_size  ,)  ,mask_feature_size=self.mask_feature_size  ,fpn_feature_size=self.mask_feature_size  ,num_channels=self.num_channels  ,num_labels=self.num_labels  ,)





							def 		a__   (  self		:Dict			):
									snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							int			       =						self.prepare_config_and_inputs()
									snake_case_		:							Dict			       =						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
									return config, inputs_dict





							def 		a__   (  self		:Any  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[Any]			):
									snake_case_		:							int			       =						output.encoder_hidden_states
									snake_case_		:							int			       =						output.pixel_decoder_hidden_states
									snake_case_		:							List[str]			       =						output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(_UpperCamelCase			)  ,len(config.backbone_config.depths			)			)
									self.parent.assertTrue(len(_UpperCamelCase			)  ,len(config.backbone_config.depths			)			)
									self.parent.assertTrue(len(_UpperCamelCase			)  ,config.decoder_config.decoder_layers			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]=False			):
									with torch.no_grad():
											snake_case_		:							int			       =						MaskFormerModel(config=_UpperCamelCase			)
											model.to(_UpperCamelCase			)
											model.eval()

											snake_case_		:							Union[str, Any]			       =						model(pixel_values=_UpperCamelCase  ,pixel_mask=_UpperCamelCase			)
											snake_case_		:							Union[str, Any]			       =						model(_UpperCamelCase  ,output_hidden_states=_UpperCamelCase			)
									# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
									# encoder and pixel decoder
									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape  ,(self.batch_size, self.num_queries, self.mask_feature_size)  ,)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
									self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

									if output_hidden_states:
											self.check_output_hidden_state(_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						MaskFormerForInstanceSegmentation(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()

									def comm_check_on_output(_UpperCamelCase		:Dict			):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
											self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape  ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,)
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape  ,(self.batch_size, self.num_queries, self.num_labels + 1)			)

									with torch.no_grad():
											snake_case_		:							List[Any]			       =						model(pixel_values=_UpperCamelCase  ,pixel_mask=_UpperCamelCase			)
											snake_case_		:							int			       =						model(_UpperCamelCase			)

											comm_check_on_output(_UpperCamelCase			)

											snake_case_		:							Any			       =						model(
											    pixel_values=_UpperCamelCase  ,pixel_mask=_UpperCamelCase  ,mask_labels=_UpperCamelCase  ,class_labels=_UpperCamelCase			)

									comm_check_on_output(_UpperCamelCase			)

									self.parent.assertTrue(result.loss is not None			)
									self.parent.assertEqual(result.loss.shape  ,torch.Size([1]			)			)





@require_torch
class 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		List[Any]   		=  (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							lowercase			:		List[str]   		=  (
							    {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							lowercase			:		Optional[int]   		=  False
							lowercase			:		Dict   		=  False
							lowercase			:		Dict   		=  False
							lowercase			:		List[Any]   		=  False





							def 		a__   (  self		:Tuple			):
									snake_case_		:							int			       =						MaskFormerModelTester(self			)
									snake_case_		:							Tuple			       =						ConfigTester(self  ,config_class=_UpperCamelCase  ,has_text_modality=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:Optional[int]			):
									snake_case_       ,      snake_case_		:							Union[str, Any]			       =						self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(_UpperCamelCase  ,**_UpperCamelCase  ,output_hidden_states=_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_UpperCamelCase			)





							@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""			)
							def 		a__   (  self		:Tuple			):
									pass





							@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""			)
							def 		a__   (  self		:Dict			):
									pass





							@unittest.skip(reason="""MaskFormer is not a generative model"""			)
							def 		a__   (  self		:Union[str, Any]			):
									pass





							@unittest.skip(reason="""MaskFormer does not use token embeddings"""			)
							def 		a__   (  self		:int			):
									pass





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





							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
							def 		a__   (  self		:List[Any]			):
									pass





							def 		a__   (  self		:Tuple			):
									snake_case_       ,      snake_case_		:							Optional[int]			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							Optional[int]			       =						model_class(_UpperCamelCase			)
											snake_case_		:							Dict			       =						inspect.signature(model.forward			)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											snake_case_		:							Union[str, Any]			       =						[*signature.parameters.keys()]

											snake_case_		:							Optional[Any]			       =						["""pixel_values"""]
											self.assertListEqual(arg_names[:1]  ,_UpperCamelCase			)





							@slow
							def 		a__   (  self		:List[str]			):
									for model_name in ["facebook/maskformer-swin-small-coco"]:
											snake_case_		:							Union[str, Any]			       =						MaskFormerModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Optional[Any]			       =						(self.model_tester.min_size,) * 2
									snake_case_		:							int			       =						{
									    """pixel_values""": torch.randn((2, 3, *size)  ,device=_UpperCamelCase			),
									    """mask_labels""": torch.randn((2, 1_0, *size)  ,device=_UpperCamelCase			),
									    """class_labels""": torch.zeros(2  ,1_0  ,device=_UpperCamelCase			).long(),
									}

									snake_case_		:							List[Any]			       =						MaskFormerForInstanceSegmentation(MaskFormerConfig()			).to(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						model(**_UpperCamelCase			)
									self.assertTrue(outputs.loss is not None			)





							def 		a__   (  self		:Any			):
									snake_case_       ,      snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(_UpperCamelCase  ,**_UpperCamelCase  ,output_hidden_states=_UpperCamelCase			)





							def 		a__   (  self		:Any			):
									snake_case_       ,      snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											snake_case_		:							Optional[int]			       =						model_class(_UpperCamelCase			).to(_UpperCamelCase			)
											snake_case_		:							int			       =						model(**_UpperCamelCase  ,output_attentions=_UpperCamelCase			)
											self.assertTrue(outputs.attentions is not None			)





							def 		a__   (  self		:int			):
									if not self.model_tester.is_training:
											return
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_		:							Dict			       =						self.all_model_classes[1]
									snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							Dict			       =						self.model_tester.prepare_config_and_inputs()

									snake_case_		:							List[str]			       =						model_class(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.train()

									snake_case_		:							int			       =						model(_UpperCamelCase  ,mask_labels=_UpperCamelCase  ,class_labels=_UpperCamelCase			).loss
									loss.backward()





							def 		a__   (  self		:Optional[Any]			):
									# only MaskFormerForInstanceSegmentation has the loss
									snake_case_		:							List[str]			       =						self.all_model_classes[1]
									snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_		:							int			       =						self.model_tester.prepare_config_and_inputs()
									snake_case_		:							Union[str, Any]			       =						True
									snake_case_		:							Union[str, Any]			       =						True

									snake_case_		:							Optional[int]			       =						model_class(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.train()

									snake_case_		:							List[Any]			       =						model(_UpperCamelCase  ,mask_labels=_UpperCamelCase  ,class_labels=_UpperCamelCase			)

									snake_case_		:							int			       =						outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									snake_case_		:							Any			       =						outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()
									# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
									snake_case_		:							Any			       =						outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									snake_case_		:							str			       =						outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=_UpperCamelCase			)

									self.assertIsNotNone(encoder_hidden_states.grad			)
									self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
									self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
									self.assertIsNotNone(attentions.grad			)





__A	:	int     =     1e-4





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






@require_vision
@slow
class 						__UpperCamelCase	(						unittest.TestCase							):





							@cached_property
							def 		a__   (  self		:List[Any]			):
									return (
									    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
									    if is_vision_available()
									    else None
									)





							def 		a__   (  self		:Tuple			):
									snake_case_		:							Tuple			       =						MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""			).to(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						self.default_image_processor
									snake_case_		:							str			       =						prepare_img()
									snake_case_		:							str			       =						image_processor(_UpperCamelCase  ,return_tensors="""pt"""			).to(_UpperCamelCase			)
									snake_case_		:							Dict			       =						inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0			)
									# check size
									self.assertEqual(_UpperCamelCase  ,(1, 3, 8_0_0, 1_0_8_8)			)

									with torch.no_grad():
											snake_case_		:							Dict			       =						model(**_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						torch.tensor(
									    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]			).to(_UpperCamelCase			)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)

									snake_case_		:							str			       =						torch.tensor(
									    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]			).to(_UpperCamelCase			)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)

									snake_case_		:							Tuple			       =						torch.tensor(
									    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]			).to(_UpperCamelCase			)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							str			       =						(
									    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
									    .to(_UpperCamelCase			)
									    .eval()
									)
									snake_case_		:							List[Any]			       =						self.default_image_processor
									snake_case_		:							Optional[int]			       =						prepare_img()
									snake_case_		:							Optional[int]			       =						image_processor(_UpperCamelCase  ,return_tensors="""pt"""			).to(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0			)
									# check size
									self.assertEqual(_UpperCamelCase  ,(1, 3, 8_0_0, 1_0_8_8)			)

									with torch.no_grad():
											snake_case_		:							List[str]			       =						model(**_UpperCamelCase			)
									# masks_queries_logits
									snake_case_		:							Dict			       =						outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,)
									snake_case_		:							Optional[int]			       =						[
									    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
									    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
									    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
									]
									snake_case_		:							Optional[int]			       =						torch.tensor(_UpperCamelCase			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)
									# class_queries_logits
									snake_case_		:							Any			       =						outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
									snake_case_		:							str			       =						torch.tensor(
									    [
									        [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
									        [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
									        [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
									    ]			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)





							def 		a__   (  self		:int			):
									snake_case_		:							Dict			       =						(
									    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""			)
									    .to(_UpperCamelCase			)
									    .eval()
									)
									snake_case_		:							List[str]			       =						self.default_image_processor
									snake_case_		:							Tuple			       =						prepare_img()
									snake_case_		:							Tuple			       =						image_processor(_UpperCamelCase  ,return_tensors="""pt"""			).to(_UpperCamelCase			)
									snake_case_		:							int			       =						inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0			)
									# check size
									self.assertEqual(_UpperCamelCase  ,(1, 3, 8_0_0, 1_0_8_8)			)

									with torch.no_grad():
											snake_case_		:							Optional[Any]			       =						model(**_UpperCamelCase			)
									# masks_queries_logits
									snake_case_		:							List[str]			       =						outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,)
									snake_case_		:							Tuple			       =						[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
									snake_case_		:							List[str]			       =						torch.tensor(_UpperCamelCase			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)
									# class_queries_logits
									snake_case_		:							List[str]			       =						outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
									snake_case_		:							int			       =						torch.tensor(
									    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]			).to(_UpperCamelCase			)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,_UpperCamelCase  ,atol=_UpperCamelCase			)			)





							def 		a__   (  self		:str			):
									snake_case_		:							Union[str, Any]			       =						(
									    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""			)
									    .to(_UpperCamelCase			)
									    .eval()
									)
									snake_case_		:							Union[str, Any]			       =						self.default_image_processor

									snake_case_		:							Dict			       =						image_processor(
									    [np.zeros((3, 8_0_0, 1_3_3_3)			), np.zeros((3, 8_0_0, 1_3_3_3)			)]  ,segmentation_maps=[np.zeros((3_8_4, 3_8_4)			).astype(np.floataa			), np.zeros((3_8_4, 3_8_4)			).astype(np.floataa			)]  ,return_tensors="""pt"""  ,)

									snake_case_		:							Union[str, Any]			       =						inputs["""pixel_values"""].to(_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						[el.to(_UpperCamelCase			) for el in inputs["""mask_labels"""]]
									snake_case_		:							Tuple			       =						[el.to(_UpperCamelCase			) for el in inputs["""class_labels"""]]

									with torch.no_grad():
											snake_case_		:							Dict			       =						model(**_UpperCamelCase			)

									self.assertTrue(outputs.loss is not None			)



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :str				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		# Return True if there is node that has not iterated.
		snake_case_		:							Optional[int]			       =						[False] * len(lowerCamelCase_ )
		snake_case_		:							Dict			       =						[]
		queue.append(lowerCamelCase_ )
		snake_case_		:							Tuple			       =						True

		while queue:
				snake_case_		:							Union[str, Any]			       =						queue.pop(0 )
				for ind in range(len(graph[u] ) ):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(lowerCamelCase_ )
								snake_case_		:							str			       =						True
								snake_case_		:							Optional[Any]			       =						u

		return visited[t]





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Dict				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		# This array is filled by BFS and to store path
		snake_case_		:							Optional[int]			       =						[-1] * (len(lowerCamelCase_ ))
		snake_case_		:							Tuple			       =						0
		while bfs(lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						float("""Inf""" )
				snake_case_		:							Any			       =						sink

				while s != source:
						# Find the minimum value in select path
						snake_case_		:							Optional[Any]			       =						min(lowerCamelCase_				,      graph[parent[s]][s] )
						snake_case_		:							Union[str, Any]			       =						parent[s]

				max_flow += path_flow
				snake_case_		:							Dict			       =						sink

				while v != source:
						snake_case_		:							Optional[int]			       =						parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						snake_case_		:							Any			       =						parent[v]
		return max_flow


__A	:	Union[str, Any]     =     [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__A,   __A	:	Union[str, Any]     =     0, 5






print(ford_fulkerson(graph, source, sink))