






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 					_snake_case						:






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=2					,UpperCamelCase=True					,UpperCamelCase=False					,UpperCamelCase=10					,UpperCamelCase=3					,UpperCamelCase=32 * 4					,UpperCamelCase=32 * 6					,UpperCamelCase=4					,UpperCamelCase=32					,)						->    str:
					snake_case__      :int         =   parent
					snake_case__      :Union[str, Any]         =   batch_size
					snake_case__      :Dict         =   is_training
					snake_case__      :Optional[Any]         =   use_auxiliary_loss
					snake_case__      :Union[str, Any]         =   num_queries
					snake_case__      :Dict         =   num_channels
					snake_case__      :List[Any]         =   min_size
					snake_case__      :int         =   max_size
					snake_case__      :Optional[int]         =   num_labels
					snake_case__      :Dict         =   mask_feature_size






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Union[str, Any]         =   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
					    __A       )

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

					snake_case__      :Optional[Any]         =   (
					    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,device=__A       ) > 0.5
					).float()
					snake_case__      :Any         =   (torch.rand((self.batch_size, self.num_labels)					,device=__A       ) > 0.5).long()

					snake_case__      :List[str]         =   self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return MaskFormerConfig.from_backbone_and_decoder_configs(
					    backbone_config=SwinConfig(
					        depths=[1, 1, 1, 1]					,)					,decoder_config=DetrConfig(
					        decoder_ffn_dim=128					,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    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   self.prepare_config_and_inputs()
					snake_case__      :List[str]         =   {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
					return config, inputs_dict






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :Optional[int]         =   output.encoder_hidden_states
					snake_case__      :Tuple         =   output.pixel_decoder_hidden_states
					snake_case__      :Any         =   output.transformer_decoder_hidden_states

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=False       )						->    Any:
					with torch.no_grad():
									snake_case__      :List[str]         =   MaskFormerModel(config=__A       )
									model.to(__A       )
									model.eval()

									snake_case__      :Optional[Any]         =   model(pixel_values=__A					,pixel_mask=__A       )
									snake_case__      :Optional[int]         =   model(__A					,output_hidden_states=__A       )
					# 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(__A					,__A       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :List[Any]         =   MaskFormerForInstanceSegmentation(config=__A       )
					model.to(__A       )
					model.eval()

					def comm_check_on_output(UpperCamelCase       ):
									# 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__      :Optional[Any]         =   model(pixel_values=__A					,pixel_mask=__A       )
									snake_case__      :List[Any]         =   model(__A       )

									comm_check_on_output(__A       )

									snake_case__      :List[str]         =   model(
									    pixel_values=__A					,pixel_mask=__A					,mask_labels=__A					,class_labels=__A       )

					comm_check_on_output(__A       )

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




@require_torch
class 					_snake_case						(			_A       ,    _A       ,    unittest.TestCase ):
	_A          =   (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
	_A          =   (
	    {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
	    if is_torch_available()
	    else {}
	)

	_A          =   False
	_A          =   False
	_A          =   False
	_A          =   False






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Any         =   MaskFormerModelTester(self       )
					snake_case__      :Optional[int]         =   ConfigTester(self					,config_class=__A					,has_text_modality=__A       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					self.config_tester.run_common_tests()






	def    lowerCAmelCase_							(		self       )						->    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(__A					,**__A					,output_hidden_states=__A       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :str         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__A       )






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






	@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method"       )
	def    lowerCAmelCase_							(		self       )						->    Any:
					pass






	@unittest.skip(reason="MaskFormer is not a generative model"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					pass






	@unittest.skip(reason="MaskFormer does not use token embeddings"       )
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					pass






	@require_torch_multi_gpu
	@unittest.skip(
	    reason="MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"       )
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					pass






	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def    lowerCAmelCase_							(		self       )						->    Any:
					pass






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__    ,						snake_case__      :int         =   self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__      :List[Any]         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,__A       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					for model_name in ["facebook/maskformer-swin-small-coco"]:
									snake_case__      :Tuple         =   MaskFormerModel.from_pretrained(__A       )
									self.assertIsNotNone(__A       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :List[str]         =   (self.model_tester.min_size,) * 2
					snake_case__      :int         =   {
					    "pixel_values": torch.randn((2, 3, *size)					,device=__A       ),
					    "mask_labels": torch.randn((2, 10, *size)					,device=__A       ),
					    "class_labels": torch.zeros(2					,10					,device=__A       ).long(),
					}

					snake_case__      :Tuple         =   MaskFormerForInstanceSegmentation(MaskFormerConfig()       ).to(__A       )
					snake_case__      :Any         =   model(**__A       )
					self.assertTrue(outputs.loss is not None       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__    ,						snake_case__      :List[str]         =   self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskformer_model(__A					,**__A					,output_hidden_states=__A       )






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__    ,						snake_case__      :str         =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case__      :int         =   model_class(__A       ).to(__A       )
									snake_case__      :int         =   model(**__A					,output_attentions=__A       )
									self.assertTrue(outputs.attentions is not None       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					if not self.model_tester.is_training:
									return
					# only MaskFormerForInstanceSegmentation has the loss
					snake_case__      :Optional[int]         =   self.all_model_classes[1]
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :Optional[Any]         =   self.model_tester.prepare_config_and_inputs()

					snake_case__      :Any         =   model_class(__A       )
					model.to(__A       )
					model.train()

					snake_case__      :Dict         =   model(__A					,mask_labels=__A					,class_labels=__A       ).loss
					loss.backward()






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					# only MaskFormerForInstanceSegmentation has the loss
					snake_case__      :Optional[Any]         =   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__      :Tuple         =   True
					snake_case__      :str         =   True

					snake_case__      :int         =   model_class(__A       )
					model.to(__A       )
					model.train()

					snake_case__      :Tuple         =   model(__A					,mask_labels=__A					,class_labels=__A       )

					snake_case__      :Any         =   outputs.encoder_hidden_states[0]
					encoder_hidden_states.retain_grad()

					snake_case__      :int         =   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__      :Optional[Any]         =   outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					snake_case__      :List[Any]         =   outputs.attentions[0]
					attentions.retain_grad()

					outputs.loss.backward(retain_graph=__A       )

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




__UpperCAmelCase							:   int							=  1e-4





def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :List[str]         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
				return image


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






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					return (
					    MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"       )
					    if is_vision_available()
					    else None
					)






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :int         =   MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"       ).to(__A       )
					snake_case__      :List[str]         =   self.default_image_processor
					snake_case__      :Dict         =   prepare_img()
					snake_case__      :List[Any]         =   image_processor(__A					,return_tensors="pt"       ).to(__A       )
					snake_case__      :Optional[Any]         =   inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
					# check size
					self.assertEqual(__A					,(1, 3, 800, 1_088)       )

					with torch.no_grad():
									snake_case__      :List[Any]         =   model(**__A       )

					snake_case__      :Tuple         =   torch.tensor(
					    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]       ).to(__A       )
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]					,__A					,atol=__A       )       )

					snake_case__      :str         =   torch.tensor(
					    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]       ).to(__A       )
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,__A					,atol=__A       )       )

					snake_case__      :Optional[int]         =   torch.tensor(
					    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]       ).to(__A       )
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,__A					,atol=__A       )       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Union[str, Any]         =   (
					    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"       )
					    .to(__A       )
					    .eval()
					)
					snake_case__      :str         =   self.default_image_processor
					snake_case__      :List[Any]         =   prepare_img()
					snake_case__      :str         =   image_processor(__A					,return_tensors="pt"       ).to(__A       )
					snake_case__      :Optional[int]         =   inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
					# check size
					self.assertEqual(__A					,(1, 3, 800, 1_088)       )

					with torch.no_grad():
									snake_case__      :Union[str, Any]         =   model(**__A       )
					# masks_queries_logits
					snake_case__      :Optional[Any]         =   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__      :str         =   [
					    [-1.3737124, -1.7724937, -1.9364233],
					    [-1.5977281, -1.9867939, -2.1523695],
					    [-1.5795398, -1.9269832, -2.093942],
					]
					snake_case__      :Any         =   torch.tensor(__A       ).to(__A       )
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,__A					,atol=__A       )       )
					# class_queries_logits
					snake_case__      :Optional[int]         =   outputs.class_queries_logits
					self.assertEqual(
					    class_queries_logits.shape					,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
					snake_case__      :List[Any]         =   torch.tensor(
					    [
					        [1.6_512E00, -5.2_572E00, -3.3_519E00],
					        [3.6_169E-02, -5.9_025E00, -2.9_313E00],
					        [1.0_766E-04, -7.7_630E00, -5.1_263E00],
					    ]       ).to(__A       )
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,__A					,atol=__A       )       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Tuple         =   (
					    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"       )
					    .to(__A       )
					    .eval()
					)
					snake_case__      :Union[str, Any]         =   self.default_image_processor
					snake_case__      :List[str]         =   prepare_img()
					snake_case__      :Dict         =   image_processor(__A					,return_tensors="pt"       ).to(__A       )
					snake_case__      :List[Any]         =   inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
					# check size
					self.assertEqual(__A					,(1, 3, 800, 1_088)       )

					with torch.no_grad():
									snake_case__      :Dict         =   model(**__A       )
					# masks_queries_logits
					snake_case__      :int         =   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__      :Dict         =   [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
					snake_case__      :int         =   torch.tensor(__A       ).to(__A       )
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,__A					,atol=__A       )       )
					# class_queries_logits
					snake_case__      :int         =   outputs.class_queries_logits
					self.assertEqual(
					    class_queries_logits.shape					,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)       )
					snake_case__      :Any         =   torch.tensor(
					    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]       ).to(__A       )
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,__A					,atol=__A       )       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Optional[int]         =   (
					    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"       )
					    .to(__A       )
					    .eval()
					)
					snake_case__      :Any         =   self.default_image_processor

					snake_case__      :str         =   image_processor(
					    [np.zeros((3, 800, 1_333)       ), np.zeros((3, 800, 1_333)       )]					,segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )]					,return_tensors="pt"					,)

					snake_case__      :Optional[int]         =   inputs["pixel_values"].to(__A       )
					snake_case__      :List[str]         =   [el.to(__A       ) for el in inputs["mask_labels"]]
					snake_case__      :Tuple         =   [el.to(__A       ) for el in inputs["class_labels"]]

					with torch.no_grad():
									snake_case__      :Dict         =   model(**__A       )

					self.assertTrue(outputs.loss is not None       )







__UpperCAmelCase							:   int							=  {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase							:   List[str]							=  ["a", "b", "c", "d", "e"]





def 					lowercase_ (    __snake_case     :							Optional[Any]	,	__snake_case     :							List[Any]	,	__snake_case     :							Tuple      )     -> Optional[int]:





				'''simple docstring'''


				snake_case__      :List[Any]         =   start
				# add current to visited
				visited.append(__snake_case      )
				snake_case__      :List[str]         =   edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # if all neighbors visited add current to sort
				sort.append(__snake_case      )
				# if all vertices haven't been visited select a new one to visit
				if len(__snake_case      ) != len(__snake_case      ):
								for vertice in vertices:
												if vertice not in visited:
																snake_case__      :Any         =   topological_sort(__snake_case	,	__snake_case	,	__snake_case      )
    # return sort
				return sort


if __name__ == "__main__":
	__UpperCAmelCase							:   Tuple							=  topological_sort("a", [], [])
	print(sort)