


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   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



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



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
              UpperCamelCase__							)

          lowerCamelCase_						:   Optional[Any]		= torch.ones([self.batch_size, self.min_size, self.max_size]		,				device=UpperCamelCase__							)

          lowerCamelCase_						:   Dict		= (
              torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,				device=UpperCamelCase__							) > 0.5
          ).float()
          lowerCamelCase_						:   Union[str, Any]		= (torch.rand((self.batch_size, self.num_labels)		,				device=UpperCamelCase__							) > 0.5).long()

          lowerCamelCase_						:   Tuple		= self.get_config()
          return config, pixel_values, pixel_mask, mask_labels, class_labels



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[int]:
          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				:				int							)					->Tuple:
          lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   List[str]		= self.prepare_config_and_inputs()
          lowerCamelCase_						:   Any		= {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
          return config, inputs_dict



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				Optional[Any]		,				__a				:				Dict							)					->List[Any]:
          lowerCamelCase_						:   Any		= output.encoder_hidden_states
          lowerCamelCase_						:   List[Any]		= output.pixel_decoder_hidden_states
          lowerCamelCase_						:   Optional[Any]		= 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 				_lowerCAmelCase							(						self				:				Any		,				__a				:				Any		,				__a				:				Optional[Any]		,				__a				:				Optional[int]		,				__a				:				Optional[Any]=False							)					->Optional[Any]:
          with torch.no_grad():
             lowerCamelCase_						:   Optional[Any]		= MaskFormerModel(config=UpperCamelCase__							)
             model.to(UpperCamelCase__							)
             model.eval()

             lowerCamelCase_						:   Optional[Any]		= model(pixel_values=UpperCamelCase__		,				pixel_mask=UpperCamelCase__							)
             lowerCamelCase_						:   int		= 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 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				Tuple		,				__a				:				Optional[Any]		,				__a				:				int		,				__a				:				Optional[Any]		,				__a				:				Tuple							)					->str:
          lowerCamelCase_						:   Tuple		= MaskFormerForInstanceSegmentation(config=UpperCamelCase__							)
          model.to(UpperCamelCase__							)
          model.eval()

          def comm_check_on_output(__a				:				Any							):
             # 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():
             lowerCamelCase_						:   Dict		= model(pixel_values=UpperCamelCase__		,				pixel_mask=UpperCamelCase__							)
             lowerCamelCase_						:   Any		= model(UpperCamelCase__							)

             comm_check_on_output(UpperCamelCase__							)

             lowerCamelCase_						:   Union[str, 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   SCREAMING_SNAKE_CASE_			(_UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase						):




       '''simple docstring'''



       _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				:				Optional[Any]							)					->Tuple:
          lowerCamelCase_						:   Dict		= MaskFormerModelTester(self							)
          lowerCamelCase_						:   Optional[int]		= ConfigTester(self		,				config_class=UpperCamelCase__		,				has_text_modality=UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->int:
          self.config_tester.run_common_tests()



       def 				_lowerCAmelCase							(						self				:				Dict							)					->int:
          lowerCamelCase_,	lowerCamelCase_						:   Optional[int]		= self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskformer_model(UpperCamelCase__		,				**UpperCamelCase__		,				output_hidden_states=UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Optional[int]:
          lowerCamelCase_						:   Any		= 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 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Any:
          pass



       @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""							)
       def 				_lowerCAmelCase							(						self				:				Dict							)					->Union[str, Any]:
          pass



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



       @unittest.skip(reason="""MaskFormer does not use token embeddings"""							)
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Union[str, Any]:
          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]							)					->Union[str, Any]:
          pass



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



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->List[str]:
          lowerCamelCase_,	lowerCamelCase_						:   Any		= self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_						:   Union[str, Any]		= ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		,				UpperCamelCase__							)



       @slow
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->Optional[int]:
          for model_name in ["facebook/maskformer-swin-small-coco"]:
             lowerCamelCase_						:   Dict		= MaskFormerModel.from_pretrained(UpperCamelCase__							)
             self.assertIsNotNone(UpperCamelCase__							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[Any]:
          lowerCamelCase_						:   Tuple		= (self.model_tester.min_size,) * 2
          lowerCamelCase_						:   Tuple		= {
              """pixel_values""": torch.randn((2, 3, *size)		,				device=UpperCamelCase__							),
              """mask_labels""": torch.randn((2, 10, *size)		,				device=UpperCamelCase__							),
              """class_labels""": torch.zeros(2		,				10		,				device=UpperCamelCase__							).long(),
          }

          lowerCamelCase_						:   Optional[int]		= MaskFormerForInstanceSegmentation(MaskFormerConfig()							).to(UpperCamelCase__							)
          lowerCamelCase_						:   str		= model(**UpperCamelCase__							)
          self.assertTrue(outputs.loss is not None							)



       def 				_lowerCAmelCase							(						self				:				Dict							)					->Dict:
          lowerCamelCase_,	lowerCamelCase_						:   List[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 				_lowerCAmelCase							(						self				:				int							)					->Tuple:
          lowerCamelCase_,	lowerCamelCase_						:   Any		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase_						:   Tuple		= model_class(UpperCamelCase__							).to(UpperCamelCase__							)
             lowerCamelCase_						:   Any		= model(**UpperCamelCase__		,				output_attentions=UpperCamelCase__							)
             self.assertTrue(outputs.attentions is not None							)



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->List[Any]:
          if not self.model_tester.is_training:
             return
          # only MaskFormerForInstanceSegmentation has the loss
          lowerCamelCase_						:   Tuple		= self.all_model_classes[1]
          lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Dict		= self.model_tester.prepare_config_and_inputs()

          lowerCamelCase_						:   Dict		= model_class(UpperCamelCase__							)
          model.to(UpperCamelCase__							)
          model.train()

          lowerCamelCase_						:   Union[str, Any]		= model(UpperCamelCase__		,				mask_labels=UpperCamelCase__		,				class_labels=UpperCamelCase__							).loss
          loss.backward()




       def 				_lowerCAmelCase							(						self				:				Dict							)					->List[str]:
          # only MaskFormerForInstanceSegmentation has the loss
          lowerCamelCase_						:   str		= self.all_model_classes[1]
          lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Optional[int]		= self.model_tester.prepare_config_and_inputs()
          lowerCamelCase_						:   Optional[int]		= True
          lowerCamelCase_						:   List[Any]		= True

          lowerCamelCase_						:   int		= model_class(UpperCamelCase__							)
          model.to(UpperCamelCase__							)
          model.train()

          lowerCamelCase_						:   List[Any]		= model(UpperCamelCase__		,				mask_labels=UpperCamelCase__		,				class_labels=UpperCamelCase__							)

          lowerCamelCase_						:   str		= outputs.encoder_hidden_states[0]
          encoder_hidden_states.retain_grad()

          lowerCamelCase_						:   List[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
          lowerCamelCase_						:   int		= outputs.transformer_decoder_hidden_states[0]
          transformer_decoder_hidden_states.retain_grad()

          lowerCamelCase_						:   Optional[int]		= 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							)







snake_case__				:	Any					       =					1e-4


def    __lowerCamelCase   (      )     ->    Any:
   lowerCamelCase_						:   Optional[Any]		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
   return image





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




       '''simple docstring'''



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



       def 				_lowerCAmelCase							(						self				:				Any							)					->List[str]:
          lowerCamelCase_						:   Union[str, Any]		= MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""							).to(UpperCamelCase__							)
          lowerCamelCase_						:   str		= self.default_image_processor
          lowerCamelCase_						:   Union[str, Any]		= prepare_img()
          lowerCamelCase_						:   str		= image_processor(UpperCamelCase__		,				return_tensors="""pt"""							).to(UpperCamelCase__							)
          lowerCamelCase_						:   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(UpperCamelCase__		,				(1, 3, 800, 1_088)							)

          with torch.no_grad():
             lowerCamelCase_						:   int		= model(**UpperCamelCase__							)

          lowerCamelCase_						:   Any		= torch.tensor(
              [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]							).to(UpperCamelCase__							)
          self.assertTrue(
              torch.allclose(
                  outputs.encoder_last_hidden_state[0, 0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)

          lowerCamelCase_						:   str		= torch.tensor(
              [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]							).to(UpperCamelCase__							)
          self.assertTrue(
              torch.allclose(
                  outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)

          lowerCamelCase_						:   str		= torch.tensor(
              [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]							).to(UpperCamelCase__							)
          self.assertTrue(
              torch.allclose(
                  outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Optional[int]:
          lowerCamelCase_						:   List[str]		= (
              MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
              .to(UpperCamelCase__							)
              .eval()
          )
          lowerCamelCase_						:   Optional[Any]		= self.default_image_processor
          lowerCamelCase_						:   Optional[Any]		= prepare_img()
          lowerCamelCase_						:   Union[str, Any]		= image_processor(UpperCamelCase__		,				return_tensors="""pt"""							).to(UpperCamelCase__							)
          lowerCamelCase_						:   Union[str, 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(UpperCamelCase__		,				(1, 3, 800, 1_088)							)

          with torch.no_grad():
             lowerCamelCase_						:   Any		= model(**UpperCamelCase__							)
          # masks_queries_logits
          lowerCamelCase_						:   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)		,				)
          lowerCamelCase_						:   str		= [
              [-1.3_737_124, -1.7_724_937, -1.9_364_233],
              [-1.5_977_281, -1.9_867_939, -2.1_523_695],
              [-1.5_795_398, -1.9_269_832, -2.093_942],
          ]
          lowerCamelCase_						:   str		= torch.tensor(UpperCamelCase__							).to(UpperCamelCase__							)
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)
          # class_queries_logits
          lowerCamelCase_						:   str		= outputs.class_queries_logits
          self.assertEqual(
              class_queries_logits.shape		,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
          lowerCamelCase_						:   str		= torch.tensor(
              [
                  [1.65_12e00, -5.25_72e00, -3.35_19e00],
                  [3.61_69e-02, -5.90_25e00, -2.93_13e00],
                  [1.07_66e-04, -7.76_30e00, -5.12_63e00],
              ]							).to(UpperCamelCase__							)
          self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Optional[Any]:
          lowerCamelCase_						:   List[Any]		= (
              MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""							)
              .to(UpperCamelCase__							)
              .eval()
          )
          lowerCamelCase_						:   Optional[Any]		= self.default_image_processor
          lowerCamelCase_						:   Dict		= prepare_img()
          lowerCamelCase_						:   Union[str, Any]		= image_processor(UpperCamelCase__		,				return_tensors="""pt"""							).to(UpperCamelCase__							)
          lowerCamelCase_						:   str		= 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(UpperCamelCase__		,				(1, 3, 800, 1_088)							)

          with torch.no_grad():
             lowerCamelCase_						:   Union[str, Any]		= model(**UpperCamelCase__							)
          # masks_queries_logits
          lowerCamelCase_						:   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)		,				)
          lowerCamelCase_						:   Dict		= [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
          lowerCamelCase_						:   List[str]		= torch.tensor(UpperCamelCase__							).to(UpperCamelCase__							)
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)
          # class_queries_logits
          lowerCamelCase_						:   Optional[Any]		= outputs.class_queries_logits
          self.assertEqual(
              class_queries_logits.shape		,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
          lowerCamelCase_						:   List[Any]		= torch.tensor(
              [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]							).to(UpperCamelCase__							)
          self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				UpperCamelCase__		,				atol=UpperCamelCase__							)							)




       def 				_lowerCAmelCase							(						self				:				Dict							)					->int:
          lowerCamelCase_						:   List[Any]		= (
              MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""							)
              .to(UpperCamelCase__							)
              .eval()
          )
          lowerCamelCase_						:   List[Any]		= self.default_image_processor

          lowerCamelCase_						:   Union[str, Any]		= 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"""		,				)

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

          with torch.no_grad():
             lowerCamelCase_						:   List[str]		= model(**UpperCamelCase__							)

          self.assertTrue(outputs.loss is not None							)






'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCAmelCase    :     List[Any]         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   pass







@slow
@require_torch_gpu
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
        pipe.to(UpperCamelCase__     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'     )
        _A    		=  torch.manual_seed(0     )
        _A    		=  pipe(
            image=UpperCamelCase__, generator=UpperCamelCase__, guidance_scale=7.5, num_inference_steps=50, output_type='numpy', ).images

        _A    		=  image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2



