


"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					KandinskyVaaControlnetImgaImgPipeline
   __UpperCAmelCase							       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __UpperCAmelCase							       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __UpperCAmelCase							       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase							       =					False





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      return 32





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return 32





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return self.time_input_dim





   @property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return self.time_input_dim * 4





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return 100





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      torch.manual_seed(0)

      _lowerCamelCase	:					str   =		{
          """in_channels""": 8,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 8,
          """addition_embed_type""": """image_hint""",
          """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
          """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
          """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
          """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
          """layers_per_block""": 1,
          """encoder_hid_dim""": self.text_embedder_hidden_size,
          """encoder_hid_dim_type""": """image_proj""",
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": None,
      }

      _lowerCamelCase	:					Dict   =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE)
      return model





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      torch.manual_seed(0)
      _lowerCamelCase	:					Optional[Any]   =		VQModel(**self.dummy_movq_kwargs)
      return model





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					int   =		self.dummy_unet
      _lowerCamelCase	:					Optional[Any]   =		self.dummy_movq

      _lowerCamelCase	:					Any   =		{
          """num_train_timesteps""": 1000,
          """beta_schedule""": """linear""",
          """beta_start""": 0.0_00_85,
          """beta_end""": 0.0_12,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      _lowerCamelCase	:					List[Any]   =		DDIMScheduler(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1)).to(
          SCREAMING_SNAKE_CASE)
      # create init_image
      _lowerCamelCase	:					Union[str, Any]   =		floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		image.cpu().permute(0      ,   2      ,   3      ,   1)[0]
      _lowerCamelCase	:					Optional[int]   =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert("""RGB""").resize((256, 256))
      # create hint
      _lowerCamelCase	:					int   =		floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)

      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[Any]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					int   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		{
          """image""": init_image,
          """image_embeds""": image_embeds,
          """negative_image_embeds""": negative_image_embeds,
          """hint""": hint,
          """generator""": generator,
          """height""": 64,
          """width""": 64,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.0,
          """strength""": 0.2,
          """output_type""": """np""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		"""cpu"""

      _lowerCamelCase	:					Any   =		self.get_dummy_components()

      _lowerCamelCase	:					int   =		self.pipeline_class(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		pipe.to(SCREAMING_SNAKE_CASE)

      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Dict   =		output.images

      _lowerCamelCase	:					Optional[int]   =		pipe(
          **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)      ,   return_dict=SCREAMING_SNAKE_CASE      ,   )[0]

      _lowerCamelCase	:					List[str]   =		image[0, -3:, -3:, -1]
      _lowerCamelCase	:					int   =		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      _lowerCamelCase	:					List[Any]   =		np.array(
          [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36])
      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







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





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Dict   =		load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""")

      _lowerCamelCase	:					List[str]   =		load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
      _lowerCamelCase	:					Dict   =		init_image.resize((512, 512))

      _lowerCamelCase	:					Union[str, Any]   =		load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/hint_image_cat.png""")
      _lowerCamelCase	:					str   =		torch.from_numpy(np.array(SCREAMING_SNAKE_CASE)).float() / 2_55.0
      _lowerCamelCase	:					Tuple   =		hint.permute(2      ,   0      ,   1).unsqueeze(0)

      _lowerCamelCase	:					Optional[int]   =		"""A robot, 4k photo"""

      _lowerCamelCase	:					Tuple   =		KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""      ,   torch_dtype=torch.floataa)
      pipe_prior.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,   torch_dtype=torch.floataa)
      _lowerCamelCase	:					Dict   =		pipeline.to(SCREAMING_SNAKE_CASE)

      pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		torch.Generator(device="""cpu""").manual_seed(0)

      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		pipe_prior(
          SCREAMING_SNAKE_CASE      ,   image=SCREAMING_SNAKE_CASE      ,   strength=0.85      ,   generator=SCREAMING_SNAKE_CASE      ,   negative_prompt=""""""      ,   ).to_tuple()

      _lowerCamelCase	:					List[str]   =		pipeline(
          image=SCREAMING_SNAKE_CASE      ,   image_embeds=SCREAMING_SNAKE_CASE      ,   negative_image_embeds=SCREAMING_SNAKE_CASE      ,   hint=SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   num_inference_steps=100      ,   height=512      ,   width=512      ,   strength=0.5      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					Any   =		output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





"""simple docstring"""
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=32 * 8      ,   SCREAMING_SNAKE_CASE=32 * 8      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=64      ,   )  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		parent
      _lowerCamelCase	:					List[Any]   =		batch_size
      _lowerCamelCase	:					Tuple   =		is_training
      _lowerCamelCase	:					Tuple   =		use_auxiliary_loss
      _lowerCamelCase	:					Any   =		num_queries
      _lowerCamelCase	:					List[str]   =		num_channels
      _lowerCamelCase	:					List[str]   =		min_size
      _lowerCamelCase	:					Tuple   =		max_size
      _lowerCamelCase	:					str   =		num_labels
      _lowerCamelCase	:					Any   =		hidden_dim
      _lowerCamelCase	:					Dict   =		hidden_dim





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
          SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		torch.ones([self.batch_size, self.min_size, self.max_size]      ,   device=SCREAMING_SNAKE_CASE)

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

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					List[str]   =		MaskaFormerConfig(
          hidden_size=self.hidden_dim      ,   )
      _lowerCamelCase	:					Any   =		self.num_queries
      _lowerCamelCase	:					int   =		self.num_labels

      _lowerCamelCase	:					int   =		[1, 1, 1, 1]
      _lowerCamelCase	:					Any   =		self.num_channels

      _lowerCamelCase	:					Optional[Any]   =		64
      _lowerCamelCase	:					str   =		128
      _lowerCamelCase	:					Optional[Any]   =		self.hidden_dim
      _lowerCamelCase	:					Any   =		self.hidden_dim
      _lowerCamelCase	:					List[Any]   =		self.hidden_dim
      return config





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.prepare_config_and_inputs()
      _lowerCamelCase	:					str   =		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					str   =		output.encoder_hidden_states
      _lowerCamelCase	:					int   =		output.pixel_decoder_hidden_states
      _lowerCamelCase	:					Optional[int]   =		output.transformer_decoder_hidden_states

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  List[str]:
      with torch.no_grad():
         _lowerCamelCase	:					Optional[int]   =		MaskaFormerModel(config=SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.eval()

         _lowerCamelCase	:					Optional[int]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)

      self.parent.assertEqual(
          output.transformer_decoder_last_hidden_state.shape      ,   (self.batch_size, self.num_queries, self.hidden_dim)      ,   )
      # 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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					str   =		MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      def comm_check_on_output(SCREAMING_SNAKE_CASE):
         # 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	:					List[Any]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)

         comm_check_on_output(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Optional[Any]   =		model(
             pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      comm_check_on_output(SCREAMING_SNAKE_CASE)

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







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   __UpperCAmelCase							       =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		MaskaFormerModelTester(self)
      _lowerCamelCase	:					Union[str, Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   has_text_modality=SCREAMING_SNAKE_CASE)





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





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE)





   @unittest.skip(reason="""Mask2Former does not use inputs_embeds""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""")
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      pass





   @unittest.skip(reason="""Mask2Former is not a generative model""")
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      pass





   @unittest.skip(reason="""Mask2Former does not use token embeddings""")
   def 		UpperCamelCase_  (							self)  ->  Any:
      pass





   @require_torch_multi_gpu
   @unittest.skip(
       reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
   def 		UpperCamelCase_  (							self)  ->  Dict:
      pass





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCamelCase	:					int   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
         _lowerCamelCase	:					Optional[int]   =		MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Dict   =		(self.model_tester.min_size,) * 2
      _lowerCamelCase	:					str   =		{
          """pixel_values""": torch.randn((2, 3, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """mask_labels""": torch.randn((2, 10, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """class_labels""": torch.zeros(2      ,   10      ,   device=SCREAMING_SNAKE_CASE).long(),
      }
      _lowerCamelCase	:					List[str]   =		self.model_tester.get_config()

      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE)
      self.assertTrue(outputs.loss is not None)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE      ,   output_attentions=SCREAMING_SNAKE_CASE)
         self.assertTrue(outputs.attentions is not None)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      if not self.model_tester.is_training:
         return

      _lowerCamelCase	:					Any   =		self.all_model_classes[1]
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs()

      _lowerCamelCase	:					List[Any]   =		model_class(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					int   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE).loss
      loss.backward()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		self.all_model_classes[1]
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		self.model_tester.prepare_config_and_inputs()
      _lowerCamelCase	:					int   =		True
      _lowerCamelCase	:					Optional[Any]   =		True

      _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      _lowerCamelCase	:					int   =		outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()

      _lowerCamelCase	:					str   =		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=SCREAMING_SNAKE_CASE)

      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				=						1e-4

def 				_snake_case      (   ):




   """simple docstring"""







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





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





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  int:
      return "facebook/mask2former-swin-small-coco-instance"





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		self.default_image_processor
      _lowerCamelCase	:					List[str]   =		prepare_img()
      _lowerCamelCase	:					Union[str, Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					Dict   =		model(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		torch.tensor(
          [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Any   =		torch.tensor(
          [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Dict   =		torch.tensor(
          [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					Optional[Any]   =		self.default_image_processor
      _lowerCamelCase	:					Any   =		prepare_img()
      _lowerCamelCase	:					Dict   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					List[str]   =		model(**SCREAMING_SNAKE_CASE)
      # masks_queries_logits
      _lowerCamelCase	:					str   =		outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape      ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
      _lowerCamelCase	:					Any   =		[
          [-8.78_39, -9.00_56, -8.81_21],
          [-7.41_04, -7.03_13, -6.54_01],
          [-6.61_05, -6.34_27, -6.46_75],
      ]
      _lowerCamelCase	:					List[Any]   =		torch.tensor(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))
      # class_queries_logits
      _lowerCamelCase	:					List[str]   =		outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape      ,   (1, model.config.num_queries, model.config.num_labels + 1))
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor(
          [
              [1.83_24, -8.08_35, -4.19_22],
              [0.84_50, -9.00_50, -3.60_53],
              [0.30_45, -7.72_93, -3.02_75],
          ]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					str   =		self.default_image_processor

      _lowerCamelCase	:					Tuple   =		image_processor(
          [np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))]      ,   segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)]      ,   return_tensors="""pt"""      ,   )

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

      with torch.no_grad():
         _lowerCamelCase	:					Any   =		model(**SCREAMING_SNAKE_CASE)

      self.assertTrue(outputs.loss is not None)

