


'''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 					lowercase_     :


    def __init__(     self  ,					a  ,					a=2  ,					a=True  ,					a=False  ,					a=10  ,					a=3  ,					a=32 * 4  ,					a=32 * 6  ,					a=4  ,					a=32  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_auxiliary_loss
          UpperCamelCase__										=				num_queries
          UpperCamelCase__										=				num_channels
          UpperCamelCase__										=				min_size
          UpperCamelCase__										=				max_size
          UpperCamelCase__										=				num_labels
          UpperCamelCase__										=				mask_feature_size


    def    __a							(     self    ):
          UpperCamelCase__										=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
              a    )

          UpperCamelCase__										=				torch.ones([self.batch_size, self.min_size, self.max_size]  ,					device=a    )

          UpperCamelCase__										=				(
              torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,					device=a    ) > 0.5
          ).float()
          UpperCamelCase__										=				(torch.rand((self.batch_size, self.num_labels)  ,					device=a    ) > 0.5).long()

          UpperCamelCase__										=				self.get_config()
          return config, pixel_values, pixel_mask, mask_labels, class_labels


    def    __a							(     self    ):
          return MaskFormerConfig.from_backbone_and_decoder_configs(
              backbone_config=SwinConfig(
                  depths=[1, 1, 1, 1]  ,					)  ,					decoder_config=DetrConfig(
                  decoder_ffn_dim=1_28  ,					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    ):
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				self.prepare_config_and_inputs()
          UpperCamelCase__										=				{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
          return config, inputs_dict


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				output.encoder_hidden_states
          UpperCamelCase__										=				output.pixel_decoder_hidden_states
          UpperCamelCase__										=				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    __a							(     self  ,					a  ,					a  ,					a  ,					a=False    ):
          with torch.no_grad():
                UpperCamelCase__										=				MaskFormerModel(config=a    )
                model.to(a    )
                model.eval()

                UpperCamelCase__										=				model(pixel_values=a  ,					pixel_mask=a    )
                UpperCamelCase__										=				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    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				MaskFormerForInstanceSegmentation(config=a    )
          model.to(a    )
          model.eval()

          def comm_check_on_output(a    ):
                # 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():
                UpperCamelCase__										=				model(pixel_values=a  ,					pixel_mask=a    )
                UpperCamelCase__										=				model(a    )

                comm_check_on_output(a    )

                UpperCamelCase__										=				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 					lowercase_     (    a__  ,						a__  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
    __UpperCAmelCase   			=	(
        {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
        if is_torch_available()
        else {}
    )

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


    def    __a							(     self    ):
          UpperCamelCase__										=				MaskFormerModelTester(self    )
          UpperCamelCase__										=				ConfigTester(self  ,					config_class=a  ,					has_text_modality=a    )


    def    __a							(     self    ):
          self.config_tester.run_common_tests()


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskformer_model(a  ,					**a  ,					output_hidden_states=a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				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    __a							(     self    ):
          pass


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


    @unittest.skip(reason="MaskFormer is not a generative model"    )
    def    __a							(     self    ):
          pass


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


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


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCamelCase__										=				["pixel_values"]
                self.assertListEqual(arg_names[:1]  ,					a    )


    @slow
    def    __a							(     self    ):
          for model_name in ["facebook/maskformer-swin-small-coco"]:
                UpperCamelCase__										=				MaskFormerModel.from_pretrained(a    )
                self.assertIsNotNone(a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				(self.model_tester.min_size,) * 2
          UpperCamelCase__										=				{
              "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(),
          }

          UpperCamelCase__										=				MaskFormerForInstanceSegmentation(MaskFormerConfig()    ).to(a    )
          UpperCamelCase__										=				model(**a    )
          self.assertTrue(outputs.loss is not None    )


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.create_and_check_maskformer_model(a  ,					**a  ,					output_hidden_states=a    )


    def    __a							(     self    ):
          UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

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


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

          UpperCamelCase__										=				model_class(a    )
          model.to(a    )
          model.train()

          UpperCamelCase__										=				model(a  ,					mask_labels=a  ,					class_labels=a    ).loss
          loss.backward()


    def    __a							(     self    ):
          # only MaskFormerForInstanceSegmentation has the loss
          UpperCamelCase__										=				self.all_model_classes[1]
          UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          UpperCamelCase__										=				True
          UpperCamelCase__										=				True

          UpperCamelCase__										=				model_class(a    )
          model.to(a    )
          model.train()

          UpperCamelCase__										=				model(a  ,					mask_labels=a  ,					class_labels=a    )

          UpperCamelCase__										=				outputs.encoder_hidden_states[0]
          encoder_hidden_states.retain_grad()

          UpperCamelCase__										=				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
          UpperCamelCase__										=				outputs.transformer_decoder_hidden_states[0]
          transformer_decoder_hidden_states.retain_grad()

          UpperCamelCase__										=				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    )

a__  :					Tuple        =       1E-4







def _UpperCamelCase       (     )							->		List[str]:


      '''simple docstring'''



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



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


    @cached_property
    def    __a							(     self    ):
          return (
              MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco"    )
              if is_vision_available()
              else None
          )


    def    __a							(     self    ):
          UpperCamelCase__										=				MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"    ).to(a    )
          UpperCamelCase__										=				self.default_image_processor
          UpperCamelCase__										=				prepare_img()
          UpperCamelCase__										=				image_processor(a  ,					return_tensors="pt"    ).to(a    )
          UpperCamelCase__										=				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, 8_00, 10_88)    )

          with torch.no_grad():
                UpperCamelCase__										=				model(**a    )

          UpperCamelCase__										=				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    )    )

          UpperCamelCase__										=				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    )    )

          UpperCamelCase__										=				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    __a							(     self    ):
          UpperCamelCase__										=				(
              MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
              .to(a    )
              .eval()
          )
          UpperCamelCase__										=				self.default_image_processor
          UpperCamelCase__										=				prepare_img()
          UpperCamelCase__										=				image_processor(a  ,					return_tensors="pt"    ).to(a    )
          UpperCamelCase__										=				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, 8_00, 10_88)    )

          with torch.no_grad():
                UpperCamelCase__										=				model(**a    )
          # masks_queries_logits
          UpperCamelCase__										=				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)  ,					)
          UpperCamelCase__										=				[
              [-1.373_7124, -1.772_4937, -1.936_4233],
              [-1.597_7281, -1.986_7939, -2.152_3695],
              [-1.579_5398, -1.926_9832, -2.09_3942],
          ]
          UpperCamelCase__										=				torch.tensor(a    ).to(a    )
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,					a  ,					atol=a    )    )
          # class_queries_logits
          UpperCamelCase__										=				outputs.class_queries_logits
          self.assertEqual(
              class_queries_logits.shape  ,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
          UpperCamelCase__										=				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(a    )
          self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,					a  ,					atol=a    )    )


    def    __a							(     self    ):
          UpperCamelCase__										=				(
              MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"    )
              .to(a    )
              .eval()
          )
          UpperCamelCase__										=				self.default_image_processor
          UpperCamelCase__										=				prepare_img()
          UpperCamelCase__										=				image_processor(a  ,					return_tensors="pt"    ).to(a    )
          UpperCamelCase__										=				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, 8_00, 10_88)    )

          with torch.no_grad():
                UpperCamelCase__										=				model(**a    )
          # masks_queries_logits
          UpperCamelCase__										=				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)  ,					)
          UpperCamelCase__										=				[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
          UpperCamelCase__										=				torch.tensor(a    ).to(a    )
          self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,					a  ,					atol=a    )    )
          # class_queries_logits
          UpperCamelCase__										=				outputs.class_queries_logits
          self.assertEqual(
              class_queries_logits.shape  ,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
          UpperCamelCase__										=				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    __a							(     self    ):
          UpperCamelCase__										=				(
              MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"    )
              .to(a    )
              .eval()
          )
          UpperCamelCase__										=				self.default_image_processor

          UpperCamelCase__										=				image_processor(
              [np.zeros((3, 8_00, 13_33)    ), np.zeros((3, 8_00, 13_33)    )]  ,					segmentation_maps=[np.zeros((3_84, 3_84)    ).astype(np.floataa    ), np.zeros((3_84, 3_84)    ).astype(np.floataa    )]  ,					return_tensors="pt"  ,					)

          UpperCamelCase__										=				inputs["pixel_values"].to(a    )
          UpperCamelCase__										=				[el.to(a    ) for el in inputs["mask_labels"]]
          UpperCamelCase__										=				[el.to(a    ) for el in inputs["class_labels"]]

          with torch.no_grad():
                UpperCamelCase__										=				model(**a    )

          self.assertTrue(outputs.loss is not None    )






'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





