

"""simple docstring"""






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import YolosForObjectDetection, YolosModel
 from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=13 ,						UpperCamelCase_=[30, 30] ,						UpperCamelCase_=2 ,						UpperCamelCase_=3 ,						UpperCamelCase_=True ,						UpperCamelCase_=True ,						UpperCamelCase_=32 ,						UpperCamelCase_=5 ,						UpperCamelCase_=4 ,						UpperCamelCase_=37 ,						UpperCamelCase_="gelu" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=10 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=3 ,						UpperCamelCase_=None ,						UpperCamelCase_=8 ,						UpperCamelCase_=10 ,						):
      __magic_name__     =      parent
      __magic_name__     =      batch_size
      __magic_name__     =      image_size
      __magic_name__     =      patch_size
      __magic_name__     =      num_channels
      __magic_name__     =      is_training
      __magic_name__     =      use_labels
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      num_attention_heads
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      attention_probs_dropout_prob
      __magic_name__     =      type_sequence_label_size
      __magic_name__     =      initializer_range
      __magic_name__     =      num_labels
      __magic_name__     =      scope
      __magic_name__     =      n_targets
      __magic_name__     =      num_detection_tokens
      # we set the expected sequence length (which is used in several tests)
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
      __magic_name__     =      (image_size[1] // patch_size) * (image_size[0] // patch_size)
      __magic_name__     =      num_patches + 1 + self.num_detection_tokens







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )

      __magic_name__     =      None
      if self.use_labels:
       # labels is a list of Dict (each Dict being the labels for a given example in the batch)
       __magic_name__     =      []
       for i in range(self.batch_size       ):
        __magic_name__     =      {}
        __magic_name__     =      torch.randint(
            high=self.num_labels ,						size=(self.n_targets,) ,						device=UpperCamelCase_       )
        __magic_name__     =      torch.rand(self.n_targets ,						4 ,						device=UpperCamelCase_       )
        labels.append(UpperCamelCase_       )

      __magic_name__     =      self.get_config()

      return config, pixel_values, labels







     def 	lowerCAmelCase__    (      self       ):
      return YolosConfig(
          image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						is_decoder=UpperCamelCase_ ,						initializer_range=self.initializer_range ,						num_detection_tokens=self.num_detection_tokens ,						num_labels=self.num_labels ,						)







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      YolosModel(config=UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()
      __magic_name__     =      model(UpperCamelCase_       )
      self.parent.assertEqual(
          result.last_hidden_state.shape ,						(self.batch_size, self.expected_seq_len, self.hidden_size)       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      YolosForObjectDetection(UpperCamelCase_       )
      model.to(UpperCamelCase_       )
      model.eval()

      __magic_name__     =      model(pixel_values=UpperCamelCase_       )
      __magic_name__     =      model(UpperCamelCase_       )

      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
      self.parent.assertEqual(result.pred_boxes.shape ,						(self.batch_size, self.num_detection_tokens, 4)       )

      __magic_name__     =      model(pixel_values=UpperCamelCase_ ,						labels=UpperCamelCase_       )

      self.parent.assertEqual(result.loss.shape ,						()       )
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
      self.parent.assertEqual(result.pred_boxes.shape ,						(self.batch_size, self.num_detection_tokens, 4)       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()
      __magic_name__   ,						__magic_name__   ,						__magic_name__     =      config_and_inputs
      __magic_name__     =      {'''pixel_values''': pixel_values}
      return config, inputs_dict


@require_torch
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     _lowerCamelCase     =       (
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=False       ):
      __magic_name__     =      super()._prepare_for_class(UpperCamelCase_ ,						UpperCamelCase_ ,						return_labels=UpperCamelCase_       )

      if return_labels:
       if model_class.__name__ == "YolosForObjectDetection":
        __magic_name__     =      []
        for i in range(self.model_tester.batch_size       ):
         __magic_name__     =      {}
         __magic_name__     =      torch.ones(
             size=(self.model_tester.n_targets,) ,						device=UpperCamelCase_ ,						dtype=torch.long       )
         __magic_name__     =      torch.ones(
             self.model_tester.n_targets ,						4 ,						device=UpperCamelCase_ ,						dtype=torch.float       )
         labels.append(UpperCamelCase_       )
        __magic_name__     =      labels

      return inputs_dict







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      YolosModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase_ ,						has_text_modality=UpperCamelCase_ ,						hidden_size=37       )







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







     def 	lowerCAmelCase__    (      self       ):
      # YOLOS does not use inputs_embeds
      pass







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __magic_name__     =      model_class(UpperCamelCase_       )
       self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module)       )
       __magic_name__     =      model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCamelCase_ ,						nn.Linear       )       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()

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

       __magic_name__     =      ['''pixel_values''']
       self.assertListEqual(arg_names[:1] ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__     =      True

      # in YOLOS, the seq_len is different
      __magic_name__     =      self.model_tester.expected_seq_len
      for model_class in self.all_model_classes:
       __magic_name__     =      True
       __magic_name__     =      False
       __magic_name__     =      True
       __magic_name__     =      model_class(UpperCamelCase_       )
       model.to(UpperCamelCase_       )
       model.eval()
       with torch.no_grad():
        __magic_name__     =      model(**self._prepare_for_class(UpperCamelCase_ ,						UpperCamelCase_       )       )
       __magic_name__     =      outputs.attentions
       self.assertEqual(len(UpperCamelCase_       ) ,						self.model_tester.num_hidden_layers       )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __magic_name__     =      True
       __magic_name__     =      model_class(UpperCamelCase_       )
       model.to(UpperCamelCase_       )
       model.eval()
       with torch.no_grad():
        __magic_name__     =      model(**self._prepare_for_class(UpperCamelCase_ ,						UpperCamelCase_       )       )
       __magic_name__     =      outputs.attentions
       self.assertEqual(len(UpperCamelCase_       ) ,						self.model_tester.num_hidden_layers       )

       self.assertListEqual(
           list(attentions[0].shape[-3:]       ) ,						[self.model_tester.num_attention_heads, seq_len, seq_len] ,						)
       __magic_name__     =      len(UpperCamelCase_       )

       # Check attention is always last and order is fine
       __magic_name__     =      True
       __magic_name__     =      True
       __magic_name__     =      model_class(UpperCamelCase_       )
       model.to(UpperCamelCase_       )
       model.eval()
       with torch.no_grad():
        __magic_name__     =      model(**self._prepare_for_class(UpperCamelCase_ ,						UpperCamelCase_       )       )

       __magic_name__     =      1
       self.assertEqual(out_len + added_hidden_states ,						len(UpperCamelCase_       )       )

       __magic_name__     =      outputs.attentions

       self.assertEqual(len(UpperCamelCase_       ) ,						self.model_tester.num_hidden_layers       )
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]       ) ,						[self.model_tester.num_attention_heads, seq_len, seq_len] ,						)







     def 	lowerCAmelCase__    (      self       ):
      def check_hidden_states_output(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
       __magic_name__     =      model_class(UpperCamelCase_       )
       model.to(UpperCamelCase_       )
       model.eval()

       with torch.no_grad():
        __magic_name__     =      model(**self._prepare_for_class(UpperCamelCase_ ,						UpperCamelCase_       )       )

       __magic_name__     =      outputs.hidden_states

       __magic_name__     =      getattr(
           self.model_tester ,						'''expected_num_hidden_layers''' ,						self.model_tester.num_hidden_layers + 1       )
       self.assertEqual(len(UpperCamelCase_       ) ,						UpperCamelCase_       )

       # YOLOS has a different seq_length
       __magic_name__     =      self.model_tester.expected_seq_len

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]       ) ,						[seq_length, self.model_tester.hidden_size] ,						)

      __magic_name__   ,						__magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __magic_name__     =      True
       check_hidden_states_output(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __magic_name__     =      True

       check_hidden_states_output(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_object_detection(*UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __magic_name__     =      YolosModel.from_pretrained(UpperCamelCase_       )
       self.assertIsNotNone(UpperCamelCase_       )





def 	lowercase      (   ) ->      List[Any]:
 __magic_name__     =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
 return image
@require_torch
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     @cached_property
     def 	lowerCAmelCase__    (      self       ):
      return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''       ) if is_vision_available() else None







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''       ).to(UpperCamelCase_       )

      __magic_name__     =      self.default_image_processor
      __magic_name__     =      prepare_img()
      __magic_name__     =      image_processor(images=UpperCamelCase_ ,						return_tensors='''pt'''       ).to(UpperCamelCase_       )

      # forward pass
      with torch.no_grad():
       __magic_name__     =      model(inputs.pixel_values       )

      # verify outputs
      __magic_name__     =      torch.Size((1, 100, 92)       )
      self.assertEqual(outputs.logits.shape ,						UpperCamelCase_       )

      __magic_name__     =      torch.tensor(
          [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] ,						device=UpperCamelCase_ ,						)
      __magic_name__     =      torch.tensor(
          [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] ,						device=UpperCamelCase_       )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,						UpperCamelCase_ ,						atol=1E-4       )       )
      self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,						UpperCamelCase_ ,						atol=1E-4       )       )

      # verify postprocessing
      __magic_name__     =      image_processor.post_process_object_detection(
          UpperCamelCase_ ,						threshold=0.3 ,						target_sizes=[image.size[::-1]]       )[0]
      __magic_name__     =      torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]       ).to(UpperCamelCase_       )
      __magic_name__     =      [75, 75, 17, 63, 17]
      __magic_name__     =      torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]       ).to(UpperCamelCase_       )

      self.assertEqual(len(results['''scores''']       ) ,						5       )
      self.assertTrue(torch.allclose(results['''scores'''] ,						UpperCamelCase_ ,						atol=1E-4       )       )
      self.assertSequenceEqual(results['''labels'''].tolist() ,						UpperCamelCase_       )
      self.assertTrue(torch.allclose(results['''boxes'''][0, :] ,						UpperCamelCase_       )       )




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      [tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]						)
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       StableDiffusionLatentUpscalePipeline
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     _lowerCamelCase     =       PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase     =       frozenset(
         []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _lowerCamelCase     =       frozenset([]     )

     _lowerCamelCase     =       True







     @property
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      1
      __magic_name__     =      4
      __magic_name__     =      (16, 16)

      __magic_name__     =      floats_tensor((batch_size, num_channels) + sizes ,						rng=random.Random(0       )       ).to(UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      UNetaDConditionModel(
          act_fn='''gelu''' ,						attention_head_dim=8 ,						norm_num_groups=UpperCamelCase_ ,						block_out_channels=[32, 32, 64, 64] ,						time_cond_proj_dim=160 ,						conv_in_kernel=1 ,						conv_out_kernel=1 ,						cross_attention_dim=32 ,						down_block_types=(
              '''KDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
          ) ,						in_channels=8 ,						mid_block_type=UpperCamelCase_ ,						only_cross_attention=UpperCamelCase_ ,						out_channels=5 ,						resnet_time_scale_shift='''scale_shift''' ,						time_embedding_type='''fourier''' ,						timestep_post_act='''gelu''' ,						up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') ,						)
      __magic_name__     =      AutoencoderKL(
          block_out_channels=[32, 32, 64, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=[
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
          ] ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,						latent_channels=4 ,						)
      __magic_name__     =      EulerDiscreteScheduler(prediction_type='''sample'''       )
      __magic_name__     =      CLIPTextConfig(
          bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-0_5 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1000 ,						hidden_act='''quick_gelu''' ,						projection_dim=512 ,						)
      __magic_name__     =      CLIPTextModel(UpperCamelCase_       )
      __magic_name__     =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

      __magic_name__     =      {
          '''unet''': model.eval(),
          '''vae''': vae.eval(),
          '''scheduler''': scheduler,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
      }

      return components







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0       ):
      if str(UpperCamelCase_       ).startswith('''mps'''       ):
       __magic_name__     =      torch.manual_seed(UpperCamelCase_       )
      else:
       __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': self.dummy_image.cpu(),
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''cpu'''

      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )
      pipe.to(UpperCamelCase_       )
      pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      pipe(**UpperCamelCase_       ).images
      __magic_name__     =      image[0, -3:, -3:, -1]

      self.assertEqual(image.shape ,						(1, 256, 256, 3)       )
      __magic_name__     =      np.array(
          [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]       )
      __magic_name__     =      np.abs(image_slice.flatten() - expected_slice       ).max()
      self.assertLessEqual(UpperCamelCase_ ,						1E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_attention_slicing_forward_pass(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_cpu_offload_forward_pass(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_inference_batch_single_identical(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_local(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_optional_components(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [
          '''DDIMScheduler''',
          '''DDPMScheduler''',
          '''PNDMScheduler''',
          '''HeunDiscreteScheduler''',
          '''EulerAncestralDiscreteScheduler''',
          '''KDPM2DiscreteScheduler''',
          '''KDPM2AncestralDiscreteScheduler''',
          '''DPMSolverSDEScheduler''',
      ]
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )

      # make sure that PNDM does not need warm-up
      pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_       )

      pipe.to(UpperCamelCase_       )
      pipe.set_progress_bar_config(disable=UpperCamelCase_       )
      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      2

      __magic_name__     =      []
      for scheduler_enum in KarrasDiffusionSchedulers:
       if scheduler_enum.name in skip_schedulers:
        # no sigma schedulers are not supported
        # no schedulers
        continue

       __magic_name__     =      getattr(UpperCamelCase_ ,						scheduler_enum.name       )
       __magic_name__     =      scheduler_cls.from_config(pipe.scheduler.config       )
       __magic_name__     =      pipe(**UpperCamelCase_       )[0]
       outputs.append(UpperCamelCase_       )

      assert check_same_shape(UpperCamelCase_       )


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







     def 	lowerCAmelCase__    (      self       ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' ,						torch_dtype=torch.floataa       )
      pipe.to('''cuda'''       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

      __magic_name__     =      pipe(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						output_type='''latent'''       ).images

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''       )
      assert np.abs((expected_image - image).mean()       ) < 5E-2







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

      __magic_name__     =      load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''       )

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''       )
      assert np.abs((expected_image - image).max()       ) < 5E-2

