

"""simple docstring"""






def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      bool:
 __magic_name__     =      len(__UpperCamelCase						)
 __magic_name__     =      [[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1						):
  __magic_name__     =      True

 # sum is not zero and set is empty then false
 for i in range(1      ,   required_sum + 1						):
  __magic_name__     =      False

 for i in range(1      ,   arr_len + 1						):
  for j in range(1      ,   required_sum + 1						):
   if arr[i - 1] > j:
    __magic_name__     =      subset[i - 1][j]
   if arr[i - 1] <= j:
    __magic_name__     =      subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




"""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

