

'''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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       A_		   = [tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]			)


class    A__ ( UpperCamelCase_  ,  UpperCamelCase_  ,  UpperCamelCase_  ,  unittest.TestCase     ):
    lowercase		   =						StableDiffusionLatentUpscalePipeline
    lowercase		   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """height""",
        """width""",
        """cross_attention_kwargs""",
        """negative_prompt_embeds""",
        """prompt_embeds""",
    }
    lowercase		   =						PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
    lowercase		   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase		   =						frozenset(
        []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase		   =						frozenset([]     )

    lowercase		   =						True



    @property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 4
           A_		   = (16, 16)

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



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = UNetaDConditionModel(
               act_fn="""gelu"""						,			attention_head_dim=8						,			norm_num_groups=_a						,			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=_a						,			only_cross_attention=_a						,			out_channels=5						,			resnet_time_scale_shift="""scale_shift"""						,			time_embedding_type="""fourier"""						,			timestep_post_act="""gelu"""						,			up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")						,			)
           A_		   = 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						,			)
           A_		   = EulerDiscreteScheduler(prediction_type="""sample"""    )
           A_		   = 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						,			)
           A_		   = CLIPTextModel(_a    )
           A_		   = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

           A_		   = {
               """unet""": model.eval(),
               """vae""": vae.eval(),
               """scheduler""": scheduler,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }

           return components



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=0    )  -> List[Any]:





           '''simple docstring'''


           if str(_a    ).startswith("""mps"""    ):
                  A_		   = torch.manual_seed(_a    )
           else:
                  A_		   = torch.Generator(device=_a    ).manual_seed(_a    )
           A_		   = {
               """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    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = """cpu"""

           A_		   = self.get_dummy_components()
           A_		   = self.pipeline_class(**_a    )
           pipe.to(_a    )
           pipe.set_progress_bar_config(disable=_a    )

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

           self.assertEqual(image.shape						,			(1, 256, 256, 3)    )
           A_		   = np.array(
               [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]    )
           A_		   = np.abs(image_slice.flatten() - expected_slice    ).max()
           self.assertLessEqual(_a						,			1e-3    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           super().test_cpu_offload_forward_pass(expected_max_diff=3e-3    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           super().test_inference_batch_single_identical(expected_max_diff=7e-3    )



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3    )



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           super().test_save_load_local(expected_max_difference=3e-3    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           super().test_save_load_optional_components(expected_max_difference=3e-3    )







    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = [
               """DDIMScheduler""",
               """DDPMScheduler""",
               """PNDMScheduler""",
               """HeunDiscreteScheduler""",
               """EulerAncestralDiscreteScheduler""",
               """KDPM2DiscreteScheduler""",
               """KDPM2AncestralDiscreteScheduler""",
               """DPMSolverSDEScheduler""",
           ]
           A_		   = self.get_dummy_components()
           A_		   = self.pipeline_class(**_a    )

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

           pipe.to(_a    )
           pipe.set_progress_bar_config(disable=_a    )
           A_		   = self.get_dummy_inputs(_a    )
           A_		   = 2

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

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

           assert check_same_shape(_a    )


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



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = torch.manual_seed(33    )

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

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

           A_		   = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""

           A_		   = pipe(_a						,			generator=_a						,			output_type="""latent"""    ).images

           A_		   = upscaler(
               prompt=_a						,			image=_a						,			num_inference_steps=20						,			guidance_scale=0						,			generator=_a						,			output_type="""np"""						,			).images[0]

           A_		   = 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    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = torch.manual_seed(33    )

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

           A_		   = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

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

           A_		   = upscaler(
               prompt=_a						,			image=_a						,			num_inference_steps=20						,			guidance_scale=0						,			generator=_a						,			output_type="""np"""						,			).images[0]

           A_		   = 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








'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[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			):
              A_		   = True

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

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