





'''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__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]    )




class 					UpperCAmelCase	(     snake_case_				,						snake_case_				,						snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  StableDiffusionLatentUpscalePipeline
      _lowercase:     Any			=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          '''height''',
          '''width''',
          '''cross_attention_kwargs''',
          '''negative_prompt_embeds''',
          '''prompt_embeds''',
      }
      _lowercase:     List[Any]			=  PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
      _lowercase:     List[str]			=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _lowercase:     str			=  frozenset(
          []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      _lowercase:     Union[str, Any]			=  frozenset([]  )

      _lowercase:     Union[str, Any]			=  True




      @property
      def 							lowercase__       (     self     :					Any    )							-> Any:
            _lowerCAmelCase					=				1
            _lowerCAmelCase					=				4
            _lowerCAmelCase					=				(16, 16)

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




      def 							lowercase__       (     self     :					List[str]    )							-> Tuple:
            torch.manual_seed(0    )
            _lowerCAmelCase					=				UNetaDConditionModel(
                act_fn="""gelu"""     ,     attention_head_dim=8     ,     norm_num_groups=__snake_case     ,     block_out_channels=[32, 32, 64, 64]     ,     time_cond_proj_dim=1_60     ,     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=__snake_case     ,     only_cross_attention=__snake_case     ,     out_channels=5     ,     resnet_time_scale_shift="""scale_shift"""     ,     time_embedding_type="""fourier"""     ,     timestep_post_act="""gelu"""     ,     up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")     ,     )
            _lowerCAmelCase					=				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     ,     )
            _lowerCAmelCase					=				EulerDiscreteScheduler(prediction_type="""sample"""    )
            _lowerCAmelCase					=				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=10_00     ,     hidden_act="""quick_gelu"""     ,     projection_dim=5_12     ,     )
            _lowerCAmelCase					=				CLIPTextModel(__snake_case    )
            _lowerCAmelCase					=				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

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

            return components




      def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					int     ,     __snake_case     :					Tuple=0    )							-> Optional[Any]:
            if str(__snake_case    ).startswith("""mps"""    ):
                  _lowerCAmelCase					=				torch.manual_seed(__snake_case    )
            else:
                  _lowerCAmelCase					=				torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
            _lowerCAmelCase					=				{
                """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 							lowercase__       (     self     :					Union[str, Any]    )							-> List[Any]:
            _lowerCAmelCase					=				"""cpu"""

            _lowerCAmelCase					=				self.get_dummy_components()
            _lowerCAmelCase					=				self.pipeline_class(**__snake_case    )
            pipe.to(__snake_case    )
            pipe.set_progress_bar_config(disable=__snake_case    )

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

            self.assertEqual(image.shape     ,     (1, 2_56, 2_56, 3)    )
            _lowerCAmelCase					=				np.array(
                [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]    )
            _lowerCAmelCase					=				np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(__snake_case     ,     1E-3    )




      def 							lowercase__       (     self     :					Any    )							-> List[str]:
            super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )




      def 							lowercase__       (     self     :					Any    )							-> Optional[int]:
            super().test_cpu_offload_forward_pass(expected_max_diff=3E-3    )




      def 							lowercase__       (     self     :					Any    )							-> Optional[Any]:
            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Union[str, Any]:
            super().test_inference_batch_single_identical(expected_max_diff=7E-3    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Any:
            super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3    )




      def 							lowercase__       (     self     :					Optional[int]    )							-> Union[str, Any]:
            super().test_save_load_local(expected_max_difference=3E-3    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Union[str, Any]:
            super().test_save_load_optional_components(expected_max_difference=3E-3    )




      def 							lowercase__       (     self     :					str    )							-> Union[str, Any]:
            _lowerCAmelCase					=				[
                """DDIMScheduler""",
                """DDPMScheduler""",
                """PNDMScheduler""",
                """HeunDiscreteScheduler""",
                """EulerAncestralDiscreteScheduler""",
                """KDPM2DiscreteScheduler""",
                """KDPM2AncestralDiscreteScheduler""",
                """DPMSolverSDEScheduler""",
            ]
            _lowerCAmelCase					=				self.get_dummy_components()
            _lowerCAmelCase					=				self.pipeline_class(**__snake_case    )

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

            pipe.to(__snake_case    )
            pipe.set_progress_bar_config(disable=__snake_case    )
            _lowerCAmelCase					=				self.get_dummy_inputs(__snake_case    )
            _lowerCAmelCase					=				2

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

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

            assert check_same_shape(__snake_case    )




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




      def 							lowercase__       (     self     :					List[str]    )							-> int:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Dict:
            _lowerCAmelCase					=				torch.manual_seed(33    )

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

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

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

            _lowerCAmelCase					=				pipe(__snake_case     ,     generator=__snake_case     ,     output_type="""latent"""    ).images

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

            _lowerCAmelCase					=				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 							lowercase__       (     self     :					List[str]    )							-> Optional[int]:
            _lowerCAmelCase					=				torch.manual_seed(33    )

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

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

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

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

            _lowerCAmelCase					=				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__ (						lowerCAmelCase    ):





      """simple docstring"""


      if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
            raise ValueError("""Input must be an integer"""    )
      if input_num <= 0:
            raise ValueError("""Input must be positive"""    )
      return sum(
          divisor for divisor in range(1				,      input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






