



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__ :Optional[Any]					):






 '''simple docstring'''

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



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

   _UpperCAmelCase     	=			True

   @property
   def 		A							(      self			:       Any							)       ->				str:


    """simple docstring"""






    a						=		1
    a						=		4
    a						=		(16, 16)

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

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


    """simple docstring"""






    torch.manual_seed(0							)
    a						=		UNetaDConditionModel(
        act_fn="gelu"							,   attention_head_dim=8							,   norm_num_groups=__lowerCAmelCase							,   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=__lowerCAmelCase							,   only_cross_attention=__lowerCAmelCase							,   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-05							,   num_attention_heads=4							,   num_hidden_layers=5							,   pad_token_id=1							,   vocab_size=1000							,   hidden_act="quick_gelu"							,   projection_dim=512							,   )
    a						=		CLIPTextModel(__lowerCAmelCase							)
    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 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Optional[Any]=0							)       ->				Optional[int]:


    """simple docstring"""






    if str(__lowerCAmelCase							).startswith("mps"							):
     a						=		torch.manual_seed(__lowerCAmelCase							)
    else:
     a						=		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
    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 		A							(      self			:       Optional[int]							)       ->				List[Any]:


    """simple docstring"""






    a						=		"cpu"

    a						=		self.get_dummy_components()
    a						=		self.pipeline_class(**__lowerCAmelCase							)
    pipe.to(__lowerCAmelCase							)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

    self.assertEqual(image.shape							,   (1, 256, 256, 3)							)
    a						=		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]							)
    a						=		np.abs(image_slice.flatten() - expected_slice							).max()
    self.assertLessEqual(__lowerCAmelCase							,   1E-3							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    super().test_attention_slicing_forward_pass(expected_max_diff=7E-3							)

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


    """simple docstring"""






    super().test_cpu_offload_forward_pass(expected_max_diff=3E-3							)

   def 		A							(      self			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)

   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    super().test_inference_batch_single_identical(expected_max_diff=7E-3							)

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


    """simple docstring"""






    super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3							)

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


    """simple docstring"""






    super().test_save_load_local(expected_max_difference=3E-3							)

   def 		A							(      self			:       int							)       ->				str:


    """simple docstring"""






    super().test_save_load_optional_components(expected_max_difference=3E-3							)




   def 		A							(      self			:       Optional[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		[
        "DDIMScheduler",
        "DDPMScheduler",
        "PNDMScheduler",
        "HeunDiscreteScheduler",
        "EulerAncestralDiscreteScheduler",
        "KDPM2DiscreteScheduler",
        "KDPM2AncestralDiscreteScheduler",
        "DPMSolverSDEScheduler",
    ]
    a						=		self.get_dummy_components()
    a						=		self.pipeline_class(**__lowerCAmelCase							)

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

    pipe.to(__lowerCAmelCase							)
    pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
    a						=		self.get_dummy_inputs(__lowerCAmelCase							)
    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(__lowerCAmelCase							,   scheduler_enum.name							)
     a						=		scheduler_cls.from_config(pipe.scheduler.config							)
     a						=		pipe(**__lowerCAmelCase							)[0]
     outputs.append(__lowerCAmelCase							)

    assert check_same_shape(__lowerCAmelCase							)



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

   def 		A							(      self			:       Optional[Any]							)       ->				Optional[int]:


    """simple docstring"""






    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

   def 		A							(      self			:       Dict							)       ->				Any:


    """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(__lowerCAmelCase							,   generator=__lowerCAmelCase							,   output_type="latent"							).images

    a						=		upscaler(
        prompt=__lowerCAmelCase							,   image=__lowerCAmelCase							,   num_inference_steps=20							,   guidance_scale=0							,   generator=__lowerCAmelCase							,   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 		A							(      self			:       int							)       ->				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=__lowerCAmelCase							,   image=__lowerCAmelCase							,   num_inference_steps=20							,   guidance_scale=0							,   generator=__lowerCAmelCase							,   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






from __future__ import annotations


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		str(UpperCAmelCase__					)
 return len(UpperCAmelCase__					) == 9 and set(UpperCAmelCase__					) == set("123456789"					)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 for base_num in range(99_99 ,					49_99 ,					-1					):
  a						=		10_00_02 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 for base_num in range(3_33 ,					99 ,					-1					):
  a						=		1_00_20_03 * base_num
  if is_9_pandigital(UpperCAmelCase__					):
   return candidate

 return None


if __name__ == "__main__":
     print(F"""{solution() = }""")

