






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 	_UpperCamelCase ( a_		,     a_		,     a_		,     unittest.TestCase							):



    '''simple docstring'''


    __lowercase    :   Any											=						StableDiffusionImageVariationPipeline
    __lowercase    :   List[Any]											=						IMAGE_VARIATION_PARAMS
    __lowercase    :   str											=						IMAGE_VARIATION_BATCH_PARAMS
    __lowercase    :   Tuple											=						frozenset([]							)
    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __lowercase    :   Union[str, Any]											=						frozenset([]							)


    def 		A__      (      self			):
     torch.manual_seed(0			)
     UpperCAmelCase__ 						=				UNetaDConditionModel(
         block_out_channels=(32, 64)   ,			layers_per_block=2   ,			sample_size=32   ,			in_channels=4   ,			out_channels=4   ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,			cross_attention_dim=32   ,			)
     UpperCAmelCase__ 						=				PNDMScheduler(skip_prk_steps=_snake_case			)
     torch.manual_seed(0			)
     UpperCAmelCase__ 						=				AutoencoderKL(
         block_out_channels=[32, 64]   ,			in_channels=3   ,			out_channels=3   ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,			latent_channels=4   ,			)
     torch.manual_seed(0			)
     UpperCAmelCase__ 						=				CLIPVisionConfig(
         hidden_size=32   ,			projection_dim=32   ,			intermediate_size=37   ,			layer_norm_eps=1e-05   ,			num_attention_heads=4   ,			num_hidden_layers=5   ,			image_size=32   ,			patch_size=4   ,			)
     UpperCAmelCase__ 						=				CLIPVisionModelWithProjection(_snake_case			)
     UpperCAmelCase__ 						=				CLIPImageProcessor(crop_size=32   ,			size=32			)

     UpperCAmelCase__ 						=				{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'image_encoder': image_encoder,
         'feature_extractor': feature_extractor,
         'safety_checker': None,
     }
     return components


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=0			):
     UpperCAmelCase__ 						=				floats_tensor((1, 3, 32, 32)   ,			rng=random.Random(_snake_case			)			)
     UpperCAmelCase__ 						=				image.cpu().permute(0   ,			2   ,			3   ,			1			)[0]
     UpperCAmelCase__ 						=				Image.fromarray(np.uinta(_snake_case			)			).convert("""RGB"""			).resize((32, 32)			)
     if str(_snake_case			).startswith("""mps"""			):
      UpperCAmelCase__ 						=				torch.manual_seed(_snake_case			)
     else:
      UpperCAmelCase__ 						=				torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
     UpperCAmelCase__ 						=				{
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
         'output_type': 'numpy',
     }
     return inputs


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				'cpu'  # ensure determinism for the device-dependent torch.Generator
     UpperCAmelCase__ 						=				self.get_dummy_components()
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline(**_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe.to(_snake_case			)
     sd_pipe.set_progress_bar_config(disable=_snake_case			)

     UpperCAmelCase__ 						=				self.get_dummy_inputs(_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe(**_snake_case			).images
     UpperCAmelCase__ 						=				image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     UpperCAmelCase__ 						=				np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				'cpu'  # ensure determinism for the device-dependent torch.Generator
     UpperCAmelCase__ 						=				self.get_dummy_components()
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline(**_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe.to(_snake_case			)
     sd_pipe.set_progress_bar_config(disable=_snake_case			)

     UpperCAmelCase__ 						=				self.get_dummy_inputs(_snake_case			)
     UpperCAmelCase__ 						=				2 * [inputs['image']]
     UpperCAmelCase__ 						=				sd_pipe(**_snake_case			)

     UpperCAmelCase__ 						=				output.images

     UpperCAmelCase__ 						=				image[-1, -3:, -3:, -1]

     assert image.shape == (2, 64, 64, 3)
     UpperCAmelCase__ 						=				np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]			)

     assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3






    def 		A__      (      self			):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



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



    '''simple docstring'''


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


    def 		A__      (      self   ,			__lowercase   ,			__lowercase="cpu"   ,			__lowercase=torch.floataa   ,			__lowercase=0			):
     UpperCAmelCase__ 						=				torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
     UpperCAmelCase__ 						=				load_image(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
         """/stable_diffusion_imgvar/input_image_vermeer.png"""			)
     UpperCAmelCase__ 						=				np.random.RandomState(_snake_case			).standard_normal((1, 4, 64, 64)			)
     UpperCAmelCase__ 						=				torch.from_numpy(_snake_case			).to(device=_snake_case   ,			dtype=_snake_case			)
     UpperCAmelCase__ 						=				{
         'image': init_image,
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 7.5,
         'output_type': 'numpy',
     }
     return inputs


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline.from_pretrained(
         """lambdalabs/sd-image-variations-diffusers"""   ,			safety_checker=_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe.to(_snake_case			)
     sd_pipe.set_progress_bar_config(disable=_snake_case			)

     UpperCAmelCase__ 						=				self.get_inputs(_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe(**_snake_case			).images
     UpperCAmelCase__ 						=				image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     UpperCAmelCase__ 						=				np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]			)
     assert np.abs(image_slice - expected_slice			).max() < 6e-3


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				0

     def callback_fn(__lowercase   ,			__lowercase   ,			__lowercase			) -> None:
      UpperCAmelCase__ 						=				True
      nonlocal number_of_steps
      number_of_steps += 1
      if step == 1:
       UpperCAmelCase__ 						=				latents.detach().cpu().numpy()
       assert latents.shape == (1, 4, 64, 64)
       UpperCAmelCase__ 						=				latents[0, -3:, -3:, -1]
       UpperCAmelCase__ 						=				np.array(
           [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]			)

       assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
      elif step == 2:
       UpperCAmelCase__ 						=				latents.detach().cpu().numpy()
       assert latents.shape == (1, 4, 64, 64)
       UpperCAmelCase__ 						=				latents[0, -3:, -3:, -1]
       UpperCAmelCase__ 						=				np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]			)

       assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2

     UpperCAmelCase__ 						=				False

     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline.from_pretrained(
         """fusing/sd-image-variations-diffusers"""   ,			safety_checker=_snake_case   ,			torch_dtype=torch.floataa   ,			)
     pipe.to(_snake_case			)
     pipe.set_progress_bar_config(disable=_snake_case			)
     pipe.enable_attention_slicing()

     UpperCAmelCase__ 						=				self.get_inputs(_snake_case   ,			dtype=torch.floataa			)
     pipe(**_snake_case   ,			callback=_snake_case   ,			callback_steps=1			)
     assert callback_fn.has_been_called
     assert number_of_steps == inputs["num_inference_steps"]






    def 		A__      (      self			):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     UpperCAmelCase__ 						=				'fusing/sd-image-variations-diffusers'
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline.from_pretrained(
         _snake_case   ,			safety_checker=_snake_case   ,			torch_dtype=torch.floataa			)
     UpperCAmelCase__ 						=				pipe.to(_snake_case			)
     pipe.set_progress_bar_config(disable=_snake_case			)
     pipe.enable_attention_slicing(1			)
     pipe.enable_sequential_cpu_offload()

     UpperCAmelCase__ 						=				self.get_inputs(_snake_case   ,			dtype=torch.floataa			)
     UpperCAmelCase__ 						=				pipe(**_snake_case			)

     UpperCAmelCase__ 						=				torch.cuda.max_memory_allocated()
     # make sure that less than 2.6 GB is allocated
     assert mem_bytes < 2.6 * 10**9



@nightly
@require_torch_gpu
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


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


    def 		A__      (      self   ,			__lowercase   ,			__lowercase="cpu"   ,			__lowercase=torch.floataa   ,			__lowercase=0			):
     UpperCAmelCase__ 						=				torch.Generator(device=_snake_case			).manual_seed(_snake_case			)
     UpperCAmelCase__ 						=				load_image(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
         """/stable_diffusion_imgvar/input_image_vermeer.png"""			)
     UpperCAmelCase__ 						=				np.random.RandomState(_snake_case			).standard_normal((1, 4, 64, 64)			)
     UpperCAmelCase__ 						=				torch.from_numpy(_snake_case			).to(device=_snake_case   ,			dtype=_snake_case			)
     UpperCAmelCase__ 						=				{
         'image': init_image,
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 50,
         'guidance_scale': 7.5,
         'output_type': 'numpy',
     }
     return inputs


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""			)
     sd_pipe.to(_snake_case			)
     sd_pipe.set_progress_bar_config(disable=_snake_case			)

     UpperCAmelCase__ 						=				self.get_inputs(_snake_case			)
     UpperCAmelCase__ 						=				sd_pipe(**_snake_case			).images[0]

     UpperCAmelCase__ 						=				load_numpy(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
         """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""			)
     UpperCAmelCase__ 						=				np.abs(expected_image - image			).max()
     assert max_diff < 1e-3






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""			)
     UpperCAmelCase__ 						=				DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
     sd_pipe.to(_snake_case			)
     sd_pipe.set_progress_bar_config(disable=_snake_case			)

     UpperCAmelCase__ 						=				self.get_inputs(_snake_case			)
     UpperCAmelCase__ 						=				25
     UpperCAmelCase__ 						=				sd_pipe(**_snake_case			).images[0]

     UpperCAmelCase__ 						=				load_numpy(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
         """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""			)
     UpperCAmelCase__ 						=				np.abs(expected_image - image			).max()
     assert max_diff < 1e-3






'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowerCAmelCase__      				=			TypeVar("T", bound=bool)



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   T													=       value
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								UpperCAmelCase_		:   SkewNode[T] | None													=       None


							@property
							def     _snake_case				(      self)				->					T:
								return self._value





							@staticmethod
							def     _snake_case				(      _snake_case     ,      _snake_case)				->					SkewNode[T] | None:
								if not roota:
									return roota

								if not roota:
									return roota

								if roota.value > roota.value:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       roota, roota

								UpperCAmelCase_		:   Dict													=       roota
								UpperCAmelCase_		:   Dict													=       roota.right
								UpperCAmelCase_		:   Optional[int]													=       roota.left
								UpperCAmelCase_		:   Dict													=       SkewNode.merge(_snake_case     ,      _snake_case)

								return result



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case = ())				->					None:
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								if data:
									for item in data:
										self.insert(_snake_case)


							def __bool__(      self)				->					bool:
								return self._root is not None


							def __iter__(      self)				->					Iterator[T]:
								UpperCAmelCase_		:   list[Any]													=       []
								while self:
									result.append(self.pop())

								# Pushing items back to the heap not to clear it.
								for item in result:
									self.insert(_snake_case)

								return iter(_snake_case)


							def     _snake_case				(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   Any													=       SkewNode.merge(self._root     ,      SkewNode(_snake_case))


							def     _snake_case				(      self)				->					T | None:
								UpperCAmelCase_		:   Union[str, Any]													=       self.top()
								UpperCAmelCase_		:   Tuple													=       (
								    SkewNode.merge(self._root.left     ,      self._root.right) if self._root else None
								)

								return result


							def     _snake_case				(      self)				->					T:
								if not self._root:
									raise IndexError('Can\'t get top element for the empty heap.')
								return self._root.value





							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Union[str, Any]													=       None


if __name__ == "__main__":
						import doctest

						doctest.testmod()






