
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__		(     __UpperCamelCase,      __UpperCamelCase,      __UpperCamelCase,      unittest.TestCase ):







	'''simple docstring'''
	_lowerCAmelCase											=					StableDiffusionImageVariationPipeline
	_lowerCAmelCase											=					IMAGE_VARIATION_PARAMS
	_lowerCAmelCase											=					IMAGE_VARIATION_BATCH_PARAMS
	_lowerCAmelCase											=					frozenset([] )
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowerCAmelCase											=					frozenset([] )





	def 	__snake_case					(      self		):
					torch.manual_seed(0		)
					A__	:						Union[str, Any]								=				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					,							)
					A__	:						Union[str, Any]								=				PNDMScheduler(skip_prk_steps=UpperCAmelCase__		)
					torch.manual_seed(0		)
					A__	:						Any								=				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		)
					A__	:						Dict								=				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					,							)
					A__	:						Optional[Any]								=				CLIPVisionModelWithProjection(UpperCAmelCase__		)
					A__	:						Tuple								=				CLIPImageProcessor(crop_size=32					,							size=32		)

					A__	:						List[str]								=				{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """image_encoder""": image_encoder,
					    """feature_extractor""": feature_extractor,
					    """safety_checker""": None,
					}
					return components





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





	def 	__snake_case					(      self		):
					A__	:						int								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A__	:						Union[str, Any]								=				self.get_dummy_components()
					A__	:						Optional[Any]								=				StableDiffusionImageVariationPipeline(**UpperCAmelCase__		)
					A__	:						List[str]								=				sd_pipe.to(UpperCAmelCase__		)
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

					A__	:						int								=				self.get_dummy_inputs(UpperCAmelCase__		)
					A__	:						str								=				sd_pipe(**UpperCAmelCase__		).images
					A__	:						List[Any]								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					A__	:						str								=				np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]		)

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





	def 	__snake_case					(      self		):
					A__	:						Union[str, Any]								=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
					A__	:						Optional[Any]								=				self.get_dummy_components()
					A__	:						str								=				StableDiffusionImageVariationPipeline(**UpperCAmelCase__		)
					A__	:						Any								=				sd_pipe.to(UpperCAmelCase__		)
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

					A__	:						List[str]								=				self.get_dummy_inputs(UpperCAmelCase__		)
					A__	:						Any								=				2 * [inputs["""image"""]]
					A__	:						List[Any]								=				sd_pipe(**UpperCAmelCase__		)

					A__	:						int								=				output.images

					A__	:						Dict								=				image[-1, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)
					A__	:						Optional[Any]								=				np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]		)

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





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







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







	'''simple docstring'''





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





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





	def 	__snake_case					(      self		):
					A__	:						Dict								=				StableDiffusionImageVariationPipeline.from_pretrained(
					    '''lambdalabs/sd-image-variations-diffusers'''					,							safety_checker=UpperCAmelCase__		)
					A__	:						List[Any]								=				sd_pipe.to(UpperCAmelCase__		)
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

					A__	:						str								=				self.get_inputs(UpperCAmelCase__		)
					A__	:						Optional[int]								=				sd_pipe(**UpperCAmelCase__		).images
					A__	:						Dict								=				image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					A__	:						List[str]								=				np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]		)
					assert np.abs(image_slice - expected_slice		).max() < 6e-3





	def 	__snake_case					(      self		):
					A__	:						str								=				0

					def callback_fn(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		) -> None:
									A__	:						Tuple								=				True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
													A__	:						Optional[Any]								=				latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													A__	:						List[Any]								=				latents[0, -3:, -3:, -1]
													A__	:						int								=				np.array(
													    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]		)

													assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
									elif step == 2:
													A__	:						Any								=				latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													A__	:						List[str]								=				latents[0, -3:, -3:, -1]
													A__	:						Optional[int]								=				np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]		)

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

					A__	:						List[str]								=				False

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

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





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

					A__	:						int								=				"""fusing/sd-image-variations-diffusers"""
					A__	:						Union[str, Any]								=				StableDiffusionImageVariationPipeline.from_pretrained(
					    UpperCAmelCase__					,							safety_checker=UpperCAmelCase__					,							torch_dtype=torch.floataa		)
					A__	:						Optional[Any]								=				pipe.to(UpperCAmelCase__		)
					pipe.set_progress_bar_config(disable=UpperCAmelCase__		)
					pipe.enable_attention_slicing(1		)
					pipe.enable_sequential_cpu_offload()

					A__	:						Any								=				self.get_inputs(UpperCAmelCase__					,							dtype=torch.floataa		)
					A__	:						Union[str, Any]								=				pipe(**UpperCAmelCase__		)

					A__	:						Dict								=				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 	__snake_case					(      self		):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





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





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

					A__	:						Union[str, Any]								=				self.get_inputs(UpperCAmelCase__		)
					A__	:						Optional[int]								=				sd_pipe(**UpperCAmelCase__		).images[0]

					A__	:						Optional[Any]								=				load_numpy(
					    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
					    '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'''		)
					A__	:						Any								=				np.abs(expected_image - image		).max()
					assert max_diff < 1e-3





	def 	__snake_case					(      self		):
					A__	:						List[str]								=				StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''		)
					A__	:						List[str]								=				DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config		)
					sd_pipe.to(UpperCAmelCase__		)
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__		)

					A__	:						Union[str, Any]								=				self.get_inputs(UpperCAmelCase__		)
					A__	:						Optional[int]								=				25
					A__	:						Optional[int]								=				sd_pipe(**UpperCAmelCase__		).images[0]

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







from __future__ import annotations

from typing import Any
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Any						):  # noqa: A002

    """simple docstring"""






    lowercase__    :						Optional[int]       =			item
    lowercase__    :						Union[str, Any]       =			next
class 							__snake_case:



 def __init__(						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Node | None       =			None
    lowercase__    :						Optional[Any]       =			0



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Any       =			Node(UpperCAmelCase__			,		self.head						)
    self.size += 1



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    if self.head is None:
       return None
    else:
       lowercase__    :						List[Any]       =			self.head.item
       lowercase__    :						Optional[Any]       =			self.head.next
       self.size -= 1
       return item



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.head is None



 def __str__(						self : List[str]						):

    """simple docstring"""






    if self.is_empty():
       return ""
    else:
       lowercase__    :						Optional[Any]       =			self.head
       lowercase__    :						str       =			""""""
       lowercase__    :						list[str]       =			[]
       while iterate:
          item_list.append(str(iterate.item						)						)
          lowercase__    :						List[str]       =			iterate.next

       lowercase__    :						List[str]       =			""" --> """.join(UpperCAmelCase__						)

       return item_str







 def __len__(						self : List[Any]						):

    """simple docstring"""






    return self.size


