





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 SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


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





					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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						,	)
										lowerCamelCase__      =							PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
										torch.manual_seed(0						)
										lowerCamelCase__      =							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						)
										lowerCamelCase__      =							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						,	)
										lowerCamelCase__      =							CLIPVisionModelWithProjection(_lowerCAmelCase						)
										lowerCamelCase__      =							CLIPImageProcessor(crop_size=32						,	size=32						)

										lowerCamelCase__      =							{
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "image_encoder": image_encoder,
										    "feature_extractor": feature_extractor,
										    "safety_checker": None,
										}
										return components





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__      =							self.get_dummy_components()
										lowerCamelCase__      =							StableDiffusionImageVariationPipeline(**_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe.to(_lowerCAmelCase						)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__      =							self.get_dummy_components()
										lowerCamelCase__      =							StableDiffusionImageVariationPipeline(**_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe.to(_lowerCAmelCase						)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							self.get_dummy_inputs(_lowerCAmelCase						)
										lowerCamelCase__      =							2 * [inputs["image"]]
										lowerCamelCase__      =							sd_pipe(**_lowerCAmelCase						)

										lowerCamelCase__      =							output.images

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

										assert image.shape == (2, 64, 64, 3)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

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





					"""simple docstring"""


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





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							StableDiffusionImageVariationPipeline.from_pretrained(
										    "lambdalabs/sd-image-variations-diffusers"						,	safety_checker=_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe.to(_lowerCAmelCase						)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							0

										def callback_fn(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) -> None:
															lowerCamelCase__      =							True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																				lowerCamelCase__      =							latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 64)
																				lowerCamelCase__      =							latents[0, -3:, -3:, -1]
																				lowerCamelCase__      =							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:
																				lowerCamelCase__      =							latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 64)
																				lowerCamelCase__      =							latents[0, -3:, -3:, -1]
																				lowerCamelCase__      =							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

										lowerCamelCase__      =							False

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

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






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

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

										lowerCamelCase__      =							self.get_inputs(_lowerCAmelCase						,	dtype=torch.floataa						)
										lowerCamelCase__      =							pipe(**_lowerCAmelCase						)

										lowerCamelCase__      =							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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


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





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"						)
										sd_pipe.to(_lowerCAmelCase						)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							self.get_inputs(_lowerCAmelCase						)
										lowerCamelCase__      =							sd_pipe(**_lowerCAmelCase						).images[0]

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






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

										lowerCamelCase__      =							self.get_inputs(_lowerCAmelCase						)
										lowerCamelCase__      =							25
										lowerCamelCase__      =							sd_pipe(**_lowerCAmelCase						).images[0]

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








def  __UpperCamelCase   (     a)			->str:
					if len(a) == 0 or not all(len(a) == 2 for pair in coordinates):
										raise ValueError("The program cannot work out a fitting polynomial.")

					if len({tuple(a) for pair in coordinates}) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							{x for x, _ in coordinates}
					if len(a) == 1:
										return f"x={coordinates[0][0]}"

					if len(a) != len(a):
										raise ValueError("The program cannot work out a fitting polynomial.")

					lowerCamelCase__      =							len(a)

					lowerCamelCase__      =							0
					lowerCamelCase__      =							[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										lowerCamelCase__      =							0
										lowerCamelCase__      =							coordinates[count_of_line][0]
										lowerCamelCase__      =							[]
										while count_in_line < x:
															count_line.append(a ** (x - (count_in_line + 1)))
															count_in_line += 1
										matrix.append(a)
										count_of_line += 1

					lowerCamelCase__      =							0
					# put the y values into a vector
					lowerCamelCase__      =							[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1])
										count_of_line += 1

					lowerCamelCase__      =							0

					while count < x:
										lowerCamelCase__      =							0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															lowerCamelCase__      =							matrix[zahlen][count] / matrix[count][count]
															for counting_columns, item in enumerate(matrix[count]):
																				# manipulating all the values in the matrix
																				matrix[zahlen][counting_columns] -= item * bruch
															# manipulating the values in the vector
															vector[zahlen] -= vector[count] * bruch
															zahlen += 1
										count += 1

					lowerCamelCase__      =							0
					# make solutions
					lowerCamelCase__      =							[]
					while count < x:
										solution.append(str(vector[count] / matrix[count][count]))
										count += 1

					lowerCamelCase__      =							0
					lowerCamelCase__      =							"f(x)="

					while count < x:
										lowerCamelCase__      =							solution[count].split("E")
										if len(a) > 1:
															lowerCamelCase__      =							f"{remove_e[0]}*10^{remove_e[1]}"
										solved += f"x^{x - (count + 1)}*{solution[count]}"
										if count + 1 != x:
															solved += "+"
										count += 1

					return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 1_1]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -1_1]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))

