






"""simple docstring"""

from __future__ import annotations

import random
from collections.abc import Iterable


class 	__lowerCamelCase       :





		'''simple docstring'''






		def __init__(     self							:			str		,    a_							:			list[str]			):
				lowerCAmelCase_     :							dict[str, bool | None]             =    {literal: None for literal in literals}





		def __str__(     self							:			str			):
				return "{" + " , ".join(self.literals			) + "}"





		def __len__(     self							:			List[str]			):
				return len(self.literals			)





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			dict[str, bool | None]			):
				for literal in self.literals:
						lowerCAmelCase_     :							Union[str, Any]             =    literal[:2]
						if symbol in model:
								lowerCAmelCase_     :							Dict             =    model[symbol]
						else:
								continue
						if value is not None:
								# Complement assignment if literal is in complemented form
								if literal.endswith("'"			):
										lowerCAmelCase_     :							int             =    not value
						lowerCAmelCase_     :							Union[str, Any]             =    value





		def      lowerCamelCase (     self							:			Union[str, Any]		,    a_							:			dict[str, bool | None]			):
				for literal in self.literals:
						lowerCAmelCase_     :							Tuple             =    literal.rstrip("'"			) if literal.endswith("'"			) else literal + "'"
						if symbol in self.literals:
								return True

				self.assign(a_			)
				for value in self.literals.values():
						if value in (True, None):
								return value
				return any(self.literals.values()			)







class 	__lowerCamelCase       :





		'''simple docstring'''






		def __init__(     self							:			Any		,    a_							:			Iterable[Clause]			):
				lowerCAmelCase_     :							int             =    list(a_			)





		def __str__(     self							:			int			):
				return "{" + " , ".join(str(a_			) for clause in self.clauses			) + "}"







def 	__lowerCamelCase			(						)		->					Clause:

		"""simple docstring"""







		lowerCAmelCase_     :							str             =    []
		lowerCAmelCase_     :							Optional[int]             =    random.randint(1   , 5 )
		lowerCAmelCase_     :							Tuple             =    "A"
		lowerCAmelCase_     :							Dict             =    0
		while i < no_of_literals:
				lowerCAmelCase_     :							List[str]             =    random.randint(1   , 5 )
				lowerCAmelCase_     :							Optional[int]             =    base_var + str(__UpperCamelCase )
				lowerCAmelCase_     :							Optional[Any]             =    random.randint(0   , 1 )
				if var_complement == 1:
						var_name += "'"
				if var_name in literals:
						i -= 1
				else:
						literals.append(__UpperCamelCase )
				i += 1
		return Clause(__UpperCamelCase )

def 	__lowerCamelCase			(						)		->					Formula:

		"""simple docstring"""







		lowerCAmelCase_     :							set[Clause]             =    set()
		lowerCAmelCase_     :							Dict             =    random.randint(1   , 10 )
		while len(__UpperCamelCase ) < no_of_clauses:
				clauses.add(generate_clause() )
		return Formula(__UpperCamelCase )

def 	__lowerCamelCase			(						__UpperCamelCase )		->					tuple[list[Clause], list[str]]:

		"""simple docstring"""







		lowerCAmelCase_     :							Dict             =    formula.clauses
		lowerCAmelCase_     :							List[Any]             =    []
		for clause in formula.clauses:
				for literal in clause.literals:
						lowerCAmelCase_     :							str             =    literal[:2]
						if symbol not in symbols_set:
								symbols_set.append(__UpperCamelCase )
		return clauses, symbols_set

def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					tuple[list[str], dict[str, bool | None]]:

		"""simple docstring"""







		lowerCAmelCase_     :							Optional[Any]             =    []
		lowerCAmelCase_     :							dict[str, bool | None]             =    {}
		lowerCAmelCase_     :							Dict             =    []

		for clause in clauses:
				if clause.evaluate(__UpperCamelCase ):
						continue
				for literal in clause.literals:
						literals.append(__UpperCamelCase )

		for s in symbols:
				lowerCAmelCase_     :							int             =    s + "'"
				if (s in literals and sym not in literals) or (
				    s not in literals and sym in literals
				):
						pure_symbols.append(__UpperCamelCase )
		for p in pure_symbols:
				lowerCAmelCase_     :							Optional[int]             =    None
		for s in pure_symbols:
				lowerCAmelCase_     :							int             =    s + "'"
				if s in literals:
						lowerCAmelCase_     :							Union[str, Any]             =    True
				elif sym in literals:
						lowerCAmelCase_     :							Union[str, Any]             =    False
		return pure_symbols, assignment

def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					tuple[list[str], dict[str, bool | None]]:

		"""simple docstring"""







		lowerCAmelCase_     :							List[str]             =    []
		for clause in clauses:
				if len(__UpperCamelCase ) == 1:
						unit_symbols.append(list(clause.literals.keys() )[0] )
				else:
						lowerCAmelCase_     :							Tuple             =    0, 0
						for literal, value in clause.literals.items():
								if value is False:
										f_count += 1
								elif value is None:
										lowerCAmelCase_     :							int             =    literal
										n_count += 1
						if f_count == len(__UpperCamelCase ) - 1 and n_count == 1:
								unit_symbols.append(__UpperCamelCase )
		lowerCAmelCase_     :							dict[str, bool | None]             =    {}
		for i in unit_symbols:
				lowerCAmelCase_     :							List[str]             =    i[:2]
				lowerCAmelCase_     :							str             =    len(__UpperCamelCase ) == 2
		lowerCAmelCase_     :							Dict             =    [i[:2] for i in unit_symbols]

		return unit_symbols, assignment

def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )		->					tuple[bool | None, dict[str, bool | None] | None]:

		"""simple docstring"""







		lowerCAmelCase_     :							Optional[Any]             =    True
		for clause in clauses:
				lowerCAmelCase_     :							Optional[int]             =    clause.evaluate(__UpperCamelCase )
				if clause_check is False:
						return False, None
				elif clause_check is None:
						lowerCAmelCase_     :							List[Any]             =    False
						continue

		if check_clause_all_true:
				return True, model

		try:
				lowerCAmelCase_     :							List[str]             =    find_pure_symbols(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )
		except RecursionError:
				print("raises a RecursionError and is" )
				return None, {}
		lowerCAmelCase_     :							Dict             =    None
		if len(__UpperCamelCase ) > 0:
				lowerCAmelCase_     :							Optional[Any]             =    pure_symbols[0], assignment[pure_symbols[0]]

		if p:
				lowerCAmelCase_     :							List[str]             =    model
				lowerCAmelCase_     :							Optional[int]             =    value
				lowerCAmelCase_     :							str             =    list(__UpperCamelCase )
				if p in tmp_symbols:
						tmp_symbols.remove(__UpperCamelCase )
				return dpll_algorithm(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )

		lowerCAmelCase_     :							Dict             =    find_unit_clauses(__UpperCamelCase   , __UpperCamelCase )
		lowerCAmelCase_     :							List[Any]             =    None
		if len(__UpperCamelCase ) > 0:
				lowerCAmelCase_     :							Dict             =    unit_symbols[0], assignment[unit_symbols[0]]
		if p:
				lowerCAmelCase_     :							str             =    model
				lowerCAmelCase_     :							Optional[Any]             =    value
				lowerCAmelCase_     :							Optional[Any]             =    list(__UpperCamelCase )
				if p in tmp_symbols:
						tmp_symbols.remove(__UpperCamelCase )
				return dpll_algorithm(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )
		lowerCAmelCase_     :							Optional[int]             =    symbols[0]
		lowerCAmelCase_     :							Tuple             =    symbols[1:]
		lowerCAmelCase_     :							Any             =    model, model
		lowerCAmelCase_     :							Dict             =    True, False

		return dpll_algorithm(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase ) or dpll_algorithm(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowercase__      =				generate_formula()
					print(F"""The formula {formula} is""", end=""" """)

					lowercase__ ,     lowercase__      =				generate_parameters(formula)
					lowercase__ ,     lowercase__      =				dpll_algorithm(clauses, symbols, {})

					if solution:
										print(F"""satisfiable with the assignment {model}.""")
					else:
										print("""not satisfiable.""")














"""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 	__lowerCamelCase       (				A__				,							A__				,							A__				,							unittest.TestCase    ):





		'''simple docstring'''






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





		def      lowerCamelCase (     self							:			Tuple			):
				torch.manual_seed(0			)
				lowerCAmelCase_     :							List[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		,    )
				lowerCAmelCase_     :							int             =    PNDMScheduler(skip_prk_steps=a_			)
				torch.manual_seed(0			)
				lowerCAmelCase_     :							List[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			)
				lowerCAmelCase_     :							Dict             =    CLIPVisionConfig(
				    hidden_size=32		,    projection_dim=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    image_size=32		,    patch_size=4		,    )
				lowerCAmelCase_     :							List[Any]             =    CLIPVisionModelWithProjection(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    CLIPImageProcessor(crop_size=32		,    size=32			)

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





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





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							int             =    "cpu"  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase_     :							Dict             =    self.get_dummy_components()
				lowerCAmelCase_     :							Union[str, Any]             =    StableDiffusionImageVariationPipeline(**a_			)
				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Tuple             =    self.get_dummy_inputs(a_			)
				lowerCAmelCase_     :							List[str]             =    sd_pipe(**a_			).images
				lowerCAmelCase_     :							List[Any]             =    image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase_     :							List[str]             =    np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]			)

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





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

				lowerCAmelCase_     :							List[str]             =    self.get_dummy_inputs(a_			)
				lowerCAmelCase_     :							Optional[Any]             =    2 * [inputs["image"]]
				lowerCAmelCase_     :							Optional[Any]             =    sd_pipe(**a_			)

				lowerCAmelCase_     :							Union[str, Any]             =    output.images

				lowerCAmelCase_     :							Optional[Any]             =    image[-1, -3:, -3:, -1]

				assert image.shape == (2, 64, 64, 3)
				lowerCAmelCase_     :							int             =    np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]			)

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





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







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





		'''simple docstring'''






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





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





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							Tuple             =    StableDiffusionImageVariationPipeline.from_pretrained(
				    "lambdalabs/sd-image-variations-diffusers"		,    safety_checker=a_			)
				lowerCAmelCase_     :							int             =    sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Union[str, Any]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							Union[str, Any]             =    sd_pipe(**a_			).images
				lowerCAmelCase_     :							Optional[int]             =    image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 5_12, 3)
				lowerCAmelCase_     :							Optional[Any]             =    np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]			)
				assert np.abs(image_slice - expected_slice			).max() < 6e-3





		def      lowerCamelCase (     self							:			Dict			):
				lowerCAmelCase_     :							List[str]             =    0

				def callback_fn(a_							:			int		,    a_							:			int		,    a_							:			torch.FloatTensor			) -> None:
						lowerCAmelCase_     :							str             =    True
						nonlocal number_of_steps
						number_of_steps += 1
						if step == 1:
								lowerCAmelCase_     :							str             =    latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								lowerCAmelCase_     :							Dict             =    latents[0, -3:, -3:, -1]
								lowerCAmelCase_     :							List[Any]             =    np.array(
								    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]			)

								assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5e-2
						elif step == 2:
								lowerCAmelCase_     :							Dict             =    latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								lowerCAmelCase_     :							Tuple             =    latents[0, -3:, -3:, -1]
								lowerCAmelCase_     :							Dict             =    np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]			)

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

				lowerCAmelCase_     :							Union[str, Any]             =    False

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

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





		def      lowerCamelCase (     self							:			Optional[Any]			):
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				lowerCAmelCase_     :							Tuple             =    "fusing/sd-image-variations-diffusers"
				lowerCAmelCase_     :							str             =    StableDiffusionImageVariationPipeline.from_pretrained(
				    a_		,    safety_checker=a_		,    torch_dtype=torch.floataa			)
				lowerCAmelCase_     :							int             =    pipe.to(a_			)
				pipe.set_progress_bar_config(disable=a_			)
				pipe.enable_attention_slicing(1			)
				pipe.enable_sequential_cpu_offload()

				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_		,    dtype=torch.floataa			)
				lowerCAmelCase_     :							Any             =    pipe(**a_			)

				lowerCAmelCase_     :							List[Any]             =    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 	__lowerCamelCase       (				unittest.TestCase    ):





		'''simple docstring'''






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





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





		def      lowerCamelCase (     self							:			Optional[int]			):
				lowerCAmelCase_     :							Any             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
				sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							List[str]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]

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





		def      lowerCamelCase (     self							:			Any			):
				lowerCAmelCase_     :							List[Any]             =    StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"			)
				lowerCAmelCase_     :							Tuple             =    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config			)
				sd_pipe.to(a_			)
				sd_pipe.set_progress_bar_config(disable=a_			)

				lowerCAmelCase_     :							Optional[int]             =    self.get_inputs(a_			)
				lowerCAmelCase_     :							Tuple             =    25
				lowerCAmelCase_     :							int             =    sd_pipe(**a_			).images[0]

				lowerCAmelCase_     :							Union[str, Any]             =    load_numpy(
				    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
				    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"			)
				lowerCAmelCase_     :							Optional[Any]             =    np.abs(expected_image - image			).max()
				assert max_diff < 1e-3
