
"""simple docstring"""




from __future__ import annotations

import random
from collections.abc import Iterable






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													__UpperCamelCase       					=     {literal: None for literal in literals}



							def __str__(					self     )->				str:





													'''simple docstring'''






													return "{" + " , ".join(self.literals     ) + "}"



							def __len__(					self     )->				int:





													'''simple docstring'''






													return len(self.literals     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													for literal in self.literals:
																			__UpperCamelCase       					=     literal[:2]
																			if symbol in model:
																									__UpperCamelCase       					=     model[symbol]
																			else:
																									continue
																			if value is not None:
																									# Complement assignment if literal is in complemented form
																									if literal.endswith('''\''''     ):
																															__UpperCamelCase       					=     not value
																			__UpperCamelCase       					=     value



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				bool | None:





													'''simple docstring'''






													for literal in self.literals:
																			__UpperCamelCase       					=     literal.rstrip('''\''''     ) if literal.endswith('''\''''     ) else literal + '''\''''
																			if symbol in self.literals:
																									return True

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






class     SCREAMING_SNAKE_CASE__      :

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				None:





													'''simple docstring'''






													__UpperCamelCase       					=     list(SCREAMING_SNAKE_CASE_     )



							def __str__(					self     )->				str:





													'''simple docstring'''






													return "{" + " , ".join(str(SCREAMING_SNAKE_CASE_     ) for clause in self.clauses     ) + "}"





def 		A_	(							) ->      Clause:



						'''simple docstring'''

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




def 		A_	(							) ->      Formula:



						'''simple docstring'''

						__UpperCamelCase       					=     set()
						__UpperCamelCase       					=     random.randint(1	, 10	)
						while len(snake_case	) < no_of_clauses:
												clauses.add(generate_clause()	)
						return Formula(snake_case	)




def 		A_	(							snake_case							:				Formula	) ->      tuple[list[Clause], list[str]]:



						'''simple docstring'''

						__UpperCamelCase       					=     formula.clauses
						__UpperCamelCase       					=     []
						for clause in formula.clauses:
												for literal in clause.literals:
																		__UpperCamelCase       					=     literal[:2]
																		if symbol not in symbols_set:
																								symbols_set.append(snake_case	)
						return clauses, symbols_set




def 		A_	(							snake_case							:				list[Clause]	, snake_case							:				list[str]	, snake_case							:				dict[str, bool | None]	) ->      tuple[list[str], dict[str, bool | None]]:



						'''simple docstring'''

						__UpperCamelCase       					=     []
						__UpperCamelCase       					=     {}
						__UpperCamelCase       					=     []

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

						for s in symbols:
												__UpperCamelCase       					=     s + '''\''''
												if (s in literals and sym not in literals) or (
												    s not in literals and sym in literals
												):
																		pure_symbols.append(snake_case	)
						for p in pure_symbols:
												__UpperCamelCase       					=     None
						for s in pure_symbols:
												__UpperCamelCase       					=     s + '''\''''
												if s in literals:
																		__UpperCamelCase       					=     True
												elif sym in literals:
																		__UpperCamelCase       					=     False
						return pure_symbols, assignment




def 		A_	(							snake_case							:				list[Clause]	, snake_case							:				dict[str, bool | None]	) ->      tuple[list[str], dict[str, bool | None]]:



						'''simple docstring'''

						__UpperCamelCase       					=     []
						for clause in clauses:
												if len(snake_case	) == 1:
																		unit_symbols.append(list(clause.literals.keys()	)[0]	)
												else:
																		__UpperCamelCase					,							__UpperCamelCase       					=     0, 0
																		for literal, value in clause.literals.items():
																								if value is False:
																														f_count += 1
																								elif value is None:
																														__UpperCamelCase       					=     literal
																														n_count += 1
																		if f_count == len(snake_case	) - 1 and n_count == 1:
																								unit_symbols.append(snake_case	)
						__UpperCamelCase       					=     {}
						for i in unit_symbols:
												__UpperCamelCase       					=     i[:2]
												__UpperCamelCase       					=     len(snake_case	) == 2
						__UpperCamelCase       					=     [i[:2] for i in unit_symbols]

						return unit_symbols, assignment




def 		A_	(							snake_case							:				list[Clause]	, snake_case							:				list[str]	, snake_case							:				dict[str, bool | None]	) ->      tuple[bool | None, dict[str, bool | None] | None]:



						'''simple docstring'''

						__UpperCamelCase       					=     True
						for clause in clauses:
												__UpperCamelCase       					=     clause.evaluate(snake_case	)
												if clause_check is False:
																		return False, None
												elif clause_check is None:
																		__UpperCamelCase       					=     False
																		continue

						if check_clause_all_true:
												return True, model

						try:
												__UpperCamelCase					,							__UpperCamelCase       					=     find_pure_symbols(snake_case	, snake_case	, snake_case	)
						except RecursionError:
												print('''raises a RecursionError and is'''	)
												return None, {}
						__UpperCamelCase       					=     None
						if len(snake_case	) > 0:
												__UpperCamelCase					,							__UpperCamelCase       					=     pure_symbols[0], assignment[pure_symbols[0]]

						if p:
												__UpperCamelCase       					=     model
												__UpperCamelCase       					=     value
												__UpperCamelCase       					=     list(snake_case	)
												if p in tmp_symbols:
																		tmp_symbols.remove(snake_case	)
												return dpll_algorithm(snake_case	, snake_case	, snake_case	)

						__UpperCamelCase					,							__UpperCamelCase       					=     find_unit_clauses(snake_case	, snake_case	)
						__UpperCamelCase       					=     None
						if len(snake_case	) > 0:
												__UpperCamelCase					,							__UpperCamelCase       					=     unit_symbols[0], assignment[unit_symbols[0]]
						if p:
												__UpperCamelCase       					=     model
												__UpperCamelCase       					=     value
												__UpperCamelCase       					=     list(snake_case	)
												if p in tmp_symbols:
																		tmp_symbols.remove(snake_case	)
												return dpll_algorithm(snake_case	, snake_case	, snake_case	)
						__UpperCamelCase       					=     symbols[0]
						__UpperCamelCase       					=     symbols[1:]
						__UpperCamelCase					,							__UpperCamelCase       					=     model, model
						__UpperCamelCase					,							__UpperCamelCase       					=     True, False

						return dpll_algorithm(snake_case	, snake_case	, snake_case	) or dpll_algorithm(snake_case	, snake_case	, snake_case	)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

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

				lowercase__							:							Optional[int]													=     generate_parameters(formula)
				lowercase__							:							List[str]													=     dpll_algorithm(clauses, symbols, {})

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






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						unittest.TestCase      ):

							"""simple docstring"""






							_snake_case       						=					StableDiffusionParadigmsPipeline
							_snake_case       						=					TEXT_TO_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case       						=					TEXT_TO_IMAGE_IMAGE_PARAMS



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													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       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=SCREAMING_SNAKE_CASE_       ,		)
													__UpperCamelCase       					=     DDIMParallelScheduler(
													    beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule='''scaled_linear'''       ,		clip_sample=SCREAMING_SNAKE_CASE_       ,		set_alpha_to_one=SCREAMING_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       ,		sample_size=128       ,		)
													torch.manual_seed(0     )
													__UpperCamelCase       					=     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='''gelu'''       ,		projection_dim=512       ,		)
													__UpperCamelCase       					=     CLIPTextModel(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

													__UpperCamelCase       					=     {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''safety_checker''': None,
													    '''feature_extractor''': None,
													}
													return components



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=0     )->				List[Any]:





													'''simple docstring'''






													if str(SCREAMING_SNAKE_CASE_     ).startswith('''mps'''     ):
																			__UpperCamelCase       					=     torch.manual_seed(SCREAMING_SNAKE_CASE_     )
													else:
																			__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''prompt''': '''a photograph of an astronaut riding a horse''',
													    '''generator''': generator,
													    '''num_inference_steps''': 10,
													    '''guidance_scale''': 6.0,
													    '''output_type''': '''numpy''',
													    '''parallel''': 3,
													    '''debug''': True,
													}
													return inputs



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.4_7_7_3, 0.5_4_1_7, 0.4_7_2_3, 0.4_9_2_5, 0.5_6_3_1, 0.4_7_5_2, 0.5_2_4_0, 0.4_9_3_5, 0.5_0_2_3]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													torch.manual_seed(0     )
													__UpperCamelCase       					=     DDPMParallelScheduler()
													torch.manual_seed(0     )
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.3_5_7_3, 0.4_4_2_0, 0.4_9_6_0, 0.4_7_9_9, 0.3_7_9_6, 0.3_8_7_9, 0.4_8_1_9, 0.4_3_6_5, 0.4_4_6_8]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2



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





													'''simple docstring'''






													super().test_inference_batch_consistent(batch_sizes=[1, 2]     )



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





													'''simple docstring'''






													super().test_inference_batch_single_identical(batch_size=2       ,		expected_max_diff=3E-3     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase       					=     self.get_dummy_components()
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline(**SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     sd_pipe.to(SCREAMING_SNAKE_CASE_     )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     '''french fries'''
													__UpperCamelCase       					=     sd_pipe(**SCREAMING_SNAKE_CASE_       ,		negative_prompt=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     output.images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													__UpperCamelCase       					=     np.array([0.4_7_7_1, 0.5_4_2_0, 0.4_6_8_3, 0.4_9_1_8, 0.5_6_3_6, 0.4_7_2_5, 0.5_2_3_0, 0.4_9_2_3, 0.5_0_1_5]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






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

							"""simple docstring"""






							def       A__						(					self     )->				Any:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=0     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {
													    '''prompt''': '''a photograph of an astronaut riding a horse''',
													    '''generator''': generator,
													    '''num_inference_steps''': 10,
													    '''guidance_scale''': 7.5,
													    '''output_type''': '''numpy''',
													    '''parallel''': 3,
													    '''debug''': True,
													}
													return inputs



							def       A__						(					self     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     '''stabilityai/stable-diffusion-2-base'''
													__UpperCamelCase       					=     DDIMParallelScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,		subfolder='''scheduler'''     )
													__UpperCamelCase       					=     StableDiffusionParadigmsPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,		scheduler=SCREAMING_SNAKE_CASE_       ,		safety_checker=SCREAMING_SNAKE_CASE_     )
													pipe.to(SCREAMING_SNAKE_CASE_     )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )
													pipe.enable_attention_slicing()

													__UpperCamelCase       					=     self.get_inputs()
													__UpperCamelCase       					=     pipe(**SCREAMING_SNAKE_CASE_     ).images
													__UpperCamelCase       					=     image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 512, 512, 3)

													__UpperCamelCase       					=     np.array([0.9_6_2_2, 0.9_6_0_2, 0.9_7_4_8, 0.9_5_9_1, 0.9_6_3_0, 0.9_6_9_1, 0.9_6_6_1, 0.9_6_3_1, 0.9_7_4_1]     )

													assert np.abs(expected_slice - image_slice     ).max() < 1E-2




