





from __future__ import annotations

import random
from collections.abc import Iterable




class 	_UpperCAmelCase						:




					def __init__(    self     ,   __lowerCAmelCase							)    ->	None:
							UpperCamelCase__								=						{literal: None for literal in literals}




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




					def __len__(    self							)    ->	int:
							return len(self.literals							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	None:
							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 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	bool | None:
							for literal in self.literals:
									UpperCamelCase__								=						literal.rstrip("""'"""							) if literal.endswith("""'"""							) else literal + """'"""
									if symbol in self.literals:
											return True

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




class 	_UpperCAmelCase						:




					def __init__(    self     ,   __lowerCAmelCase							)    ->	None:
							UpperCamelCase__								=						list(__lowerCAmelCase							)




					def __str__(    self							)    ->	str:
							return "{" + " , ".join(str(__lowerCAmelCase							) for clause in self.clauses							) + "}"



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

def      a    (   )  ->Formula:
		UpperCamelCase__								=						set()
		UpperCamelCase__								=						random.randint(1			,    1_0							)
		while len(_lowercase							) < no_of_clauses:
				clauses.add(generate_clause()							)
		return Formula(_lowercase							)

def      a    (   _lowercase :  Formula							)  ->tuple[list[Clause], list[str]]:
		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(_lowercase							)
		return clauses, symbols_set

def      a    (   _lowercase :  list[Clause]			,    _lowercase :  list[str]			,    _lowercase :  dict[str, bool | None]							)  ->tuple[list[str], dict[str, bool | None]]:
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						{}
		UpperCamelCase__								=						[]

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

		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(_lowercase							)
		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    (   _lowercase :  list[Clause]			,    _lowercase :  dict[str, bool | None]							)  ->tuple[list[str], dict[str, bool | None]]:
		UpperCamelCase__								=						[]
		for clause in clauses:
				if len(_lowercase							) == 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(_lowercase							) - 1 and n_count == 1:
								unit_symbols.append(_lowercase							)
		UpperCamelCase__								=						{}
		for i in unit_symbols:
				UpperCamelCase__								=						i[:2]
				UpperCamelCase__								=						len(_lowercase							) == 2
		UpperCamelCase__								=						[i[:2] for i in unit_symbols]

		return unit_symbols, assignment

def      a    (   _lowercase :  list[Clause]			,    _lowercase :  list[str]			,    _lowercase :  dict[str, bool | None]							)  ->tuple[bool | None, dict[str, bool | None] | None]:
		UpperCamelCase__								=						True
		for clause in clauses:
				UpperCamelCase__								=						clause.evaluate(_lowercase							)
				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(_lowercase			,    _lowercase			,    _lowercase							)
		except RecursionError:
				print("""raises a RecursionError and is"""							)
				return None, {}
		UpperCamelCase__								=						None
		if len(_lowercase							) > 0:
				UpperCamelCase__	,      UpperCamelCase__								=						pure_symbols[0], assignment[pure_symbols[0]]

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

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

		return dpll_algorithm(_lowercase			,    _lowercase			,    _lowercase							) or dpll_algorithm(_lowercase			,    _lowercase			,    _lowercase							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      generate_formula()
				print(f"""The formula {formula} is""", end=" ")

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

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












import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 	_UpperCAmelCase						( _lowercase			,							unittest.TestCase       ):
					__A        = KandinskyPipeline
					__A        = [
					    "prompt",
					    "image_embeds",
					    "negative_image_embeds",
					]
					__A        = ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
					__A        = [
					    "generator",
					    "height",
					    "width",
					    "latents",
					    "guidance_scale",
					    "negative_prompt",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					__A        = False




					@property
					def 						snake_case_		(    self							)    ->	Any:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							return 32




					@property
					def 						snake_case_		(    self							)    ->	List[Any]:
							return self.time_input_dim




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return self.time_input_dim * 4




					@property
					def 						snake_case_		(    self							)    ->	Dict:
							return 100




					@property
					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
							return tokenizer




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						MCLIPConfig(
							    numDims=self.cross_attention_dim     ,   transformerDimensions=self.text_embedder_hidden_size     ,   hidden_size=self.text_embedder_hidden_size     ,   intermediate_size=37     ,   num_attention_heads=4     ,   num_hidden_layers=5     ,   vocab_size=1_005     ,   )

							UpperCamelCase__								=						MultilingualCLIP(__lowerCAmelCase							)
							UpperCamelCase__								=						text_encoder.eval()

							return text_encoder




					@property
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							torch.manual_seed(0							)

							UpperCamelCase__								=						{
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							UpperCamelCase__								=						UNetaDConditionModel(**__lowerCAmelCase							)
							return model




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}




					@property
					def 						snake_case_		(    self							)    ->	Optional[int]:
							torch.manual_seed(0							)
							UpperCamelCase__								=						VQModel(**self.dummy_movq_kwargs							)
							return model




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						self.dummy_text_encoder
							UpperCamelCase__								=						self.dummy_tokenizer
							UpperCamelCase__								=						self.dummy_unet
							UpperCamelCase__								=						self.dummy_movq

							UpperCamelCase__								=						DDIMScheduler(
							    num_train_timesteps=1_000     ,   beta_schedule="""linear"""     ,   beta_start=0.0_0_0_8_5     ,   beta_end=0.0_1_2     ,   clip_sample=__lowerCAmelCase     ,   set_alpha_to_one=__lowerCAmelCase     ,   steps_offset=1     ,   prediction_type="""epsilon"""     ,   thresholding=__lowerCAmelCase     ,   )

							UpperCamelCase__								=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}
							return components




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=0							)    ->	str:
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
							UpperCamelCase__								=						floats_tensor((1, self.cross_attention_dim)     ,   rng=random.Random(seed + 1							)							).to(__lowerCAmelCase							)
							if str(__lowerCAmelCase							).startswith("""mps"""							):
									UpperCamelCase__								=						torch.manual_seed(__lowerCAmelCase							)
							else:
									UpperCamelCase__								=						torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
							UpperCamelCase__								=						{
							    """prompt""": """horse""",
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """guidance_scale""": 4.0,
							    """num_inference_steps""": 2,
							    """output_type""": """np""",
							}
							return inputs




					def 						snake_case_		(    self							)    ->	Tuple:
							UpperCamelCase__								=						"""cpu"""

							UpperCamelCase__								=						self.get_dummy_components()

							UpperCamelCase__								=						self.pipeline_class(**__lowerCAmelCase							)
							UpperCamelCase__								=						pipe.to(__lowerCAmelCase							)

							pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
							UpperCamelCase__								=						output.images

							UpperCamelCase__								=						pipe(
							    **self.get_dummy_inputs(__lowerCAmelCase							)     ,   return_dict=__lowerCAmelCase     ,   )[0]

							UpperCamelCase__								=						image[0, -3:, -3:, -1]
							UpperCamelCase__								=						image_from_tuple[0, -3:, -3:, -1]

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

							UpperCamelCase__								=						np.array(
							    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

							assert (
							    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
							), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




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




					def 						snake_case_		(    self							)    ->	str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

							UpperCamelCase__								=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""     ,   torch_dtype=torch.floataa							)
							pipe_prior.to(__lowerCAmelCase							)

							UpperCamelCase__								=						KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,   torch_dtype=torch.floataa							)
							UpperCamelCase__								=						pipeline.to(__lowerCAmelCase							)
							pipeline.set_progress_bar_config(disable=__lowerCAmelCase							)

							UpperCamelCase__								=						"""red cat, 4k photo"""

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__	,      UpperCamelCase__								=						pipe_prior(
							    __lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=5     ,   negative_prompt=""""""     ,   ).to_tuple()

							UpperCamelCase__								=						torch.Generator(device="""cuda"""							).manual_seed(0							)
							UpperCamelCase__								=						pipeline(
							    __lowerCAmelCase     ,   image_embeds=__lowerCAmelCase     ,   negative_image_embeds=__lowerCAmelCase     ,   generator=__lowerCAmelCase     ,   num_inference_steps=100     ,   output_type="""np"""     ,   )

							UpperCamelCase__								=						output.images[0]

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

							assert_mean_pixel_difference(__lowerCAmelCase     ,   __lowerCAmelCase							)





