



from __future__ import annotations

import random
from collections.abc import Iterable

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_		):
											a__					   =		{literal: None for literal in literals}


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


							def __len__(				self		):
											return len(self.literals		)


							def _a   (				self	,	a_		):
											for literal in self.literals:
															a__					   =		literal[:2]
															if symbol in model:
																			a__					   =		model[symbol]
															else:
																			continue
															if value is not None:
																			# Complement assignment if literal is in complemented form
																			if literal.endswith("""'"""		):
																							a__					   =		not value
															a__					   =		value







							def _a   (				self	,	a_		):
											for literal in self.literals:
															a__					   =		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 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_		):
											a__					   =		list(a_		)







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




def       A_   (						):

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

def       A_   (						):

				"""simple docstring"""
				a__					   =		set()
				a__					   =		random.randint(1			,		10		)
				while len(__a		) < no_of_clauses:
								clauses.add(generate_clause()		)
				return Formula(__a		)

def       A_   (						__a  :					Formula		):

				"""simple docstring"""
				a__					   =		formula.clauses
				a__					   =		[]
				for clause in formula.clauses:
								for literal in clause.literals:
												a__					   =		literal[:2]
												if symbol not in symbols_set:
																symbols_set.append(__a		)
				return clauses, symbols_set

def       A_   (						__a  :					list[Clause]			,		__a  :					list[str]			,		__a  :					dict[str, bool | None]		):

				"""simple docstring"""
				a__					   =		[]
				a__					   =		{}
				a__					   =		[]

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

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

def       A_   (						__a  :					list[Clause]			,		__a  :					dict[str, bool | None]		):

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

				return unit_symbols, assignment

def       A_   (						__a  :					list[Clause]			,		__a  :					list[str]			,		__a  :					dict[str, bool | None]		):

				"""simple docstring"""
				a__					   =		True
				for clause in clauses:
								a__					   =		clause.evaluate(__a		)
								if clause_check is False:
												return False, None
								elif clause_check is None:
												a__					   =		False
												continue

				if check_clause_all_true:
								return True, model

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

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

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

				return dpll_algorithm(__a			,		__a			,		__a		) or dpll_algorithm(__a			,		__a			,		__a		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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

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

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


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    AutoencoderKL     ,       lowercase__   :    CLIPTextModel     ,       lowercase__   :    CLIPTokenizer     ,       lowercase__   :    UNetaDConditionModel     ,       lowercase__   :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]     ,       ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(vae=lowercase__     ,       text_encoder=lowercase__     ,       tokenizer=lowercase__     ,       unet=lowercase__     ,       scheduler=lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.encode(tfms.ToTensor()(lowercase__).unsqueeze(0).to(self.device) * 2 - 1)
               lowerCAmelCase__	      =   0.18_215 * latent.latent_dist.sample()
        return latent




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   (1 / 0.18_215) * latent
        with torch.no_grad():
               lowerCAmelCase__	      =   self.vae.decode(lowercase__).sample
        lowerCAmelCase__	      =   (img / 2 + 0.5).clamp(0     ,       1)
        lowerCAmelCase__	      =   img.detach().cpu().permute(0     ,       2     ,       3     ,       1).numpy()
        lowerCAmelCase__	      =   (img * 255).round().astype('uint8')
        return Image.fromarray(img[0])




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer(
            lowercase__     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(text_input.input_ids.to(self.device))[0]

        lowerCAmelCase__	      =   self.tokenizer(
            ''     ,       padding='max_length'     ,       max_length=self.tokenizer.model_max_length     ,       truncation=lowercase__     ,       return_tensors='pt'     ,       )

        lowerCAmelCase__	      =   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])




 def __call__(     self   :    List[Any]     ,       lowercase__   :    Image.Image     ,       lowercase__   :    str     ,       lowercase__   :    float = 0.3     ,       lowercase__   :    float = 0.6     ,       lowercase__   :    float = 0.5     ,       lowercase__   :    int = 42     ,       lowercase__   :    int = 50     ,       lowercase__   :    float = 7.5     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   steps - int(kmin * steps)
        lowerCAmelCase__	      =   steps - int(kmax * steps)

        lowerCAmelCase__	      =   self.prep_text(lowercase__)

        self.scheduler.set_timesteps(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   img.size
        lowerCAmelCase__	      =   self.encode(lowercase__)

        torch.manual_seed(lowercase__)
        lowerCAmelCase__	      =   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)     ,       ).to(self.device)

        lowerCAmelCase__	      =   self.scheduler.add_noise(
            lowercase__     ,       lowercase__     ,       timesteps=self.scheduler.timesteps[tmax]     ,       )

        lowerCAmelCase__	      =   torch.cat([latents] * 2)

        lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       self.scheduler.timesteps[tmax])

        with torch.no_grad():
               lowerCAmelCase__	      =   self.unet(
                   lowercase__     ,       self.scheduler.timesteps[tmax]     ,       encoder_hidden_states=lowercase__     ,       ).sample

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
        lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       self.scheduler.timesteps[tmax]     ,       lowercase__).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
               if i > tmax:
                      if i < tmin:  # layout generation phase
                             lowerCAmelCase__	      =   self.scheduler.add_noise(
                                 lowercase__     ,       lowercase__     ,       timesteps=lowercase__     ,       )

                             lowerCAmelCase__	      =   (mix_factor * latents) + (
                                 1 - mix_factor
                             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                             lowerCAmelCase__	      =   torch.cat([input] * 2)

                      else:  # content generation phase
                             lowerCAmelCase__	      =   torch.cat([latents] * 2)

                      lowerCAmelCase__	      =   self.scheduler.scale_model_input(lowercase__     ,       lowercase__)

                      with torch.no_grad():
                             lowerCAmelCase__	      =   self.unet(
                                 lowercase__     ,       lowercase__     ,       encoder_hidden_states=lowercase__     ,       ).sample

                      lowerCAmelCase__      ,   lowerCAmelCase__	      =   pred.chunk(2)
                      lowerCAmelCase__	      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      lowerCAmelCase__	      =   self.scheduler.step(lowercase__     ,       lowercase__     ,       lowercase__).prev_sample

        return self.decode(lowercase__)
