
"""simple docstring"""

from __future__ import annotations

import random
from collections.abc import Iterable






class        _lowercase   :




	def __init__( self		, UpperCAmelCase_					)		->							None:
			lowerCamelCase     :			dict[str, bool | None]		=    {literal: None for literal in literals}




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




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




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							None:
			for literal in self.literals:
					lowerCamelCase     :			List[str]		=    literal[:2]
					if symbol in model:
							lowerCamelCase     :			Optional[int]		=    model[symbol]
					else:
							continue
					if value is not None:
							# Complement assignment if literal is in complemented form
							if literal.endswith('\''					):
									lowerCamelCase     :			Optional[Any]		=    not value
					lowerCamelCase     :			List[Any]		=    value




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							bool | None:
			for literal in self.literals:
					lowerCamelCase     :			Union[str, Any]		=    literal.rstrip('\''					) if literal.endswith('\''					) else literal + '\''
					if symbol in self.literals:
							return True

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






class        _lowercase   :




	def __init__( self		, UpperCAmelCase_					)		->							None:
			lowerCamelCase     :			Optional[Any]		=    list(UpperCAmelCase_					)




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


def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			Optional[int]		=    []
		lowerCamelCase     :			int		=    random.randint(1,						5			)
		lowerCamelCase     :			str		=    'A'
		lowerCamelCase     :			Dict		=    0
		while i < no_of_literals:
				lowerCamelCase     :			str		=    random.randint(1,						5			)
				lowerCamelCase     :			str		=    base_var + str(a_			)
				lowerCamelCase     :			Dict		=    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 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			set[Clause]		=    set()
		lowerCamelCase     :			Optional[int]		=    random.randint(1,						10			)
		while len(a_			) < no_of_clauses:
				clauses.add(generate_clause()			)
		return Formula(a_			)
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		lowerCamelCase     :			int		=    formula.clauses
		lowerCamelCase     :			str		=    []
		for clause in formula.clauses:
				for literal in clause.literals:
						lowerCamelCase     :			Dict		=    literal[:2]
						if symbol not in symbols_set:
								symbols_set.append(a_			)
		return clauses, symbols_set
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			dict[str, bool | None]		=    {}
		lowerCamelCase     :			Union[str, Any]		=    []

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

		for s in symbols:
				lowerCamelCase     :			List[str]		=    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:
				lowerCamelCase     :			Dict		=    None
		for s in pure_symbols:
				lowerCamelCase     :			List[Any]		=    s + '\''
				if s in literals:
						lowerCamelCase     :			Optional[int]		=    True
				elif sym in literals:
						lowerCamelCase     :			Dict		=    False
		return pure_symbols, assignment
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


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

		return unit_symbols, assignment
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


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

		if check_clause_all_true:
				return True, model

		try:
				lowerCamelCase	,    lowerCamelCase     :			str		=    find_pure_symbols(a_,						a_,						a_			)
		except RecursionError:
				print('raises a RecursionError and is'			)
				return None, {}
		lowerCamelCase     :			Optional[int]		=    None
		if len(a_			) > 0:
				lowerCamelCase	,    lowerCamelCase     :			int		=    pure_symbols[0], assignment[pure_symbols[0]]

		if p:
				lowerCamelCase     :			int		=    model
				lowerCamelCase     :			str		=    value
				lowerCamelCase     :			Tuple		=    list(a_			)
				if p in tmp_symbols:
						tmp_symbols.remove(a_			)
				return dpll_algorithm(a_,						a_,						a_			)

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

		return dpll_algorithm(a_,						a_,						a_			) or dpll_algorithm(a_,						a_,						a_			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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

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

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








"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(KDPMaAncestralDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Optional[int]:
			lowerCamelCase     :			Any		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Any		=    self.scheduler_classes[0]
			lowerCamelCase     :			List[str]		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[Any]		=    torch.manual_seed(0					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Tuple		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Union[str, Any]		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Tuple		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			Optional[int]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3877					) < 1E-2
			assert abs(result_mean.item() - 18.0331					) < 5E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Dict		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Optional[int]		=    sample.to(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Optional[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Optional[Any]		=    output.prev_sample

			lowerCamelCase     :			Union[str, Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 328.9970					) < 1E-2
			assert abs(result_mean.item() - 0.4284					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Any:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Union[str, Any]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Dict		=    self.get_scheduler_config()
			lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps		, device=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    torch.manual_seed(0					)

			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Dict		=    output.prev_sample

			lowerCamelCase     :			List[str]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3818					) < 1E-1
			assert abs(result_mean.item() - 18.0331					) < 1E-3






