


"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[int]      							=					(EulerDiscreteScheduler,)
				_lowerCAmelCase      :      Optional[int]      							=					10



				def   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				{
								    "num_train_timesteps": 1100,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**UpperCAmelCase__     )
								return config



				def   lowerCAmelCase__		(						self     ):
								for timesteps in [10, 50, 100, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,		[0.0_002, 0.002, 0.02]     ):
												self.check_over_configs(beta_start=UpperCAmelCase__       ,		beta_end=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE__     							=				self.get_scheduler_config()
								SCREAMING_SNAKE_CASE__     							=				scheduler_class(**UpperCAmelCase__     )

								scheduler.set_timesteps(self.num_inference_steps     )

								SCREAMING_SNAKE_CASE__     							=				torch.manual_seed(0     )

								SCREAMING_SNAKE_CASE__     							=				self.dummy_model()
								SCREAMING_SNAKE_CASE__     							=				self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE__     							=				sample.to(UpperCAmelCase__     )

								for i, t in enumerate(scheduler.timesteps     ):
												SCREAMING_SNAKE_CASE__     							=				scheduler.scale_model_input(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				scheduler.step(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		generator=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				output.prev_sample

								SCREAMING_SNAKE_CASE__     							=				torch.sum(torch.abs(UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				torch.mean(torch.abs(UpperCAmelCase__     )     )

								assert abs(result_sum.item() - 10.0_807     ) < 1e-2
								assert abs(result_mean.item() - 0.0_131     ) < 1e-3



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE__     							=				self.get_scheduler_config(prediction_type="v_prediction"     )
								SCREAMING_SNAKE_CASE__     							=				scheduler_class(**UpperCAmelCase__     )

								scheduler.set_timesteps(self.num_inference_steps     )

								SCREAMING_SNAKE_CASE__     							=				torch.manual_seed(0     )

								SCREAMING_SNAKE_CASE__     							=				self.dummy_model()
								SCREAMING_SNAKE_CASE__     							=				self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE__     							=				sample.to(UpperCAmelCase__     )

								for i, t in enumerate(scheduler.timesteps     ):
												SCREAMING_SNAKE_CASE__     							=				scheduler.scale_model_input(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				scheduler.step(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		generator=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				output.prev_sample

								SCREAMING_SNAKE_CASE__     							=				torch.sum(torch.abs(UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				torch.mean(torch.abs(UpperCAmelCase__     )     )

								assert abs(result_sum.item() - 0.0_002     ) < 1e-2
								assert abs(result_mean.item() - 2.2676e-06     ) < 1e-3



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE__     							=				self.get_scheduler_config()
								SCREAMING_SNAKE_CASE__     							=				scheduler_class(**UpperCAmelCase__     )

								scheduler.set_timesteps(self.num_inference_steps       ,		device=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				torch.manual_seed(0     )

								SCREAMING_SNAKE_CASE__     							=				self.dummy_model()
								SCREAMING_SNAKE_CASE__     							=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								SCREAMING_SNAKE_CASE__     							=				sample.to(UpperCAmelCase__     )

								for t in scheduler.timesteps:
												SCREAMING_SNAKE_CASE__     							=				scheduler.scale_model_input(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				scheduler.step(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		generator=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				output.prev_sample

								SCREAMING_SNAKE_CASE__     							=				torch.sum(torch.abs(UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				torch.mean(torch.abs(UpperCAmelCase__     )     )

								assert abs(result_sum.item() - 10.0_807     ) < 1e-2
								assert abs(result_mean.item() - 0.0_131     ) < 1e-3






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE__     							=				self.get_scheduler_config()
								SCREAMING_SNAKE_CASE__     							=				scheduler_class(**UpperCAmelCase__       ,		use_karras_sigmas=UpperCAmelCase__     )

								scheduler.set_timesteps(self.num_inference_steps       ,		device=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				torch.manual_seed(0     )

								SCREAMING_SNAKE_CASE__     							=				self.dummy_model()
								SCREAMING_SNAKE_CASE__     							=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								SCREAMING_SNAKE_CASE__     							=				sample.to(UpperCAmelCase__     )

								for t in scheduler.timesteps:
												SCREAMING_SNAKE_CASE__     							=				scheduler.scale_model_input(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				model(UpperCAmelCase__       ,		UpperCAmelCase__     )

												SCREAMING_SNAKE_CASE__     							=				scheduler.step(UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		generator=UpperCAmelCase__     )
												SCREAMING_SNAKE_CASE__     							=				output.prev_sample

								SCREAMING_SNAKE_CASE__     							=				torch.sum(torch.abs(UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				torch.mean(torch.abs(UpperCAmelCase__     )     )

								assert abs(result_sum.item() - 124.52_299_499_511_719     ) < 1e-2
								assert abs(result_mean.item() - 0.16_213_932_633_399_963     ) < 1e-3




"""simple docstring"""






from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				0
				for i in range(len(lowerCamelCase_  )  ):
								if lista[i] != lista[i]:
												count += 1
												SCREAMING_SNAKE_CASE__     							=				"_"
				if count > 1:
								return False
				else:
								return "".join(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				while True:
								SCREAMING_SNAKE_CASE__     							=				["$"] * len(lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				[]
								for i in range(len(lowerCamelCase_  )  ):
												for j in range(i + 1						,					len(lowerCamelCase_  )  ):
																SCREAMING_SNAKE_CASE__     							=				compare_string(binary[i]						,					binary[j]  )
																if k is False:
																				SCREAMING_SNAKE_CASE__     							=				"*"
																				SCREAMING_SNAKE_CASE__     							=				"*"
																				temp.append("X"  )
								for i in range(len(lowerCamelCase_  )  ):
												if checka[i] == "$":
																pi.append(binary[i]  )
								if len(lowerCamelCase_  ) == 0:
												return pi
								SCREAMING_SNAKE_CASE__     							=				list(set(lowerCamelCase_  )  )


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Sequence[float]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				for minterm in minterms:
								SCREAMING_SNAKE_CASE__     							=				""
								for _ in range(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				str(minterm % 2  ) + string
												minterm //= 2
								temp.append(lowerCamelCase_  )
				return temp


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				0
				for i in range(len(lowerCamelCase_  )  ):
								if lista[i] != lista[i]:
												count_n += 1
				return count_n == count


def __lowercase     (      lowerCamelCase_  :      list[list[int]]						,					lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				[0] * len(lowerCamelCase_  )
				for i in range(len(chart[0]  )  ):
								SCREAMING_SNAKE_CASE__     							=				0
								SCREAMING_SNAKE_CASE__     							=				-1
								for j in range(len(lowerCamelCase_  )  ):
												if chart[j][i] == 1:
																count += 1
																SCREAMING_SNAKE_CASE__     							=				j
								if count == 1:
												SCREAMING_SNAKE_CASE__     							=				1
				for i in range(len(lowerCamelCase_  )  ):
								if select[i] == 1:
												for j in range(len(chart[0]  )  ):
																if chart[i][j] == 1:
																				for k in range(len(lowerCamelCase_  )  ):
																								SCREAMING_SNAKE_CASE__     							=				0
												temp.append(prime_implicants[i]  )
				while True:
								SCREAMING_SNAKE_CASE__     							=				0
								SCREAMING_SNAKE_CASE__     							=				-1
								SCREAMING_SNAKE_CASE__     							=				0
								for i in range(len(lowerCamelCase_  )  ):
												SCREAMING_SNAKE_CASE__     							=				chart[i].count(1  )
												if count_n > max_n:
																SCREAMING_SNAKE_CASE__     							=				count_n
																SCREAMING_SNAKE_CASE__     							=				i

								if max_n == 0:
												return temp

								temp.append(prime_implicants[rem]  )

								for i in range(len(chart[0]  )  ):
												if chart[rem][i] == 1:
																for j in range(len(lowerCamelCase_  )  ):
																				SCREAMING_SNAKE_CASE__     							=				0


def __lowercase     (      lowerCamelCase_  :      list[str]						,					lowerCamelCase_  :      list[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[[0 for x in range(len(lowerCamelCase_  )  )] for x in range(len(lowerCamelCase_  )  )]
				for i in range(len(lowerCamelCase_  )  ):
								SCREAMING_SNAKE_CASE__     							=				prime_implicants[i].count("_"  )
								for j in range(len(lowerCamelCase_  )  ):
												if is_for_table(prime_implicants[i]						,					binary[j]						,					lowerCamelCase_  ):
																SCREAMING_SNAKE_CASE__     							=				1

				return chart


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				int(input("Enter the no. of variables\n"  )  )
				SCREAMING_SNAKE_CASE__     							=				[
				    float(lowerCamelCase_  )
				    for x in input(
				        "Enter the decimal representation of Minterms 'Spaces Separated'\n"  ).split()
				]
				SCREAMING_SNAKE_CASE__     							=				decimal_to_binary(lowerCamelCase_						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				check(lowerCamelCase_  )
				print("Prime Implicants are:"  )
				print(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				prime_implicant_chart(lowerCamelCase_						,					lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				selection(lowerCamelCase_						,					lowerCamelCase_  )
				print("Essential Prime Implicants are:"  )
				print(lowerCamelCase_  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
