



"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							(HeunDiscreteScheduler,)
						__UpperCamelCase	       =							10






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						**lowercase_				:str      )       ->    Dict:
												UpperCAmelCase    =				{
												    'num_train_timesteps': 11_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												}

												config.update(**lowercase_      )
												return config






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    str:
												for timesteps in [10, 50, 1_00, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_      )






						def 			UpperCAmelCase__  (      self				:Any      )       ->    int:
												for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]     ,						[0.0002, 0.002, 0.02]      ):
																		self.check_over_configs(beta_start=lowercase_     ,						beta_end=lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    List[str]:
												for schedule in ["linear", "scaled_linear", "exp"]:
																		self.check_over_configs(beta_schedule=lowercase_      )






						def 			UpperCAmelCase__  (      self				:str      )       ->    Dict:
												for clip_sample_range in [1.0, 2.0, 3.0]:
																		self.check_over_configs(clip_sample_range=lowercase_     ,						clip_sample=lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    Tuple:
												for prediction_type in ["epsilon", "v_prediction", "sample"]:
																		self.check_over_configs(prediction_type=lowercase_      )






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    str:
												UpperCAmelCase    =				self.scheduler_classes[0]
												UpperCAmelCase    =				self.get_scheduler_config()
												UpperCAmelCase    =				scheduler_class(**lowercase_      )

												scheduler.set_timesteps(self.num_inference_steps      )

												UpperCAmelCase    =				self.dummy_model()
												UpperCAmelCase    =				self.dummy_sample_deter * scheduler.init_noise_sigma
												UpperCAmelCase    =				sample.to(lowercase_      )

												for i, t in enumerate(scheduler.timesteps      ):
																		UpperCAmelCase    =				scheduler.scale_model_input(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				model(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				scheduler.step(lowercase_     ,						lowercase_     ,						lowercase_      )
																		UpperCAmelCase    =				output.prev_sample

												UpperCAmelCase    =				torch.sum(torch.abs(lowercase_      )      )
												UpperCAmelCase    =				torch.mean(torch.abs(lowercase_      )      )

												if torch_device in ["cpu", "mps"]:
																		assert abs(result_sum.item() - 0.1233      ) < 1E-2
																		assert abs(result_mean.item() - 0.0002      ) < 1E-3
												else:
																		# CUDA
																		assert abs(result_sum.item() - 0.1233      ) < 1E-2
																		assert abs(result_mean.item() - 0.0002      ) < 1E-3






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    int:
												UpperCAmelCase    =				self.scheduler_classes[0]
												UpperCAmelCase    =				self.get_scheduler_config(prediction_type='v_prediction'      )
												UpperCAmelCase    =				scheduler_class(**lowercase_      )

												scheduler.set_timesteps(self.num_inference_steps      )

												UpperCAmelCase    =				self.dummy_model()
												UpperCAmelCase    =				self.dummy_sample_deter * scheduler.init_noise_sigma
												UpperCAmelCase    =				sample.to(lowercase_      )

												for i, t in enumerate(scheduler.timesteps      ):
																		UpperCAmelCase    =				scheduler.scale_model_input(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				model(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				scheduler.step(lowercase_     ,						lowercase_     ,						lowercase_      )
																		UpperCAmelCase    =				output.prev_sample

												UpperCAmelCase    =				torch.sum(torch.abs(lowercase_      )      )
												UpperCAmelCase    =				torch.mean(torch.abs(lowercase_      )      )

												if torch_device in ["cpu", "mps"]:
																		assert abs(result_sum.item() - 4.6_934E-07      ) < 1E-2
																		assert abs(result_mean.item() - 6.1_112E-10      ) < 1E-3
												else:
																		# CUDA
																		assert abs(result_sum.item() - 4.693_428_650_170_972E-07      ) < 1E-2
																		assert abs(result_mean.item() - 0.0002      ) < 1E-3






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    Dict:
												UpperCAmelCase    =				self.scheduler_classes[0]
												UpperCAmelCase    =				self.get_scheduler_config()
												UpperCAmelCase    =				scheduler_class(**lowercase_      )

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

												UpperCAmelCase    =				self.dummy_model()
												UpperCAmelCase    =				self.dummy_sample_deter.to(lowercase_      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																		UpperCAmelCase    =				scheduler.scale_model_input(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				model(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				scheduler.step(lowercase_     ,						lowercase_     ,						lowercase_      )
																		UpperCAmelCase    =				output.prev_sample

												UpperCAmelCase    =				torch.sum(torch.abs(lowercase_      )      )
												UpperCAmelCase    =				torch.mean(torch.abs(lowercase_      )      )

												if str(lowercase_      ).startswith('cpu'      ):
																		# The following sum varies between 148 and 156 on mps. Why?
																		assert abs(result_sum.item() - 0.1233      ) < 1E-2
																		assert abs(result_mean.item() - 0.0002      ) < 1E-3
												elif str(lowercase_      ).startswith('mps'      ):
																		# Larger tolerance on mps
																		assert abs(result_mean.item() - 0.0002      ) < 1E-2
												else:
																		# CUDA
																		assert abs(result_sum.item() - 0.1233      ) < 1E-2
																		assert abs(result_mean.item() - 0.0002      ) < 1E-3






						def 			UpperCAmelCase__  (      self				:str      )       ->    Union[str, Any]:
												UpperCAmelCase    =				self.scheduler_classes[0]
												UpperCAmelCase    =				self.get_scheduler_config()
												UpperCAmelCase    =				scheduler_class(**lowercase_     ,						use_karras_sigmas=lowercase_      )

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

												UpperCAmelCase    =				self.dummy_model()
												UpperCAmelCase    =				self.dummy_sample_deter.to(lowercase_      ) * scheduler.init_noise_sigma
												UpperCAmelCase    =				sample.to(lowercase_      )

												for t in scheduler.timesteps:
																		UpperCAmelCase    =				scheduler.scale_model_input(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				model(lowercase_     ,						lowercase_      )

																		UpperCAmelCase    =				scheduler.step(lowercase_     ,						lowercase_     ,						lowercase_      )
																		UpperCAmelCase    =				output.prev_sample

												UpperCAmelCase    =				torch.sum(torch.abs(lowercase_      )      )
												UpperCAmelCase    =				torch.mean(torch.abs(lowercase_      )      )

												assert abs(result_sum.item() - 0.0_0015      ) < 1E-2
												assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07      ) < 1E-2










"""simple docstring"""


from typing import Any

import numpy as np







class    A_    :



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:np.ndarray     ,						lowercase_				:int      )       ->    None:
												# Check if RHS is negative
												if np.any(tableau[:, -1]     ,						where=tableau[:, -1] < 0      ):
																		raise ValueError('RHS must be > 0'      )

												UpperCAmelCase    =				tableau
												UpperCAmelCase   ,					UpperCAmelCase    =				tableau.shape

												# Number of decision variables x1, x2, x3...
												UpperCAmelCase    =				n_vars

												# Number of artificial variables to be minimised
												UpperCAmelCase    =				len(np.where(tableau[self.n_vars : -1] == -1      )[0]      )

												# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
												UpperCAmelCase    =				(self.n_art_vars > 0) + 1

												# Number of slack variables added to make inequalities into equalities
												UpperCAmelCase    =				self.n_rows - self.n_stages

												# Objectives for each stage
												UpperCAmelCase    =				['max']

												# In two stage simplex, first minimise then maximise
												if self.n_art_vars:
																		self.objectives.append('min'      )

												UpperCAmelCase    =				['']

												# Index of current pivot row and column
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None

												# Does objective row only contain (non)-negative values?
												UpperCAmelCase    =				False






						@staticmethod
						def 			UpperCAmelCase__  (      *lowercase_				:int      )       ->    list[str]:
												if len(lowercase_      ) != 3:
																		raise ValueError('Must provide n_vars, n_slack, and n_art_vars'      )

												if not all(x >= 0 and isinstance(lowercase_     ,						lowercase_      ) for x in args      ):
																		raise ValueError('All arguments must be non-negative integers'      )

												# decision | slack | artificial
												UpperCAmelCase    =				['x', 's', 'a']
												UpperCAmelCase    =				[]
												for i in range(3      ):
																		for j in range(args[i]      ):
																								titles.append(string_starts[i] + str(j + 1      )      )
												titles.append('RHS'      )
												return titles






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:np.ndarray      )       ->    tuple[Any, Any]:
												UpperCAmelCase    =				self.objectives[-1]

												# Find entries of highest magnitude in objective rows
												UpperCAmelCase    =				(objective == 'min') - (objective == 'max')
												UpperCAmelCase    =				np.argmax(sign * tableau[0, : self.n_vars]      )

												# Choice is only valid if below 0 for maximise, and above for minimise
												if sign * self.tableau[0, col_idx] <= 0:
																		UpperCAmelCase    =				True
																		return 0, 0

												# Pivot row is chosen as having the lowest quotient when elements of
												# the pivot column divide the right-hand side

												# Slice excluding the objective rows
												UpperCAmelCase    =				slice(self.n_stages     ,						self.n_rows      )

												# RHS
												UpperCAmelCase    =				tableau[s, -1]

												# Elements of pivot column within slice
												UpperCAmelCase    =				tableau[s, col_idx]

												# Array filled with nans
												UpperCAmelCase    =				np.full(self.n_rows - self.n_stages     ,						np.nan      )

												# If element in pivot column is greater than zeron_stages, return
												# quotient or nan otherwise
												UpperCAmelCase    =				np.divide(lowercase_     ,						lowercase_     ,						out=lowercase_     ,						where=divisor > 0      )

												# Arg of minimum quotient excluding the nan values. n_stages is added
												# to compensate for earlier exclusion of objective columns
												UpperCAmelCase    =				np.nanargmin(lowercase_      ) + self.n_stages
												return row_idx, col_idx






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:int     ,						lowercase_				:int      )       ->    np.ndarray:
												UpperCAmelCase    =				tableau[row_idx].copy()

												UpperCAmelCase    =				piv_row[col_idx]

												# Entry becomes 1
												piv_row *= 1 / piv_val

												# Variable in pivot column becomes basic, ie the only non-zero entry
												for idx, coeff in enumerate(tableau[:, col_idx]      ):
																		tableau[idx] += -coeff * piv_row
												UpperCAmelCase    =				piv_row
												return tableau






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:np.ndarray      )       ->    np.ndarray:
												self.objectives.pop()

												if not self.objectives:
																		return tableau

												# Slice containing ids for artificial columns
												UpperCAmelCase    =				slice(-self.n_art_vars - 1     ,						-1      )

												# Delete the artificial variable columns
												UpperCAmelCase    =				np.delete(lowercase_     ,						lowercase_     ,						axis=1      )

												# Delete the objective row of the first stage
												UpperCAmelCase    =				np.delete(lowercase_     ,						0     ,						axis=0      )

												UpperCAmelCase    =				1
												self.n_rows -= 1
												UpperCAmelCase    =				0
												UpperCAmelCase    =				False
												return tableau






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    dict[Any, Any]:
												# Stop simplex algorithm from cycling.
												for _ in range(1_00      ):
																		# Completion of each stage removes an objective. If both stages
																		# are complete, then no objectives are left
																		if not self.objectives:
																								UpperCAmelCase    =				self.generate_col_titles(
																								    self.n_vars     ,						self.n_slack     ,						self.n_art_vars      )

																								# Find the values of each variable at optimal solution
																								return self.interpret_tableau(self.tableau     ,						self.col_titles      )

																		UpperCAmelCase   ,					UpperCAmelCase    =				self.find_pivot(self.tableau      )

																		# If there are no more negative values in objective row
																		if self.stop_iter:
																								# Delete artificial variable columns and rows. Update attributes
																								UpperCAmelCase    =				self.change_stage(self.tableau      )
																		else:
																								UpperCAmelCase    =				self.pivot(self.tableau     ,						lowercase_     ,						lowercase_      )
												return {}






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:np.ndarray     ,						lowercase_				:list[str]      )       ->    dict[str, float]:
												UpperCAmelCase    =				{'P': abs(tableau[0, -1]      )}

												for i in range(self.n_vars      ):
																		# Gives ids of nonzero entries in the ith column
																		UpperCAmelCase    =				np.nonzero(tableau[:, i]      )
																		UpperCAmelCase    =				len(nonzero[0]      )

																		# First entry in the nonzero ids
																		UpperCAmelCase    =				nonzero[0][0]
																		UpperCAmelCase    =				tableau[nonzero_rowidx, i]

																		# If there is only one nonzero value in column, which is one
																		if n_nonzero == nonzero_val == 1:
																								UpperCAmelCase    =				tableau[nonzero_rowidx, -1]
																								UpperCAmelCase    =				rhs_val

        # Check for basic variables
												for title in col_titles:
																		# Don't add RHS or slack variables to output dict
																		if title[0] not in "R-s-a":
																								output_dict.setdefault(lowercase_     ,						0      )
												return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()





