






"""simple docstring"""



from typing import Any

import numpy as np




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											if np.any(tableau[:, -1]     ,    where=tableau[:, -1] < 0):
																raise ValueError('RHS must be > 0')

											lowerCamelCase__					=tableau
											lowerCamelCase__					,  lowerCamelCase__					=tableau.shape

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

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

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

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

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

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

											lowerCamelCase__					=['']

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

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






						@staticmethod
						def 		lowerCAmelCase (       *_SCREAMING_SNAKE_CASE)      ->	list[str]:




											"""simple docstring"""





											if len(_SCREAMING_SNAKE_CASE) != 3:
																raise ValueError('Must provide n_vars, n_slack, and n_art_vars')

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

											# decision | slack | artificial
											lowerCamelCase__					=['x', 's', 'a']
											lowerCamelCase__					=[]
											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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	tuple[Any, Any]:




											"""simple docstring"""





											lowerCamelCase__					=self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											lowerCamelCase__					=(objective == 'min') - (objective == 'max')
											lowerCamelCase__					=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:
																lowerCamelCase__					=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
											lowerCamelCase__					=slice(self.n_stages     ,    self.n_rows)

											# RHS
											lowerCamelCase__					=tableau[s, -1]

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

											# Array filled with nans
											lowerCamelCase__					=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
											lowerCamelCase__					=np.divide(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    out=_SCREAMING_SNAKE_CASE     ,    where=divisor > 0)

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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	np.ndarray:




											"""simple docstring"""





											lowerCamelCase__					=tableau[row_idx].copy()

											lowerCamelCase__					=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
											lowerCamelCase__					=piv_row
											return tableau






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	np.ndarray:




											"""simple docstring"""





											self.objectives.pop()

											if not self.objectives:
																return tableau

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

											# Delete the artificial variable columns
											lowerCamelCase__					=np.delete(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    axis=1)

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

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






						def 		lowerCAmelCase (       self)      ->	dict[Any, Any]:




											"""simple docstring"""





											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:
																					lowerCamelCase__					=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)

																lowerCamelCase__					,  lowerCamelCase__					=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
																					lowerCamelCase__					=self.change_stage(self.tableau)
																else:
																					lowerCamelCase__					=self.pivot(self.tableau     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return {}






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	dict[str, float]:




											"""simple docstring"""





											lowerCamelCase__					={'P': abs(tableau[0, -1])}

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

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

																# If there is only one nonzero value in column, which is one
																if n_nonzero == nonzero_val == 1:
																					lowerCamelCase__					=tableau[nonzero_rowidx, -1]
																					lowerCamelCase__					=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(_SCREAMING_SNAKE_CASE     ,    0)
											return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()







"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											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=_SCREAMING_SNAKE_CASE     ,    beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3