
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class 							UpperCAmelCase   (						SCREAMING_SNAKE_CASE__						):




	'''simple docstring'''





	snake_case_      					=  (DDIMScheduler,)
	snake_case_      					=  (("eta", 0.0), ("num_inference_steps", 50))






	def        UpperCamelCase_						( self      :				Tuple							,**A      :				List[str]				):
					__A         =				{
					    "num_train_timesteps": 10_00,
					    "beta_start": 0.00_01,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					    "clip_sample": True,
					}

					config.update(**A				)
					return config






	def        UpperCamelCase_						( self      :				Dict							,**A      :				Optional[int]				):
					__A         =				self.scheduler_classes[0]
					__A         =				self.get_scheduler_config(**A				)
					__A         =				scheduler_class(**A				)

					__A    ,							__A         =				10, 0.0

					__A         =				self.dummy_model()
					__A         =				self.dummy_sample_deter

					scheduler.set_timesteps(A				)

					for t in scheduler.timesteps:
									__A         =				model(A							,A				)
									__A         =				scheduler.step(A							,A							,A							,A				).prev_sample

					return sample






	def        UpperCamelCase_						( self      :				List[str]				):



					for timesteps in [1_00, 5_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A				)






	def        UpperCamelCase_						( self      :				Dict				):



					for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=A				)

					__A         =				self.scheduler_classes[0]
					__A         =				self.get_scheduler_config(steps_offset=1				)
					__A         =				scheduler_class(**A				)
					scheduler.set_timesteps(5				)
					assert torch.equal(scheduler.timesteps							,torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]				)				)






	def        UpperCamelCase_						( self      :				Optional[Any]				):



					for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]							,[0.0_02, 0.02, 0.2, 2]				):
									self.check_over_configs(beta_start=A							,beta_end=A				)






	def        UpperCamelCase_						( self      :				Union[str, Any]				):



					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=A				)






	def        UpperCamelCase_						( self      :				int				):



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






	def        UpperCamelCase_						( self      :				Dict				):



					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=A				)






	def        UpperCamelCase_						( self      :				str				):



					for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=A				)






	def        UpperCamelCase_						( self      :				Any				):



					for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=A				)






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					self.check_over_configs(thresholding=A				)
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=A							,prediction_type=A							,sample_max_value=A							,)






	def        UpperCamelCase_						( self      :				Optional[int]				):



					for t in [1, 10, 49]:
									self.check_over_forward(time_step=A				)






	def        UpperCamelCase_						( self      :				Tuple				):



					for t, num_inference_steps in zip([1, 10, 50]							,[10, 50, 5_00]				):
									self.check_over_forward(time_step=A							,num_inference_steps=A				)






	def        UpperCamelCase_						( self      :				List[Any]				):



					for t, eta in zip([1, 10, 49]							,[0.0, 0.5, 1.0]				):
									self.check_over_forward(time_step=A							,eta=A				)






	def        UpperCamelCase_						( self      :				int				):
					__A         =				self.scheduler_classes[0]
					__A         =				self.get_scheduler_config()
					__A         =				scheduler_class(**A				)

					assert torch.sum(torch.abs(scheduler._get_variance(0							,0				) - 0.0				)				) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_20							,4_00				) - 0.1_47_71				)				) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80							,9_60				) - 0.3_24_60				)				) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0							,0				) - 0.0				)				) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87							,4_86				) - 0.0_09_79				)				) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99							,9_98				) - 0.02				)				) < 1E-5






	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				self.full_loop()

					__A         =				torch.sum(torch.abs(A				)				)
					__A         =				torch.mean(torch.abs(A				)				)

					assert abs(result_sum.item() - 1_72.00_67				) < 1E-2
					assert abs(result_mean.item() - 0.22_39_67				) < 1E-3






	def        UpperCamelCase_						( self      :				Optional[int]				):
					__A         =				self.full_loop(prediction_type="v_prediction"				)

					__A         =				torch.sum(torch.abs(A				)				)
					__A         =				torch.mean(torch.abs(A				)				)

					assert abs(result_sum.item() - 52.53_02				) < 1E-2
					assert abs(result_mean.item() - 0.06_84				) < 1E-3






	def        UpperCamelCase_						( self      :				Optional[int]				):
					__A         =				self.full_loop(set_alpha_to_one=A							,beta_start=0.01				)
					__A         =				torch.sum(torch.abs(A				)				)
					__A         =				torch.mean(torch.abs(A				)				)

					assert abs(result_sum.item() - 1_49.82_95				) < 1E-2
					assert abs(result_mean.item() - 0.19_51				) < 1E-3







	def        UpperCamelCase_						( self      :				str				):
					__A         =				self.full_loop(set_alpha_to_one=A							,beta_start=0.01				)
					__A         =				torch.sum(torch.abs(A				)				)
					__A         =				torch.mean(torch.abs(A				)				)

					assert abs(result_sum.item() - 1_49.07_84				) < 1E-2
					assert abs(result_mean.item() - 0.19_41				) < 1E-3


from typing import Any

import numpy as np





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				np.ndarray							,A      :				int				):
					# Check if RHS is negative
					if np.any(tableau[:, -1]							,where=tableau[:, -1] < 0				):
									raise ValueError("RHS must be > 0"				)

					__A         =				tableau
					__A    ,							__A         =				tableau.shape

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

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

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

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

					# Objectives for each stage
					__A         =				["max"]

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

					__A         =				[""]

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

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






	@staticmethod
	def        UpperCamelCase_						( *A      :				int				):
					if len(A				) != 3:
									raise ValueError("Must provide n_vars, n_slack, and n_art_vars"				)

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

					# decision | slack | artificial
					__A         =				["x", "s", "a"]
					__A         =				[]
					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[Any]							,A      :				np.ndarray				):
					__A         =				self.objectives[-1]

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

					# RHS
					__A         =				tableau[s, -1]

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

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

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






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray							,A      :				int							,A      :				int				):
					__A         =				tableau[row_idx].copy()

					__A         =				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
					__A         =				piv_row
					return tableau






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				np.ndarray				):
					self.objectives.pop()

					if not self.objectives:
									return tableau

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

					# Delete the artificial variable columns
					__A         =				np.delete(A							,A							,axis=1				)

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

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






	def        UpperCamelCase_						( self      :				Dict				):
					# 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:
													__A         =				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				)

									__A    ,							__A         =				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
													__A         =				self.change_stage(self.tableau				)
									else:
													__A         =				self.pivot(self.tableau							,A							,A				)
					return {}







	def        UpperCamelCase_						( self      :				Dict							,A      :				np.ndarray							,A      :				list[str]				):
					__A         =				{"P": abs(tableau[0, -1]				)}

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

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
