






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  (DDPMScheduler,)







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**__lowerCAmelCase     )
								return config







				def 			A	(						self     ):
								"""simple docstring"""
								for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,					[0.002, 0.02, 0.2, 2]     ):
												self.check_over_configs(beta_start=__lowerCAmelCase       ,					beta_end=__lowerCAmelCase     )







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







				def 			A	(						self     ):
								"""simple docstring"""
								for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.check_over_configs(thresholding=__lowerCAmelCase     )
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=__lowerCAmelCase       ,					prediction_type=__lowerCAmelCase       ,					sample_max_value=__lowerCAmelCase       ,					)







				def 			A	(						self     ):
								"""simple docstring"""
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								for t in [0, 5_0_0, 9_9_9]:
												self.check_over_forward(time_step=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.scheduler_classes[0]
								__magic_name__		:Dict        =      self.get_scheduler_config()
								__magic_name__		:List[Any]        =      scheduler_class(**__lowerCAmelCase     )

								assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.00979     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.02     )     ) < 1E-5







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.scheduler_classes[0]
								__magic_name__		:Dict        =      self.get_scheduler_config()
								__magic_name__		:Optional[int]        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      len(__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      self.dummy_model()
								__magic_name__		:Tuple        =      self.dummy_sample_deter
								__magic_name__		:Dict        =      torch.manual_seed(0     )

								for t in reversed(range(__lowerCAmelCase     )     ):
												# 1. predict noise residual
												__magic_name__		:Any        =      model(__lowerCAmelCase       ,					__lowerCAmelCase     )

												# 2. predict previous mean of sample x_t-1
												__magic_name__		:List[Any]        =      scheduler.step(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					generator=__lowerCAmelCase     ).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__magic_name__		:List[Any]        =      pred_prev_sample

								__magic_name__		:Any        =      torch.sum(torch.abs(__lowerCAmelCase     )     )
								__magic_name__		:Tuple        =      torch.mean(torch.abs(__lowerCAmelCase     )     )

								assert abs(result_sum.item() - 258.9606     ) < 1E-2
								assert abs(result_mean.item() - 0.3372     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.scheduler_classes[0]
								__magic_name__		:Tuple        =      self.get_scheduler_config(prediction_type='''v_prediction'''     )
								__magic_name__		:Any        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      len(__lowerCAmelCase     )

								__magic_name__		:List[str]        =      self.dummy_model()
								__magic_name__		:List[Any]        =      self.dummy_sample_deter
								__magic_name__		:int        =      torch.manual_seed(0     )

								for t in reversed(range(__lowerCAmelCase     )     ):
												# 1. predict noise residual
												__magic_name__		:Optional[Any]        =      model(__lowerCAmelCase       ,					__lowerCAmelCase     )

												# 2. predict previous mean of sample x_t-1
												__magic_name__		:Optional[int]        =      scheduler.step(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					generator=__lowerCAmelCase     ).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__magic_name__		:Any        =      pred_prev_sample

								__magic_name__		:Any        =      torch.sum(torch.abs(__lowerCAmelCase     )     )
								__magic_name__		:List[Any]        =      torch.mean(torch.abs(__lowerCAmelCase     )     )

								assert abs(result_sum.item() - 202.0296     ) < 1E-2
								assert abs(result_mean.item() - 0.2631     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.scheduler_classes[0]
								__magic_name__		:Union[str, Any]        =      self.get_scheduler_config()
								__magic_name__		:Any        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:List[str]        =      [1_0_0, 8_7, 5_0, 1, 0]

								scheduler.set_timesteps(timesteps=__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      scheduler.timesteps

								for i, timestep in enumerate(__lowerCAmelCase     ):
												if i == len(__lowerCAmelCase     ) - 1:
																__magic_name__		:Union[str, Any]        =      -1
												else:
																__magic_name__		:Dict        =      timesteps[i + 1]

												__magic_name__		:int        =      scheduler.previous_timestep(__lowerCAmelCase     )
												__magic_name__		:Tuple        =      prev_t.item()

												self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.scheduler_classes[0]
								__magic_name__		:str        =      self.get_scheduler_config()
								__magic_name__		:List[str]        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      [1_0_0, 8_7, 5_0, 5_1, 0]

								with self.assertRaises(__lowerCAmelCase       ,					msg='''`custom_timesteps` must be in descending order.'''     ):
												scheduler.set_timesteps(timesteps=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.scheduler_classes[0]
								__magic_name__		:Any        =      self.get_scheduler_config()
								__magic_name__		:Dict        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      [1_0_0, 8_7, 5_0, 1, 0]
								__magic_name__		:List[Any]        =      len(__lowerCAmelCase     )

								with self.assertRaises(__lowerCAmelCase       ,					msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''     ):
												scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.scheduler_classes[0]
								__magic_name__		:List[str]        =      self.get_scheduler_config()
								__magic_name__		:Tuple        =      scheduler_class(**__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    __lowerCAmelCase       ,					msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,					):
												scheduler.set_timesteps(timesteps=__lowerCAmelCase     )














from __future__ import annotations

from typing import Any
class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = 0     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Any        =      row, column
								__magic_name__		:Union[str, Any]        =      [[default_value for c in range(__lowerCAmelCase     )] for r in range(__lowerCAmelCase     )]







				def __str__(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

								# Make string identifier
								__magic_name__		:Dict        =      0
								for row_vector in self.array:
												for obj in row_vector:
																__magic_name__		:Dict        =      max(__lowerCAmelCase       ,					len(str(__lowerCAmelCase     )     )     )
								__magic_name__		:List[str]        =      F'''%{max_element_length}s'''

								# Make string and return
								def single_line(__lowerCAmelCase     ) -> str:
												nonlocal string_format_identifier
												__magic_name__		:int        =      '''['''
												line += ", ".join(string_format_identifier % (obj,) for obj in row_vector     )
												line += "]"
												return line

								s += "\n".join(single_line(__lowerCAmelCase     ) for row_vector in self.array     )
								return s







				def __repr__(						self     ):
								"""simple docstring"""
								return str(self     )







				def 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								if not (isinstance(__lowerCAmelCase       ,					(list, tuple)     ) and len(__lowerCAmelCase     ) == 2):
												return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
												return False
								else:
												return True







				def __getitem__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								return self.array[loc[0]][loc[1]]







				def __setitem__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert self.validate_indicies(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      value







				def __add__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == another.row and self.column == another.column

								# Add
								__magic_name__		:List[Any]        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      self[r, c] + another[r, c]
								return result







				def __neg__(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix(self.row       ,					self.column     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:Any        =      -self[r, c]
								return result







				def __sub__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return self + (-another)







				def __mul__(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""

								if isinstance(__lowerCAmelCase       ,					(int, float)     ):  # Scalar multiplication
												__magic_name__		:List[str]        =      Matrix(self.row       ,					self.column     )
												for r in range(self.row     ):
																for c in range(self.column     ):
																				__magic_name__		:Tuple        =      self[r, c] * another
												return result
								elif isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ):  # Matrix multiplication
												assert self.column == another.row
												__magic_name__		:List[Any]        =      Matrix(self.row       ,					another.column     )
												for r in range(self.row     ):
																for c in range(another.column     ):
																				for i in range(self.column     ):
																								result[r, c] += self[r, i] * another[i, c]
												return result
								else:
												__magic_name__		:List[str]        =      F'''Unsupported type given for another ({type(__lowerCAmelCase     )})'''
												raise TypeError(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix(self.column       ,					self.row     )
								for r in range(self.row     ):
												for c in range(self.column     ):
																__magic_name__		:List[str]        =      self[r, c]
								return result







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) and isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								__magic_name__		:int        =      v.transpose()
								__magic_name__		:str        =      (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
												return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":

					def      __lowercase					( ):







									"""simple docstring"""






									__magic_name__		:Optional[int]        =      Matrix(3,  3,  0 )
									for i in range(3 ):
													__magic_name__		:Tuple        =      1
									print(f'''a^(-1) is {ainv}''' )
									# u, v
									__magic_name__		:Optional[int]        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      1, 2, -3
									__magic_name__		:int        =      Matrix(3,  1,  0 )
									__magic_name__     ,				__magic_name__     ,				__magic_name__		:str        =      4, -2, 5
									print(f'''u is {u}''' )
									print(f'''v is {v}''' )
									print(f'''uv^T is {u * v.transpose()}''' )
									# Sherman Morrison
									print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case,  snake_case )}''' )

					def      __lowercase					( ):







									"""simple docstring"""






									import doctest

									doctest.testmod()

					testa()






