



'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		UpperCamelCase__      ):
					a      :     Tuple        =							(DDIMParallelScheduler,)
					a      :     Optional[int]        =							(("eta", 0.0), ("num_inference_steps", 5_0))



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Dict:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.00_01,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**UpperCamelCase     )
						return config



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config(**UpperCamelCase     )
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase							,       __lowerCAmelCase												=  10, 0.0

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter

						scheduler.set_timesteps(UpperCamelCase     )

						for t in scheduler.timesteps:
							__lowerCAmelCase												=  model(UpperCamelCase			,				UpperCamelCase     )
							__lowerCAmelCase												=  scheduler.step(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     ).prev_sample

						return sample



					def        UpperCAmelCase_ (    self     )     ->			Tuple:




						for timesteps in [100, 500, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase     )



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




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

						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config(steps_offset=1     )
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )
						scheduler.set_timesteps(5     )
						assert torch.equal(scheduler.timesteps			,				torch.LongTensor([801, 601, 401, 201, 1]     )     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:




						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=UpperCamelCase			,				beta_end=UpperCamelCase     )



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




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



					def        UpperCAmelCase_ (    self     )     ->			str:




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



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:




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



					def        UpperCAmelCase_ (    self     )     ->			Any:




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



					def        UpperCAmelCase_ (    self     )     ->			str:




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



					def        UpperCAmelCase_ (    self     )     ->			int:
						self.check_over_configs(thresholding=UpperCamelCase     )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=UpperCamelCase			,				prediction_type=UpperCamelCase			,				sample_max_value=UpperCamelCase			,				)



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




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



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




						for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 500]     ):
							self.check_over_forward(time_step=UpperCamelCase			,				num_inference_steps=UpperCamelCase     )



					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=UpperCamelCase			,				eta=UpperCamelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						assert torch.sum(torch.abs(scheduler._get_variance(0			,				0     ) - 0.0     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420			,				400     ) - 0.1_47_71     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980			,				960     ) - 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(487			,				486     ) - 0.0_09_79     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999			,				998     ) - 0.02     )     ) < 1E-5



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**UpperCamelCase     )

						__lowerCAmelCase							,       __lowerCAmelCase												=  10, 0.0
						scheduler.set_timesteps(UpperCamelCase     )

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter
						__lowerCAmelCase												=  self.dummy_sample_deter + 0.1
						__lowerCAmelCase												=  self.dummy_sample_deter - 0.1

						__lowerCAmelCase												=  samplea.shape[0]
						__lowerCAmelCase												=  torch.stack([samplea, samplea, samplea]			,				dim=0     )
						__lowerCAmelCase												=  torch.arange(UpperCamelCase     )[0:3, None].repeat(1			,				UpperCamelCase     )

						__lowerCAmelCase												=  model(samples.flatten(0			,				1     )			,				timesteps.flatten(0			,				1     )     )
						__lowerCAmelCase												=  scheduler.batch_step_no_noise(UpperCamelCase			,				timesteps.flatten(0			,				1     )			,				samples.flatten(0			,				1     )			,				UpperCamelCase     )

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						assert abs(result_sum.item() - 11_47.79_04     ) < 1E-2
						assert abs(result_mean.item() - 0.49_82     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.full_loop()

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

						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     )     ->			Any:
						__lowerCAmelCase												=  self.full_loop(prediction_type="v_prediction"     )

						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

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



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.full_loop(set_alpha_to_one=UpperCamelCase			,				beta_start=0.01     )
						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

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



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.full_loop(set_alpha_to_one=UpperCamelCase			,				beta_start=0.01     )
						__lowerCAmelCase												=  torch.sum(torch.abs(UpperCamelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(UpperCamelCase     )     )

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




'''simple docstring'''



from __future__ import annotations

import math



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	if len(lowerCamelCase					) != 2 or len(a[0]					) != 2 or len(lowerCamelCase					) != 2 or len(b[0]					) != 2:
		raise Exception("Matrices are not 2x2"					)
	__lowerCAmelCase												=  [
	    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
	    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
	]
	return new_matrix



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	return [
	    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]					)					)]
	    for row in range(len(lowerCamelCase					)					)
	]



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	return [
	    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]					)					)]
	    for row in range(len(lowerCamelCase					)					)
	]



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	if len(lowerCamelCase					) % 2 != 0 or len(a[0]					) % 2 != 0:
		raise Exception("Odd matrices are not supported!"					)

	__lowerCAmelCase												=  len(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_length // 2

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [
	    [a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)
	]

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)]

	return top_left, top_right, bot_left, bot_right



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	return len(lowerCamelCase					), len(matrix[0]					)



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




	print("\n".join(str(lowerCamelCase					) for line in matrix					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	if matrix_dimensions(lowerCamelCase					) == (2, 2):
		return default_matrix_multiplication(lowerCamelCase				,		lowerCamelCase					)

	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)
	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)

	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)

	__lowerCAmelCase												=  matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)

	# construct the new matrix from our 4 quadrants
	__lowerCAmelCase												=  []
	for i in range(len(lowerCamelCase					)					):
		new_matrix.append(top_left[i] + top_right[i]					)
	for i in range(len(lowerCamelCase					)					):
		new_matrix.append(bot_left[i] + bot_right[i]					)
	return new_matrix



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	if matrix_dimensions(lowerCamelCase					)[1] != matrix_dimensions(lowerCamelCase					)[0]:
		__lowerCAmelCase												=  (
		    "Unable to multiply these matrices, please check the dimensions.\n"
		    f'''Matrix A: {matrixa}\n'''
		    f'''Matrix B: {matrixa}'''
		)
		raise Exception(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_dimensions(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_dimensions(lowerCamelCase					)

	if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
		return [matrixa, matrixa]

	__lowerCAmelCase												=  max(*lowerCamelCase				,		*lowerCamelCase					)
	__lowerCAmelCase												=  int(math.pow(2				,		math.ceil(math.loga(lowerCamelCase					)					)					)					)
	__lowerCAmelCase												=  matrixa
	__lowerCAmelCase												=  matrixa

	# Adding zeros to the matrices so that the arrays dimensions are the same and also
	# power of 2
	for i in range(0				,		lowerCamelCase					):
		if i < dimensiona[0]:
			for _ in range(dimensiona[1]				,		lowerCamelCase					):
				new_matrixa[i].append(0					)
		else:
			new_matrixa.append([0] * maxim					)
		if i < dimensiona[0]:
			for _ in range(dimensiona[1]				,		lowerCamelCase					):
				new_matrixa[i].append(0					)
		else:
			new_matrixa.append([0] * maxim					)

	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		lowerCamelCase					)

	# Removing the additional zeros
	for i in range(0				,		lowerCamelCase					):
		if i < dimensiona[0]:
			for _ in range(dimensiona[1]				,		lowerCamelCase					):
				final_matrix[i].pop()
		else:
			final_matrix.pop()
	return final_matrix


if __name__ == "__main__":
	lowerCAmelCase	:		Tuple				=   [
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	lowerCAmelCase	:		Any				=   [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
	print(strassen(matrixa, matrixa))