






"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class      a						(					_UpperCamelCase		):
    SCREAMING_SNAKE_CASE						:     str						= (DDIMParallelScheduler,)
    SCREAMING_SNAKE_CASE						:     Union[str, Any]						= (("""eta""", 0.0), ("""num_inference_steps""", 5_0))






    def    UpperCamelCase       (       self     :							List[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Dict:
      lowerCamelCase_         =						{
          'num_train_timesteps': 1000,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'clip_sample': True,
      }

      config.update(**__lowerCamelCase					)
      return config






    def    UpperCamelCase       (       self     :							Any       ,						**__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config(**__lowerCamelCase					)
      lowerCamelCase_         =						scheduler_class(**__lowerCamelCase					)

      lowerCamelCase_     ,	lowerCamelCase_         =						10, 0.0

      lowerCamelCase_         =						self.dummy_model()
      lowerCamelCase_         =						self.dummy_sample_deter

      scheduler.set_timesteps(__lowerCamelCase					)

      for t in scheduler.timesteps:
        lowerCamelCase_         =						model(__lowerCamelCase       ,						__lowerCamelCase					)
        lowerCamelCase_         =						scheduler.step(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase					).prev_sample

      return sample






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Optional[int]:
      for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							int					)     ->		Union[str, Any]:
      for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=__lowerCamelCase					)

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






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Optional[Any]:
      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,						[0.002, 0.02, 0.2, 2]					):
        self.check_over_configs(beta_start=__lowerCamelCase       ,						beta_end=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Optional[int]:
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Tuple:
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		List[str]:
      for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							int					)     ->		Tuple:
      for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase					)






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






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      for t in [1, 10, 49]:
        self.check_over_forward(time_step=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							int					)     ->		List[Any]:
      for t, num_inference_steps in zip([1, 10, 50]       ,						[10, 50, 500]					):
        self.check_over_forward(time_step=__lowerCamelCase       ,						num_inference_steps=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Dict:
      for t, eta in zip([1, 10, 49]       ,						[0.0, 0.5, 1.0]					):
        self.check_over_forward(time_step=__lowerCamelCase       ,						eta=__lowerCamelCase					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		str:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__lowerCamelCase					)

      assert torch.sum(torch.abs(scheduler._get_variance(0       ,						0					) - 0.0					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(420       ,						400					) - 0.14_771					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(980       ,						960					) - 0.32_460					)					) < 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					) - 0.00_979					)					) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(999       ,						998					) - 0.02					)					) < 1e-5






    def    UpperCamelCase       (       self     :							int					)     ->		int:
      lowerCamelCase_         =						self.scheduler_classes[0]
      lowerCamelCase_         =						self.get_scheduler_config()
      lowerCamelCase_         =						scheduler_class(**__lowerCamelCase					)

      lowerCamelCase_     ,	lowerCamelCase_         =						10, 0.0
      scheduler.set_timesteps(__lowerCamelCase					)

      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(__lowerCamelCase					)[0:3, None].repeat(1       ,						__lowerCamelCase					)

      lowerCamelCase_         =						model(samples.flatten(0       ,						1					)       ,						timesteps.flatten(0       ,						1					)					)
      lowerCamelCase_         =						scheduler.batch_step_no_noise(__lowerCamelCase       ,						timesteps.flatten(0       ,						1					)       ,						samples.flatten(0       ,						1					)       ,						__lowerCamelCase					)

      lowerCamelCase_         =						torch.sum(torch.abs(__lowerCamelCase					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 1147.7904					) < 1e-2
      assert abs(result_mean.item() - 0.4_982					) < 1e-3






    def    UpperCamelCase       (       self     :							int					)     ->		Union[str, Any]:
      lowerCamelCase_         =						self.full_loop()

      lowerCamelCase_         =						torch.sum(torch.abs(__lowerCamelCase					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 172.0_067					) < 1e-2
      assert abs(result_mean.item() - 0.223_967					) < 1e-3






    def    UpperCamelCase       (       self     :							Dict					)     ->		List[str]:
      lowerCamelCase_         =						self.full_loop(prediction_type='v_prediction'					)

      lowerCamelCase_         =						torch.sum(torch.abs(__lowerCamelCase					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 52.5_302					) < 1e-2
      assert abs(result_mean.item() - 0.0_684					) < 1e-3






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[Any]:
      # We specify different beta, so that the first alpha is 0.99
      lowerCamelCase_         =						self.full_loop(set_alpha_to_one=__lowerCamelCase       ,						beta_start=0.01					)
      lowerCamelCase_         =						torch.sum(torch.abs(__lowerCamelCase					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 149.8_295					) < 1e-2
      assert abs(result_mean.item() - 0.1_951					) < 1e-3






    def    UpperCamelCase       (       self     :							int					)     ->		Optional[int]:
      # We specify different beta, so that the first alpha is 0.99
      lowerCamelCase_         =						self.full_loop(set_alpha_to_one=__lowerCamelCase       ,						beta_start=0.01					)
      lowerCamelCase_         =						torch.sum(torch.abs(__lowerCamelCase					)					)
      lowerCamelCase_         =						torch.mean(torch.abs(__lowerCamelCase					)					)

      assert abs(result_sum.item() - 149.0_784					) < 1e-2
      assert abs(result_mean.item() - 0.1_941					) < 1e-3










from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

			if len(A   ) != rowsa:
						a    =			(
						    "Number of initial values must be equal to number of rows in coefficient "
						    F"""matrix but received {len(A   )} and {rowsa}"""
						)
						raise ValueError(A   )

			if iterations <= 0:
						raise ValueError("Iterations must be at least 1"   )

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

			# Iterates the whole matrix for given number of times
			for _ in range(A   ):
						a    =			[]
						for row in range(A   ):
									a    =			0
									for col in range(A   ):
												if col == row:
															a    =			table[row][col]
												elif col == cols - 1:
															a    =			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									a    =			(temp + val) / denom
									new_val.append(A   )
						a    =			new_val

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			0
						for j in range(0,  cols - 1   ):
									if i == j:
												continue
									else:
												total += table[i][j]

						if table[i][i] <= total:
									raise ValueError("Coefficient matrix is not strictly diagonally dominant"   )

			return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()






