


'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						(DDIMParallelScheduler,)
 __lowercase				:		Optional[int]    							=						(('''eta''', 0.0), ('''num_inference_steps''', 5_0))


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Dict       =			{
      'num_train_timesteps': 1_000,
      'beta_start': 0.0_0_0_1,
      'beta_end': 0.0_2,
      'beta_schedule': 'linear',
      'clip_sample': True,
  }

  config.update(**__lowercase						)
  return config


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			self.scheduler_classes[0]
  __A  :		Optional[Any]       =			self.get_scheduler_config(**__lowercase						)
  __A  :		int       =			scheduler_class(**__lowercase						)

  __A     ,__A  :		List[str]       =			10, 0.0

  __A  :		Tuple       =			self.dummy_model()
  __A  :		Any       =			self.dummy_sample_deter

  scheduler.set_timesteps(__lowercase						)

  for t in scheduler.timesteps:
   __A  :		Dict       =			model(__lowercase      ,      __lowercase						)
   __A  :		List[Any]       =			scheduler.step(__lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						).prev_sample

  return sample


 def  snake_case__   (      self						):

  """simple docstring"""







  for timesteps in [100, 500, 1_000]:
   self.check_over_configs(num_train_timesteps=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







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

  __A  :		str       =			self.scheduler_classes[0]
  __A  :		Optional[Any]       =			self.get_scheduler_config(steps_offset=1						)
  __A  :		List[str]       =			scheduler_class(**__lowercase						)
  scheduler.set_timesteps(5						)
  assert torch.equal(scheduler.timesteps      ,      torch.LongTensor([801, 601, 401, 201, 1]						)						)


 def  snake_case__   (      self						):

  """simple docstring"""







  for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,      [0.0_0_2, 0.0_2, 0.2, 2]						):
   self.check_over_configs(beta_start=__lowercase      ,      beta_end=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  self.check_over_configs(thresholding=__lowercase						)
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(
        thresholding=__lowercase      ,      prediction_type=__lowercase      ,      sample_max_value=__lowercase      ,      )


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.scheduler_classes[0]
  __A  :		Tuple       =			self.get_scheduler_config()
  __A  :		Optional[int]       =			scheduler_class(**__lowercase						)

  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_4_7_7_1						)						) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(980      ,      960						) - 0.3_2_4_6_0						)						) < 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_0_9_7_9						)						) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(999      ,      998						) - 0.0_2						)						) < 1E-5


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.scheduler_classes[0]
  __A  :		Tuple       =			self.get_scheduler_config()
  __A  :		Optional[int]       =			scheduler_class(**__lowercase						)

  __A     ,__A  :		Dict       =			10, 0.0
  scheduler.set_timesteps(__lowercase						)

  __A  :		int       =			self.dummy_model()
  __A  :		int       =			self.dummy_sample_deter
  __A  :		List[str]       =			self.dummy_sample_deter + 0.1
  __A  :		Union[str, Any]       =			self.dummy_sample_deter - 0.1

  __A  :		Union[str, Any]       =			samplea.shape[0]
  __A  :		Any       =			torch.stack([samplea, samplea, samplea]      ,      dim=0						)
  __A  :		Dict       =			torch.arange(__lowercase						)[0:3, None].repeat(1      ,      __lowercase						)

  __A  :		List[Any]       =			model(samples.flatten(0      ,      1						)      ,      timesteps.flatten(0      ,      1						)						)
  __A  :		str       =			scheduler.batch_step_no_noise(__lowercase      ,      timesteps.flatten(0      ,      1						)      ,      samples.flatten(0      ,      1						)      ,      __lowercase						)

  __A  :		Tuple       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		List[Any]       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 1_1_4_7.7_9_0_4						) < 1E-2
  assert abs(result_mean.item() - 0.4_9_8_2						) < 1E-3


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.full_loop()

  __A  :		List[str]       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		int       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1E-2
  assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1E-3


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			self.full_loop(prediction_type='v_prediction'						)

  __A  :		List[str]       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		Union[str, Any]       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1E-2
  assert abs(result_mean.item() - 0.0_6_8_4						) < 1E-3


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.full_loop(set_alpha_to_one=__lowercase      ,      beta_start=0.0_1						)
  __A  :		Dict       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		str       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1E-2
  assert abs(result_mean.item() - 0.1_9_5_1						) < 1E-3







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.full_loop(set_alpha_to_one=__lowercase      ,      beta_start=0.0_1						)
  __A  :		Any       =			torch.sum(torch.abs(__lowercase						)						)
  __A  :		Optional[Any]       =			torch.mean(torch.abs(__lowercase						)						)

  assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1E-2
  assert abs(result_mean.item() - 0.1_9_4_1						) < 1E-3










'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = None						):

  """simple docstring"""







  if components is None:
   __A  :		Optional[int]       =			[]
  __A  :		Dict       =			list(__lowercase						)


 def __len__(      self						):

  """simple docstring"""







  return len(self.__components						)


 def __str__(      self						):

  """simple docstring"""







  return "(" + ",".join(map(__lowercase      ,      self.__components						)						) + ")"


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			len(self						)
  if size == len(__lowercase						):
   __A  :		Dict       =			[self.__components[i] + other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:
   raise Exception('must have the same size'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			len(self						)
  if size == len(__lowercase						):
   __A  :		str       =			[self.__components[i] - other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:  # error case
   raise Exception('must have the same size'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      (float, int)						):
   __A  :		Dict       =			[c * other for c in self.__components]
   return Vector(__lowercase						)
  elif isinstance(__lowercase      ,      __lowercase						) and len(self						) == len(__lowercase						):
   __A  :		Optional[Any]       =			len(self						)
   __A  :		Optional[int]       =			[self.__components[i] * other.component(__lowercase						) for i in range(__lowercase						)]
   return sum(__lowercase						)
  else:  # error case
   raise Exception('invalid operand!'						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return Vector(self.__components						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						) and -len(self.__components						) <= i < len(self.__components						):
   return self.__components[i]
  else:
   raise Exception('index out of range'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  assert -len(self.__components						) <= pos < len(self.__components						)
  __A  :		str       =			value


 def  snake_case__   (      self						):

  """simple docstring"""







  if len(self.__components						) == 0:
   raise Exception('Vector is empty'						)
  __A  :		List[Any]       =			[c**2 for c in self.__components]
  return math.sqrt(sum(__lowercase						)						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		List[Any]       =			self * other
  __A  :		Any       =			self.euclidean_length() * other.euclidean_length()
  if deg:
   return math.degrees(math.acos(num / den						)						)
  else:
   return math.acos(num / den						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			)
 return Vector([0] * dimension			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			) and (isinstance(UpperCamelCase__,				UpperCamelCase__			))
 __A  :		List[str]       =			[0] * dimension
 __A  :		Tuple       =			1
 return Vector(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					Vector,				UpperCamelCase__       :					Vector			):
 assert (
     isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and (isinstance(UpperCamelCase__,				(int, float)			))
 )
 return x * scalar + y


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		Any       =			[random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)]
 return Vector(UpperCamelCase__			)

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			matrix
  __A  :		str       =			w
  __A  :		Dict       =			h


 def __str__(      self						):

  """simple docstring"""







  __A  :		Tuple       =			''
  for i in range(self.__height						):
   ans += "|"
   for j in range(self.__width						):
    if j < self.__width - 1:
     ans += str(self.__matrix[i][j]						) + ","
    else:
     ans += str(self.__matrix[i][j]						) + "|\n"
  return ans


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Union[str, Any]       =			[]
   for i in range(self.__height						):
    __A  :		List[Any]       =			[
        self.__matrix[i][j] + other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrix must have the same dimension!'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Dict       =			[]
   for i in range(self.__height						):
    __A  :		str       =			[
        self.__matrix[i][j] - other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrices must have the same dimension!'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						):  # matrix-vector
   if len(__lowercase						) == self.__width:
    __A  :		Tuple       =			zero_vector(self.__height						)
    for i in range(self.__height						):
     __A  :		Any       =			[
         self.__matrix[i][j] * other.component(__lowercase						)
         for j in range(self.__width						)
     ]
     ans.change_component(__lowercase      ,      sum(__lowercase						)						)
    return ans
   else:
    raise Exception(
        'vector must have the same size as the '
        'number of columns of the matrix!'						)
  elif isinstance(__lowercase      ,      (int, float)						):  # matrix-scalar
   __A  :		Optional[Any]       =			[
       [self.__matrix[i][j] * other for j in range(self.__width						)]
       for i in range(self.__height						)
   ]
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  return None


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__height


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__width


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   return self.__matrix[x][y]
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   __A  :		Dict       =			value
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  __A  :		Union[str, Any]       =			self.__matrix[:x] + self.__matrix[x + 1 :]
  for i in range(len(__lowercase						)						):
   __A  :		Dict       =			minor[i][:y] + minor[i][y + 1 :]
  return Matrix(__lowercase      ,      self.__width - 1      ,      self.__height - 1						).determinant()


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if 0 <= x < self.__height and 0 <= y < self.__width:
   return (-1) ** (x + y) * self.minor(__lowercase      ,      __lowercase						)
  else:
   raise Exception('Indices out of bounds'						)







 def  snake_case__   (      self						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if self.__height < 1:
   raise Exception('Matrix has no element'						)
  elif self.__height == 1:
   return self.__matrix[0][0]
  elif self.__height == 2:
   return (
       self.__matrix[0][0] * self.__matrix[1][1]
       - self.__matrix[0][1] * self.__matrix[1][0]
   )
  else:
   __A  :		List[str]       =			[
       self.__matrix[0][y] * self.cofactor(0      ,      __lowercase						) for y in range(self.__width						)
   ]
   return sum(__lowercase						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		list[list[float]]       =			[[0] * n for _ in range(UpperCamelCase__			)]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		list[list[float]]       =			[
     [random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)] for _ in range(UpperCamelCase__			)
 ]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)






