
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:UNetaDModel
	a			:KarrasVeScheduler
	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			UNetaDModel       ,							SCREAMING_SNAKE_CASE_  :			KarrasVeScheduler       )       ->     List[str]:
					super().__init__()
					self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       )




	@torch.no_grad()
	def __call__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			int = 5_0       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "pil"       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							**SCREAMING_SNAKE_CASE_  :			int       ,							)       ->     Union[Tuple, ImagePipelineOutput]:
					lowercase_       =					self.unet.config.sample_size
					lowercase_       =					(batch_size, 3, img_size, img_size)

					lowercase_       =					self.unet

					# sample x_0 ~ N(0, sigma_0^2 * I)
					lowercase_       =					randn_tensor(SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       ,							device=self.device       ) * self.scheduler.init_noise_sigma

					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       )

					for t in self.progress_bar(self.scheduler.timesteps       ):
									# here sigma_t == t_i from the paper
									lowercase_       =					self.scheduler.schedule[t]
									lowercase_       =					self.scheduler.schedule[t - 1] if t > 0 else 0

									# 1. Select temporarily increased noise level sigma_hat
									# 2. Add new noise to move from sample_i to sample_hat
									lowercase_  ,	lowercase_       =					self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							generator=SCREAMING_SNAKE_CASE_       )

									# 3. Predict the noise residual given the noise magnitude `sigma_hat`
									# The model inputs and output are adjusted by following eq. (213) in [1].
									lowercase_       =					(sigma_hat / 2) * model((sample_hat + 1) / 2       ,							sigma_hat / 2       ).sample

									# 4. Evaluate dx/dt at sigma_hat
									# 5. Take Euler step from sigma to sigma_prev
									lowercase_       =					self.scheduler.step(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

									if sigma_prev != 0:
													# 6. Apply 2nd order correction
													# The model inputs and output are adjusted by following eq. (213) in [1].
													lowercase_       =					(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,							sigma_prev / 2       ).sample
													lowercase_       =					self.scheduler.step_correct(
													    SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							step_output.prev_sample       ,							step_output['''derivative''']       ,							)
									lowercase_       =					step_output.prev_sample

					lowercase_       =					(sample / 2 + 0.5).clamp(0       ,							1       )
					lowercase_       =					sample.cpu().permute(0       ,							2       ,							3       ,							1       ).numpy()
					if output_type == "pil":
									lowercase_       =					self.numpy_to_pil(SCREAMING_SNAKE_CASE_       )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_       )











from __future__ import annotations

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





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



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



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



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





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



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



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



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



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



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									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(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									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     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




