
import timeit







class 							UpperCAmelCase	:

					def __init__(			self      :					int ,      lowerCAmelCase      :					Tuple					):
											if isinstance(lowerCAmelCase ,      lowerCAmelCase					):  # Initializes a matrix identical to the one provided.
																	lowercase       :    Tuple											=  arg
																	lowercase       :    Dict											=  len(lowerCAmelCase					)
											else:  # Initializes a square matrix of the given size and set values to zero.
																	lowercase       :    Optional[Any]											=  arg
																	lowercase       :    Union[str, Any]											=  [[0 for _ in range(self.n					)] for _ in range(self.n					)]




					def __mul__(			self      :					Tuple ,      lowerCAmelCase      :					str					):
											lowercase       :    Optional[Any]											=  Matrix(self.n					)
											for i in range(self.n					):
																	for j in range(self.n					):
																							for k in range(self.n					):
																													matrix.t[i][j] += self.t[i][k] * b.t[k][j]
											return matrix





def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    List[Any]											=  Matrix([[1, 0], [0, 1]]       )
						while b > 0:
												if b & 1:
																		matrix *= a
												a *= a
												b >>= 1
						return matrix

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Tuple       ):
						# Trivial Cases
						if n == 1:
												return fa
						elif n == 2:
												return fa
						lowercase       :    List[Any]											=  Matrix([[1, 1], [1, 0]]       )
						lowercase       :    Dict											=  modular_exponentiation(UpperCAmelCase_							,			n - 2       )
						return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				str       ):
						# Trivial Cases
						if n == 1:
												return fa
						elif n == 2:
												return fa

						lowercase       :    Tuple											=  fa
						lowercase       :    List[Any]											=  fa
						n -= 2

						while n > 0:
												lowercase       :    Dict											=  fn_a + fn_a, fn_a
												n -= 1

						return fn_a

def        lowerCamelCase_      (			):
						lowercase       :    Tuple											=  '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
						lowercase       :    Tuple											=  '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
						lowercase       :    Any											=  timeit.timeit(setup=UpperCAmelCase_							,			stmt=UpperCAmelCase_							,			number=1_00       )
						print('''With matrix exponentiation the average execution time is '''							,			exec_time / 1_00       )
						return exec_time

def        lowerCamelCase_      (			):
						lowercase       :    str											=  '''
from random import randint
from __main__ import simple_fibonacci
'''
						lowercase       :    int											=  '''simple_fibonacci(randint(1,70000), 1, 1)'''
						lowercase       :    Tuple											=  timeit.timeit(setup=UpperCAmelCase_							,			stmt=UpperCAmelCase_							,			number=1_00       )
						print(
						    '''Without matrix exponentiation the average execution time is '''							,			exec_time / 1_00       )
						return exec_time

def        lowerCamelCase_      (			):
						matrix_exponentiation_time()
						simple_fibonacci_time()


if __name__ == "__main__":
						main()




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

											self.register_modules(unet=lowerCAmelCase ,      scheduler=lowerCAmelCase					)




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

											lowercase       :    Optional[Any]											=  (image / 2 + 0.5).clamp(0 ,      1					)
											lowercase       :    Optional[int]											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).numpy()
											if output_type == "pil":
																	lowercase       :    Union[str, Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


