'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			[]
    UpperCamelCase__      =			len(lowerCAmelCase_	)
    for i in range(lowerCAmelCase_	):
        UpperCamelCase__      =			[]
        for j in range(lowerCAmelCase_	):
            UpperCamelCase__      =			0
            for k in range(lowerCAmelCase_	):
                UpperCamelCase__      =			val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(lowerCAmelCase_	)
        matrix_c.append(lowerCAmelCase_	)
    return matrix_c





def 						snake_case__ (lowerCAmelCase_	):
    return [[int(row == column	) for column in range(lowerCAmelCase_	)] for row in range(lowerCAmelCase_	)]





def 						snake_case__ (lowerCAmelCase_	):
    if n <= 1:
        return n
    UpperCamelCase__      =			identity(2	)
    UpperCamelCase__      =			[[1, 1], [1, 0]]
    UpperCamelCase__      =			n - 1
    while n > 0:
        if n % 2 == 1:
            UpperCamelCase__      =			multiply(lowerCAmelCase_					,  lowerCAmelCase_	)
        UpperCamelCase__      =			multiply(lowerCAmelCase_					,  lowerCAmelCase_	)
        UpperCamelCase__      =			int(n / 2	)
    return res_matrix[0][0]





def 						snake_case__ (lowerCAmelCase_	):
    if n <= 1:
        return n
    UpperCamelCase__      =			0
    UpperCamelCase__      =			1
    for _ in range(2					,  n + 1	):
        UpperCamelCase__    ,  UpperCamelCase__      =			fiba, fiba + fiba
    return fiba





def 						snake_case__ ():
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        UpperCamelCase__      =			int("".join(c for c in ordinal if c in "0123456789"	)	)  # 1000th --> 1000
        print(
            F'{ordinal} fibonacci number using matrix exponentiation is '
            F'{nth_fibonacci_matrix(lowerCAmelCase_	)} and using bruteforce is '
            F'{nth_fibonacci_bruteforce(lowerCAmelCase_	)}\n'	)
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()









'''simple docstring'''







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       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

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







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

       UpperCamelCase__      =			(image / 2 + 0.5).clamp(0     ,						1 )
       UpperCamelCase__      =			image.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       if output_type == "pil":
           UpperCamelCase__      =			self.numpy_to_pil(__UpperCamelCase )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




