
'''simple docstring'''





def   A			(							A_				:       list[list[int]]     ,				A_				:       list[list[int]]							):
						snake_case							:  List[Any]			=     []
						snake_case							:  Optional[Any]			=     len(A_							)
						for i in range(A_							):
												snake_case							:  Optional[Any]			=     []
												for j in range(A_							):
																		snake_case							:  Optional[Any]			=     0
																		for k in range(A_							):
																								snake_case							:  str			=     val + matrix_a[i][k] * matrix_b[k][j]
																		list_a.append(A_							)
												matrix_c.append(A_							)
						return matrix_c

def   A			(							A_				:       int							):
						return [[int(row == column							) for column in range(A_							)] for row in range(A_							)]

def   A			(							A_				:       int							):
						if n <= 1:
												return n
						snake_case							:  Optional[Any]			=     identity(2							)
						snake_case							:  str			=     [[1, 1], [1, 0]]
						snake_case							:  Optional[Any]			=     n - 1
						while n > 0:
												if n % 2 == 1:
																		snake_case							:  Tuple			=     multiply(A_     ,				A_							)
												snake_case							:  Dict			=     multiply(A_     ,				A_							)
												snake_case							:  Tuple			=     int(n / 2							)
						return res_matrix[0][0]

def   A			(							A_				:       int							):
						if n <= 1:
												return n
						snake_case							:  Optional[int]			=     0
						snake_case							:  Any			=     1
						for _ in range(2     ,				n + 1							):
												snake_case,       snake_case							:  Union[str, Any]			=     fiba, fiba + fiba
						return fiba

def   A			(							):
						for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
												snake_case							:  List[str]			=     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(A_							)} and using bruteforce is """
												    F"""{nth_fibonacci_bruteforce(A_							)}\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

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




