
'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 					a_					:




							def __init__(       self				,				snake_case_				,				snake_case_=sys.maxsize					):
									_lowerCAmelCase    :					int							=   """bilinear"""
									_lowerCAmelCase    :					List[str]							=   max_size
									_lowerCAmelCase    :					List[Any]							=   short_edge_length







							def __call__(       self				,				snake_case_					):
									_lowerCAmelCase    :					Optional[int]							=   []
									for img in imgs:
											_lowerCAmelCase			,   _lowerCAmelCase    :					List[Any]							=   img.shape[:2]
											# later: provide list and randomly choose index for resize
											_lowerCAmelCase    :					Tuple							=   np.random.randint(self.short_edge_length[0]				,				self.short_edge_length[1] + 1					)
											if size == 0:
													return img
											_lowerCAmelCase    :					List[Any]							=   size * 1.0 / min(snake_case_				,				snake_case_					)
											if h < w:
													_lowerCAmelCase			,   _lowerCAmelCase    :					str							=   size, scale * w
											else:
													_lowerCAmelCase			,   _lowerCAmelCase    :					Tuple							=   scale * h, size
											if max(snake_case_				,				snake_case_					) > self.max_size:
													_lowerCAmelCase    :					Any							=   self.max_size * 1.0 / max(snake_case_				,				snake_case_					)
													_lowerCAmelCase    :					Tuple							=   newh * scale
													_lowerCAmelCase    :					Tuple							=   neww * scale
											_lowerCAmelCase    :					int							=   int(neww + 0.5					)
											_lowerCAmelCase    :					Any							=   int(newh + 0.5					)

											if img.dtype == np.uinta:
													_lowerCAmelCase    :					Optional[int]							=   Image.fromarray(snake_case_					)
													_lowerCAmelCase    :					Optional[Any]							=   pil_image.resize((neww, newh)				,				PILImageResampling.BILINEAR					)
													_lowerCAmelCase    :					List[Any]							=   np.asarray(snake_case_					)
											else:
													_lowerCAmelCase    :					Optional[Any]							=   img.permute(2				,				0				,				1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
													_lowerCAmelCase    :					Optional[Any]							=   nn.functional.interpolate(
													    snake_case_				,				(newh, neww)				,				mode=self.interp_method				,				align_corners=snake_case_					).squeeze(0					)
											img_augs.append(snake_case_					)

									return img_augs



class 					a_					:




							def __init__(       self				,				snake_case_					):
									_lowerCAmelCase    :					Tuple							=   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,				cfg.INPUT.MAX_SIZE_TEST					)
									_lowerCAmelCase    :					Optional[Any]							=   cfg.INPUT.FORMAT
									_lowerCAmelCase    :					int							=   cfg.SIZE_DIVISIBILITY
									_lowerCAmelCase    :					Union[str, Any]							=   cfg.PAD_VALUE
									_lowerCAmelCase    :					Union[str, Any]							=   cfg.INPUT.MAX_SIZE_TEST
									_lowerCAmelCase    :					Any							=   cfg.MODEL.DEVICE
									_lowerCAmelCase    :					List[str]							=   torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,				1				,				1					)
									_lowerCAmelCase    :					Union[str, Any]							=   torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,				1				,				1					)
									_lowerCAmelCase    :					Union[str, Any]							=   lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std




							def 							__UpperCamelCase    (       self				,				snake_case_					):
									_lowerCAmelCase    :					Optional[Any]							=   tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									_lowerCAmelCase    :					Dict							=   [im.shape[-2:] for im in images]
									_lowerCAmelCase    :					List[Any]							=   [
									    nn.functional.pad(
									        snake_case_				,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,				value=self.pad_value				,				)
									    for size, im in zip(snake_case_				,				snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)







							def __call__(       self				,				snake_case_				,				snake_case_=False					):
									with torch.no_grad():
											if not isinstance(snake_case_				,				snake_case_					):
													_lowerCAmelCase    :					Optional[int]							=   [images]
											if single_image:
													assert len(snake_case_					) == 1
											for i in range(len(snake_case_					)					):
													if isinstance(images[i]				,				torch.Tensor					):
															images.insert(snake_case_				,				images.pop(snake_case_					).to(self.device					).float()					)
													elif not isinstance(images[i]				,				torch.Tensor					):
															images.insert(
															    snake_case_				,				torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,				input_format=self.input_format					)					)
															    .to(self.device					)
															    .float()				,				)
            # resize smallest edge
											_lowerCAmelCase    :					Optional[int]							=   torch.tensor([im.shape[:2] for im in images]					)
											_lowerCAmelCase    :					Optional[Any]							=   self.aug(snake_case_					)
											# transpose images and convert to torch tensors
											# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
											# now normalize before pad to avoid useless arithmetic
											_lowerCAmelCase    :					Optional[Any]							=   [self.normalizer(snake_case_					) for x in images]
											# now pad them to do the following operations
											_lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   self.pad(snake_case_					)
											# Normalize

											if self.size_divisibility > 0:
													raise NotImplementedError()
											# pad
											_lowerCAmelCase    :					List[Any]							=   torch.true_divide(snake_case_				,				snake_case_					)
											if single_image:
													return images[0], sizes[0], scales_yx[0]
											else:
													return images, sizes, scales_yx



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Any					, _lowerCamelCase  :    List[str]						)      ->  Optional[Any]:
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes



def 		_UpperCAmelCase			(					_lowerCamelCase  :    int					, _lowerCamelCase  :    Tuple[int, int]						)      ->  List[str]:
		assert torch.isfinite(snake_case__						).all(), "Box tensor contains infinite or NaN!"
		_lowerCAmelCase			,   _lowerCAmelCase    :					Optional[int]							=   box_size
		tensor[:, 0].clamp_(min=0					, max=snake_case__						)
		tensor[:, 1].clamp_(min=0					, max=snake_case__						)
		tensor[:, 2].clamp_(min=0					, max=snake_case__						)
		tensor[:, 3].clamp_(min=0					, max=snake_case__						)




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    list[list[int]]					, _lowerCamelCase  :    list[list[int]]						)      ->  list[list[int]]:
  _lowerCAmelCase    :					int							=   []
  _lowerCAmelCase    :					Union[str, Any]							=   len(_lowerCamelCase						)
  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   []
    for j in range(_lowerCamelCase						):
      _lowerCAmelCase    :					Union[str, Any]							=   0
      for k in range(_lowerCamelCase						):
        _lowerCAmelCase    :					Any							=   val + matrix_a[i][k] * matrix_b[k][j]
      list_a.append(_lowerCamelCase						)
    matrix_c.append(_lowerCamelCase						)
  return matrix_c


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  list[list[int]]:
  return [[int(row == column						) for column in range(_lowerCamelCase						)] for row in range(_lowerCamelCase						)]


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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Any							=   0
  _lowerCAmelCase    :					Dict							=   1
  for _ in range(2					, n + 1						):
    _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   fiba, fiba + fiba
  return fiba


def 		_UpperCAmelCase			(					)      ->  None:
  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    _lowerCAmelCase    :					Union[str, Any]							=   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()


