
"""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 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_=sys.maxsize   )						->				int:
			lowerCamelCase	:     Tuple            =       'bilinear'
			lowerCamelCase	:     int            =       max_size
			lowerCamelCase	:     Any            =       short_edge_length




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

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

			return img_augs






class 			_lowercase  :




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




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				int:
			lowerCamelCase	:     Tuple            =       tuple(max(UpperCAmelCase_   ) for s in zip(*[img.shape for img in images]   )   )
			lowerCamelCase	:     int            =       [im.shape[-2:] for im in images]
			lowerCamelCase	:     List[str]            =       [
			    nn.functional.pad(
			        UpperCAmelCase_						,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,					value=self.pad_value						,					)
			    for size, im in zip(UpperCAmelCase_						,					UpperCAmelCase_   )
			]

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




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_=False   )						->				List[str]:
			with torch.no_grad():
					if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     Dict            =       [images]
					if single_image:
							assert len(UpperCAmelCase_   ) == 1
					for i in range(len(UpperCAmelCase_   )   ):
							if isinstance(images[i]						,					torch.Tensor   ):
									images.insert(UpperCAmelCase_						,					images.pop(UpperCAmelCase_   ).to(self.device   ).float()   )
							elif not isinstance(images[i]						,					torch.Tensor   ):
									images.insert(
									    UpperCAmelCase_						,					torch.as_tensor(img_tensorize(images.pop(UpperCAmelCase_   )						,					input_format=self.input_format   )   )
									    .to(self.device   )
									    .float()						,					)
            # resize smallest edge
					lowerCamelCase	:     Optional[Any]            =       torch.tensor([im.shape[:2] for im in images]   )
					lowerCamelCase	:     int            =       self.aug(UpperCAmelCase_   )
					# 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	:     Union[str, Any]            =       [self.normalizer(UpperCAmelCase_   ) for x in images]
					# now pad them to do the following operations
					lowerCamelCase      ,       lowerCamelCase	:     str            =       self.pad(UpperCAmelCase_   )
					# Normalize

					if self.size_divisibility > 0:
							raise NotImplementedError()
					# pad
					lowerCamelCase	:     Dict            =       torch.true_divide(UpperCAmelCase_						,					UpperCAmelCase_   )
					if single_image:
							return images[0], sizes[0], scales_yx[0]
					else:
							return images, sizes, scales_yx


def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes





def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		assert torch.isfinite(a_     ).all(), "Box tensor contains infinite or NaN!"
		lowerCamelCase      ,       lowerCamelCase	:     str            =       box_size
		tensor[:, 0].clamp_(min=0,		max=a_     )
		tensor[:, 1].clamp_(min=0,		max=a_     )
		tensor[:, 2].clamp_(min=0,		max=a_     )
		tensor[:, 3].clamp_(min=0,		max=a_     )








"""simple docstring"""

def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('recursive_lucas_number accepts only integer arguments.'     )
		if n_th_number == 0:
				return 2
		if n_th_number == 1:
				return 1

		return recursive_lucas_number(n_th_number - 1     ) + recursive_lucas_number(
		    n_th_number - 2     )
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if not isinstance(a_,		a_     ):
				raise TypeError('dynamic_lucas_number accepts only integer arguments.'     )
		lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       2, 1
		for _ in range(a_     ):
				lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       b, a + b
		return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					_A				=					int(input('Enter the number of terms in lucas series:\n').strip())
					print('Using recursive function to calculate lucas series:')
					print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('\nUsing dynamic function to calculate lucas series:')
					print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))






