
"""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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=sys.maxsize):
				__SCREAMING_SNAKE_CASE            = """bilinear"""
				__SCREAMING_SNAKE_CASE            = max_size
				__SCREAMING_SNAKE_CASE            = short_edge_length




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

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

				return img_augs



class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							,  cfg.INPUT.MAX_SIZE_TEST)
				__SCREAMING_SNAKE_CASE            = cfg.INPUT.FORMAT
				__SCREAMING_SNAKE_CASE            = cfg.SIZE_DIVISIBILITY
				__SCREAMING_SNAKE_CASE            = cfg.PAD_VALUE
				__SCREAMING_SNAKE_CASE            = cfg.INPUT.MAX_SIZE_TEST
				__SCREAMING_SNAKE_CASE            = cfg.MODEL.DEVICE
				__SCREAMING_SNAKE_CASE            = torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)							,  1							,  1)
				__SCREAMING_SNAKE_CASE            = torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)							,  1							,  1)
				__SCREAMING_SNAKE_CASE            = lambda lowerCAmelCase__: (x - self.pixel_mean) / self.pixel_std




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

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




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

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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes



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






"""simple docstring"""





__magic_name__       =			"Tobias Carryer"

from time import time
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=int(time())):  # noqa: B008
				__SCREAMING_SNAKE_CASE            = multiplier
				__SCREAMING_SNAKE_CASE            = increment
				__SCREAMING_SNAKE_CASE            = modulo
				__SCREAMING_SNAKE_CASE            = seed




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (self.multiplier * self.seed + self.increment) % self.modulo
				return self.seed


if __name__ == "__main__":
							# Show the LCG in action.
							__magic_name__       =			LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
							while True:
														print(lcg.next_number())




