


"""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 			lowerCamelCase__						:







						def __init__(							self   :    str			,			_lowercase   :    List[str]			,			_lowercase   :    str=sys.maxsize		):
								A					       = 'bilinear'
								A					       = max_size
								A					       = short_edge_length







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

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

								return img_augs



class 			lowerCamelCase__						:







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







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

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







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

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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		assert torch.isfinite(UpperCamelCase__	).all(), "Box tensor contains infinite or NaN!"
		A       , A					       = 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"""


from math import factorial







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		float:







		"""simple docstring"""





		if successes > trials:
				raise ValueError('successes must be lower or equal to trials'	)
		if trials < 0 or successes < 0:
				raise ValueError('the function is defined for non-negative integers'	)
		if not isinstance(UpperCamelCase__ ,			UpperCamelCase__	) or not isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
				raise ValueError('the function is defined for non-negative integers'	)
		if not 0 < prob < 1:
				raise ValueError('prob has to be in range of 1 - 0'	)
		A					       = (prob**successes) * ((1 - prob) ** (trials - successes))
		# Calculate the binomial coefficient: n! / k!(n-k)!
		A					       = float(factorial(UpperCamelCase__	)	)
		coefficient /= factorial(UpperCamelCase__	) * factorial(trials - successes	)
		return probability * coefficient


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

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))





