

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 						__UpperCamelCase    :




	def __init__(		self	:						List[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						int=sys.maxsize							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"bilinear"
		UpperCAmelCase_       				=			max_size
		UpperCAmelCase_       				=			short_edge_length




	def __call__(		self	:						str		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







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

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

		return img_augs


class 						__UpperCamelCase    :




	def __init__(		self	:						Any		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						str		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			tuple(max(lowerCAmelCase							) for s in zip(*[img.shape for img in images]							)							)
		UpperCAmelCase_       				=			[im.shape[-2:] for im in images]
		UpperCAmelCase_       				=			[
		    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	:						List[Any]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Dict=False							):


		'''simple docstring'''







		with torch.no_grad():
			if not isinstance(lowerCAmelCase		,	lowerCAmelCase							):
				UpperCAmelCase_       				=			[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
			UpperCAmelCase_       				=			torch.tensor([im.shape[:2] for im in images]							)
			UpperCAmelCase_       				=			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
			UpperCAmelCase_       				=			[self.normalizer(lowerCAmelCase							) for x in images]
			# now pad them to do the following operations
			UpperCAmelCase_				,   UpperCAmelCase_       				=			self.pad(lowerCAmelCase							)
			# Normalize

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



def       __lowerCAmelCase      (						A       ,     A    ):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes




def       __lowerCAmelCase      (						A       ,     A    ):
	assert torch.isfinite(A    ).all(), "Box tensor contains infinite or NaN!"
	UpperCAmelCase_				,   UpperCAmelCase_       				=			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    )


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()