

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						:







    """simple docstring"""





    def __init__(				self			:	List[Any],lowercase_			:	Union[str, Any],lowercase_			:	int=sys.maxsize		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       """bilinear"""
        A__   				=       max_size
        A__   				=       short_edge_length


    def __call__(				self			:	Dict,lowercase_			:	Dict		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       []
        for img in imgs:
            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(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_		)
            if h < w:
                A__   				=       size, scale * w
            else:
                A__   				=       scale * h, size
            if max(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_		) > self.max_size:
                A__   				=       self.max_size * 1.0 / max(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_		)
                A__   				=       newh * scale
                A__   				=       neww * scale
            A__   				=       int(neww + 0.5		)
            A__   				=       int(newh + 0.5		)

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

        return img_augs




class 			A						:







    """simple docstring"""





    def __init__(				self			:	Dict,lowercase_			:	str		)->   Optional[Any]:





        '''simple docstring'''







        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 						snake_case__					(				self			:	Tuple,lowercase_			:	str		)->   List[str]:





        '''simple docstring'''







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

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


    def __call__(				self			:	Tuple,lowercase_			:	Optional[Any],lowercase_			:	Any=False		)->   Tuple:





        '''simple docstring'''







        with torch.no_grad():
            if not isinstance(SCREAMING_SNAKE_CASE_,SCREAMING_SNAKE_CASE_		):
                A__   				=       [images]
            if single_image:
                assert len(SCREAMING_SNAKE_CASE_		) == 1
            for i in range(len(SCREAMING_SNAKE_CASE_		)		):
                if isinstance(images[i],torch.Tensor		):
                    images.insert(SCREAMING_SNAKE_CASE_,images.pop(SCREAMING_SNAKE_CASE_		).to(self.device		).float()		)
                elif not isinstance(images[i],torch.Tensor		):
                    images.insert(
                        SCREAMING_SNAKE_CASE_,torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE_		),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(SCREAMING_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
            A__   				=       [self.normalizer(SCREAMING_SNAKE_CASE_		) for x in images]
            # now pad them to do the following operations
            A__   				=       self.pad(SCREAMING_SNAKE_CASE_		)
            # Normalize

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




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Any				)							->				Dict:





    '''simple docstring'''






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

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple[int, int]				)							->				Optional[Any]:





    '''simple docstring'''






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




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase_			=  (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_			=  (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42




class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Iterable[int]		)->   None:





        '''simple docstring'''







        A__   				=       None
        for i in sorted(lowercase_,reverse=lowercase_		):
            A__   				=       Node(lowercase_,self.head		)


    def __iter__(				self			:	List[str]		)->   Iterator[int]:





        '''simple docstring'''







        A__   				=       self.head
        while node:
            yield node.data
            A__   				=       node.next_node


    def __len__(				self			:	str		)->   int:





        '''simple docstring'''







        return sum(1 for _ in self		)


    def __str__(				self			:	Optional[int]		)->   str:





        '''simple docstring'''







        return " -> ".join([str(lowercase_		) for node in self]		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     SortedLinkedList   ,      SCREAMING_SNAKE_CASE__      :     SortedLinkedList				)							->				SortedLinkedList:





    '''simple docstring'''






    return SortedLinkedList(list(SCREAMING_SNAKE_CASE__				) + list(SCREAMING_SNAKE_CASE__				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     lowercase_			=  SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

