






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:   Union[str, Any]		,     __A:   Dict		,     __A:   Union[str, Any]=sys.maxsize   )    ->				Tuple:
												_A								= '''bilinear'''
												_A								= max_size
												_A								= short_edge_length



							def __call__(  self:   Dict		,     __A:   Optional[int]   )    ->				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(__A		,     __A   )
																	if h < w:
																						_A							,_A								= size, scale * w
																	else:
																						_A							,_A								= scale * h, size
																	if max(__A		,     __A   ) > self.max_size:
																						_A								= self.max_size * 1.0 / max(__A		,     __A   )
																						_A								= newh * scale
																						_A								= neww * scale
																	_A								= int(neww + 0.5   )
																	_A								= int(newh + 0.5   )

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

												return img_augs





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   int   )    ->				List[str]:
												_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 __A   : (x - self.pixel_mean) / self.pixel_std

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

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



							def __call__(  self:   int		,     __A:   Dict		,     __A:   List[str]=False   )    ->				List[str]:
												with torch.no_grad():
																	if not isinstance(__A		,     __A   ):
																						_A								= [images]
																	if single_image:
																						assert len(__A   ) == 1
																	for i in range(len(__A   )   ):
																						if isinstance(images[i]		,     torch.Tensor   ):
																											images.insert(__A		,     images.pop(__A   ).to(self.device   ).float()   )
																						elif not isinstance(images[i]		,     torch.Tensor   ):
																											images.insert(
																											    __A		,     torch.as_tensor(img_tensorize(images.pop(__A   )		,     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(__A   )
																	# 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(__A   ) for x in images]
																	# now pad them to do the following operations
																	_A							,_A								= self.pad(__A   )
																	# Normalize

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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= len(_lowercase ), len(grid[0] )
					if (
					    min(_lowercase					,			_lowercase ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col) )

					_A								= 0
					count += depth_first_search(_lowercase					,			row + 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			row - 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col + 1					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col - 1					,			_lowercase )

					visit.remove((row, col) )
					return count


if __name__ == "__main__":
				import doctest

				doctest.testmod()



