

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: str  , __UpperCamelCase: int  , __UpperCamelCase: Union[str, Any]=sys.maxsize      ):

						'''simple docstring'''





						__magic_name__							=    '''bilinear'''
						__magic_name__							=    max_size
						__magic_name__							=    short_edge_length







	def __call__(   self: int  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





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

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

						return img_augs




class 				__UpperCamelCase				:







	def __init__(   self: Tuple  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





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

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







	def __call__(   self: List[Any]  , __UpperCamelCase: str  , __UpperCamelCase: Union[str, Any]=False      ):

						'''simple docstring'''





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

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




def 					_lowercase     (				a_				:					Tuple					,a_				:					Union[str, Any]				)				->	Optional[int]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Dict					,a_				:					Tuple[int, int]				)				->	Union[str, Any]:




					'''simple docstring'''







					assert torch.isfinite(__A				).all(), "Box tensor contains infinite or NaN!"
					__magic_name__							=    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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__						=							{"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "VanForImageClassification",
						    "VanModel",
						    "VanPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						A__						=							_LazyModule(__name__, globals()["__file__"], _import_structure)




