
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    ,    _UpperCamelCase    ,    _UpperCamelCase=sys.maxsize					)    ->				Any:
							lowerCAmelCase_      =     "bilinear"
							lowerCAmelCase_      =     max_size
							lowerCAmelCase_      =     short_edge_length




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

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

							return img_augs





class      _lowerCAmelCase :




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




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     tuple(max(_UpperCamelCase					) for s in zip(*[img.shape for img in images]					)					)
							lowerCAmelCase_      =     [im.shape[-2:] for im in images]
							lowerCAmelCase_      =     [
							    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    ,    _UpperCamelCase    ,    _UpperCamelCase=False					)    ->				int:
							with torch.no_grad():
										if not isinstance(_UpperCamelCase    ,    _UpperCamelCase					):
													lowerCAmelCase_      =     [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
										lowerCAmelCase_      =     torch.tensor([im.shape[:2] for im in images]					)
										lowerCAmelCase_      =     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
										lowerCAmelCase_      =     [self.normalizer(_UpperCamelCase					) for x in images]
										# now pad them to do the following operations
										lowerCAmelCase_     ,  lowerCAmelCase_      =     self.pad(_UpperCamelCase					)
										# Normalize

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






def        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  Optional[int]    ):




			"""simple docstring"""


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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Tuple[int, int]    ):




			"""simple docstring"""


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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



