


'''simple docstring'''



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      :			Optional[Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			int=sys.maxsize				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     '''bilinear'''
										UpperCAmelCase					    =     max_size
										UpperCAmelCase					    =     short_edge_length





				def __call__(		self      :			Any				,   lowerCAmelCase      :			int				)->					Union[str, 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      :			int				,   lowerCAmelCase      :			str				)->					Tuple:

										"""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      :			Tuple				,   lowerCAmelCase      :			Optional[int]				)->					List[Any]:

										"""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      :			Any				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[Any]=False				)->					Union[str, Any]:

										"""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       :      Dict				,       A       :      Optional[int]					):




						'''simple docstring'''






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




def 				lowerCamelCase__				(	A       :      Any				,       A       :      Tuple[int, int]					):




						'''simple docstring'''






						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					)









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase      :      Union[str, Any]            =					{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					["""PoolFormerFeatureExtractor"""]
	_lowercase      :      Any            =					["""PoolFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Tuple            =					[
	    """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PoolFormerForImageClassification""",
	    """PoolFormerModel""",
	    """PoolFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_poolformer import (
	    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    PoolFormerConfig,
	    PoolFormerOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_poolformer import PoolFormerFeatureExtractor
		from .image_processing_poolformer import PoolFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_poolformer import (
		    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PoolFormerForImageClassification,
		    PoolFormerModel,
		    PoolFormerPreTrainedModel,
		)


else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





