



'''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 ,				_lowerCAmelCase ,				_lowerCAmelCase=sys.maxsize						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							"""bilinear"""
							lowercase        =							max_size
							lowercase        =							short_edge_length







	def __call__(    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






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

													if img.dtype == np.uinta:
																			lowercase        =							Image.fromarray(_lowerCAmelCase						)
																			lowercase        =							pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR						)
																			lowercase        =							np.asarray(_lowerCAmelCase						)
													else:
																			lowercase        =							img.permute(2 ,				0 ,				1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																			lowercase        =							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 ,				_lowerCAmelCase						)    ->    int:
							'''simple docstring'''






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







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							tuple(max(_lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
							lowercase        =							[im.shape[-2:] for im in images]
							lowercase        =							[
							    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 ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    Dict:
							'''simple docstring'''






							with torch.no_grad():
													if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																			lowercase        =							[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
													lowercase        =							torch.tensor([im.shape[:2] for im in images]						)
													lowercase        =							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
													lowercase        =							[self.normalizer(_lowerCAmelCase						) for x in images]
													# now pad them to do the following operations
													lowercase     ,   lowercase        =							self.pad(_lowerCAmelCase						)
													# Normalize

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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Dict						):
						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Tuple[int, int]						):
						assert torch.isfinite(lowercase_						).all(), "Box tensor contains infinite or NaN!"
						lowercase     ,   lowercase        =							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_						)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


