






'''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    A_	:
							"""simple docstring"""




							def __init__(		self			:Dict       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Union[str, Any]=sys.maxsize				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       "bilinear"
										snake_case_				:					str   =       max_size
										snake_case_				:					Union[str, Any]   =       short_edge_length






							def __call__(		self			:Tuple       ,							lowerCAmelCase__			:Any				)				->  int:


										'''simple docstring'''




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

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

										return img_augs







class    A_	:
							"""simple docstring"""




							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Dict				)				->  int:


										'''simple docstring'''




										snake_case_				:					Dict   =       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,							cfg.INPUT.MAX_SIZE_TEST				)
										snake_case_				:					Optional[Any]   =       cfg.INPUT.FORMAT
										snake_case_				:					List[str]   =       cfg.SIZE_DIVISIBILITY
										snake_case_				:					Tuple   =       cfg.PAD_VALUE
										snake_case_				:					Tuple   =       cfg.INPUT.MAX_SIZE_TEST
										snake_case_				:					Any   =       cfg.MODEL.DEVICE
										snake_case_				:					Union[str, Any]   =       torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,							1       ,							1				)
										snake_case_				:					List[Any]   =       torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,							1       ,							1				)
										snake_case_				:					List[Any]   =       lambda lowerCAmelCase__				: (x - self.pixel_mean) / self.pixel_std

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Any				)				->  int:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       tuple(max(lowerCAmelCase__				) for s in zip(*[img.shape for img in images]				)				)
										snake_case_				:					List[Any]   =       [im.shape[-2:] for im in images]
										snake_case_				:					Any   =       [
										    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__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  Optional[int]:


										'''simple docstring'''




										with torch.no_grad():
													if not isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				):
																snake_case_				:					Union[str, Any]   =       [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
													snake_case_				:					Optional[Any]   =       torch.tensor([im.shape[:2] for im in images]				)
													snake_case_				:					Any   =       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
													snake_case_				:					str   =       [self.normalizer(lowerCAmelCase__				) for x in images]
													# now pad them to do the following operations
													snake_case_				:					List[str]   =       self.pad(lowerCAmelCase__				)
													# Normalize

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



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






def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Dict:



			"""simple docstring"""



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












'''simple docstring'''




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


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




