"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_a  :  Any=				logging.get_logger(__name__)

_a  :  Tuple=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   List[Any]					=	"""conditional_detr"""
				UpperCAmelCase       :   int					=	["""past_key_values"""]
				UpperCAmelCase       :   Any					=	{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}



				def __init__(self       :   Any			,						_A       :   Optional[Any]=True			,						_A       :   Dict=None			,						_A       :   Dict=3			,						_A       :   Union[str, Any]=3_00			,						_A       :   Dict=6			,						_A       :   Any=20_48			,						_A       :   List[str]=8			,						_A       :   Any=6			,						_A       :   Optional[Any]=20_48			,						_A       :   Any=8			,						_A       :   Union[str, Any]=0.0			,						_A       :   Union[str, Any]=0.0			,						_A       :   List[Any]=True			,						_A       :   Union[str, Any]="relu"			,						_A       :   Optional[int]=2_56			,						_A       :   Dict=0.1			,						_A       :   List[str]=0.0			,						_A       :   Dict=0.0			,						_A       :   Union[str, Any]=0.02			,						_A       :   List[Any]=1.0			,						_A       :   List[Any]=False			,						_A       :   Tuple="sine"			,						_A       :   Optional[Any]="resnet50"			,						_A       :   List[Any]=True			,						_A       :   Union[str, Any]=False			,						_A       :   Any=2			,						_A       :   Optional[Any]=5			,						_A       :   int=2			,						_A       :   List[Any]=1			,						_A       :   List[str]=1			,						_A       :   Optional[int]=2			,						_A       :   Optional[Any]=5			,						_A       :   Union[str, Any]=2			,						_A       :   Optional[Any]=0.25			,						**_A       :   Any			,						)					->			List[Any]:
					if backbone_config is not None and use_timm_backbone:
						raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')

					if not use_timm_backbone:
						if backbone_config is None:
							logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
							__snake_case    :							Union[str, Any]   	=				CONFIG_MAPPING['resnet'](out_features=['stage4'])
						elif isinstance(_A			,						_A):
							__snake_case    :							List[Any]   	=				backbone_config.get('model_type')
							__snake_case    :							Optional[int]   	=				CONFIG_MAPPING[backbone_model_type]
							__snake_case    :							int   	=				config_class.from_dict(_A)

					__snake_case    :							str   	=				use_timm_backbone
					__snake_case    :							Any   	=				backbone_config
					__snake_case    :							List[str]   	=				num_channels
					__snake_case    :							str   	=				num_queries
					__snake_case    :							int   	=				d_model
					__snake_case    :							Union[str, Any]   	=				encoder_ffn_dim
					__snake_case    :							str   	=				encoder_layers
					__snake_case    :							Dict   	=				encoder_attention_heads
					__snake_case    :							int   	=				decoder_ffn_dim
					__snake_case    :							List[Any]   	=				decoder_layers
					__snake_case    :							List[Any]   	=				decoder_attention_heads
					__snake_case    :							List[str]   	=				dropout
					__snake_case    :							Union[str, Any]   	=				attention_dropout
					__snake_case    :							Any   	=				activation_dropout
					__snake_case    :							int   	=				activation_function
					__snake_case    :							Union[str, Any]   	=				init_std
					__snake_case    :							List[str]   	=				init_xavier_std
					__snake_case    :							int   	=				encoder_layerdrop
					__snake_case    :							str   	=				decoder_layerdrop
					__snake_case    :							List[str]   	=				encoder_layers
					__snake_case    :							str   	=				auxiliary_loss
					__snake_case    :							Any   	=				position_embedding_type
					__snake_case    :							str   	=				backbone
					__snake_case    :							Optional[int]   	=				use_pretrained_backbone
					__snake_case    :							Dict   	=				dilation
					# Hungarian matcher
					__snake_case    :							Union[str, Any]   	=				class_cost
					__snake_case    :							Optional[Any]   	=				bbox_cost
					__snake_case    :							Dict   	=				giou_cost
					# Loss coefficients
					__snake_case    :							Optional[Any]   	=				mask_loss_coefficient
					__snake_case    :							Tuple   	=				dice_loss_coefficient
					__snake_case    :							Union[str, Any]   	=				cls_loss_coefficient
					__snake_case    :							Optional[Any]   	=				bbox_loss_coefficient
					__snake_case    :							Tuple   	=				giou_loss_coefficient
					__snake_case    :							Tuple   	=				focal_alpha
					super().__init__(is_encoder_decoder=_A			,						**_A)



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			int:
					return self.encoder_attention_heads



				@property
				def       _lowercase  (self       :   Optional[int])					->			int:
					return self.d_model






				def       _lowercase  (self       :   Any)					->			Tuple:
					__snake_case    :							List[str]   	=				copy.deepcopy(self.__dict__)
					if self.backbone_config is not None:
						__snake_case    :							List[str]   	=				self.backbone_config.to_dict()
					__snake_case    :							Dict   	=				self.__class__.model_type
					return output







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[Any]					=	version.parse("""1.11"""		)



				@property
				def       _lowercase  (self       :   List[Any])					->			Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					        ('pixel_mask', {0: 'batch'}),
					    ])



				@property
				def       _lowercase  (self       :   int)					->			float:
					return 1E-5






				@property
				def       _lowercase  (self       :   str)					->			int:
					return 12



"""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       :   Tuple			,						_A       :   List[Any]			,						_A       :   int=sys.maxsize)					->			int:
					__snake_case    :							Dict   	=				'bilinear'
					__snake_case    :							int   	=				max_size
					__snake_case    :							Any   	=				short_edge_length






				def __call__(self       :   Optional[Any]			,						_A       :   Any)					->			Dict:
					__snake_case    :							Any   	=				[]
					for img in imgs:
						__snake_case	,       __snake_case    :							int   	=				img.shape[:2]
						# later: provide list and randomly choose index for resize
						__snake_case    :							Optional[Any]   	=				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(_A			,						_A)
						if h < w:
							__snake_case	,       __snake_case    :							str   	=				size, scale * w
						else:
							__snake_case	,       __snake_case    :							List[str]   	=				scale * h, size
						if max(_A			,						_A) > self.max_size:
							__snake_case    :							str   	=				self.max_size * 1.0 / max(_A			,						_A)
							__snake_case    :							Any   	=				newh * scale
							__snake_case    :							Any   	=				neww * scale
						__snake_case    :							Optional[int]   	=				int(neww + 0.5)
						__snake_case    :							str   	=				int(newh + 0.5)

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

					return img_augs







class 	UpperCamelCase    :



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



				def       _lowercase  (self       :   Tuple			,						_A       :   str)					->			int:
					__snake_case    :							str   	=				tuple(max(_A) for s in zip(*[img.shape for img in images]))
					__snake_case    :							int   	=				[im.shape[-2:] for im in images]
					__snake_case    :							int   	=				[
					    nn.functional.pad(
					        _A			,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,						value=self.pad_value			,						)
					    for size, im in zip(_A			,						_A)
					]

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






				def __call__(self       :   Optional[Any]			,						_A       :   Dict			,						_A       :   Union[str, Any]=False)					->			Optional[int]:
					with torch.no_grad():
						if not isinstance(_A			,						_A):
							__snake_case    :							str   	=				[images]
						if single_image:
							assert len(_A) == 1
						for i in range(len(_A)):
							if isinstance(images[i]			,						torch.Tensor):
								images.insert(_A			,						images.pop(_A).to(self.device).float())
							elif not isinstance(images[i]			,						torch.Tensor):
								images.insert(
								    _A			,						torch.as_tensor(img_tensorize(images.pop(_A)			,						input_format=self.input_format))
								    .to(self.device)
								    .float()			,						)
            # resize smallest edge
						__snake_case    :							Any   	=				torch.tensor([im.shape[:2] for im in images])
						__snake_case    :							Tuple   	=				self.aug(_A)
						# 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    :							List[str]   	=				[self.normalizer(_A) for x in images]
						# now pad them to do the following operations
						__snake_case	,       __snake_case    :							List[Any]   	=				self.pad(_A)
						# Normalize

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







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Any						)							->    int:

	'''simple docstring'''





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

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Tuple[int, int]						)							->    Union[str, Any]:

	'''simple docstring'''





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





