
'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


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







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[Any]:
	_snake_case     		=	SwinConfig(
	    embed_dim=192    ,       depths=(2, 2, 18, 2)    ,       num_heads=(6, 12, 24, 48)    ,       window_size=12    ,       out_features=['stage2', 'stage3', 'stage4']    ,       )

	_snake_case     		=	DetaConfig(
	    backbone_config=lowercase_    ,       num_queries=900    ,       encoder_ffn_dim=2_048    ,       decoder_ffn_dim=2_048    ,       num_feature_levels=5    ,       assign_first_stage=lowercase_    ,       with_box_refine=lowercase_    ,       two_stage=lowercase_    ,       )

	# set labels
	_snake_case     		=	"huggingface/label-files"
	if "o365" in model_name:
		_snake_case     		=	366
		_snake_case     		=	"object365-id2label.json"
	else:
		_snake_case     		=	91
		_snake_case     		=	"coco-detection-id2label.json"

	_snake_case     		=	num_labels
	_snake_case     		=	json.load(open(cached_download(hf_hub_url(lowercase_    ,       lowercase_    ,       repo_type='dataset'			)			)    ,       'r'			)			)
	_snake_case     		=	{int(lowercase_			): v for k, v in idalabel.items()}
	_snake_case     		=	idalabel
	_snake_case     		=	{v: k for k, v in idalabel.items()}

	return config







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	[]

	# stem
	# fmt: off
	rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')			)
	rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')			)
	rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')			)
	rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')			)
	# stages
	for i in range(len(config.backbone_config.depths			)			):
		for j in range(config.backbone_config.depths[i]			):
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

		if i < 3:
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')			)
			rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')			)

	rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')			)
	rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')			)
	rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')			)
	rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')			)
	rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')			)
	rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')			)

	# transformer encoder
	for i in range(config.encoder_layers			):
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')			)
		rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')			)

	# transformer decoder
	for i in range(config.decoder_layers			):
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')			)
		rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')			)

	# fmt: on

	return rename_keys







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Union[str, Any]:
	_snake_case     		=	dct.pop(lowercase_			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Tuple:
	_snake_case     		=	[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
	for i in range(len(backbone_config.depths			)			):
		_snake_case     		=	num_features[i]
		for j in range(backbone_config.depths[i]			):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			_snake_case     		=	state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'			)
			_snake_case     		=	state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'			)
			# next, add query, keys and values (in that order) to the state dict
			_snake_case     		=	in_proj_weight[:dim, :]
			_snake_case     		=	in_proj_bias[: dim]
			_snake_case     		=	in_proj_weight[
			    dim : dim * 2, :
			]
			_snake_case     		=	in_proj_bias[
			    dim : dim * 2
			]
			_snake_case     		=	in_proj_weight[
			    -dim :, :
			]
			_snake_case     		=	in_proj_bias[-dim :]
			# fmt: on







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			int:
	_snake_case     		=	config.d_model
	for i in range(config.decoder_layers			):
		# read in weights + bias of input projection layer of self-attention
		_snake_case     		=	state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'			)
		_snake_case     		=	state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'			)
		# next, add query, keys and values (in that order) to the state dict
		_snake_case     		=	in_proj_weight[:hidden_size, :]
		_snake_case     		=	in_proj_bias[:hidden_size]
		_snake_case     		=	in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		_snake_case     		=	in_proj_bias[hidden_size : hidden_size * 2]
		_snake_case     		=	in_proj_weight[-hidden_size:, :]
		_snake_case     		=	in_proj_bias[-hidden_size:]







def        SCREAMING_SNAKE_CASE__			(     ) ->			Optional[int]:
	_snake_case     		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
	_snake_case     		=	Image.open(requests.get(lowercase_    ,       stream=lowercase_			).raw			)

	return im







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Union[str, Any]:
	_snake_case     		=	get_deta_config(lowercase_			)

	# load original state dict
	if model_name == "deta-swin-large":
		_snake_case     		=	hf_hub_download(repo_id='nielsr/deta-checkpoints'    ,       filename='adet_swin_ft.pth'			)
	elif model_name == "deta-swin-large-o365":
		_snake_case     		=	hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'    ,       filename='deta_swin_pt_o365.pth'			)
	else:
		raise ValueError(F'Model name {model_name} not supported'			)

	_snake_case     		=	torch.load(lowercase_    ,       map_location='cpu'			)["model"]

	# original state dict
	for name, param in state_dict.items():
		print(lowercase_    ,       param.shape			)

	# rename keys
	_snake_case     		=	create_rename_keys(lowercase_			)
	for src, dest in rename_keys:
		rename_key(lowercase_    ,       lowercase_    ,       lowercase_			)
	read_in_swin_q_k_v(lowercase_    ,       config.backbone_config			)
	read_in_decoder_q_k_v(lowercase_    ,       lowercase_			)

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			_snake_case     		=	state_dict.pop(lowercase_			)
			_snake_case     		=	val
		if "input_proj" in key:
			_snake_case     		=	state_dict.pop(lowercase_			)
			_snake_case     		=	val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			_snake_case     		=	state_dict.pop(lowercase_			)
			_snake_case     		=	val

    # finally, create HuggingFace model and load state dict
	_snake_case     		=	DetaForObjectDetection(lowercase_			)
	model.load_state_dict(lowercase_			)
	model.eval()

	_snake_case     		=	"cuda" if torch.cuda.is_available() else "cpu"
	model.to(lowercase_			)

	# load image processor
	_snake_case     		=	DetaImageProcessor(format='coco_detection'			)

	# verify our conversion on image
	_snake_case     		=	prepare_img()
	_snake_case     		=	processor(images=lowercase_    ,       return_tensors='pt'			)
	_snake_case     		=	encoding["pixel_values"]
	_snake_case     		=	model(pixel_values.to(lowercase_			)			)

	# verify logits
	print('Logits:'    ,       outputs.logits[0, :3, :3]			)
	print('Boxes:'    ,       outputs.pred_boxes[0, :3, :3]			)
	if model_name == "deta-swin-large":
		_snake_case     		=	torch.tensor(
		    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]			)
		_snake_case     		=	torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]			)
	elif model_name == "deta-swin-large-o365":
		_snake_case     		=	torch.tensor(
		    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]			)
		_snake_case     		=	torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]			)
	assert torch.allclose(outputs.logits[0, :3, :3]    ,       expected_logits.to(lowercase_			)    ,       atol=1e-4			)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]    ,       expected_boxes.to(lowercase_			)    ,       atol=1e-4			)
	print('Everything ok!'			)

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'			)
		Path(lowercase_			).mkdir(exist_ok=lowercase_			)
		model.save_pretrained(lowercase_			)
		processor.save_pretrained(lowercase_			)

	# Push to hub
	if push_to_hub:
		print('Pushing model and processor to hub...'			)
		model.push_to_hub(F'jozhang97/{model_name}'			)
		processor.push_to_hub(F'jozhang97/{model_name}'			)


if __name__ == "__main__":
					lowercase					:							Tuple   =      argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name",
					    type=str,
					    default="deta-swin-large",
					    choices=["deta-swin-large", "deta-swin-large-o365"],
					    help="Name of the model you'd like to convert.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    help="Path to the folder to output PyTorch model.",
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)
					lowercase					:							List[str]   =      parser.parse_args()
					convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''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						):





								"""simple docstring"""






								_snake_case     		=	'bilinear'
								_snake_case     		=	max_size
								_snake_case     		=	short_edge_length




							def __call__( self					,     lowerCAmelCase_						):





								"""simple docstring"""






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

									if img.dtype == np.uinta:
										_snake_case     		=	Image.fromarray(lowerCAmelCase_						)
										_snake_case     		=	pil_image.resize((neww, newh)					,     PILImageResampling.BILINEAR						)
										_snake_case     		=	np.asarray(lowerCAmelCase_						)
									else:
										_snake_case     		=	img.permute(2					,     0					,     1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
										_snake_case     		=	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_						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	tuple(max(lowerCAmelCase_						) for s in zip(*[img.shape for img in images]						)						)
								_snake_case     		=	[im.shape[-2:] for im in images]
								_snake_case     		=	[
								    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						):





								"""simple docstring"""






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

									if self.size_divisibility > 0:
										raise NotImplementedError()
									# pad
									_snake_case     		=	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__			(     __A    ,       __A			) ->			List[Any]:
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			List[Any]:
	assert torch.isfinite(__A			).all(), "Box tensor contains infinite or NaN!"
	_snake_case		,				_snake_case     		=	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			)

