






import argparse
import json
from pathlib import Path

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

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__							= logging.get_logger(__name__)





def 							lowercase__		(				lowerCamelCase					):
				if "backbone.0.body" in orig_key:
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						orig_key.replace('backbone.0.body',	'backbone.conv_encoder.model'					)
				if "transformer" in orig_key:
								_SCREAMING_SNAKE_CASE      :		int							=						orig_key.replace('transformer.',	''					)
				if "norm1" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      :		str							=						orig_key.replace('norm1',	'self_attn_layer_norm'					)
								else:
												_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						orig_key.replace('norm1',	'encoder_attn_layer_norm'					)
				if "norm2" in orig_key:
								if "encoder" in orig_key:
												_SCREAMING_SNAKE_CASE      :		Dict							=						orig_key.replace('norm2',	'final_layer_norm'					)
								else:
												_SCREAMING_SNAKE_CASE      :		Any							=						orig_key.replace('norm2',	'self_attn_layer_norm'					)
				if "norm3" in orig_key:
								_SCREAMING_SNAKE_CASE      :		Dict							=						orig_key.replace('norm3',	'final_layer_norm'					)
				if "linear1" in orig_key:
								_SCREAMING_SNAKE_CASE      :		Any							=						orig_key.replace('linear1',	'fc1'					)
				if "linear2" in orig_key:
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						orig_key.replace('linear2',	'fc2'					)
				if "query_embed" in orig_key:
								_SCREAMING_SNAKE_CASE      :		int							=						orig_key.replace('query_embed',	'query_position_embeddings'					)
				if "cross_attn" in orig_key:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						orig_key.replace('cross_attn',	'encoder_attn'					)

				return orig_key





def 							lowercase__		(				lowerCamelCase					):
				# transformer decoder self-attention layers
				for i in range(6					):
								# read in weights + bias of input projection layer of self-attention
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""					)
								_SCREAMING_SNAKE_CASE      :		Any							=						state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""					)
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE      :		List[str]							=						in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE      :		Tuple							=						in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE      :		Any							=						in_proj_bias[-256:]





def 							lowercase__		(				):
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						'http://images.cocodataset.org/val2017/000000039769.jpg'
				_SCREAMING_SNAKE_CASE      :		Any							=						Image.open(requests.get(_UpperCAmelCase,	stream=_UpperCAmelCase					).raw					)

				return im





@torch.no_grad()
def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	):
				_SCREAMING_SNAKE_CASE      :		List[str]							=						DeformableDetrConfig()
				# set config attributes
				if single_scale:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						1
				_SCREAMING_SNAKE_CASE      :		str							=						dilation
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						with_box_refine
				_SCREAMING_SNAKE_CASE      :		int							=						two_stage
				# set labels
				_SCREAMING_SNAKE_CASE      :		List[str]							=						91
				_SCREAMING_SNAKE_CASE      :		int							=						'huggingface/label-files'
				_SCREAMING_SNAKE_CASE      :		int							=						'coco-detection-id2label.json'
				_SCREAMING_SNAKE_CASE      :		Any							=						json.load(open(cached_download(hf_hub_url(_UpperCAmelCase,	_UpperCAmelCase,	repo_type='dataset'					)					),	'r'					)					)
				_SCREAMING_SNAKE_CASE      :		Dict							=						{int(_UpperCAmelCase					): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE      :		Tuple							=						idalabel
				_SCREAMING_SNAKE_CASE      :		str							=						{v: k for k, v in idalabel.items()}

				# load image processor
				_SCREAMING_SNAKE_CASE      :		int							=						DeformableDetrImageProcessor(format='coco_detection'					)

				# prepare image
				_SCREAMING_SNAKE_CASE      :		Dict							=						prepare_img()
				_SCREAMING_SNAKE_CASE      :		List[str]							=						image_processor(images=_UpperCAmelCase,	return_tensors='pt'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						encoding['pixel_values']

				logger.info('Converting model...'					)

				# load original state dict
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						torch.load(_UpperCAmelCase,	map_location='cpu'					)['model']
				# rename keys
				for key in state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE      :		int							=						state_dict.pop(_UpperCAmelCase					)
								_SCREAMING_SNAKE_CASE      :		str							=						val
				# query, key and value matrices need special treatment
				read_in_q_k_v(_UpperCAmelCase					)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				_SCREAMING_SNAKE_CASE      :		int							=						'model.'
				for key in state_dict.copy().keys():
								if not key.startswith('class_embed'					) and not key.startswith('bbox_embed'					):
												_SCREAMING_SNAKE_CASE      :		str							=						state_dict.pop(_UpperCAmelCase					)
												_SCREAMING_SNAKE_CASE      :		Optional[int]							=						val
    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE      :		str							=						DeformableDetrForObjectDetection(_UpperCAmelCase					)
				model.load_state_dict(_UpperCAmelCase					)
				model.eval()

				_SCREAMING_SNAKE_CASE      :		str							=						'cuda' if torch.cuda.is_available() else 'cpu'
				model.to(_UpperCAmelCase					)
				# verify our conversion
				_SCREAMING_SNAKE_CASE      :		Tuple							=						model(pixel_values.to(_UpperCAmelCase					)					)

				_SCREAMING_SNAKE_CASE      :		Dict							=						torch.tensor(
				    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]					)
				_SCREAMING_SNAKE_CASE      :		str							=						torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]					)

				if single_scale:
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						torch.tensor(
								    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]					)
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]					)

				if single_scale and dilation:
								_SCREAMING_SNAKE_CASE      :		List[Any]							=						torch.tensor(
								    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]					)
								_SCREAMING_SNAKE_CASE      :		str							=						torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]					)

				if with_box_refine:
								_SCREAMING_SNAKE_CASE      :		Dict							=						torch.tensor(
								    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]					)
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]					)

				if with_box_refine and two_stage:
								_SCREAMING_SNAKE_CASE      :		int							=						torch.tensor(
								    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]					)
								_SCREAMING_SNAKE_CASE      :		Dict							=						torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]					)

				print('Logits:',	outputs.logits[0, :3, :3]					)

				assert torch.allclose(outputs.logits[0, :3, :3],	expected_logits.to(_UpperCAmelCase					),	atol=1E-4					)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3],	expected_boxes.to(_UpperCAmelCase					),	atol=1E-4					)

				print('Everything ok!'					)

				# Save model and image processor
				logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
				Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
				model.save_pretrained(_UpperCAmelCase					)
				image_processor.save_pretrained(_UpperCAmelCase					)

				# Push to hub
				if push_to_hub:
								_SCREAMING_SNAKE_CASE      :		str							=						'deformable-detr'
								model_name += "-single-scale" if single_scale else ""
								model_name += "-dc5" if dilation else ""
								model_name += "-with-box-refine" if with_box_refine else ""
								model_name += "-two-stage" if two_stage else ""
								print('Pushing model to hub...'					)
								model.push_to_hub(repo_path_or_name=_UpperCAmelCase,	organization='nielsr',	commit_message='Add model'					)


if __name__ == "__main__":
			lowerCAmelCase__							= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    type=str,
			    default='/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth',
			    help='Path to Pytorch checkpoint (.pth file) you\'d like to convert.',
			)
			parser.add_argument('--single_scale', action='store_true', help='Whether to set config.num_features_levels = 1.')
			parser.add_argument('--dilation', action='store_true', help='Whether to set config.dilation=True.')
			parser.add_argument('--with_box_refine', action='store_true', help='Whether to set config.with_box_refine=True.')
			parser.add_argument('--two_stage', action='store_true', help='Whether to set config.two_stage=True.')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    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.'
			)
			lowerCAmelCase__							= parser.parse_args()
			convert_deformable_detr_checkpoint(
			    args.checkpoint_path,
			    args.single_scale,
			    args.dilation,
			    args.with_box_refine,
			    args.two_stage,
			    args.pytorch_dump_folder_path,
			    args.push_to_hub,
			)









"""simple docstring"""
def 							lowercase__		(				lowerCamelCase					):
				return -num if num < 0 else num





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_min() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						x[0]
				for i in x:
								if abs_val(lowerCamelCase					) < abs_val(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						x[0]
				for i in x:
								if abs(lowerCamelCase					) > abs(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max_sort() arg is an empty sequence'					)
				return sorted(lowerCamelCase,	key=lowerCamelCase					)[-1]





def 							lowercase__		(				):
				assert abs_val(0					) == 0
				assert abs_val(34					) == 34
				assert abs_val(-100_000_000_000					) == 100_000_000_000

				_SCREAMING_SNAKE_CASE      :		Tuple							=						[-3, -1, 2, -11]
				assert abs_max(lowerCamelCase					) == -11
				assert abs_max_sort(lowerCamelCase					) == -11
				assert abs_min(lowerCamelCase					) == -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			test_abs_val()
			print(abs_val(-34))  # --> 34

