




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()
lowercase_		=							logging.get_logger(__name__)







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Dict:






					"""simple docstring"""






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

					return orig_key







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->str:






					"""simple docstring"""






					for i in range(6	):
										# read in weights + bias of input projection layer of self-attention
										__magic_name__ :							str		       =							state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
										__magic_name__ :							List[str]		       =							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
										__magic_name__ :							Optional[Any]		       =							in_proj_weight[:256, :]
										__magic_name__ :							int		       =							in_proj_bias[:256]
										__magic_name__ :							str		       =							in_proj_weight[256:512, :]
										__magic_name__ :							Optional[Any]		       =							in_proj_bias[256:512]
										__magic_name__ :							Tuple		       =							in_proj_weight[-256:, :]
										__magic_name__ :							Any		       =							in_proj_bias[-256:]







def 			lowerCAmelCase  (     )  ->Tuple:






					"""simple docstring"""






					__magic_name__ :							Optional[Any]		       =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__ :							Tuple		       =							Image.open(requests.get(UpperCAmelCase,     stream=UpperCAmelCase	).raw	)

					return im







@torch.no_grad()
def 			lowerCAmelCase  (     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase,     )  ->List[str]:






					"""simple docstring"""






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

					# load image processor
					__magic_name__ :							Union[str, Any]		       =							DeformableDetrImageProcessor(format='''coco_detection'''	)

					# prepare image
					__magic_name__ :							Optional[int]		       =							prepare_img()
					__magic_name__ :							Dict		       =							image_processor(images=UpperCAmelCase,     return_tensors='''pt'''	)
					__magic_name__ :							str		       =							encoding['''pixel_values''']

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

					# load original state dict
					__magic_name__ :							int		       =							torch.load(UpperCAmelCase,     map_location='''cpu'''	)['''model''']
					# rename keys
					for key in state_dict.copy().keys():
										__magic_name__ :							Tuple		       =							state_dict.pop(UpperCAmelCase	)
										__magic_name__ :							List[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
					__magic_name__ :							str		       =							'''model.'''
					for key in state_dict.copy().keys():
										if not key.startswith('''class_embed'''	) and not key.startswith('''bbox_embed'''	):
															__magic_name__ :							Union[str, Any]		       =							state_dict.pop(UpperCAmelCase	)
															__magic_name__ :							List[str]		       =							val
    # finally, create HuggingFace model and load state dict
					__magic_name__ :							List[Any]		       =							DeformableDetrForObjectDetection(UpperCAmelCase	)
					model.load_state_dict(UpperCAmelCase	)
					model.eval()

					__magic_name__ :							List[str]		       =							'''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(UpperCAmelCase	)
					# verify our conversion
					__magic_name__ :							Tuple		       =							model(pixel_values.to(UpperCAmelCase	)	)

					__magic_name__ :							Dict		       =							torch.tensor(
					    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]	)
					__magic_name__ :							List[str]		       =							torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]	)

					if single_scale:
										__magic_name__ :							Dict		       =							torch.tensor(
										    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]	)
										__magic_name__ :							Dict		       =							torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]	)

					if single_scale and dilation:
										__magic_name__ :							Optional[int]		       =							torch.tensor(
										    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]	)
										__magic_name__ :							Any		       =							torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]	)

					if with_box_refine:
										__magic_name__ :							int		       =							torch.tensor(
										    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]	)
										__magic_name__ :							str		       =							torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]	)

					if with_box_refine and two_stage:
										__magic_name__ :							Optional[Any]		       =							torch.tensor(
										    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]	)
										__magic_name__ :							int		       =							torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]	)

					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:
										__magic_name__ :							Dict		       =							'''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__":
				lowercase_		=							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.'''
				)
				lowercase_		=							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,
				)









from ..utils import requires_backends


lowercase_		=							None





class 		A__    :






	def __init__(     self    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       Optional[Any]:




						"""simple docstring"""






						requires_backends(self    ,					['''detectron2''']						)






	@classmethod
	def 			lowercase				(     cls    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						requires_backends(cls    ,					['''detectron2''']						)



