
'''simple docstring'''






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

		return orig_key







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		# transformer decoder self-attention layers
		for i in range(6  ):
				# read in weights + bias of input projection layer of self-attention
				lowerCAmelCase_   :       Dict    							=state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'  )
				lowerCAmelCase_   :       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
				lowerCAmelCase_   :       Any    							=in_proj_weight[:256, :]
				lowerCAmelCase_   :       Optional[Any]    							=in_proj_bias[:256]
				lowerCAmelCase_   :       Optional[int]    							=in_proj_weight[256:512, :]
				lowerCAmelCase_   :       List[Any]    							=in_proj_bias[256:512]
				lowerCAmelCase_   :       Any    							=in_proj_weight[-256:, :]
				lowerCAmelCase_   :       Optional[Any]    							=in_proj_bias[-256:]







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

		return im







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

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

		# prepare image
		lowerCAmelCase_   :       Optional[int]    							=prepare_img()
		lowerCAmelCase_   :       Union[str, Any]    							=image_processor(images=_SCREAMING_SNAKE_CASE  ,					return_tensors='''pt'''  )
		lowerCAmelCase_   :       List[str]    							=encoding['''pixel_values''']

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

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

		lowerCAmelCase_   :       Dict    							='''cuda''' if torch.cuda.is_available() else '''cpu'''
		model.to(_SCREAMING_SNAKE_CASE  )
		# verify our conversion
		lowerCAmelCase_   :       Any    							=model(pixel_values.to(_SCREAMING_SNAKE_CASE  )  )

		lowerCAmelCase_   :       Tuple    							=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]]  )
		lowerCAmelCase_   :       Any    							=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:
				lowerCAmelCase_   :       int    							=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]]  )
				lowerCAmelCase_   :       int    							=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:
				lowerCAmelCase_   :       Union[str, Any]    							=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]]  )
				lowerCAmelCase_   :       Tuple    							=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:
				lowerCAmelCase_   :       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]]  )
				lowerCAmelCase_   :       Optional[Any]    							=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:
				lowerCAmelCase_   :       List[str]    							=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]]  )
				lowerCAmelCase_   :       Dict    							=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(_SCREAMING_SNAKE_CASE  )  ,					atol=1e-4  )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,					expected_boxes.to(_SCREAMING_SNAKE_CASE  )  ,					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(_SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )
		image_processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

		# Push to hub
		if push_to_hub:
				lowerCAmelCase_   :       Optional[int]    							='''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=_SCREAMING_SNAKE_CASE  ,					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,
							)








'''simple docstring'''






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if len(_SCREAMING_SNAKE_CASE  ) == 0:
				raise ValueError('''find_min() arg is an empty sequence'''  )
		if (
		    left >= len(_SCREAMING_SNAKE_CASE  )
		    or left < -len(_SCREAMING_SNAKE_CASE  )
		    or right >= len(_SCREAMING_SNAKE_CASE  )
		    or right < -len(_SCREAMING_SNAKE_CASE  )
		):
				raise IndexError('''list index out of range'''  )
		if left == right:
				return nums[left]
		lowerCAmelCase_   :       Dict    							=(left + right) >> 1  # the middle
		lowerCAmelCase_   :       Optional[int]    							=find_min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[left, mid]
		lowerCAmelCase_   :       Any    							=find_min(_SCREAMING_SNAKE_CASE  ,					mid + 1  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[mid + 1, right]

		return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






