





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase_        =							[]
for i in range(6):
	# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
	# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
	    )
	)
	rename_keys.append(
	    (
	        F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
	        F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
	    )
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

	# q, k, v projections in self/cross-attention in decoder for conditional DETR
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
	)

	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
	)
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
	)
	# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
	)
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
	rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
	rename_keys.append(
	    (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
	)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       Any           =    state_dict.pop(_UpperCamelCase					)
				snake_case_		:       Any           =    val






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       Dict           =    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												snake_case_		:       Optional[int]           =    key.replace('''backbone.0.body'''					,							'''backbone.conv_encoder.model'''					)
												snake_case_		:       Dict           =    value
								else:
												snake_case_		:       Dict           =    value

				return new_state_dict






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=False					)						->		List[Any]:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    ''''''
				if is_panoptic:
								snake_case_		:       List[str]           =    '''conditional_detr.'''

				# first: transformer encoder
				for i in range(6					):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								snake_case_		:       Any           =    state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
								snake_case_		:       Optional[Any]           =    state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''					)
								# next, add query, keys and values (in that order) to the state dict
								snake_case_		:       Any           =    in_proj_weight[:256, :]
								snake_case_		:       Union[str, Any]           =    in_proj_bias[:256]
								snake_case_		:       List[str]           =    in_proj_weight[256:512, :]
								snake_case_		:       Optional[int]           =    in_proj_bias[256:512]
								snake_case_		:       Dict           =    in_proj_weight[-256:, :]
								snake_case_		:       Tuple           =    in_proj_bias[-256:]






def 	lowerCamelCase_		(							)						->		Optional[Any]:






				"""simple docstring"""


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

				return im






@torch.no_grad()
def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				snake_case_		:       str           =    ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								snake_case_		:       Optional[int]           =    '''resnet101'''
				if "dc5" in model_name:
								snake_case_		:       Dict           =    True
				snake_case_		:       Tuple           =    '''panoptic''' in model_name
				if is_panoptic:
								snake_case_		:       Tuple           =    250
				else:
								snake_case_		:       List[Any]           =    91
								snake_case_		:       List[str]           =    '''huggingface/label-files'''
								snake_case_		:       Union[str, Any]           =    '''coco-detection-id2label.json'''
								snake_case_		:       Any           =    json.load(open(hf_hub_download(_UpperCamelCase					,							_UpperCamelCase					,							repo_type='''dataset'''					)					,							'''r'''					)					)
								snake_case_		:       Optional[Any]           =    {int(_UpperCamelCase					): v for k, v in idalabel.items()}
								snake_case_		:       List[str]           =    idalabel
								snake_case_		:       Optional[int]           =    {v: k for k, v in idalabel.items()}

				# load image processor
				snake_case_		:       List[str]           =    '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				snake_case_		:       Any           =    ConditionalDetrImageProcessor(format=_UpperCamelCase					)

				# prepare image
				snake_case_		:       Tuple           =    prepare_img()
				snake_case_		:       Union[str, Any]           =    image_processor(images=_UpperCamelCase					,							return_tensors='''pt'''					)
				snake_case_		:       Optional[Any]           =    encoding['''pixel_values''']

				logger.info(f'''Converting model {model_name}...'''					)

				# load original model from torch hub
				snake_case_		:       Union[str, Any]           =    torch.hub.load('''DeppMeng/ConditionalDETR'''					,							_UpperCamelCase					,							pretrained=_UpperCamelCase					).eval()
				snake_case_		:       Union[str, Any]           =    conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												snake_case_		:       Optional[Any]           =    '''conditional_detr.''' + src
								rename_key(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)
				snake_case_		:       Union[str, Any]           =    rename_backbone_keys(_UpperCamelCase					)
				# query, key and value matrices need special treatment
				read_in_q_k_v(_UpperCamelCase					,							is_panoptic=_UpperCamelCase					)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				snake_case_		:       Union[str, Any]           =    '''conditional_detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''conditional_detr'''					)
												    and not key.startswith('''class_labels_classifier'''					)
												    and not key.startswith('''bbox_predictor'''					)
												):
																snake_case_		:       Optional[int]           =    state_dict.pop(_UpperCamelCase					)
																snake_case_		:       List[Any]           =    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																snake_case_		:       str           =    state_dict.pop(_UpperCamelCase					)
																snake_case_		:       List[Any]           =    val
												elif key.startswith('''bbox_attention'''					) or key.startswith('''mask_head'''					):
																continue
												else:
																snake_case_		:       str           =    state_dict.pop(_UpperCamelCase					)
																snake_case_		:       Any           =    val
								else:
												if not key.startswith('''class_labels_classifier'''					) and not key.startswith('''bbox_predictor'''					):
																snake_case_		:       Dict           =    state_dict.pop(_UpperCamelCase					)
																snake_case_		:       Tuple           =    val
    # finally, create HuggingFace model and load state dict
				snake_case_		:       Optional[Any]           =    ConditionalDetrForSegmentation(_UpperCamelCase					) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCamelCase					)
				model.load_state_dict(_UpperCamelCase					)
				model.eval()
				model.push_to_hub(repo_id=_UpperCamelCase					,							organization='''DepuMeng'''					,							commit_message='''Add model'''					)
				# verify our conversion
				snake_case_		:       Optional[int]           =    conditional_detr(_UpperCamelCase					)
				snake_case_		:       Union[str, Any]           =    model(_UpperCamelCase					)
				assert torch.allclose(outputs.logits					,							original_outputs['''pred_logits''']					,							atol=1E-4					)
				assert torch.allclose(outputs.pred_boxes					,							original_outputs['''pred_boxes''']					,							atol=1E-4					)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks					,							original_outputs['''pred_masks''']					,							atol=1E-4					)

				# 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					)


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

	parser.add_argument(
	    '''--model_name''',
	    default='''conditional_detr_resnet50''',
	    type=str,
	    help='''Name of the CONDITIONAL_DETR 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.'''
	)
	lowerCAmelCase_        =							parser.parse_args()
	convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



def 			_A	(      SCREAMING_SNAKE_CASE      ):  # noqa: E741
	UpperCAmelCase__:	int									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[int]									= [0] * n
	UpperCAmelCase__:	List[str]									= [False] * n
	UpperCAmelCase__:	List[str]									= [False] * n

	def dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		if parent == root:
			out_edge_count += 1
		UpperCAmelCase__:	List[str]									= True
		UpperCAmelCase__:	List[Any]									= at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				UpperCAmelCase__:	str									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				UpperCAmelCase__:	Optional[Any]									= min(low[at]	,low[to]      )

				# AP found via bridge
				if at < low[to]:
					UpperCAmelCase__:	List[Any]									= True
				# AP found via cycle
				if at == low[to]:
					UpperCAmelCase__:	str									= True
			else:
				UpperCAmelCase__:	Union[str, Any]									= min(low[at]	,SCREAMING_SNAKE_CASE      )
		return out_edge_count

	for i in range(SCREAMING_SNAKE_CASE      ):
		if not visited[i]:
			UpperCAmelCase__:	Optional[Any]									= 0
			UpperCAmelCase__:	Union[str, Any]									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,-1	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Tuple									= out_edge_count > 1

	for x in range(len(SCREAMING_SNAKE_CASE      )      ):
		if is_art[x] is True:
			print(SCREAMING_SNAKE_CASE      )


# Adjacency list of graph
_lowerCAmelCase  :      Any			 ={
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(data)