






"""simple docstring"""
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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    state_dict.pop(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		str       		=    val





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Any       		=    OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    key.replace('backbone.0.body',						'backbone.conv_encoder.model'       )
												_SCREAMING_SNAKE_CASE   :		str       		=    value
								else:
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    value

				return new_state_dict





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase=False       ):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ''
				if is_panoptic:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    '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)
								_SCREAMING_SNAKE_CASE   :		int       		=    state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
								_SCREAMING_SNAKE_CASE   :		List[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
								_SCREAMING_SNAKE_CASE   :		Dict       		=    in_proj_weight[:256, :]
								_SCREAMING_SNAKE_CASE   :		Dict       		=    in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    in_proj_bias[-256:]





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

				return im





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

				# load image processor
				_SCREAMING_SNAKE_CASE   :		str       		=    'coco_panoptic' if is_panoptic else 'coco_detection'
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ConditionalDetrImageProcessor(format=lowerCamelCase       )

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

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

				# load original model from torch hub
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.hub.load('DeppMeng/ConditionalDETR',						lowerCamelCase,						pretrained=lowerCamelCase       ).eval()
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    conditional_detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												_SCREAMING_SNAKE_CASE   :		Any       		=    'conditional_detr.' + src
								rename_key(lowerCamelCase,						lowerCamelCase,						lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    rename_backbone_keys(lowerCamelCase       )
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase,						is_panoptic=lowerCamelCase       )
				# 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   :		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'       )
												):
																_SCREAMING_SNAKE_CASE   :		Tuple       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    val
												elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
																continue
												else:
																_SCREAMING_SNAKE_CASE   :		str       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		int       		=    val
								else:
												if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    state_dict.pop(lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    val
    # finally, create HuggingFace model and load state dict
				_SCREAMING_SNAKE_CASE   :		int       		=    ConditionalDetrForSegmentation(lowerCamelCase       ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				model.eval()
				model.push_to_hub(repo_id=lowerCamelCase,						organization='DepuMeng',						commit_message='Add model'       )
				# verify our conversion
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    conditional_detr(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCamelCase       )
				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(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
				model.save_pretrained(lowerCamelCase       )
				image_processor.save_pretrained(lowerCamelCase       )


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)









"""simple docstring"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    size
										_SCREAMING_SNAKE_CASE   :		str       		=    [0] * size
										_SCREAMING_SNAKE_CASE   :		Any       		=    [0] * size







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return index | (index + 1)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return (index & (index + 1)) - 1







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		str       		=    value
										while index < self.size:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.get_prev(lowerCAmelCase_							) + 1
														if current_left_border == index:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    value
														else:
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		str       		=    self.get_next(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										right -= 1  # Because of right is exclusive
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0
										while left <= right:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.get_prev(lowerCAmelCase_							)
														if left <= current_left:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    max(lowerCAmelCase_   ,				self.tree[right]							)
																		_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current_left
														else:
																		_SCREAMING_SNAKE_CASE   :		List[str]       		=    max(lowerCAmelCase_   ,				self.arr[right]							)
																		right -= 1
										return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()

