






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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_			:Optional[Any]	    =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A_			:int	    =		[]
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}.multihead_attn.out_proj.weight",
							        f"decoder.layers.{i}.encoder_attn.out_proj.weight",
							    )
							)
							rename_keys.append(
							    (
							        f"transformer.decoder.layers.{i}.multihead_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"))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
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'''),
    ]
)






def 				A	(			a_				,a_				,a_   )	->		Dict:
					__UpperCamelCase :   List[Any]									=state_dict.pop(a_   )
					__UpperCamelCase :   Optional[int]									=val






def 				A	(			a_   )	->		Union[str, Any]:
					__UpperCamelCase :   List[Any]									=OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															__UpperCamelCase :   Tuple									=key.replace('backbone.0.body'				,'backbone.conv_encoder.model'   )
															__UpperCamelCase :   Union[str, Any]									=value
										else:
															__UpperCamelCase :   Tuple									=value

					return new_state_dict






def 				A	(			a_				,a_=False   )	->		Dict:
					__UpperCamelCase :   Optional[int]									=''
					if is_panoptic:
										__UpperCamelCase :   Any									='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)
										__UpperCamelCase :   Tuple									=state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
										__UpperCamelCase :   str									=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
										__UpperCamelCase :   str									=in_proj_weight[:256, :]
										__UpperCamelCase :   Tuple									=in_proj_bias[:256]
										__UpperCamelCase :   Union[str, Any]									=in_proj_weight[256:512, :]
										__UpperCamelCase :   Any									=in_proj_bias[256:512]
										__UpperCamelCase :   Union[str, Any]									=in_proj_weight[-256:, :]
										__UpperCamelCase :   Union[str, Any]									=in_proj_bias[-256:]
					# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
					for i in range(6   ):
										# read in weights + bias of input projection layer of self-attention
										__UpperCamelCase :   int									=state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'   )
										__UpperCamelCase :   List[Any]									=state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'   )
										# next, add query, keys and values (in that order) to the state dict
										__UpperCamelCase :   Tuple									=in_proj_weight[:256, :]
										__UpperCamelCase :   Any									=in_proj_bias[:256]
										__UpperCamelCase :   Union[str, Any]									=in_proj_weight[256:512, :]
										__UpperCamelCase :   Optional[Any]									=in_proj_bias[256:512]
										__UpperCamelCase :   List[str]									=in_proj_weight[-256:, :]
										__UpperCamelCase :   Optional[int]									=in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										__UpperCamelCase :   Optional[Any]									=state_dict.pop(
										    F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'   )
										__UpperCamelCase :   Tuple									=state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'   )
										# next, add query, keys and values (in that order) of cross-attention to the state dict
										__UpperCamelCase :   int									=in_proj_weight_cross_attn[:256, :]
										__UpperCamelCase :   Any									=in_proj_bias_cross_attn[:256]
										__UpperCamelCase :   int									=in_proj_weight_cross_attn[256:512, :]
										__UpperCamelCase :   List[str]									=in_proj_bias_cross_attn[256:512]
										__UpperCamelCase :   str									=in_proj_weight_cross_attn[-256:, :]
										__UpperCamelCase :   Optional[Any]									=in_proj_bias_cross_attn[-256:]






def 				A	(			)	->		int:
					__UpperCamelCase :   List[str]									='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase :   Dict									=Image.open(requests.get(a_				,stream=a_   ).raw   )

					return im






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

					# load image processor
					__UpperCamelCase :   Any									='coco_panoptic' if is_panoptic else 'coco_detection'
					__UpperCamelCase :   Any									=DetrImageProcessor(format=a_   )

					# prepare image
					__UpperCamelCase :   Optional[int]									=prepare_img()
					__UpperCamelCase :   Optional[int]									=image_processor(images=a_				,return_tensors='pt'   )
					__UpperCamelCase :   Any									=encoding['pixel_values']

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

					# load original model from torch hub
					__UpperCamelCase :   str									=torch.hub.load('facebookresearch/detr'				,a_				,pretrained=a_   ).eval()
					__UpperCamelCase :   List[str]									=detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															__UpperCamelCase :   List[str]									='detr.' + src
										rename_key(a_				,a_				,a_   )
					__UpperCamelCase :   Dict									=rename_backbone_keys(a_   )
					# query, key and value matrices need special treatment
					read_in_q_k_v(a_				,is_panoptic=a_   )
					# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
					__UpperCamelCase :   List[str]									='detr.model.' if is_panoptic else 'model.'
					for key in state_dict.copy().keys():
										if is_panoptic:
															if (
															    key.startswith('detr'   )
															    and not key.startswith('class_labels_classifier'   )
															    and not key.startswith('bbox_predictor'   )
															):
																				__UpperCamelCase :   Tuple									=state_dict.pop(a_   )
																				__UpperCamelCase :   Union[str, Any]									=val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				__UpperCamelCase :   Any									=state_dict.pop(a_   )
																				__UpperCamelCase :   Tuple									=val
															elif key.startswith('bbox_attention'   ) or key.startswith('mask_head'   ):
																				continue
															else:
																				__UpperCamelCase :   Tuple									=state_dict.pop(a_   )
																				__UpperCamelCase :   Optional[int]									=val
										else:
															if not key.startswith('class_labels_classifier'   ) and not key.startswith('bbox_predictor'   ):
																				__UpperCamelCase :   int									=state_dict.pop(a_   )
																				__UpperCamelCase :   Tuple									=val
    # finally, create HuggingFace model and load state dict
					__UpperCamelCase :   int									=DetrForSegmentation(a_   ) if is_panoptic else DetrForObjectDetection(a_   )
					model.load_state_dict(a_   )
					model.eval()
					# verify our conversion
					__UpperCamelCase :   Optional[int]									=detr(a_   )
					__UpperCamelCase :   Tuple									=model(a_   )
					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(a_   ).mkdir(exist_ok=a_   )
					model.save_pretrained(a_   )
					image_processor.save_pretrained(a_   )


if __name__ == "__main__":
							A_			:Tuple	    =		argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''', default='''detr_resnet50''', type=str, help='''Name of the 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.'''
							)
							A_			:Any	    =		parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



'''simple docstring'''





class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:int      )					->      None:







       '''simple docstring'''



       __A													=      data
       __A													=      None
       __A													=      None





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      BinaryTreeNode | None:







      """simple docstring"""
      if node is None:
            __A													=      BinaryTreeNode(UpperCAmelCase						)
            return node

      # binary search tree is not empty,
      # so we will insert it into the tree
      # if new_value is less than value of data in node,
      #  add it to left subtree and proceed recursively
      if new_value < node.data:
            __A													=      insert(node.left_child   ,	UpperCAmelCase						)
      else:
            # if new_value is greater than value of data in node,
            #  add it to right subtree and proceed recursively
            __A													=      insert(node.right_child   ,	UpperCAmelCase						)
      return node





def 							snake_case				(		UpperCAmelCase						)->      list[int]:  # if node is None,return







      """simple docstring"""
      if node:
            __A													=      inorder(node.left_child						)
            __A													=      [*inorder_array, node.data]
            __A													=      inorder_array + inorder(node.right_child						)
      else:
            __A													=      []
      return inorder_array





def 							snake_case				(		)->      BinaryTreeNode | None:







      """simple docstring"""
      __A													=      insert(UpperCAmelCase   ,	1_5						)
      insert(UpperCAmelCase   ,	1_0						)
      insert(UpperCAmelCase   ,	2_5						)
      insert(UpperCAmelCase   ,	6						)
      insert(UpperCAmelCase   ,	1_4						)
      insert(UpperCAmelCase   ,	2_0						)
      insert(UpperCAmelCase   ,	6_0						)
      return root





def 							snake_case				(		)->      None:







      """simple docstring"""
      # main function
      __A													=      make_tree()
      print('Printing values of binary search tree in Inorder Traversal.'						)
      inorder(UpperCAmelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






