
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	  =					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_A	  =					[]
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 				lowerCamelCase__				(						a__  :  Any	,      a__  :  int	,      a__  :  Any     )		->	Any:
							UpperCamelCase_							       =       state_dict.pop(a__     )
							UpperCamelCase_							       =       val




def 				lowerCamelCase__				(						a__  :  Any     )		->	Union[str, Any]:
							UpperCamelCase_							       =       OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCamelCase_							       =       key.replace("""backbone.0.body"""	,      """backbone.conv_encoder.model"""     )
																					UpperCamelCase_							       =       value
														else:
																					UpperCamelCase_							       =       value

							return new_state_dict




def 				lowerCamelCase__				(						a__  :  str	,      a__  :  str=False     )		->	Tuple:
							UpperCamelCase_							       =       """"""
							if is_panoptic:
														UpperCamelCase_							       =       """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_							       =       state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''     )
														UpperCamelCase_							       =       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_							       =       in_proj_weight[:256, :]
														UpperCamelCase_							       =       in_proj_bias[:256]
														UpperCamelCase_							       =       in_proj_weight[256:512, :]
														UpperCamelCase_							       =       in_proj_bias[256:512]
														UpperCamelCase_							       =       in_proj_weight[-256:, :]
														UpperCamelCase_							       =       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_							       =       state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''     )
														UpperCamelCase_							       =       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_							       =       in_proj_weight[:256, :]
														UpperCamelCase_							       =       in_proj_bias[:256]
														UpperCamelCase_							       =       in_proj_weight[256:512, :]
														UpperCamelCase_							       =       in_proj_bias[256:512]
														UpperCamelCase_							       =       in_proj_weight[-256:, :]
														UpperCamelCase_							       =       in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														UpperCamelCase_							       =       state_dict.pop(
														    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''     )
														UpperCamelCase_							       =       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_							       =       in_proj_weight_cross_attn[:256, :]
														UpperCamelCase_							       =       in_proj_bias_cross_attn[:256]
														UpperCamelCase_							       =       in_proj_weight_cross_attn[256:512, :]
														UpperCamelCase_							       =       in_proj_bias_cross_attn[256:512]
														UpperCamelCase_							       =       in_proj_weight_cross_attn[-256:, :]
														UpperCamelCase_							       =       in_proj_bias_cross_attn[-256:]




def 				lowerCamelCase__				(						)		->	List[str]:
							UpperCamelCase_							       =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCamelCase_							       =       Image.open(requests.get(a__	,      stream=a__     ).raw     )

							return im




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

							# load image processor
							UpperCamelCase_							       =       """coco_panoptic""" if is_panoptic else """coco_detection"""
							UpperCamelCase_							       =       DetrImageProcessor(format=a__     )

							# prepare image
							UpperCamelCase_							       =       prepare_img()
							UpperCamelCase_							       =       image_processor(images=a__	,      return_tensors="""pt"""     )
							UpperCamelCase_							       =       encoding["""pixel_values"""]

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

							# load original model from torch hub
							UpperCamelCase_							       =       torch.hub.load("""facebookresearch/detr"""	,      a__	,      pretrained=a__     ).eval()
							UpperCamelCase_							       =       detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					UpperCamelCase_							       =       """detr.""" + src
														rename_key(a__	,      a__	,      a__     )
							UpperCamelCase_							       =       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_							       =       """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_							       =       state_dict.pop(a__     )
																												UpperCamelCase_							       =       val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCamelCase_							       =       state_dict.pop(a__     )
																												UpperCamelCase_							       =       val
																					elif key.startswith("""bbox_attention"""     ) or key.startswith("""mask_head"""     ):
																												continue
																					else:
																												UpperCamelCase_							       =       state_dict.pop(a__     )
																												UpperCamelCase_							       =       val
														else:
																					if not key.startswith("""class_labels_classifier"""     ) and not key.startswith("""bbox_predictor"""     ):
																												UpperCamelCase_							       =       state_dict.pop(a__     )
																												UpperCamelCase_							       =       val
    # finally, create HuggingFace model and load state dict
							UpperCamelCase_							       =       DetrForSegmentation(a__     ) if is_panoptic else DetrForObjectDetection(a__     )
							model.load_state_dict(a__     )
							model.eval()
							# verify our conversion
							UpperCamelCase_							       =       detr(a__     )
							UpperCamelCase_							       =       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	  =					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	  =					parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int     )		->	Optional[Any]:
       if not root or root not in binary_tree_mirror_dictionary:
              return
       UpperCamelCase_       ,				UpperCamelCase_							       =       binary_tree_mirror_dictionary[root][:2]
       UpperCamelCase_							       =       [right_child, left_child]
       binary_tree_mirror_dict(a__	,      a__     )
       binary_tree_mirror_dict(a__	,      a__     )




def 				lowerCamelCase__				(						a__  :  dict	,      a__  :  int = 1     )		->	dict:
       if not binary_tree:
              raise ValueError("""binary tree cannot be empty"""     )
       if root not in binary_tree:
              UpperCamelCase_							       =       f'''root {root} is not present in the binary_tree'''
              raise ValueError(a__     )
       UpperCamelCase_							       =       dict(a__     )
       binary_tree_mirror_dict(a__	,      a__     )
       return binary_tree_mirror_dictionary


if __name__ == "__main__":
      _A	  =					{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
      print(F'''Binary tree: {binary_tree}''')
      _A	  =					binary_tree_mirror(binary_tree, 5)
      print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')



