






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


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

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCamelCase						:    Tuple		=						[]
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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Tuple						,   __snake_case     :    Dict       ):





  '''simple docstring'''





  lowercase				       =			state_dict.pop(__snake_case       )
  lowercase				       =			val
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowercase				       =			key.replace('backbone.0.body'						,   'backbone.conv_encoder.model'       )
      lowercase				       =			value
    else:
      lowercase				       =			value

  return new_state_dict
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Optional[int]=False       ):





  '''simple docstring'''





  lowercase				       =			''
  if is_panoptic:
    lowercase				       =			'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)
    lowercase				       =			state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
    lowercase				       =			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
    lowercase				       =			in_proj_weight[:2_56, :]
    lowercase				       =			in_proj_bias[:2_56]
    lowercase				       =			in_proj_weight[2_56:5_12, :]
    lowercase				       =			in_proj_bias[2_56:5_12]
    lowercase				       =			in_proj_weight[-2_56:, :]
    lowercase				       =			in_proj_bias[-2_56:]
  # 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
    lowercase				       =			state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'       )
    lowercase				       =			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
    lowercase				       =			in_proj_weight[:2_56, :]
    lowercase				       =			in_proj_bias[:2_56]
    lowercase				       =			in_proj_weight[2_56:5_12, :]
    lowercase				       =			in_proj_bias[2_56:5_12]
    lowercase				       =			in_proj_weight[-2_56:, :]
    lowercase				       =			in_proj_bias[-2_56:]
    # read in weights + bias of input projection layer of cross-attention
    lowercase				       =			state_dict.pop(
        f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'       )
    lowercase				       =			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
    lowercase				       =			in_proj_weight_cross_attn[:2_56, :]
    lowercase				       =			in_proj_bias_cross_attn[:2_56]
    lowercase				       =			in_proj_weight_cross_attn[2_56:5_12, :]
    lowercase				       =			in_proj_bias_cross_attn[2_56:5_12]
    lowercase				       =			in_proj_weight_cross_attn[-2_56:, :]
    lowercase				       =			in_proj_bias_cross_attn[-2_56:]
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase				       =			Image.open(requests.get(__snake_case						,   stream=__snake_case       ).raw       )

  return im
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowercase				       =			'resnet101'
  if "dc5" in model_name:
    lowercase				       =			True
  lowercase				       =			'panoptic' in model_name
  if is_panoptic:
    lowercase				       =			2_50
  else:
    lowercase				       =			91
    lowercase				       =			'huggingface/label-files'
    lowercase				       =			'coco-detection-id2label.json'
    lowercase				       =			json.load(open(hf_hub_download(__snake_case						,   __snake_case						,   repo_type='dataset'       )						,   'r'       )       )
    lowercase				       =			{int(__snake_case       ): v for k, v in idalabel.items()}
    lowercase				       =			idalabel
    lowercase				       =			{v: k for k, v in idalabel.items()}

  # load image processor
  lowercase				       =			'coco_panoptic' if is_panoptic else 'coco_detection'
  lowercase				       =			DetrImageProcessor(format=__snake_case       )

  # prepare image
  lowercase				       =			prepare_img()
  lowercase				       =			image_processor(images=__snake_case						,   return_tensors='pt'       )
  lowercase				       =			encoding['pixel_values']

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

  # load original model from torch hub
  lowercase				       =			torch.hub.load('facebookresearch/detr'						,   __snake_case						,   pretrained=__snake_case       ).eval()
  lowercase				       =			detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowercase				       =			'detr.' + src
    rename_key(__snake_case						,   __snake_case						,   __snake_case       )
  lowercase				       =			rename_backbone_keys(__snake_case       )
  # query, key and value matrices need special treatment
  read_in_q_k_v(__snake_case						,   is_panoptic=__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
  lowercase				       =			'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'       )
      ):
        lowercase				       =			state_dict.pop(__snake_case       )
        lowercase				       =			val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowercase				       =			state_dict.pop(__snake_case       )
        lowercase				       =			val
      elif key.startswith('bbox_attention'       ) or key.startswith('mask_head'       ):
        continue
      else:
        lowercase				       =			state_dict.pop(__snake_case       )
        lowercase				       =			val
    else:
      if not key.startswith('class_labels_classifier'       ) and not key.startswith('bbox_predictor'       ):
        lowercase				       =			state_dict.pop(__snake_case       )
        lowercase				       =			val
    # finally, create HuggingFace model and load state dict
  lowercase				       =			DetrForSegmentation(__snake_case       ) if is_panoptic else DetrForObjectDetection(__snake_case       )
  model.load_state_dict(__snake_case       )
  model.eval()
  # verify our conversion
  lowercase				       =			detr(__snake_case       )
  lowercase				       =			model(__snake_case       )
  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(__snake_case       ).mkdir(exist_ok=__snake_case       )
  model.save_pretrained(__snake_case       )
  image_processor.save_pretrained(__snake_case       )


if __name__ == "__main__":
    _UpperCamelCase						:    str		=						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.'
    )
    _UpperCamelCase						:    Any		=						parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  sorted(lowerCAmelCase_						)
      _snake_case		:						Any         =  len(lowerCAmelCase_						)
      _snake_case		:						List[str]         =  length >> 1
      return (
          (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
          if length % 2 == 0
          else sorted_list[mid_index]
      )




def  _a     (				):





      """simple docstring"""



      import doctest

      doctest.testmod()


if __name__ == "__main__":
   main()


