

'''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()
snake_case_					  =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case_					  =	[]
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      (				SCREAMING_SNAKE_CASE_					:     Tuple  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     List[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Any					=	state_dict.pop(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	val


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Any   )							->       Dict:
      """simple docstring"""

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

      return new_state_dict


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     Union[str, Any]=False   )							->       Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Dict					=	""
      if is_panoptic:
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	"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_				:      List[str]					=	state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
            SCREAMING_SNAKE_CASE_				:      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
            SCREAMING_SNAKE_CASE_				:      int					=	in_proj_weight[:2_5_6, :]
            SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	in_proj_bias[:2_5_6]
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	in_proj_weight[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	in_proj_bias[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_				:      Tuple					=	in_proj_weight[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_				:      Dict					=	in_proj_bias[-2_5_6:]
      # 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
            SCREAMING_SNAKE_CASE_				:      Dict					=	state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
            SCREAMING_SNAKE_CASE_				:      Union[str, 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
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	in_proj_weight[:2_5_6, :]
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	in_proj_bias[:2_5_6]
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	in_proj_weight[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	in_proj_bias[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	in_proj_weight[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_				:      int					=	in_proj_bias[-2_5_6:]
            # read in weights + bias of input projection layer of cross-attention
            SCREAMING_SNAKE_CASE_				:      Optional[int]					=	state_dict.pop(
                F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"   )
            SCREAMING_SNAKE_CASE_				:      List[str]					=	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
            SCREAMING_SNAKE_CASE_				:      Tuple					=	in_proj_weight_cross_attn[:2_5_6, :]
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	in_proj_bias_cross_attn[:2_5_6]
            SCREAMING_SNAKE_CASE_				:      str					=	in_proj_weight_cross_attn[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_				:      str					=	in_proj_bias_cross_attn[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	in_proj_weight_cross_attn[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	in_proj_bias_cross_attn[-2_5_6:]


def  __lowerCamelCase      (				)							->       Optional[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      int					=	"http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,  stream=SCREAMING_SNAKE_CASE_   ).raw   )

      return im


@torch.no_grad()
def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Tuple  ,  SCREAMING_SNAKE_CASE_					:     List[Any]   )							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DetrConfig()
      # set backbone and dilation attributes
      if "resnet101" in model_name:
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	"resnet101"
      if "dc5" in model_name:
            SCREAMING_SNAKE_CASE_				:      int					=	True
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	"panoptic" in model_name
      if is_panoptic:
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	2_5_0
      else:
            SCREAMING_SNAKE_CASE_				:      Dict					=	9_1
            SCREAMING_SNAKE_CASE_				:      Dict					=	"huggingface/label-files"
            SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	"coco-detection-id2label.json"
            SCREAMING_SNAKE_CASE_				:      List[str]					=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  repo_type="dataset"   )  ,  "r"   )   )
            SCREAMING_SNAKE_CASE_				:      Tuple					=	{int(SCREAMING_SNAKE_CASE_   ): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_				:      Tuple					=	idalabel
            SCREAMING_SNAKE_CASE_				:      List[str]					=	{v: k for k, v in idalabel.items()}

      # load image processor
      SCREAMING_SNAKE_CASE_				:      List[str]					=	"coco_panoptic" if is_panoptic else "coco_detection"
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DetrImageProcessor(format=SCREAMING_SNAKE_CASE_   )

      # prepare image
      SCREAMING_SNAKE_CASE_				:      Tuple					=	prepare_img()
      SCREAMING_SNAKE_CASE_				:      Dict					=	image_processor(images=SCREAMING_SNAKE_CASE_  ,  return_tensors="pt"   )
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	encoding["pixel_values"]

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

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


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




'''simple docstring'''






def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(boundary[1] - boundary[0]) / steps
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	boundary[0]
      SCREAMING_SNAKE_CASE_				:      int					=	boundary[1]
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	make_points(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	0.0
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	2
      for i in x_i:
            y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE_   )
            cnt += 1
      y += (h / 3.0) * f(SCREAMING_SNAKE_CASE_   )
      return y


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Optional[Any]  ,  SCREAMING_SNAKE_CASE_					:     List[Any]  ,  SCREAMING_SNAKE_CASE_					:     Optional[Any]   )							->       List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	a + h
      while x < (b - h):
            yield x
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	x + h


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str   )							->       List[Any]:  # enter your function here
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(x - 0) * (x - 0)
      return y


def  __lowerCamelCase      (				)							->       int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[Any]					=	0.0  # Lower bound of integration
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	1.0  # Upper bound of integration
      SCREAMING_SNAKE_CASE_				:      str					=	10.0  # define number of steps or resolution
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	[a, b]  # define boundary of integration
      SCREAMING_SNAKE_CASE_				:      List[str]					=	method_a(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )
      print(F"y = {y}"   )


if __name__ == "__main__":
      main()

