
import argparse
import json
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, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       =logging.get_logger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ):
 # initialize config
 if "resnet-50" in model_name:
  __a :  int						=  ResNetConfig.from_pretrained('''microsoft/resnet-50'''  )
 elif "resnet-101" in model_name:
  __a :  str						=  ResNetConfig.from_pretrained('''microsoft/resnet-101'''  )
 else:
  raise ValueError('''Model name should include either resnet50 or resnet101'''  )

 __a :  Union[str, Any]						=  DetrConfig(use_timm_backbone=lowerCAmelCase__  ,   backbone_config=lowerCAmelCase__  )

 # set label attributes
 __a :  Any						=  '''panoptic''' in model_name
 if is_panoptic:
  __a :  Tuple						=  2_5_0
 else:
  __a :  Optional[Any]						=  9_1
  __a :  Any						=  '''huggingface/label-files'''
  __a :  Tuple						=  '''coco-detection-id2label.json'''
  __a :  Optional[int]						=  json.load(open(hf_hub_download(lowerCAmelCase__  ,   lowerCAmelCase__  ,   repo_type='''dataset'''  )  ,   '''r'''  )  )
  __a :  str						=  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
  __a :  List[str]						=  idalabel
  __a :  Optional[Any]						=  {v: k for k, v in idalabel.items()}

 return config, is_panoptic




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ):
 # here we list all keys to be renamed (original name on the left, our name on the right)
 __a :  str						=  []

 # stem
 # fmt: off
 rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')  )
 rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')  )
 rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')  )
 rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')  )
 rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')  )
 # stages
 for stage_idx in range(len(config.backbone_config.depths  )  ):
  for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
   # shortcut
   if layer_idx == 0:
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
        )  )
   # 3 convs
   for i in range(3  ):
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
        )  )
    rename_keys.append(
        (
            f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
            f"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
        )  )
    # fmt: on

 for i in range(config.encoder_layers  ):
  # 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'''),
     ]  )

 return rename_keys




def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				List[Any]  ):
 __a :  str						=  state_dict.pop(lowerCAmelCase__  )
 __a :  Optional[int]						=  val




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




def    __UpperCamelCase     (				):
 __a :  Optional[int]						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a :  Tuple						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )

 return im




@torch.no_grad()
def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Optional[int]=None  ,   lowerCAmelCase__							:				Optional[Any]=False  ):
 __a				,       __a :  Union[str, Any]						=  get_detr_config(lowerCAmelCase__  )

 # load original model from torch hub
 __a :  Tuple						=  {
     '''detr-resnet-50''': '''detr_resnet50''',
     '''detr-resnet-101''': '''detr_resnet101''',
 }
 logger.info(f"Converting model {model_name}..."  )
 __a :  Tuple						=  torch.hub.load('''facebookresearch/detr'''  ,   model_name_to_original_name[model_name]  ,   pretrained=lowerCAmelCase__  ).eval()
 __a :  int						=  detr.state_dict()
 # rename keys
 for src, dest in create_rename_keys(lowerCAmelCase__  ):
  if is_panoptic:
   __a :  List[Any]						=  '''detr.''' + src
  rename_key(lowerCAmelCase__  ,   lowerCAmelCase__  ,   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
 __a :  int						=  '''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'''  )
   ):
    __a :  Any						=  state_dict.pop(lowerCAmelCase__  )
    __a :  Optional[Any]						=  val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __a :  str						=  state_dict.pop(lowerCAmelCase__  )
    __a :  Optional[int]						=  val
   elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
    continue
   else:
    __a :  List[Any]						=  state_dict.pop(lowerCAmelCase__  )
    __a :  Optional[Any]						=  val
  else:
   if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
    __a :  Dict						=  state_dict.pop(lowerCAmelCase__  )
    __a :  str						=  val

    # finally, create HuggingFace model and load state dict
 __a :  List[Any]						=  DetrForSegmentation(lowerCAmelCase__  ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__  )
 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

 # verify our conversion on an image
 __a :  Tuple						=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
 __a :  Any						=  DetrImageProcessor(format=lowerCAmelCase__  )

 __a :  int						=  processor(images=prepare_img()  ,   return_tensors='''pt'''  )
 __a :  List[Any]						=  encoding['''pixel_values''']

 __a :  Optional[int]						=  detr(lowerCAmelCase__  )
 __a :  Dict						=  model(lowerCAmelCase__  )

 assert torch.allclose(outputs.logits  ,   original_outputs['''pred_logits''']  ,   atol=1e-3  )
 assert torch.allclose(outputs.pred_boxes  ,   original_outputs['''pred_boxes''']  ,   atol=1e-3  )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks  ,   original_outputs['''pred_masks''']  ,   atol=1e-4  )
 print('''Looks ok!'''  )

 if pytorch_dump_folder_path is not None:
  # 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__  )
  processor.save_pretrained(lowerCAmelCase__  )

 if push_to_hub:
  # Upload model and image processor to the hub
  logger.info('''Uploading PyTorch model and image processor to the hub...'''  )
  model.push_to_hub(f"nielsr/{model_name}"  )
  processor.push_to_hub(f"nielsr/{model_name}"  )


if __name__ == "__main__":
      lowercase__       =argparse.ArgumentParser()

      parser.add_argument(
          '--model_name',
          default='detr-resnet-50',
          type=str,
          choices=['detr-resnet-50', 'detr-resnet-101'],
          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.'
      )
      parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
      lowercase__       =parser.parse_args()
      convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ):
 __a :  int						=  int(number**0.5  )
 return number == sq * sq




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ):
 __a :  int						=  x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
 __a :  int						=  x_den * y_den * z_den
 __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
 top //= hcf
 bottom //= hcf
 return top, bottom




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 3_5  ):
 __a :  set						=  set()
 __a :  int
 __a :  Fraction						=  Fraction(0  )
 __a :  tuple[int, int]

 for x_num in range(1  ,   order + 1  ):
  for x_den in range(x_num + 1  ,   order + 1  ):
   for y_num in range(1  ,   order + 1  ):
    for y_den in range(y_num + 1  ,   order + 1  ):
     # n=1
     __a :  Any						=  x_num * y_den + x_den * y_num
     __a :  Dict						=  x_den * y_den
     __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
     z_num //= hcf
     z_den //= hcf
     if 0 < z_num < z_den <= order:
      __a :  str						=  add_three(
          lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
      unique_s.add(lowerCAmelCase__  )

     # n=2
     __a :  Union[str, Any]						=  (
         x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
     )
     __a :  Optional[Any]						=  x_den * x_den * y_den * y_den
     if is_sq(lowerCAmelCase__  ) and is_sq(lowerCAmelCase__  ):
      __a :  Any						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  Optional[Any]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
      z_num //= hcf
      z_den //= hcf
      if 0 < z_num < z_den <= order:
       __a :  str						=  add_three(
           lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
       unique_s.add(lowerCAmelCase__  )

                    # n=-1
     __a :  List[str]						=  x_num * y_num
     __a :  List[str]						=  x_den * y_num + x_num * y_den
     __a :  str						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
     z_num //= hcf
     z_den //= hcf
     if 0 < z_num < z_den <= order:
      __a :  Optional[int]						=  add_three(
          lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
      unique_s.add(lowerCAmelCase__  )

     # n=2
     __a :  List[str]						=  x_num * x_num * y_num * y_num
     __a :  Optional[int]						=  (
         x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
     )
     if is_sq(lowerCAmelCase__  ) and is_sq(lowerCAmelCase__  ):
      __a :  Union[str, Any]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  List[str]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  Optional[int]						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
      z_num //= hcf
      z_den //= hcf
      if 0 < z_num < z_den <= order:
       __a :  int						=  add_three(
           lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
       unique_s.add(lowerCAmelCase__  )

 for num, den in unique_s:
  total += Fraction(lowerCAmelCase__  ,   lowerCAmelCase__  )

 return total.denominator + total.numerator


if __name__ == "__main__":
      print(F"""{solution() = }""")
