


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()
UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      if "resnet-50" in model_name:
            snake_case_ =   ResNetConfig.from_pretrained('''microsoft/resnet-50'''      )
      elif "resnet-101" in model_name:
            snake_case_ =   ResNetConfig.from_pretrained('''microsoft/resnet-101'''      )
      else:
            raise ValueError('''Model name should include either resnet50 or resnet101'''      )

      snake_case_ =   DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE      ,      backbone_config=SCREAMING_SNAKE_CASE      )

      # set label attributes
      snake_case_ =   '''panoptic''' in model_name
      if is_panoptic:
            snake_case_ =   250
      else:
            snake_case_ =   91
            snake_case_ =   '''huggingface/label-files'''
            snake_case_ =   '''coco-detection-id2label.json'''
            snake_case_ =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      repo_type='''dataset'''      )      ,      '''r'''      )      )
            snake_case_ =   {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
            snake_case_ =   idalabel
            snake_case_ =   {v: k for k, v in idalabel.items()}

      return config, is_panoptic






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   []

      # 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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   state_dict.pop(SCREAMING_SNAKE_CASE      )
      snake_case_ =   val






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						str:




      """simple docstring"""

      snake_case_ =   ''''''
      if is_panoptic:
            snake_case_ =   '''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)
            snake_case_ =   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''      )
            snake_case_ =   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
            snake_case_ =   in_proj_weight[:256, :]
            snake_case_ =   in_proj_bias[:256]
            snake_case_ =   in_proj_weight[256:512, :]
            snake_case_ =   in_proj_bias[256:512]
            snake_case_ =   in_proj_weight[-256:, :]
            snake_case_ =   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
            snake_case_ =   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
            snake_case_ =   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
            snake_case_ =   in_proj_weight[:256, :]
            snake_case_ =   in_proj_bias[:256]
            snake_case_ =   in_proj_weight[256:512, :]
            snake_case_ =   in_proj_bias[256:512]
            snake_case_ =   in_proj_weight[-256:, :]
            snake_case_ =   in_proj_bias[-256:]
            # read in weights + bias of input projection layer of cross-attention
            snake_case_ =   state_dict.pop(
                f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''      )
            snake_case_ =   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
            snake_case_ =   in_proj_weight_cross_attn[:256, :]
            snake_case_ =   in_proj_bias_cross_attn[:256]
            snake_case_ =   in_proj_weight_cross_attn[256:512, :]
            snake_case_ =   in_proj_bias_cross_attn[256:512]
            snake_case_ =   in_proj_weight_cross_attn[-256:, :]
            snake_case_ =   in_proj_bias_cross_attn[-256:]






def  __lowerCAmelCase					()->						str:




      """simple docstring"""

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

      return im






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=False      )->						Dict:




      """simple docstring"""

      snake_case_ ,					snake_case_ =   get_detr_config(SCREAMING_SNAKE_CASE      )

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

    # finally, create HuggingFace model and load state dict
      snake_case_ =   DetrForSegmentation(SCREAMING_SNAKE_CASE      ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE      )
      model.load_state_dict(SCREAMING_SNAKE_CASE      )
      model.eval()

      # verify our conversion on an image
      snake_case_ =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
      snake_case_ =   DetrImageProcessor(format=SCREAMING_SNAKE_CASE      )

      snake_case_ =   processor(images=prepare_img()      ,      return_tensors='''pt'''      )
      snake_case_ =   encoding['''pixel_values''']

      snake_case_ =   detr(SCREAMING_SNAKE_CASE      )
      snake_case_ =   model(SCREAMING_SNAKE_CASE      )

      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(SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
            model.save_pretrained(SCREAMING_SNAKE_CASE      )
            processor.save_pretrained(SCREAMING_SNAKE_CASE      )

      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__":
   UpperCAmelCase    =					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.""")
   UpperCAmelCase    =					parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


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