

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()
snake_case_        =			logging.get_logger(__name__)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

     lowercase__   :       Optional[Any]				=	DetrConfig(use_timm_backbone=SCREAMING_SNAKE_CASE_     ,					backbone_config=SCREAMING_SNAKE_CASE_      )

     # set label attributes
     lowercase__   :       int				=	'panoptic' in model_name
     if is_panoptic:
          lowercase__   :       int				=	250
     else:
          lowercase__   :       Union[str, Any]				=	91
          lowercase__   :       List[str]				=	'huggingface/label-files'
          lowercase__   :       List[str]				=	'coco-detection-id2label.json'
          lowercase__   :       int				=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					repo_type='dataset'      )     ,					'r'      )      )
          lowercase__   :       Tuple				=	{int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}
          lowercase__   :       List[Any]				=	idalabel
          lowercase__   :       Optional[int]				=	{v: k for k, v in idalabel.items()}

     return config, is_panoptic

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



     '''simple docstring'''





     lowercase__   :       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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       str				=	state_dict.pop(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	val

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   int=False      ):



     '''simple docstring'''





     lowercase__   :       Any				=	''
     if is_panoptic:
          lowercase__   :       str				=	'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__   :       str				=	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""      )
          lowercase__   :       Any				=	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__   :       List[Any]				=	in_proj_weight[:256, :]
          lowercase__   :       int				=	in_proj_bias[:256]
          lowercase__   :       List[Any]				=	in_proj_weight[256:512, :]
          lowercase__   :       Union[str, Any]				=	in_proj_bias[256:512]
          lowercase__   :       int				=	in_proj_weight[-256:, :]
          lowercase__   :       Optional[Any]				=	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
          lowercase__   :       Optional[Any]				=	state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""      )
          lowercase__   :       List[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
          lowercase__   :       Dict				=	in_proj_weight[:256, :]
          lowercase__   :       Tuple				=	in_proj_bias[:256]
          lowercase__   :       Optional[int]				=	in_proj_weight[256:512, :]
          lowercase__   :       Tuple				=	in_proj_bias[256:512]
          lowercase__   :       str				=	in_proj_weight[-256:, :]
          lowercase__   :       Any				=	in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          lowercase__   :       List[str]				=	state_dict.pop(
              f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""      )
          lowercase__   :       Any				=	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__   :       Dict				=	in_proj_weight_cross_attn[:256, :]
          lowercase__   :       int				=	in_proj_bias_cross_attn[:256]
          lowercase__   :       str				=	in_proj_weight_cross_attn[256:512, :]
          lowercase__   :       List[str]				=	in_proj_bias_cross_attn[256:512]
          lowercase__   :       Optional[int]				=	in_proj_weight_cross_attn[-256:, :]
          lowercase__   :       Tuple				=	in_proj_bias_cross_attn[-256:]

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase__   :       Tuple				=	Image.open(requests.get(SCREAMING_SNAKE_CASE_     ,					stream=SCREAMING_SNAKE_CASE_      ).raw      )

     return im

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]=False      ):



     '''simple docstring'''





     lowercase__				,			lowercase__   :       List[str]				=	get_detr_config(SCREAMING_SNAKE_CASE_      )

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

    # finally, create HuggingFace model and load state dict
     lowercase__   :       Optional[int]				=	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
     lowercase__   :       Tuple				=	'coco_panoptic' if is_panoptic else 'coco_detection'
     lowercase__   :       Dict				=	DetrImageProcessor(format=SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	processor(images=prepare_img()     ,					return_tensors='pt'      )
     lowercase__   :       Tuple				=	encoding['pixel_values']

     lowercase__   :       Union[str, Any]				=	detr(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	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__":
       snake_case_        =			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.''')
       snake_case_        =			parser.parse_args()
       convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
          raise ValueError('Length must be a positive.'      )
     return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

