
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Any:
       lowercase							:  Tuple 			=   fname.split(os.path.sep       )[-1]
       return re.search(R"""^(.*)_\d+\.jpg$"""      ,			SCREAMING_SNAKE_CASE__       ).groups()[0]

class __snake_case	(   lowerCAmelCase  ):
    def __init__(						self	,snake_case	,snake_case=None	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  int 			=   file_names
           lowercase							:  Optional[int] 			=   image_transform
           lowercase							:  int 			=   label_to_id
    def __len__(						self   ):



           '''simple docstring'''







           return len(self.file_names   )
    def __getitem__(						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.file_names[idx]
           lowercase							:  Tuple 			=   PIL.Image.open(snake_case   )
           lowercase							:  Dict 			=   raw_image.convert("""RGB"""   )
           if self.image_transform is not None:
                  lowercase							:  Any 			=   self.image_transform(snake_case   )
           lowercase							:  Tuple 			=   extract_label(snake_case   )
           if self.label_to_id is not None:
                  lowercase							:  str 			=   self.label_to_id[label]
           return {"image": image, "label": label}







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Tuple:
       # Initialize accelerator
       lowercase							:  str 			=   Accelerator(cpu=args.cpu      ,			mixed_precision=args.mixed_precision       )

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase							:  Optional[Any] 			=   config["""lr"""]
       lowercase							:  int 			=   int(config["""num_epochs"""]       )
       lowercase							:  Union[str, Any] 			=   int(config["""seed"""]       )
       lowercase							:  List[str] 			=   int(config["""batch_size"""]       )
       lowercase							:  Dict 			=   config["""image_size"""]
       if not isinstance(SCREAMING_SNAKE_CASE__      ,			(list, tuple)       ):
              lowercase							:  Tuple 			=   (image_size, image_size)

       # Grab all the image filenames
       lowercase							:  Dict 			=   [os.path.join(args.data_dir      ,			SCREAMING_SNAKE_CASE__       ) for fname in os.listdir(args.data_dir       ) if fname.endswith(""".jpg"""       )]

       # Build the label correspondences
       lowercase							:  List[str] 			=   [extract_label(SCREAMING_SNAKE_CASE__       ) for fname in file_names]
       lowercase							:  int 			=   list(set(SCREAMING_SNAKE_CASE__       )       )
       id_to_label.sort()
       lowercase							:  Dict 			=   {lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE__       )}

       # Set the seed before splitting the data.
       np.random.seed(SCREAMING_SNAKE_CASE__       )
       torch.manual_seed(SCREAMING_SNAKE_CASE__       )
       torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE__       )

       # Split our filenames between train and validation
       lowercase							:  Tuple 			=   np.random.permutation(len(SCREAMING_SNAKE_CASE__       )       )
       lowercase							:  Dict 			=   int(0.8 * len(SCREAMING_SNAKE_CASE__       )       )
       lowercase							:  Optional[Any] 			=   random_perm[:cut]
       lowercase							:  str 			=   random_perm[cut:]

       # For training we use a simple RandomResizedCrop
       lowercase							:  Tuple 			=   Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__      ,			scale=(0.5, 1.0)       ), ToTensor()]       )
       lowercase							:  Tuple 			=   PetsDataset(
           [file_names[i] for i in train_split]      ,			image_transform=SCREAMING_SNAKE_CASE__      ,			label_to_id=SCREAMING_SNAKE_CASE__       )

       # For evaluation, we use a deterministic Resize
       lowercase							:  Tuple 			=   Compose([Resize(SCREAMING_SNAKE_CASE__       ), ToTensor()]       )
       lowercase							:  str 			=   PetsDataset([file_names[i] for i in eval_split]      ,			image_transform=SCREAMING_SNAKE_CASE__      ,			label_to_id=SCREAMING_SNAKE_CASE__       )

       # Instantiate dataloaders.
       lowercase							:  List[str] 			=   DataLoader(SCREAMING_SNAKE_CASE__      ,			shuffle=SCREAMING_SNAKE_CASE__      ,			batch_size=SCREAMING_SNAKE_CASE__      ,			num_workers=4       )
       lowercase							:  Tuple 			=   DataLoader(SCREAMING_SNAKE_CASE__      ,			shuffle=SCREAMING_SNAKE_CASE__      ,			batch_size=SCREAMING_SNAKE_CASE__      ,			num_workers=4       )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase							:  List[Any] 			=   create_model("""resnet50d"""      ,			pretrained=SCREAMING_SNAKE_CASE__      ,			num_classes=len(SCREAMING_SNAKE_CASE__       )       )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       lowercase							:  Tuple 			=   model.to(accelerator.device       )

       # Freezing the base model
       for param in model.parameters():
              lowercase							:  str 			=   False
       for param in model.get_classifier().parameters():
              lowercase							:  str 			=   True

       # We normalize the batches of images to be a bit faster.
       lowercase							:  str 			=   torch.tensor(model.default_cfg["""mean"""]       )[None, :, None, None].to(accelerator.device       )
       lowercase							:  Dict 			=   torch.tensor(model.default_cfg["""std"""]       )[None, :, None, None].to(accelerator.device       )

       # Instantiate optimizer
       lowercase							:  Optional[Any] 			=   torch.optim.Adam(params=model.parameters()      ,			lr=lr / 25       )

       # Instantiate learning rate scheduler
       lowercase							:  List[Any] 			=   OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__      ,			max_lr=SCREAMING_SNAKE_CASE__      ,			epochs=SCREAMING_SNAKE_CASE__      ,			steps_per_epoch=len(SCREAMING_SNAKE_CASE__       )       )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase							:  List[Any] 			=   accelerator.prepare(
           SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE__       ):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     lowercase							:  int 			=   {k: v.to(accelerator.device       ) for k, v in batch.items()}
                     lowercase							:  Tuple 			=   (batch["""image"""] - mean) / std
                     lowercase							:  Union[str, Any] 			=   model(SCREAMING_SNAKE_CASE__       )
                     lowercase							:  List[Any] 			=   torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__      ,			batch["""label"""]       )
                     accelerator.backward(SCREAMING_SNAKE_CASE__       )
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()

              model.eval()
              lowercase							:  List[Any] 			=   0
              lowercase							:  Optional[int] 			=   0
              for _, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     lowercase							:  Optional[int] 			=   {k: v.to(accelerator.device       ) for k, v in batch.items()}
                     lowercase							:  List[Any] 			=   (batch["""image"""] - mean) / std
                     with torch.no_grad():
                            lowercase							:  List[str] 			=   model(SCREAMING_SNAKE_CASE__       )
                     lowercase							:  Any 			=   outputs.argmax(dim=-1       )
                     lowercase			,    lowercase							:  Any 			=   accelerator.gather_for_metrics((predictions, batch["""label"""])       )
                     lowercase							:  Optional[int] 			=   predictions == references
                     num_elems += accurate_preds.shape[0]
                     accurate += accurate_preds.long().sum()

              lowercase							:  Tuple 			=   accurate.item() / num_elems
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"       )







def 		_snake_case(						)				->					Union[str, Any]:
       lowercase							:  Optional[Any] 			=   argparse.ArgumentParser(description="""Simple example of training script."""       )
       parser.add_argument("""--data_dir"""      ,			required=SCREAMING_SNAKE_CASE__      ,			help="""The data folder on disk."""       )
       parser.add_argument(
           """--mixed_precision"""      ,			type=SCREAMING_SNAKE_CASE__      ,			default=SCREAMING_SNAKE_CASE__      ,			choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,			help="""Whether to use mixed precision. Choose"""
           """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
           """and an Nvidia Ampere GPU."""      ,			)
       parser.add_argument(
           """--checkpointing_steps"""      ,			type=SCREAMING_SNAKE_CASE__      ,			default=SCREAMING_SNAKE_CASE__      ,			help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""      ,			)
       parser.add_argument("""--cpu"""      ,			action="""store_true"""      ,			help="""If passed, will train on the CPU."""       )
       lowercase							:  Any 			=   parser.parse_args()
       lowercase							:  Optional[int] 			=   {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
       training_function(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  main()


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()
lowercase						:      Union[str, Any]							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase						:      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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[int] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   val







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:  Optional[Any] 			=   key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""       )
                     lowercase							:  Optional[int] 			=   value
              else:
                     lowercase							:  Dict 			=   value

       return new_state_dict







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







def 		_snake_case(						)				->					str:
       lowercase							:  Tuple 			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:  int 			=   Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ).raw       )

       return im







@torch.no_grad()
def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Any:
       lowercase							:  List[Any] 			=   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowercase							:  Union[str, Any] 			=   """resnet101"""
       if "dc5" in model_name:
              lowercase							:  List[str] 			=   True
       lowercase							:  Union[str, Any] 			=   """panoptic""" in model_name
       if is_panoptic:
              lowercase							:  List[str] 			=   250
       else:
              lowercase							:  List[Any] 			=   91
              lowercase							:  List[str] 			=   """huggingface/label-files"""
              lowercase							:  Union[str, Any] 			=   """coco-detection-id2label.json"""
              lowercase							:  Optional[int] 			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			repo_type="""dataset"""       )      ,			"""r"""       )       )
              lowercase							:  int 			=   {int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
              lowercase							:  List[Any] 			=   idalabel
              lowercase							:  List[Any] 			=   {v: k for k, v in idalabel.items()}

       # load image processor
       lowercase							:  Any 			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:  Dict 			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

       # prepare image
       lowercase							:  Optional[Any] 			=   prepare_img()
       lowercase							:  int 			=   image_processor(images=SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )
       lowercase							:  int 			=   encoding["""pixel_values"""]

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

       # load original model from torch hub
       lowercase							:  Optional[int] 			=   torch.hub.load("""facebookresearch/detr"""      ,			SCREAMING_SNAKE_CASE__      ,			pretrained=SCREAMING_SNAKE_CASE__       ).eval()
       lowercase							:  Optional[int] 			=   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:  Optional[Any] 			=   """detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   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
       lowercase							:  List[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"""       )
                     ):
                            lowercase							:  Optional[Any] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Optional[Any] 			=   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:  Tuple 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  str 			=   val
                     elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
                            continue
                     else:
                            lowercase							:  Any 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Dict 			=   val
              else:
                     if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
                            lowercase							:  List[str] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Union[str, Any] 			=   val
    # finally, create HuggingFace model and load state dict
       lowercase							:  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
       lowercase							:  Union[str, Any] 			=   detr(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   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__":
  lowercase						:      Any							=							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."""
  )
  lowercase						:      List[Any]							=							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
