

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()
UpperCAmelCase__			:  Dict					     =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__			:  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        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = state_dict.pop(_A      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = val






def        __lowercase   (  _A      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Dict	  = OrderedDict()
      for key, value in state_dict.items():
            if "backbone.0.body" in key:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = key.replace("""backbone.0.body"""    ,     """backbone.conv_encoder.model"""      )
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = value
            else:
                  SCREAMING_SNAKE_CASE			:   int	  = value

      return new_state_dict






def        __lowercase   (  _A    ,     _A=False      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Tuple	  = """"""
      if is_panoptic:
            SCREAMING_SNAKE_CASE			:   List[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)
            SCREAMING_SNAKE_CASE			:   Any	  = state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
            SCREAMING_SNAKE_CASE			:   Union[str, 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
            SCREAMING_SNAKE_CASE			:   List[str]	  = in_proj_weight[:256, :]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_bias[:256]
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight[256:512, :]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_bias[256:512]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_weight[-256:, :]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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
            SCREAMING_SNAKE_CASE			:   Tuple	  = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
            SCREAMING_SNAKE_CASE			:   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
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight[:256, :]
            SCREAMING_SNAKE_CASE			:   str	  = in_proj_bias[:256]
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = in_proj_weight[256:512, :]
            SCREAMING_SNAKE_CASE			:   Any	  = in_proj_bias[256:512]
            SCREAMING_SNAKE_CASE			:   Any	  = in_proj_weight[-256:, :]
            SCREAMING_SNAKE_CASE			:   int	  = in_proj_bias[-256:]
            # 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			:   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			:   int	  = in_proj_weight_cross_attn[:256, :]
            SCREAMING_SNAKE_CASE			:   Any	  = in_proj_bias_cross_attn[:256]
            SCREAMING_SNAKE_CASE			:   Tuple	  = in_proj_weight_cross_attn[256:512, :]
            SCREAMING_SNAKE_CASE			:   Any	  = in_proj_bias_cross_attn[256:512]
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = in_proj_weight_cross_attn[-256:, :]
            SCREAMING_SNAKE_CASE			:   Any	  = in_proj_bias_cross_attn[-256:]






def        __lowercase   (  )  ->    List[str]:
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE			:   List[str]	  = Image.open(requests.get(_A    ,     stream=_A      ).raw      )

      return im






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

      # load image processor
      SCREAMING_SNAKE_CASE			:   Dict	  = """coco_panoptic""" if is_panoptic else """coco_detection"""
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = DetrImageProcessor(format=_A      )

      # prepare image
      SCREAMING_SNAKE_CASE			:   Any	  = prepare_img()
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = image_processor(images=_A    ,     return_tensors="""pt"""      )
      SCREAMING_SNAKE_CASE			:   int	  = encoding["""pixel_values"""]

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

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


if __name__ == "__main__":
  UpperCAmelCase__			:  Optional[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."""
  )
  UpperCAmelCase__			:  Union[str, Any]					     =     parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json






def        __lowercase   (  _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = filter(lambda _A      : p.requires_grad    ,     model.parameters()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
        pl_module.logger.log_metrics(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Tuple=True		)       ->None:
        """simple docstring"""







        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
        # Log results
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = od / """test_results.txt"""
              SCREAMING_SNAKE_CASE			:   int	  = od / """test_generations.txt"""
        else:
              # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
              # If people want this it will be easy enough to add back.
              SCREAMING_SNAKE_CASE			:   Any	  = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
              SCREAMING_SNAKE_CASE			:   int	  = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
              results_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
              generations_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
        with open(UpperCAmelCase__					,					"""a+"""		) as writer:
              for key in sorted(UpperCAmelCase__		):
                    if key in ["log", "progress_bar", "preds"]:
                          continue
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = metrics[key]
                    if isinstance(UpperCAmelCase__					,					torch.Tensor		):
                          SCREAMING_SNAKE_CASE			:   Optional[int]	  = val.item()
                    SCREAMING_SNAKE_CASE			:   List[str]	  = f"{key}: {val:.6f}\n"
                    writer.write(UpperCAmelCase__		)

        if not save_generations:
              return

        if "preds" in metrics:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = """\n""".join(metrics["""preds"""]		)
              generations_file.open("""w+"""		).write(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = count_trainable_parameters(UpperCAmelCase__		)
        # mp stands for million parameters
        trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}		)


  @rank_zero_only
  def 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







        save_json(pl_module.metrics					,					pl_module.metrics_save_path		)
        return self._write_logs(UpperCAmelCase__					,					UpperCAmelCase__					,					"""test"""		)




  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """simple docstring"""







        save_json(pl_module.metrics					,					pl_module.metrics_save_path		)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")






def        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   Any	  = """{val_avg_loss:.4f}-{step_count}"""
      else:
            raise NotImplementedError(
                F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
                """ this function."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




