






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()
__a :    Any								=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__a :    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      _SCREAMING_SNAKE_CASE  (	__lowercase				:     List[str]     ,			__lowercase				:     int     ,			__lowercase				:     List[Any]      )  -> Dict:





    """simple docstring"""

    __A             =		state_dict.pop(__lowercase      )
    __A             =		val



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[int]      )  -> List[str]:





    """simple docstring"""

    __A             =		OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __A             =		key.replace("""backbone.0.body"""     ,			"""backbone.conv_encoder.model"""      )
            __A             =		value
        else:
            __A             =		value

    return new_state_dict



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Dict     ,			__lowercase				:     Optional[int]=False      )  -> int:





    """simple docstring"""

    __A             =		""""""
    if is_panoptic:
        __A             =		"""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             =		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
        __A             =		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             =		in_proj_weight[:2_5_6, :]
        __A             =		in_proj_bias[:2_5_6]
        __A             =		in_proj_weight[2_5_6:5_1_2, :]
        __A             =		in_proj_bias[2_5_6:5_1_2]
        __A             =		in_proj_weight[-2_5_6:, :]
        __A             =		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             =		state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
        __A             =		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             =		in_proj_weight[:2_5_6, :]
        __A             =		in_proj_bias[:2_5_6]
        __A             =		in_proj_weight[2_5_6:5_1_2, :]
        __A             =		in_proj_bias[2_5_6:5_1_2]
        __A             =		in_proj_weight[-2_5_6:, :]
        __A             =		in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        __A             =		state_dict.pop(
            f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"      )
        __A             =		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             =		in_proj_weight_cross_attn[:2_5_6, :]
        __A             =		in_proj_bias_cross_attn[:2_5_6]
        __A             =		in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        __A             =		in_proj_bias_cross_attn[2_5_6:5_1_2]
        __A             =		in_proj_weight_cross_attn[-2_5_6:, :]
        __A             =		in_proj_bias_cross_attn[-2_5_6:]



def      _SCREAMING_SNAKE_CASE  (	)  -> Optional[int]:





    """simple docstring"""

    __A             =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    __A             =		Image.open(requests.get(__lowercase     ,			stream=__lowercase      ).raw      )

    return im



@torch.no_grad()
def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     List[Any]      )  -> str:





    """simple docstring"""

    __A             =		DetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        __A             =		"""resnet101"""
    if "dc5" in model_name:
        __A             =		True
    __A             =		"""panoptic""" in model_name
    if is_panoptic:
        __A             =		2_5_0
    else:
        __A             =		9_1
        __A             =		"""huggingface/label-files"""
        __A             =		"""coco-detection-id2label.json"""
        __A             =		json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="""dataset"""      )     ,			"""r"""      )      )
        __A             =		{int(__lowercase      ): v for k, v in idalabel.items()}
        __A             =		idalabel
        __A             =		{v: k for k, v in idalabel.items()}

    # load image processor
    __A             =		"""coco_panoptic""" if is_panoptic else """coco_detection"""
    __A             =		DetrImageProcessor(format=__lowercase      )

    # prepare image
    __A             =		prepare_img()
    __A             =		image_processor(images=__lowercase     ,			return_tensors="""pt"""      )
    __A             =		encoding["""pixel_values"""]

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

    # load original model from torch hub
    __A             =		torch.hub.load("""facebookresearch/detr"""     ,			__lowercase     ,			pretrained=__lowercase      ).eval()
    __A             =		detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            __A             =		"""detr.""" + src
        rename_key(__lowercase     ,			__lowercase     ,			__lowercase      )
    __A             =		rename_backbone_keys(__lowercase      )
    # query, key and value matrices need special treatment
    read_in_q_k_v(__lowercase     ,			is_panoptic=__lowercase      )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    __A             =		"""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             =		state_dict.pop(__lowercase      )
                __A             =		val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __A             =		state_dict.pop(__lowercase      )
                __A             =		val
            elif key.startswith("""bbox_attention"""      ) or key.startswith("""mask_head"""      ):
                continue
            else:
                __A             =		state_dict.pop(__lowercase      )
                __A             =		val
        else:
            if not key.startswith("""class_labels_classifier"""      ) and not key.startswith("""bbox_predictor"""      ):
                __A             =		state_dict.pop(__lowercase      )
                __A             =		val
    # finally, create HuggingFace model and load state dict
    __A             =		DetrForSegmentation(__lowercase      ) if is_panoptic else DetrForObjectDetection(__lowercase      )
    model.load_state_dict(__lowercase      )
    model.eval()
    # verify our conversion
    __A             =		detr(__lowercase      )
    __A             =		model(__lowercase      )
    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(__lowercase      ).mkdir(exist_ok=__lowercase      )
    model.save_pretrained(__lowercase      )
    image_processor.save_pretrained(__lowercase      )


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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




