





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase       = {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase       = ['''DetrFeatureExtractor''']
     _UpperCamelCase       = ['''DetrImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase       = [
         '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DetrForObjectDetection''',
         '''DetrForSegmentation''',
         '''DetrModel''',
         '''DetrPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_detr import DetrFeatureExtractor
          from .image_processing_detr import DetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_detr import (
              DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetrForObjectDetection,
              DetrForSegmentation,
              DetrModel,
              DetrPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase       = logging.get_logger(__name__)







def       lowerCAmelCase__(	lowercase       :    Tuple				) ->    int:
  if "backbone.0.body" in orig_key:
    __snake_case   :				str        =       orig_key.replace("backbone.0.body"   ,     "backbone.conv_encoder.model"				)
  if "transformer" in orig_key:
    __snake_case   :				List[Any]        =       orig_key.replace("transformer."   ,     ""				)
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("norm1"   ,     "self_attn_layer_norm"				)
    else:
      __snake_case   :				Optional[int]        =       orig_key.replace("norm1"   ,     "encoder_attn_layer_norm"				)
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      __snake_case   :				Optional[Any]        =       orig_key.replace("norm2"   ,     "final_layer_norm"				)
    else:
      __snake_case   :				List[str]        =       orig_key.replace("norm2"   ,     "self_attn_layer_norm"				)
  if "norm3" in orig_key:
    __snake_case   :				Dict        =       orig_key.replace("norm3"   ,     "final_layer_norm"				)
  if "linear1" in orig_key:
    __snake_case   :				Optional[Any]        =       orig_key.replace("linear1"   ,     "fc1"				)
  if "linear2" in orig_key:
    __snake_case   :				Dict        =       orig_key.replace("linear2"   ,     "fc2"				)
  if "query_embed" in orig_key:
    __snake_case   :				Optional[int]        =       orig_key.replace("query_embed"   ,     "query_position_embeddings"				)
  if "cross_attn" in orig_key:
    __snake_case   :				str        =       orig_key.replace("cross_attn"   ,     "encoder_attn"				)

  return orig_key







def       lowerCAmelCase__(	lowercase       :    Optional[int]				) ->    Tuple:
  # transformer decoder self-attention layers
  for i in range(6				):
    # read in weights + bias of input projection layer of self-attention
    __snake_case   :				str        =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
    __snake_case   :				Optional[Any]        =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""				)
    # next, add query, keys and values (in that order) to the state dict
    __snake_case   :				Tuple        =       in_proj_weight[:256, :]
    __snake_case   :				Dict        =       in_proj_bias[:256]
    __snake_case   :				List[str]        =       in_proj_weight[256:512, :]
    __snake_case   :				Optional[Any]        =       in_proj_bias[256:512]
    __snake_case   :				Optional[Any]        =       in_proj_weight[-256:, :]
    __snake_case   :				List[Any]        =       in_proj_bias[-256:]







def       lowerCAmelCase__(	) ->    str:
  __snake_case   :				Union[str, Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __snake_case   :				Optional[Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)

  return im







@torch.no_grad()
def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    Any   ,     lowercase       :    Optional[int]   ,     lowercase       :    List[Any]   ,     lowercase       :    str   ,     lowercase       :    int   ,     ) ->    Optional[Any]:
  __snake_case   :				Optional[int]        =       DeformableDetrConfig()
  # set config attributes
  if single_scale:
    __snake_case   :				List[str]        =       1
  __snake_case   :				Any        =       dilation
  __snake_case   :				List[str]        =       with_box_refine
  __snake_case   :				List[str]        =       two_stage
  # set labels
  __snake_case   :				int        =       91
  __snake_case   :				Any        =       "huggingface/label-files"
  __snake_case   :				List[Any]        =       "coco-detection-id2label.json"
  __snake_case   :				Tuple        =       json.load(open(cached_download(hf_hub_url(lowercase   ,     lowercase   ,     repo_type="dataset"				)				)   ,     "r"				)				)
  __snake_case   :				List[str]        =       {int(lowercase				): v for k, v in idalabel.items()}
  __snake_case   :				Optional[int]        =       idalabel
  __snake_case   :				str        =       {v: k for k, v in idalabel.items()}

  # load image processor
  __snake_case   :				Union[str, Any]        =       DeformableDetrImageProcessor(format="coco_detection"				)

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

  logger.info("Converting model..."				)

  # load original state dict
  __snake_case   :				Optional[Any]        =       torch.load(lowercase   ,     map_location="cpu"				)["model"]
  # rename keys
  for key in state_dict.copy().keys():
    __snake_case   :				List[Any]        =       state_dict.pop(lowercase				)
    __snake_case   :				Dict        =       val
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowercase				)
  # 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   :				Union[str, Any]        =       "model."
  for key in state_dict.copy().keys():
    if not key.startswith("class_embed"				) and not key.startswith("bbox_embed"				):
      __snake_case   :				List[str]        =       state_dict.pop(lowercase				)
      __snake_case   :				int        =       val
    # finally, create HuggingFace model and load state dict
  __snake_case   :				int        =       DeformableDetrForObjectDetection(lowercase				)
  model.load_state_dict(lowercase				)
  model.eval()

  __snake_case   :				str        =       "cuda" if torch.cuda.is_available() else "cpu"
  model.to(lowercase				)
  # verify our conversion
  __snake_case   :				Any        =       model(pixel_values.to(lowercase				)				)

  __snake_case   :				Union[str, Any]        =       torch.tensor(
      [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]				)
  __snake_case   :				Any        =       torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]				)

  if single_scale:
    __snake_case   :				Union[str, Any]        =       torch.tensor(
        [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]				)
    __snake_case   :				Any        =       torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]				)

  if single_scale and dilation:
    __snake_case   :				Dict        =       torch.tensor(
        [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]				)
    __snake_case   :				List[str]        =       torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]				)

  if with_box_refine:
    __snake_case   :				List[str]        =       torch.tensor(
        [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]				)
    __snake_case   :				List[str]        =       torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]				)

  if with_box_refine and two_stage:
    __snake_case   :				Tuple        =       torch.tensor(
        [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]				)
    __snake_case   :				Any        =       torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]				)

  print("Logits:"   ,     outputs.logits[0, :3, :3]				)

  assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(lowercase				)   ,     atol=1E-4				)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(lowercase				)   ,     atol=1E-4				)

  print("Everything ok!"				)

  # 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				)

  # Push to hub
  if push_to_hub:
    __snake_case   :				int        =       "deformable-detr"
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print("Pushing model to hub..."				)
    model.push_to_hub(repo_path_or_name=lowercase   ,     organization="nielsr"   ,     commit_message="Add model"				)


if __name__ == "__main__":
     _UpperCamelCase       = argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''',
         type=str,
         default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
         help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
     )
     parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
     parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
     parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
     parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the folder to output PyTorch model.''',
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
     )
     _UpperCamelCase       = parser.parse_args()
     convert_deformable_detr_checkpoint(
         args.checkpoint_path,
         args.single_scale,
         args.dilation,
         args.with_box_refine,
         args.two_stage,
         args.pytorch_dump_folder_path,
         args.push_to_hub,
     )

