




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						:    int      =  logging.get_logger(__name__)



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

  return orig_key



def    UpperCAmelCase__	(  lowerCamelCase ):
  # transformer decoder self-attention layers
  for i in range(6 ):
    # read in weights + bias of input projection layer of self-attention
    lowercase     :str   						=    state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight" )
    lowercase     :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
    lowercase     :Optional[int]   						=    in_proj_weight[:256, :]
    lowercase     :int   						=    in_proj_bias[:256]
    lowercase     :List[Any]   						=    in_proj_weight[256:512, :]
    lowercase     :Any   						=    in_proj_bias[256:512]
    lowercase     :Tuple   						=    in_proj_weight[-256:, :]
    lowercase     :List[Any]   						=    in_proj_bias[-256:]



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowercase     :Tuple   						=    Image.open(requests.get(lowerCamelCase,      stream=lowerCamelCase ).raw )

  return im



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

  # load image processor
  lowercase     :Tuple   						=    DeformableDetrImageProcessor(format="coco_detection" )

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

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

  # load original state dict
  lowercase     :int   						=    torch.load(lowerCamelCase,      map_location="cpu" )["model"]
  # rename keys
  for key in state_dict.copy().keys():
    lowercase     :Optional[Any]   						=    state_dict.pop(lowerCamelCase )
    lowercase     :List[Any]   						=    val
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCamelCase )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowercase     :Any   						=    "model."
  for key in state_dict.copy().keys():
    if not key.startswith("class_embed" ) and not key.startswith("bbox_embed" ):
      lowercase     :Dict   						=    state_dict.pop(lowerCamelCase )
      lowercase     :List[Any]   						=    val
    # finally, create HuggingFace model and load state dict
  lowercase     :Optional[int]   						=    DeformableDetrForObjectDetection(lowerCamelCase )
  model.load_state_dict(lowerCamelCase )
  model.eval()

  lowercase     :Tuple   						=    "cuda" if torch.cuda.is_available() else "cpu"
  model.to(lowerCamelCase )
  # verify our conversion
  lowercase     :Union[str, Any]   						=    model(pixel_values.to(lowerCamelCase ) )

  lowercase     :Optional[Any]   						=    torch.tensor(
      [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]] )
  lowercase     :Union[str, Any]   						=    torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]] )

  if single_scale:
    lowercase     :Optional[Any]   						=    torch.tensor(
        [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]] )
    lowercase     :Tuple   						=    torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]] )

  if single_scale and dilation:
    lowercase     :List[Any]   						=    torch.tensor(
        [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]] )
    lowercase     :Tuple   						=    torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]] )

  if with_box_refine:
    lowercase     :Optional[int]   						=    torch.tensor(
        [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]] )
    lowercase     :Union[str, Any]   						=    torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]] )

  if with_box_refine and two_stage:
    lowercase     :Dict   						=    torch.tensor(
        [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]] )
    lowercase     :Union[str, Any]   						=    torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]] )

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

  assert torch.allclose(outputs.logits[0, :3, :3],      expected_logits.to(lowerCamelCase ),      atol=1e-4 )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3],      expected_boxes.to(lowerCamelCase ),      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(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
  model.save_pretrained(lowerCamelCase )
  image_processor.save_pretrained(lowerCamelCase )

  # Push to hub
  if push_to_hub:
    lowercase     :List[str]   						=    "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=lowerCamelCase,      organization="nielsr",      commit_message="Add model" )


if __name__ == "__main__":
     _UpperCAmelCase						:    List[Any]      =  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						:    Tuple      =  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,
     )









from unittest.mock import patch

import datasets
from datasets import Dataset



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    Dataset.from_dict({"col_1": [3, 2, 0, 1]} )

  with patch("tqdm.auto.tqdm" ) as mock_tqdm:
    datasets.disable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_not_called()

    mock_tqdm.reset_mock()

    datasets.enable_progress_bar()
    dset.map(lambda lowerCamelCase : {"col_2": x["col_1"] + 1} )
    mock_tqdm.assert_called()



