
'''simple docstring'''
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()
a_     =  logging.get_logger(__name__)

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    if "backbone.0.body" in orig_key:
        snake_case_	:      List[Any]            =  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_	:      int            =  orig_key.replace("norm1",						"self_attn_layer_norm"    )
        else:
            snake_case_	:      Any            =  orig_key.replace("norm1",						"encoder_attn_layer_norm"    )
    if "norm2" in orig_key:
        if "encoder" in orig_key:
            snake_case_	:      Optional[int]            =  orig_key.replace("norm2",						"final_layer_norm"    )
        else:
            snake_case_	:      Dict            =  orig_key.replace("norm2",						"self_attn_layer_norm"    )
    if "norm3" in orig_key:
        snake_case_	:      Optional[int]            =  orig_key.replace("norm3",						"final_layer_norm"    )
    if "linear1" in orig_key:
        snake_case_	:      Any            =  orig_key.replace("linear1",						"fc1"    )
    if "linear2" in orig_key:
        snake_case_	:      List[Any]            =  orig_key.replace("linear2",						"fc2"    )
    if "query_embed" in orig_key:
        snake_case_	:      str            =  orig_key.replace("query_embed",						"query_position_embeddings"    )
    if "cross_attn" in orig_key:
        snake_case_	:      int            =  orig_key.replace("cross_attn",						"encoder_attn"    )

    return orig_key

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    for i in range(6    ):
        # read in weights + bias of input projection layer of self-attention
        snake_case_	:      List[str]            =  state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'    )
        snake_case_	:      Dict            =  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_	:      Dict            =  in_proj_weight[:2_5_6, :]
        snake_case_	:      Dict            =  in_proj_bias[:2_5_6]
        snake_case_	:      int            =  in_proj_weight[2_5_6:5_1_2, :]
        snake_case_	:      List[Any]            =  in_proj_bias[2_5_6:5_1_2]
        snake_case_	:      Optional[int]            =  in_proj_weight[-2_5_6:, :]
        snake_case_	:      Optional[Any]            =  in_proj_bias[-2_5_6:]

def   UpperCamelCase_       (		):
    """simple docstring"""






    snake_case_	:      List[Any]            =  "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case_	:      List[Any]            =  Image.open(requests.get(__SCREAMING_SNAKE_CASE,						stream=__SCREAMING_SNAKE_CASE    ).raw    )

    return im

@torch.no_grad()
def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						):
    """simple docstring"""






    snake_case_	:      Any            =  DeformableDetrConfig()
    # set config attributes
    if single_scale:
        snake_case_	:      int            =  1
    snake_case_	:      Optional[int]            =  dilation
    snake_case_	:      Optional[int]            =  with_box_refine
    snake_case_	:      str            =  two_stage
    # set labels
    snake_case_	:      Any            =  9_1
    snake_case_	:      List[Any]            =  "huggingface/label-files"
    snake_case_	:      Optional[Any]            =  "coco-detection-id2label.json"
    snake_case_	:      Tuple            =  json.load(open(cached_download(hf_hub_url(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						repo_type="dataset"    )    ),						"r"    )    )
    snake_case_	:      int            =  {int(__SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
    snake_case_	:      Any            =  idalabel
    snake_case_	:      Any            =  {v: k for k, v in idalabel.items()}

    # load image processor
    snake_case_	:      Optional[int]            =  DeformableDetrImageProcessor(format="coco_detection"    )

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

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

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

    snake_case_	:      List[str]            =  "cuda" if torch.cuda.is_available() else "cpu"
    model.to(__SCREAMING_SNAKE_CASE    )
    # verify our conversion
    snake_case_	:      Dict            =  model(pixel_values.to(__SCREAMING_SNAKE_CASE    )    )

    snake_case_	:      List[str]            =  torch.tensor(
        [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]    )
    snake_case_	:      Any            =  torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]    )

    if single_scale:
        snake_case_	:      List[str]            =  torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]    )
        snake_case_	:      Optional[int]            =  torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]    )

    if single_scale and dilation:
        snake_case_	:      Optional[Any]            =  torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]    )
        snake_case_	:      Optional[int]            =  torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]    )

    if with_box_refine:
        snake_case_	:      Tuple            =  torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]    )
        snake_case_	:      str            =  torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]    )

    if with_box_refine and two_stage:
        snake_case_	:      List[str]            =  torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]    )
        snake_case_	:      int            =  torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]    )

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

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

    # Push to hub
    if push_to_hub:
        snake_case_	:      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=__SCREAMING_SNAKE_CASE,						organization="nielsr",						commit_message="Add model"    )


if __name__ == "__main__":
  a_     =  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."
  )
  a_     =  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,
  )






'''simple docstring'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




