



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_		:  Union[str, Any]										=  logging.get_logger(__name__)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Dict       ):






 '''simple docstring'''

 if "backbone.0.body" in orig_key:
  a							=		orig_key.replace("backbone.0.body"			,       "backbone.conv_encoder.model"       )
 if "transformer" in orig_key:
  a							=		orig_key.replace("transformer."			,       ""       )
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   a							=		orig_key.replace("norm1"			,       "self_attn_layer_norm"       )
  else:
   a							=		orig_key.replace("norm1"			,       "encoder_attn_layer_norm"       )
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   a							=		orig_key.replace("norm2"			,       "final_layer_norm"       )
  else:
   a							=		orig_key.replace("norm2"			,       "self_attn_layer_norm"       )
 if "norm3" in orig_key:
  a							=		orig_key.replace("norm3"			,       "final_layer_norm"       )
 if "linear1" in orig_key:
  a							=		orig_key.replace("linear1"			,       "fc1"       )
 if "linear2" in orig_key:
  a							=		orig_key.replace("linear2"			,       "fc2"       )
 if "query_embed" in orig_key:
  a							=		orig_key.replace("query_embed"			,       "query_position_embeddings"       )
 if "cross_attn" in orig_key:
  a							=		orig_key.replace("cross_attn"			,       "encoder_attn"       )

 return orig_key


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 for i in range(6       ):
  # read in weights + bias of input projection layer of self-attention
  a							=		state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""       )
  a							=		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
  a							=		in_proj_weight[:2_56, :]
  a							=		in_proj_bias[:2_56]
  a							=		in_proj_weight[2_56:5_12, :]
  a							=		in_proj_bias[2_56:5_12]
  a							=		in_proj_weight[-2_56:, :]
  a							=		in_proj_bias[-2_56:]


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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

 return im


@torch.no_grad()
def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Dict			,       UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:Dict			,       UpperCAmelCase__			:List[Any]			,       ):






 '''simple docstring'''

 a							=		DeformableDetrConfig()
 # set config attributes
 if single_scale:
  a							=		1
 a							=		dilation
 a							=		with_box_refine
 a							=		two_stage
 # set labels
 a							=		91
 a							=		"huggingface/label-files"
 a							=		"coco-detection-id2label.json"
 a							=		json.load(open(cached_download(hf_hub_url(UpperCAmelCase__			,       UpperCAmelCase__			,       repo_type="dataset"       )       )			,       "r"       )       )
 a							=		{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
 a							=		idalabel
 a							=		{v: k for k, v in idalabel.items()}

 # load image processor
 a							=		DeformableDetrImageProcessor(format="coco_detection"       )

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

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

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

 a							=		"cuda" if torch.cuda.is_available() else "cpu"
 model.to(UpperCAmelCase__       )
 # verify our conversion
 a							=		model(pixel_values.to(UpperCAmelCase__       )       )

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

 if single_scale:
  a							=		torch.tensor(
      [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]       )
  a							=		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:
  a							=		torch.tensor(
      [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]       )
  a							=		torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]       )

 if with_box_refine:
  a							=		torch.tensor(
      [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]       )
  a							=		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:
  a							=		torch.tensor(
      [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]       )
  a							=		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(UpperCAmelCase__       )			,       atol=1E-4       )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,       expected_boxes.to(UpperCAmelCase__       )			,       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(UpperCAmelCase__       ).mkdir(exist_ok=UpperCAmelCase__       )
 model.save_pretrained(UpperCAmelCase__       )
 image_processor.save_pretrained(UpperCAmelCase__       )

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


if __name__ == "__main__":
     A_		:  Tuple										=  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_		:  List[str]										=  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,
     )








class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self      )->   None:
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A    =  {}
     # indexmap keys are an index and it's values are the number at that index
     __A    =  {}

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == item:
           array.pop(UpperCAmelCase      )
           return array
        elif array[mid] < item:
           __A    =  mid + 1
        else:
           __A    =  mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   list[int]:
     if isinstance(UpperCAmelCase    ,							(range, str)      ):
        __A    =  list(UpperCAmelCase      )
     elif not isinstance(UpperCAmelCase    ,							UpperCAmelCase      ):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''      )

     __A    =  0
     __A    =  len(UpperCAmelCase      ) - 1

     while low <= high:
        __A    =  (low + high) // 2
        if array[mid] == index:
           # If the item already exists in the array,
           # insert it after the existing item
           array.insert(mid + 1    ,							UpperCAmelCase      )
           return array
        elif array[mid] < index:
           __A    =  mid + 1
        else:
           __A    =  mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(UpperCAmelCase    ,							UpperCAmelCase      )
     return array

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   None:
     # Remove previous index
     if index in self.indexmap:
        __A    =  self.indexmap[index]
        if len(self.numbermap[n]      ) == 1:
           del self.numbermap[n]
        else:
           __A    =  self.binary_search_delete(self.numbermap[n]    ,							UpperCAmelCase      )

        # Set new index
     __A    =  number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
        __A    =  [index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
        __A    =  self.binary_search_insert(
            self.numbermap[number]    ,							UpperCAmelCase      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:
     return self.numbermap.get(UpperCAmelCase    ,							[-1]      )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

