

def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			list[int]:



  """simple docstring"""






  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''  )

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''  )

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''  )

  _snake_case		=							value * saturation
  _snake_case		=							hue / 60
  _snake_case		=							chroma * (1 - abs(hue_section % 2 - 1  ))
  _snake_case		=							value - chroma

  if hue_section >= 0 and hue_section <= 1:
    _snake_case		=							round(255 * (chroma + match_value)  )
    _snake_case		=							round(255 * (second_largest_component + match_value)  )
    _snake_case		=							round(255 * (match_value)  )
  elif hue_section > 1 and hue_section <= 2:
    _snake_case		=							round(255 * (second_largest_component + match_value)  )
    _snake_case		=							round(255 * (chroma + match_value)  )
    _snake_case		=							round(255 * (match_value)  )
  elif hue_section > 2 and hue_section <= 3:
    _snake_case		=							round(255 * (match_value)  )
    _snake_case		=							round(255 * (chroma + match_value)  )
    _snake_case		=							round(255 * (second_largest_component + match_value)  )
  elif hue_section > 3 and hue_section <= 4:
    _snake_case		=							round(255 * (match_value)  )
    _snake_case		=							round(255 * (second_largest_component + match_value)  )
    _snake_case		=							round(255 * (chroma + match_value)  )
  elif hue_section > 4 and hue_section <= 5:
    _snake_case		=							round(255 * (second_largest_component + match_value)  )
    _snake_case		=							round(255 * (match_value)  )
    _snake_case		=							round(255 * (chroma + match_value)  )
  else:
    _snake_case		=							round(255 * (chroma + match_value)  )
    _snake_case		=							round(255 * (match_value)  )
    _snake_case		=							round(255 * (second_largest_component + match_value)  )

  return [red, green, blue]



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			list[float]:



  """simple docstring"""






  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''  )

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''  )

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''  )

  _snake_case		=							red / 255
  _snake_case		=							green / 255
  _snake_case		=							blue / 255
  _snake_case		=							max(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							value - min(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )
  _snake_case		=							0 if value == 0 else chroma / value

  if chroma == 0:
    _snake_case		=							0.0
  elif value == float_red:
    _snake_case		=							60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    _snake_case		=							60 * (2 + (float_blue - float_red) / chroma)
  else:
    _snake_case		=							60 * (4 + (float_red - float_green) / chroma)

  _snake_case		=							(hue + 360) % 360

  return [hue, saturation, value]



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			bool:



  """simple docstring"""






  _snake_case		=							abs(hsv_a[0] - hsv_a[0]  ) < 0.2
  _snake_case		=							abs(hsv_a[1] - hsv_a[1]  ) < 0.002
  _snake_case		=							abs(hsv_a[2] - hsv_a[2]  ) < 0.002

  return check_hue and check_saturation and check_value




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    snake_case_(_UpperCamelCase  )							->			Tuple:



  """simple docstring"""






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

  return orig_key



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""  )
    _snake_case		=							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		=							in_proj_weight[:256, :]
    _snake_case		=							in_proj_bias[:256]
    _snake_case		=							in_proj_weight[256:512, :]
    _snake_case		=							in_proj_bias[256:512]
    _snake_case		=							in_proj_weight[-256:, :]
    _snake_case		=							in_proj_bias[-256:]



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case		=							Image.open(requests.get(_UpperCamelCase				, stream=_UpperCamelCase  ).raw  )

  return im



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, )							->			Dict:



  """simple docstring"""






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

  # load image processor
  _snake_case		=							DeformableDetrImageProcessor(format='''coco_detection'''  )

  # prepare image
  _snake_case		=							prepare_img()
  _snake_case		=							image_processor(images=_UpperCamelCase				, return_tensors='''pt'''  )
  _snake_case		=							encoding['''pixel_values''']

  logger.info('''Converting model...'''  )

  # load original state dict
  _snake_case		=							torch.load(_UpperCamelCase				, map_location='''cpu'''  )['''model''']
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case		=							state_dict.pop(_UpperCamelCase  )
    _snake_case		=							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
  _snake_case		=							'''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
      _snake_case		=							state_dict.pop(_UpperCamelCase  )
      _snake_case		=							val
    # finally, create HuggingFace model and load state dict
  _snake_case		=							DeformableDetrForObjectDetection(_UpperCamelCase  )
  model.load_state_dict(_UpperCamelCase  )
  model.eval()

  _snake_case		=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _snake_case		=							model(pixel_values.to(_UpperCamelCase  )  )

  _snake_case		=							torch.tensor(
      [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
  _snake_case		=							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		=							torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
    _snake_case		=							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		=							torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
    _snake_case		=							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		=							torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
    _snake_case		=							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		=							torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
    _snake_case		=							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:
    _snake_case		=							'''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    	=			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,
 )

