

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	)				->      Any:



  """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	)				->      List[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_()				->      Union[str, Any]:



  """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					,			)				->      Tuple:



  """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,
 )




from __future__ import annotations



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  assert n > 1 and a > 0 and greatest_common_divisor(_UpperCamelCase					,			_UpperCamelCase	) == 1
  ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  _snake_case		=				(b * s) % n
  return x



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			_UpperCamelCase	)  # Implemented below
  if b < 0:
    _snake_case		=				(b % n + n) % n
  return b



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				invert_modulo(_UpperCamelCase					,			_UpperCamelCase	)
  _snake_case		=				(b * s) % n
  return x



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



  """simple docstring"""






  assert a >= 0 and b >= 0

  if b == 0:
    _snake_case,	_snake_case,	_snake_case		=				a, 1, 0
  else:
    ((_snake_case),	(_snake_case),	(_snake_case))		=				extended_gcd(_UpperCamelCase					,			a % b	)
    _snake_case		=				q
    _snake_case		=				p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)



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



  """simple docstring"""






  if b == 0:
    return (1, 0)
  ((_snake_case),	(_snake_case))		=				extended_euclid(_UpperCamelCase					,			a % b	)
  _snake_case		=				a // b
  return (y, x - k * y)



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  if a < b:
    _snake_case,	_snake_case		=				b, a

  while a % b != 0:
    _snake_case,	_snake_case		=				b, a % b

  return b


if __name__ == "__main__":
 from doctest import testmod

 testmod(name='''modular_division''', verbose=True)
 testmod(name='''modular_division2''', verbose=True)
 testmod(name='''invert_modulo''', verbose=True)
 testmod(name='''extended_gcd''', verbose=True)
 testmod(name='''extended_euclid''', verbose=True)
 testmod(name='''greatest_common_divisor''', verbose=True)

