

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()
lowerCamelCase_    =					logging.get_logger(__name__)



def    UpperCamelCase(					lowercase_							)	->				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    UpperCamelCase(					lowercase_							)	->				Union[str, 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    UpperCamelCase(					)	->				List[Any]:





  '''simple docstring'''





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

  return im



@torch.no_grad()
def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					)	->				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(lowercase_			,					lowercase_			,					repo_type="""dataset"""							)							)			,					"""r"""							)							)
  snake_case_       						=      {int(lowercase_							): 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=lowercase_			,					return_tensors="""pt"""							)
  snake_case_       						=      encoding["""pixel_values"""]

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

  # load original state dict
  snake_case_       						=      torch.load(lowercase_			,					map_location="""cpu"""							)["""model"""]
  # rename keys
  for key in state_dict.copy().keys():
    snake_case_       						=      state_dict.pop(lowercase_							)
    snake_case_       						=      val
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowercase_							)
  # 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(lowercase_							)
      snake_case_       						=      val
    # finally, create HuggingFace model and load state dict
  snake_case_       						=      DeformableDetrForObjectDetection(lowercase_							)
  model.load_state_dict(lowercase_							)
  model.eval()

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

  snake_case_       						=      torch.tensor(
      [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]							)
  snake_case_       						=      torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]							)

  if single_scale:
    snake_case_       						=      torch.tensor(
        [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]							)
    snake_case_       						=      torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]							)

  if single_scale and dilation:
    snake_case_       						=      torch.tensor(
        [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]							)
    snake_case_       						=      torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]							)

  if with_box_refine:
    snake_case_       						=      torch.tensor(
        [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]							)
    snake_case_       						=      torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]							)

  if with_box_refine and two_stage:
    snake_case_       						=      torch.tensor(
        [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]							)
    snake_case_       						=      torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]							)

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

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

  # 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=lowercase_			,					organization="""nielsr"""			,					commit_message="""Add model"""							)


if __name__ == "__main__":
    lowerCamelCase_    =					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.'''
    )
    lowerCamelCase_    =					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 typing import Callable



def    UpperCamelCase(					lowercase_							)	->				List[str]:





  '''simple docstring'''





  def wrapper(lowercase_							):
    snake_case_       						=      function_with_docstring.__doc__
    return target_function

  return wrapper