



"""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()
lowerCamelCase				:     Tuple								=  logging.get_logger(__name__)


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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

  return orig_key


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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


def 							A__ (     ):


  '''simple docstring'''


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

  return im


@torch.no_grad()
def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							):


  '''simple docstring'''


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

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

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

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

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

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

  _SCREAMING_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]]    )
  _SCREAMING_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:
    _SCREAMING_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]]    )
    _SCREAMING_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:
    _SCREAMING_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]]    )
    _SCREAMING_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:
    _SCREAMING_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]]    )
    _SCREAMING_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:
    _SCREAMING_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]]    )
    _SCREAMING_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(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:
    _SCREAMING_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__":
    lowerCamelCase				:     List[str]								=  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				:     Dict								=  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"""






from collections import defaultdict

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE = 5				)		->							int:






       """simple docstring"""




       _UpperCAmelCase			     =  defaultdict(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  0

       while True:
              _UpperCAmelCase			     =  get_digits(SCREAMING_SNAKE_CASE				)
              freqs[digits].append(SCREAMING_SNAKE_CASE				)

              if len(freqs[digits]				) == max_base:
                     _UpperCAmelCase			     =  freqs[digits][0] ** 3
                     return base

              num += 1

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       return "".join(sorted(str(num**3				)				)				)


if __name__ == "__main__":
    print(F'''{solution() = }''')






