




'''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()
lowercase__								=						logging.get_logger(__name__)


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

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

   return orig_key


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   for i in range(6 ):
      # read in weights + bias of input projection layer of self-attention
      SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight""" )
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			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__      :					int							    =			in_proj_weight[:2_56, :]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			in_proj_bias[:2_56]
      SCREAMING_SNAKE_CASE__      :					int							    =			in_proj_weight[2_56:5_12, :]
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			in_proj_bias[2_56:5_12]
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			in_proj_weight[-2_56:, :]
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			in_proj_bias[-2_56:]


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Dict							    =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE__      :					Any							    =			Image.open(requests.get(snake_case						,  stream=snake_case ).raw )

   return im


@torch.no_grad()
def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  ):




   '''simple docstring'''

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

   # load image processor
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			DeformableDetrImageProcessor(format='''coco_detection''' )

   # prepare image
   SCREAMING_SNAKE_CASE__      :					Dict							    =			prepare_img()
   SCREAMING_SNAKE_CASE__      :					str							    =			image_processor(images=snake_case						,  return_tensors='''pt''' )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			encoding['''pixel_values''']

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

   # load original state dict
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.load(snake_case						,  map_location='''cpu''' )['''model''']
   # rename keys
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE__      :					Any							    =			state_dict.pop(snake_case )
      SCREAMING_SNAKE_CASE__      :					Any							    =			val
   # query, key and value matrices need special treatment
   read_in_q_k_v(snake_case )
   # 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__      :					Optional[int]							    =			'''model.'''
   for key in state_dict.copy().keys():
      if not key.startswith('''class_embed''' ) and not key.startswith('''bbox_embed''' ):
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			state_dict.pop(snake_case )
         SCREAMING_SNAKE_CASE__      :					str							    =			val
    # finally, create HuggingFace model and load state dict
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			DeformableDetrForObjectDetection(snake_case )
   model.load_state_dict(snake_case )
   model.eval()

   SCREAMING_SNAKE_CASE__      :					int							    =			'''cuda''' if torch.cuda.is_available() else '''cpu'''
   model.to(snake_case )
   # verify our conversion
   SCREAMING_SNAKE_CASE__      :					Any							    =			model(pixel_values.to(snake_case ) )

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

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

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

   # Push to hub
   if push_to_hub:
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''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=snake_case						,  organization='''nielsr'''						,  commit_message='''Add model''' )


if __name__ == "__main__":
       lowercase__								=						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.'''
       )
       lowercase__								=						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'''





import math
from decimal import Decimal, getcontext


def 					UpperCamelCase						(	snake_case = 4_00_00_00 ):




   '''simple docstring'''

   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			int(snake_case )
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.''' )
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.''' )
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			1_00
   SCREAMING_SNAKE_CASE__      :					int							    =			(Decimal(5 ) ** Decimal(0.5 ) + 1) / Decimal(2 )

   SCREAMING_SNAKE_CASE__      :					int							    =			(math.floor(math.log(n * (phi + 2)						,  snake_case ) - 1 ) // 3) * 3 + 2
   SCREAMING_SNAKE_CASE__      :					int							    =			Decimal(round(phi ** Decimal(index + 1 ) ) ) / (phi + 2)
   SCREAMING_SNAKE_CASE__      :					Any							    =			num // 2
   return int(snake_case )


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

