


"""simple docstring"""





from typing import Dict, Optional, Tuple

import torch


def   _snake_case    (    _snake_case					:      torch.Tensor      ):
      lowerCAmelCase       :						Tuple							= boundaries[1] - boundaries[0]
      lowerCAmelCase       :						Optional[int]							= boundaries + step / 2
      lowerCAmelCase       :						int							= torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )]			, dim=0      )
      return bin_centers


def   _snake_case    (    _snake_case					:      torch.Tensor			, _snake_case					:      torch.Tensor			, ):
      lowerCAmelCase       :						Union[str, Any]							= _calculate_bin_centers(_snake_case      )
      return (
          torch.sum(aligned_distance_error_probs * bin_centers			, dim=-1      ),
          bin_centers[-1],
      )


def   _snake_case    (    _snake_case					:      torch.Tensor			, _snake_case					:      int = 31			, _snake_case					:      int = 64			, **_snake_case					:      Tuple			, ):
      lowerCAmelCase       :						Optional[int]							= torch.linspace(0			, _snake_case			, steps=(no_bins - 1)			, device=logits.device      )

      lowerCAmelCase       :						str							= torch.nn.functional.softmax(_snake_case			, dim=-1      )
      lowerCAmelCase,   lowerCAmelCase       :						List[str]							= _calculate_expected_aligned_error(
          alignment_confidence_breaks=_snake_case			, aligned_distance_error_probs=_snake_case			, )

      return {
          "aligned_confidence_probs": aligned_confidence_probs,
          "predicted_aligned_error": predicted_aligned_error,
          "max_predicted_aligned_error": max_predicted_aligned_error,
      }






def   _snake_case    (    _snake_case					:      torch.Tensor			, _snake_case					:      Optional[torch.Tensor] = None			, _snake_case					:      int = 31			, _snake_case					:      int = 64			, _snake_case					:      float = 1E-8			, **_snake_case					:      Union[str, Any]			, ):
      if residue_weights is None:
            lowerCAmelCase       :						Optional[int]							= logits.new_ones(logits.shape[-2]      )

      lowerCAmelCase       :						List[str]							= torch.linspace(0			, _snake_case			, steps=(no_bins - 1)			, device=logits.device      )

      lowerCAmelCase       :						List[str]							= _calculate_bin_centers(_snake_case      )
      torch.sum(_snake_case      )
      lowerCAmelCase       :						Optional[int]							= logits.shape[-2]
      lowerCAmelCase       :						List[Any]							= max(_snake_case			, 19      )

      lowerCAmelCase       :						Tuple							= 1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

      lowerCAmelCase       :						str							= torch.nn.functional.softmax(_snake_case			, dim=-1      )

      lowerCAmelCase       :						Optional[int]							= 1.0 / (1 + (bin_centers**2) / (da**2))
      lowerCAmelCase       :						Tuple							= torch.sum(probs * tm_per_bin			, dim=-1      )

      lowerCAmelCase       :						Union[str, Any]							= residue_weights / (eps + residue_weights.sum())
      lowerCAmelCase       :						Any							= torch.sum(predicted_tm_term * normed_residue_mask			, dim=-1      )

      lowerCAmelCase       :						List[str]							= per_alignment * residue_weights

      lowerCAmelCase       :						int							= (weighted == torch.max(_snake_case      )).nonzero()[0]
      return per_alignment[tuple(_snake_case      )]






"""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()
snake_case__		:  List[str]     					=					logging.get_logger(__name__)


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

      return orig_key


def   _snake_case    (    _snake_case					:      List[str]      ):
      # transformer decoder self-attention layers
      for i in range(6      ):
            # read in weights + bias of input projection layer of self-attention
            lowerCAmelCase       :						str							= state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''      )
            lowerCAmelCase       :						str							= 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
            lowerCAmelCase       :						Tuple							= in_proj_weight[:256, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[:256]
            lowerCAmelCase       :						int							= in_proj_weight[256:512, :]
            lowerCAmelCase       :						Optional[int]							= in_proj_bias[256:512]
            lowerCAmelCase       :						Any							= in_proj_weight[-256:, :]
            lowerCAmelCase       :						str							= in_proj_bias[-256:]


def   _snake_case    (    ):
      lowerCAmelCase       :						List[str]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase       :						Dict							= Image.open(requests.get(_snake_case			, stream=_snake_case      ).raw      )

      return im


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

      # load image processor
      lowerCAmelCase       :						str							= DeformableDetrImageProcessor(format='''coco_detection'''      )

      # prepare image
      lowerCAmelCase       :						Tuple							= prepare_img()
      lowerCAmelCase       :						int							= image_processor(images=_snake_case			, return_tensors='''pt'''      )
      lowerCAmelCase       :						int							= encoding['''pixel_values''']

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

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

      lowerCAmelCase       :						List[str]							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(_snake_case      )
      # verify our conversion
      lowerCAmelCase       :						Dict							= model(pixel_values.to(_snake_case      )      )

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

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

      if with_box_refine:
            lowerCAmelCase       :						Optional[Any]							= torch.tensor(
                [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
            lowerCAmelCase       :						List[str]							= 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:
            lowerCAmelCase       :						Dict							= torch.tensor(
                [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
            lowerCAmelCase       :						Union[str, Any]							= 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:
            lowerCAmelCase       :						Optional[Any]							= '''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__":
 snake_case__		:  Union[str, Any]     					=					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.'''
 )
 snake_case__		:  Union[str, Any]     					=					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,
 )


