"""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()
_UpperCamelCase:				Union[str, Any]    							=  logging.get_logger(__name__)


def   lowercase__     (			_UpperCAmelCase				)     ->			Union[str, Any]:






      '''simple docstring'''


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

      return orig_key


def   lowercase__     (			_UpperCAmelCase				)     ->			Tuple:






      '''simple docstring'''


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


def   lowercase__     (			)     ->			Optional[Any]:






      '''simple docstring'''


      lowercase  :      Dict					=   'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase  :      str					=   Image.open(requests.get(_UpperCAmelCase	,      stream=_UpperCAmelCase				).raw				)

      return im


@torch.no_grad()
def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      )     ->			Optional[int]:






      '''simple docstring'''


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

      # load image processor
      lowercase  :      Optional[int]					=   DeformableDetrImageProcessor(format='coco_detection'				)

      # prepare image
      lowercase  :      Optional[Any]					=   prepare_img()
      lowercase  :      int					=   image_processor(images=_UpperCAmelCase	,      return_tensors='pt'				)
      lowercase  :      Tuple					=   encoding['pixel_values']

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

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

      lowercase  :      int					=   'cuda' if torch.cuda.is_available() else 'cpu'
      model.to(_UpperCAmelCase				)
      # verify our conversion
      lowercase  :      List[str]					=   model(pixel_values.to(_UpperCAmelCase				)				)

      lowercase  :      str					=   torch.tensor(
          [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-10.56_34, -5.3_3_7_9, -7.5_1_1_6]]				)
      lowercase  :      List[Any]					=   torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]				)

      if single_scale:
            lowercase  :      Optional[Any]					=   torch.tensor(
                [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-10.06_65, -5.8_4_7_0, -7.7_0_0_3]]				)
            lowercase  :      Dict					=   torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]				)

      if single_scale and dilation:
            lowercase  :      List[Any]					=   torch.tensor(
                [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-10.11_78, -4.5_2_7_5, -6.2_6_7_1]]				)
            lowercase  :      List[Any]					=   torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]				)

      if with_box_refine:
            lowercase  :      int					=   torch.tensor(
                [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]				)
            lowercase  :      List[Any]					=   torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]				)

      if with_box_refine and two_stage:
            lowercase  :      int					=   torch.tensor(
                [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]				)
            lowercase  :      Tuple					=   torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]				)

      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:
            lowercase  :      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=_UpperCAmelCase	,      organization='nielsr'	,      commit_message='Add model'				)


if __name__ == "__main__":
     _UpperCamelCase:				Optional[int]    							=  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.'
     )
     _UpperCamelCase:				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"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_UpperCamelCase:				int    							=  TypeVar('T', bound='TaskTemplate')


@dataclass(frozen=SCREAMING_SNAKE_CASE__     )
class   a__		(						abc.ABC     ):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42
   _lowerCamelCase												=				42


   def        lowercase				(							self      :							T,     lowerCAmelCase      :							Features			)    ->       T:
         return copy.deepcopy(self			)


   @property
   def        lowercase				(							self      :							Union[str, Any]			)    ->       Features:
         return Features(**self.input_schema,     **self.label_schema			)


   @property
   @abc.abstractmethod
   def        lowercase				(							self      :							Tuple			)    ->       Dict[str, str]:
         raise NotImplementedError






   @classmethod
   def        lowercase				(							cls      :							Type[T],     lowerCAmelCase      :							dict			)    ->       T:
         lowercase  :      Union[str, Any]					=   {f.name for f in dataclasses.fields(cls			)}
         return cls(**{k: v for k, v in template_dict.items() if k in field_names}			)
