





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__					:							Optional[int]													=     logging.get_logger(__name__)



def _a				(	lowerCamelCase:   List[Any] )	->						Dict:







      '''simple docstring'''






      __A          =				SwinConfig(
          embed_dim=1_92					,				depths=(2, 2, 18, 2)					,				num_heads=(6, 12, 24, 48)					,				window_size=12					,				out_features=['''stage2''', '''stage3''', '''stage4''']					,				)

      __A          =				DetaConfig(
          backbone_config=lowerCamelCase					,				num_queries=9_00					,				encoder_ffn_dim=20_48					,				decoder_ffn_dim=20_48					,				num_feature_levels=5					,				assign_first_stage=lowerCamelCase					,				with_box_refine=lowerCamelCase					,				two_stage=lowerCamelCase					,				)

      # set labels
      __A          =				'''huggingface/label-files'''
      if "o365" in model_name:
            __A          =				3_66
            __A          =				'''object365-id2label.json'''
      else:
            __A          =				91
            __A          =				'''coco-detection-id2label.json'''

      __A          =				num_labels
      __A          =				json.load(open(cached_download(hf_hub_url(lowerCamelCase					,				lowerCamelCase					,				repo_type='''dataset''' ) )					,				'''r''' ) )
      __A          =				{int(lowerCamelCase ): v for k, v in idalabel.items()}
      __A          =				idalabel
      __A          =				{v: k for k, v in idalabel.items()}

      return config



def _a				(	lowerCamelCase:   Tuple )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				[]

      # stem
      # fmt: off
      rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''') )
      rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''') )
      rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''') )
      rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''') )
      # stages
      for i in range(len(config.backbone_config.depths ) ):
            for j in range(config.backbone_config.depths[i] ):
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )

            if i < 3:
                  rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.reduction.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""") )
                  rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.bias""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""") )

      rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''') )
      rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''') )
      rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''') )
      rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''') )
      rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''') )
      rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''') )

      # transformer encoder
      for i in range(config.encoder_layers ):
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""") )

      # transformer decoder
      for i in range(config.decoder_layers ):
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""") )

      # fmt: on

      return rename_keys



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   Dict )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				dct.pop(lowerCamelCase )
      __A          =				val



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Any )	->						Dict:







      '''simple docstring'''






      __A          =				[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
      for i in range(len(backbone_config.depths ) ):
            __A          =				num_features[i]
            for j in range(backbone_config.depths[i] ):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  __A          =				state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
                  __A          =				state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias""" )
                  # next, add query, keys and values (in that order) to the state dict
                  __A          =				in_proj_weight[:dim, :]
                  __A          =				in_proj_bias[: dim]
                  __A          =				in_proj_weight[
                      dim : dim * 2, :
                  ]
                  __A          =				in_proj_bias[
                      dim : dim * 2
                  ]
                  __A          =				in_proj_weight[
                      -dim :, :
                  ]
                  __A          =				in_proj_bias[-dim :]
                  # fmt: on



def _a				(	lowerCamelCase:   str					,				lowerCamelCase:   Optional[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				config.d_model
      for i in range(config.decoder_layers ):
            # read in weights + bias of input projection layer of self-attention
            __A          =				state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
            __A          =				state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            __A          =				in_proj_weight[:hidden_size, :]
            __A          =				in_proj_bias[:hidden_size]
            __A          =				in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            __A          =				in_proj_bias[hidden_size : hidden_size * 2]
            __A          =				in_proj_weight[-hidden_size:, :]
            __A          =				in_proj_bias[-hidden_size:]



def _a				(	)	->						List[str]:







      '''simple docstring'''






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

      return im



@torch.no_grad()
def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Dict )	->						Optional[int]:







      '''simple docstring'''






      __A          =				get_deta_config(lowerCamelCase )

      # load original state dict
      if model_name == "deta-swin-large":
            __A          =				hf_hub_download(repo_id='''nielsr/deta-checkpoints'''					,				filename='''adet_swin_ft.pth''' )
      elif model_name == "deta-swin-large-o365":
            __A          =				hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''					,				filename='''deta_swin_pt_o365.pth''' )
      else:
            raise ValueError(F"""Model name {model_name} not supported""" )

      __A          =				torch.load(lowerCamelCase					,				map_location='''cpu''' )['''model''']

      # original state dict
      for name, param in state_dict.items():
            print(lowerCamelCase					,				param.shape )

      # rename keys
      __A          =				create_rename_keys(lowerCamelCase )
      for src, dest in rename_keys:
            rename_key(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      read_in_swin_q_k_v(lowerCamelCase					,				config.backbone_config )
      read_in_decoder_q_k_v(lowerCamelCase					,				lowerCamelCase )

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  __A          =				state_dict.pop(lowerCamelCase )
                  __A          =				val
            if "input_proj" in key:
                  __A          =				state_dict.pop(lowerCamelCase )
                  __A          =				val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  __A          =				state_dict.pop(lowerCamelCase )
                  __A          =				val

    # finally, create HuggingFace model and load state dict
      __A          =				DetaForObjectDetection(lowerCamelCase )
      model.load_state_dict(lowerCamelCase )
      model.eval()

      __A          =				'''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(lowerCamelCase )

      # load image processor
      __A          =				DetaImageProcessor(format='''coco_detection''' )

      # verify our conversion on image
      __A          =				prepare_img()
      __A          =				processor(images=lowerCamelCase					,				return_tensors='''pt''' )
      __A          =				encoding['''pixel_values''']
      __A          =				model(pixel_values.to(lowerCamelCase ) )

      # verify logits
      print('''Logits:'''					,				outputs.logits[0, :3, :3] )
      print('''Boxes:'''					,				outputs.pred_boxes[0, :3, :3] )
      if model_name == "deta-swin-large":
            __A          =				torch.tensor(
                [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]] )
            __A          =				torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]] )
      elif model_name == "deta-swin-large-o365":
            __A          =				torch.tensor(
                [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]] )
            __A          =				torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]] )
      assert torch.allclose(outputs.logits[0, :3, :3]					,				expected_logits.to(lowerCamelCase )					,				atol=1e-4 )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,				expected_boxes.to(lowerCamelCase )					,				atol=1e-4 )
      print('''Everything ok!''' )

      if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}...""" )
            Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
            model.save_pretrained(lowerCamelCase )
            processor.save_pretrained(lowerCamelCase )

      # Push to hub
      if push_to_hub:
            print('''Pushing model and processor to hub...''' )
            model.push_to_hub(F"""jozhang97/{model_name}""" )
            processor.push_to_hub(F"""jozhang97/{model_name}""" )


if __name__ == "__main__":
       snake_case__					:							List[str]													=     argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           type=str,
           default='deta-swin-large',
           choices=['deta-swin-large', 'deta-swin-large-o365'],
           help='Name of the model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default=None,
           type=str,
           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__					:							List[Any]													=     parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





