





'''simple docstring'''




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






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					Any:
     __A						:			List[Any]								=	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						:			Any								=	DetaConfig(
         backbone_config=__snake_case			,							num_queries=9_00			,							encoder_ffn_dim=20_48			,							decoder_ffn_dim=20_48			,							num_feature_levels=5			,							assign_first_stage=__snake_case			,							with_box_refine=__snake_case			,							two_stage=__snake_case			,							)

     # set labels
     __A						:			Dict								=	'huggingface/label-files'
     if "o365" in model_name:
          __A						:			List[Any]								=	3_66
          __A						:			str								=	'object365-id2label.json'
     else:
          __A						:			Optional[int]								=	91
          __A						:			List[Any]								=	'coco-detection-id2label.json'

     __A						:			str								=	num_labels
     __A						:			Any								=	json.load(open(cached_download(hf_hub_url(__snake_case			,							__snake_case			,							repo_type='dataset'      )      )			,							'r'      )      )
     __A						:			List[str]								=	{int(__snake_case      ): v for k, v in idalabel.items()}
     __A						:			str								=	idalabel
     __A						:			Dict								=	{v: k for k, v in idalabel.items()}

     return config






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					Union[str, Any]:
     __A						:			Union[str, Any]								=	[]

     # 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 	_lowerCAmelCase      (  __snake_case  :			Union[str, Any]			,							__snake_case  :			Tuple			,							__snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Union[str, Any]								=	dct.pop(__snake_case      )
     __A						:			Tuple								=	val






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Union[str, Any]      )					->					Optional[Any]:
     __A						:			Union[str, Any]								=	[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
     for i in range(len(backbone_config.depths      )      ):
          __A						:			Any								=	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						:			Dict								=	state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'      )
               __A						:			Union[str, Any]								=	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						:			List[str]								=	in_proj_weight[:dim, :]
               __A						:			Dict								=	in_proj_bias[: dim]
               __A						:			int								=	in_proj_weight[
                   dim : dim * 2, :
               ]
               __A						:			Dict								=	in_proj_bias[
                   dim : dim * 2
               ]
               __A						:			List[Any]								=	in_proj_weight[
                   -dim :, :
               ]
               __A						:			Dict								=	in_proj_bias[-dim :]
               # fmt: on






def 	_lowerCAmelCase      (  __snake_case  :			int			,							__snake_case  :			Dict      )					->					List[Any]:
     # transformer decoder self-attention layers
     __A						:			Optional[int]								=	config.d_model
     for i in range(config.decoder_layers      ):
          # read in weights + bias of input projection layer of self-attention
          __A						:			Optional[int]								=	state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
          __A						:			Any								=	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						:			int								=	in_proj_weight[:hidden_size, :]
          __A						:			Optional[int]								=	in_proj_bias[:hidden_size]
          __A						:			List[str]								=	in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          __A						:			Union[str, Any]								=	in_proj_bias[hidden_size : hidden_size * 2]
          __A						:			List[str]								=	in_proj_weight[-hidden_size:, :]
          __A						:			List[Any]								=	in_proj_bias[-hidden_size:]






def 	_lowerCAmelCase      (  )					->					Union[str, Any]:
     __A						:			str								=	'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						:			Tuple								=	Image.open(requests.get(__snake_case			,							stream=__snake_case      ).raw      )

     return im






@torch.no_grad()
def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str			,							__snake_case  :			List[str]      )					->					Tuple:
     __A						:			Optional[int]								=	get_deta_config(__snake_case      )

     # load original state dict
     if model_name == "deta-swin-large":
          __A						:			List[Any]								=	hf_hub_download(repo_id='nielsr/deta-checkpoints'			,							filename='adet_swin_ft.pth'      )
     elif model_name == "deta-swin-large-o365":
          __A						:			Optional[Any]								=	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						:			List[str]								=	torch.load(__snake_case			,							map_location='cpu'      )['model']

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

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

     # 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						:			int								=	state_dict.pop(__snake_case      )
               __A						:			List[Any]								=	val
          if "input_proj" in key:
               __A						:			Union[str, Any]								=	state_dict.pop(__snake_case      )
               __A						:			List[str]								=	val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               __A						:			Optional[int]								=	state_dict.pop(__snake_case      )
               __A						:			Optional[int]								=	val

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

     __A						:			List[str]								=	'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(__snake_case      )

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

     # verify our conversion on image
     __A						:			Optional[int]								=	prepare_img()
     __A						:			List[str]								=	processor(images=__snake_case			,							return_tensors='pt'      )
     __A						:			List[Any]								=	encoding['pixel_values']
     __A						:			Any								=	model(pixel_values.to(__snake_case      )      )

     # verify logits
     print('Logits:'			,							outputs.logits[0, :3, :3]      )
     print('Boxes:'			,							outputs.pred_boxes[0, :3, :3]      )
     if model_name == "deta-swin-large":
          __A						:			Any								=	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						:			int								=	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						:			str								=	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						:			Union[str, Any]								=	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(__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!'      )

     if pytorch_dump_folder_path:
          # Save model and processor
          logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'      )
          Path(__snake_case      ).mkdir(exist_ok=__snake_case      )
          model.save_pretrained(__snake_case      )
          processor.save_pretrained(__snake_case      )

     # 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__":
  lowercase__   :				List[Any]      =   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.'''
  )
  lowercase__   :				List[Any]      =   parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					int:

     if not isinstance(__snake_case			,							__snake_case      ):
          __A						:			List[Any]								=	f'Input value of [number={number}] must be an integer'
          raise TypeError(__snake_case      )

     if number < 1:
          __A						:			Union[str, Any]								=	f'Input value of [number={number}] must be > 0'
          raise ValueError(__snake_case      )
     elif number == 1:
          return 3
     elif number == 2:
          return 5
     else:
          __A						:			Optional[Any]								=	int(math.log(number // 3			,							2      )      ) + 2

          __A						:			Union[str, Any]								=	[3, 5]
          __A						:			List[Any]								=	2
          __A						:			Optional[Any]								=	3
          for block in range(1			,							__snake_case      ):
               for _ in range(__snake_case      ):
                    proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]      )
                    proth_index += 1
               increment *= 2

     return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    lowercase__   :				str      =   0
    try:
      lowercase__   :				List[str]      =   proth(number)
    except ValueError:
      print(f"""ValueError: there is no {number}th Proth number""")
      continue

    print(f"""The {number}th Proth number: {value}""")