


"""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()
_snake_case          =							logging.get_logger(__name__)



def   snake_case      (				_a:     Optional[int]							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		SwinConfig(
          embed_dim=192		,   depths=(2, 2, 18, 2)		,   num_heads=(6, 12, 24, 48)		,   window_size=12		,   out_features=['stage2', 'stage3', 'stage4']		,   )

      lowerCamelCase__           =		DetaConfig(
          backbone_config=_a		,   num_queries=900		,   encoder_ffn_dim=2048		,   decoder_ffn_dim=2048		,   num_feature_levels=5		,   assign_first_stage=_a		,   with_box_refine=_a		,   two_stage=_a		,   )

      # set labels
      lowerCamelCase__           =		'huggingface/label-files'
      if "o365" in model_name:
            lowerCamelCase__           =		366
            lowerCamelCase__           =		'object365-id2label.json'
      else:
            lowerCamelCase__           =		91
            lowerCamelCase__           =		'coco-detection-id2label.json'

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

      return config



def   snake_case      (				_a:     int							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[]

      # 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   snake_case      (				_a:     Tuple		,   _a:     Optional[int]		,   _a:     Optional[Any]							)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		dct.pop(_a							)
      lowerCamelCase__           =		val



def   snake_case      (				_a:     List[str]		,   _a:     Optional[int]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
      for i in range(len(backbone_config.depths							)							):
            lowerCamelCase__           =		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)
                  lowerCamelCase__           =		state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'							)
                  lowerCamelCase__           =		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
                  lowerCamelCase__           =		in_proj_weight[:dim, :]
                  lowerCamelCase__           =		in_proj_bias[: dim]
                  lowerCamelCase__           =		in_proj_weight[
                      dim : dim * 2, :
                  ]
                  lowerCamelCase__           =		in_proj_bias[
                      dim : dim * 2
                  ]
                  lowerCamelCase__           =		in_proj_weight[
                      -dim :, :
                  ]
                  lowerCamelCase__           =		in_proj_bias[-dim :]
                  # fmt: on



def   snake_case      (				_a:     Union[str, Any]		,   _a:     List[str]							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		config.d_model
      for i in range(config.decoder_layers							):
            # read in weights + bias of input projection layer of self-attention
            lowerCamelCase__           =		state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'							)
            lowerCamelCase__           =		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
            lowerCamelCase__           =		in_proj_weight[:hidden_size, :]
            lowerCamelCase__           =		in_proj_bias[:hidden_size]
            lowerCamelCase__           =		in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            lowerCamelCase__           =		in_proj_bias[hidden_size : hidden_size * 2]
            lowerCamelCase__           =		in_proj_weight[-hidden_size:, :]
            lowerCamelCase__           =		in_proj_bias[-hidden_size:]



def   snake_case      (				)->		Dict:
      '''simple docstring'''


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

      return im



@torch.no_grad()
def   snake_case      (				_a:     Optional[Any]		,   _a:     int		,   _a:     int							)->		Any:
      '''simple docstring'''


      lowerCamelCase__           =		get_deta_config(_a							)

      # load original state dict
      if model_name == "deta-swin-large":
            lowerCamelCase__           =		hf_hub_download(repo_id='nielsr/deta-checkpoints'		,   filename='adet_swin_ft.pth'							)
      elif model_name == "deta-swin-large-o365":
            lowerCamelCase__           =		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'							)

      lowerCamelCase__           =		torch.load(_a		,   map_location='cpu'							)['model']

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

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

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

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

      lowerCamelCase__           =		'cuda' if torch.cuda.is_available() else 'cpu'
      model.to(_a							)

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

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

      # verify logits
      print('Logits:'		,   outputs.logits[0, :3, :3]							)
      print('Boxes:'		,   outputs.pred_boxes[0, :3, :3]							)
      if model_name == "deta-swin-large":
            lowerCamelCase__           =		torch.tensor(
                [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]							)
            lowerCamelCase__           =		torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]							)
      elif model_name == "deta-swin-large-o365":
            lowerCamelCase__           =		torch.tensor(
                [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]							)
            lowerCamelCase__           =		torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]							)
      assert torch.allclose(outputs.logits[0, :3, :3]		,   expected_logits.to(_a							)		,   atol=1E-4							)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,   expected_boxes.to(_a							)		,   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(_a							).mkdir(exist_ok=_a							)
            model.save_pretrained(_a							)
            processor.save_pretrained(_a							)

      # 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          =							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          =							parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
   from .camera import create_pan_cameras
   from .pipeline_shap_e import ShapEPipeline
   from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
   from .renderer import (
       BoundingBoxVolume,
       ImportanceRaySampler,
       MLPNeRFModelOutput,
       MLPNeRSTFModel,
       ShapEParamsProjModel,
       ShapERenderer,
       StratifiedRaySampler,
       VoidNeRFModel,
   )



