

"""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
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_  :      List[str]							=      logging.get_logger(__name__)



def 						UpperCAmelCase__ (					)   ->   Any:




   """simple docstring"""
   lowerCamelCase__						       =      DetaConfig(
       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__						       =      91
   lowerCamelCase__						       =      "huggingface/label-files"
   lowerCamelCase__						       =      "coco-detection-id2label.json"
   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 						UpperCAmelCase__ (					A__ )   ->   int:




   """simple docstring"""
   lowerCamelCase__						       =      []

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight") )
   rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight") )
   rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias") )
   rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean") )
   rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var") )
   # stages
   for stage_idx in range(len(config.backbone_config.depths ) ):
      for layer_idx in range(config.backbone_config.depths[stage_idx] ):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                ) )
         # 3 convs
         for i in range(3 ):
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                ) )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                    f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                ) )
    # 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 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   Optional[int]:




   """simple docstring"""
   lowerCamelCase__						       =      dct.pop(A__ )
   lowerCamelCase__						       =      val



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   List[str]:




   """simple docstring"""
   # transformer decoder self-attention layers
   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 						UpperCAmelCase__ (					)   ->   Any:




   """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 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   Union[str, Any]:




   """simple docstring"""
   lowerCamelCase__						       =      get_deta_config()

   # load original state dict
   if model_name == "deta-resnet-50":
      lowerCamelCase__						       =      "adet_checkpoint0011.pth"
   elif model_name == "deta-resnet-50-24-epochs":
      lowerCamelCase__						       =      "adet_2x_checkpoint0023.pth"
   else:
      raise ValueError(f'Model name {model_name} not supported' )
   lowerCamelCase__						       =      hf_hub_download(repo_id="nielsr/deta-checkpoints"						,     filename=A__ )
   lowerCamelCase__						       =      torch.load(A__						,     map_location="cpu" )["model"]

   # rename keys
   lowerCamelCase__						       =      create_rename_keys(A__ )
   for src, dest in rename_keys:
      rename_key(A__						,     A__						,     A__ )
   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
   if model_name == "deta-resnet-50":
      lowerCamelCase__						       =      torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] )
      lowerCamelCase__						       =      torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]] )
   elif model_name == "deta-resnet-50-24-epochs":
      lowerCamelCase__						       =      torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] )
      lowerCamelCase__						       =      torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]] )

   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__":
     SCREAMING_SNAKE_CASE_  :      int							=      argparse.ArgumentParser()

     parser.add_argument(
         '''--model_name''',
         type=str,
         default='''deta-resnet-50''',
         choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
         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.'''
     )
     SCREAMING_SNAKE_CASE_  :      Optional[int]							=      parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




