






"""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()
_UpperCamelCase    :   Optional[Any]   			=						logging.get_logger(__name__)




def       snake_case  (A_				:Optional[int]				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       SwinConfig(
      embed_dim=1_9_2     ,      depths=(2, 2, 1_8, 2)     ,      num_heads=(6, 1_2, 2_4, 4_8)     ,      window_size=1_2     ,      out_features=['stage2', 'stage3', 'stage4']     ,      )

  a					:      Tuple    =       DetaConfig(
      backbone_config=A_     ,      num_queries=9_0_0     ,      encoder_ffn_dim=2_0_4_8     ,      decoder_ffn_dim=2_0_4_8     ,      num_feature_levels=5     ,      assign_first_stage=A_     ,      with_box_refine=A_     ,      two_stage=A_     ,      )

  # set labels
  a					:      Union[str, Any]    =       'huggingface/label-files'
  if "o365" in model_name:
    a					:      Dict    =       3_6_6
    a					:      Tuple    =       'object365-id2label.json'
  else:
    a					:      Any    =       9_1
    a					:      str    =       'coco-detection-id2label.json'

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

  return config




def       snake_case  (A_				:Union[str, Any]				):







  '''simple docstring'''

  a					:      List[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       snake_case  (A_				:Dict     ,      A_				:List[str]     ,      A_				:str				):







  '''simple docstring'''

  a					:      Tuple    =       dct.pop(A_				)
  a					:      Tuple    =       val




def       snake_case  (A_				:Optional[Any]     ,      A_				:Tuple				):







  '''simple docstring'''

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




def       snake_case  (A_				:Any     ,      A_				:Union[str, Any]				):







  '''simple docstring'''

  a					:      Union[str, Any]    =       config.d_model
  for i in range(config.decoder_layers				):
    # read in weights + bias of input projection layer of self-attention
    a					:      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					:      Tuple    =       in_proj_weight[:hidden_size, :]
    a					:      Union[str, Any]    =       in_proj_bias[:hidden_size]
    a					:      List[Any]    =       in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    a					:      Optional[int]    =       in_proj_bias[hidden_size : hidden_size * 2]
    a					:      List[Any]    =       in_proj_weight[-hidden_size:, :]
    a					:      Optional[Any]    =       in_proj_bias[-hidden_size:]




def       snake_case  ():







  '''simple docstring'''

  a					:      Dict    =       'http://images.cocodataset.org/val2017/000000039769.jpg'
  a					:      Tuple    =       Image.open(requests.get(A_     ,      stream=A_				).raw				)

  return im




@torch.no_grad()
def       snake_case  (A_				:int     ,      A_				:int     ,      A_				:str				):







  '''simple docstring'''

  a					:      List[Any]    =       get_deta_config(A_				)

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

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

  # rename keys
  a					:      int    =       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:
      a					:      int    =       state_dict.pop(A_				)
      a					:      Union[str, Any]    =       val
    if "input_proj" in key:
      a					:      List[str]    =       state_dict.pop(A_				)
      a					:      str    =       val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      a					:      Union[str, Any]    =       state_dict.pop(A_				)
      a					:      str    =       val

    # finally, create HuggingFace model and load state dict
  a					:      int    =       DetaForObjectDetection(A_				)
  model.load_state_dict(A_				)
  model.eval()

  a					:      int    =       'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(A_				)

  # load image processor
  a					:      List[str]    =       DetaImageProcessor(format='coco_detection'				)

  # verify our conversion on image
  a					:      Optional[int]    =       prepare_img()
  a					:      Optional[Any]    =       processor(images=A_     ,      return_tensors='pt'				)
  a					:      List[Any]    =       encoding['pixel_values']
  a					:      Optional[Any]    =       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":
    a					:      int    =       torch.tensor(
        [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]				)
    a					:      Optional[Any]    =       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":
    a					:      Any    =       torch.tensor(
        [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]				)
    a					:      List[str]    =       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__":
 _UpperCamelCase    :   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.'
 )
 _UpperCamelCase    :   str   			=						parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




