





'''simple docstring'''






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


__lowerCamelCase						: Union[str, Any]		=   [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  for pegasus_name, hf_name in PATTERNS:
    _UpperCamelCase       			=k.replace(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return k


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=DEFAULTS.copy()
  cfg_kwargs.update(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=PegasusConfig(**__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=torch_model.model.state_dict()
  _UpperCamelCase       			={}
  for k, v in tf_weights.items():
    _UpperCamelCase       			=rename_state_dict_key(__SCREAMING_SNAKE_CASE   )
    if new_k not in sd:
      raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''   )

    if "dense" in k or "proj" in new_k:
      _UpperCamelCase       			=v.T
    _UpperCamelCase       			=torch.tensor(__SCREAMING_SNAKE_CASE  ,      dtype=sd[new_k].dtype   )
    assert v.shape == sd[new_k].shape, f'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  _UpperCamelCase       			=torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]   )
  _UpperCamelCase       			=mapping['''shared.weight''']
  _UpperCamelCase       			=mapping['''shared.weight''']
  _UpperCamelCase       			={k: torch.zeros_like(__SCREAMING_SNAKE_CASE   ) for k, v in sd.items() if k.endswith('''bias'''   ) and k not in mapping}
  mapping.update(**__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase			,     _UpperCamelCase       			=torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE  ,      strict=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[
      k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
  ]
  assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], f'''no matches found for the following tf keys {extra}'''
  return torch_model


def 					_a   (__SCREAMING_SNAKE_CASE="./ckpt/aeslc/model.ckpt-32000"   ):


  """simple docstring"""

  _UpperCamelCase       			=tf.train.list_variables(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			={}
  _UpperCamelCase       			=['''Adafactor''', '''global_step''']
  for name, shape in tqdm(__SCREAMING_SNAKE_CASE  ,      desc='''converting tf checkpoint to dict'''   ):
    _UpperCamelCase       			=any(pat in name for pat in ignore_name   )
    if skip_key:
      continue
    _UpperCamelCase       			=tf.train.load_variable(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=array
  return tf_weights


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=Path(__SCREAMING_SNAKE_CASE   ).parent.name
  _UpperCamelCase       			=task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
  _UpperCamelCase       			=PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''  ,      model_max_length=__SCREAMING_SNAKE_CASE   )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(__SCREAMING_SNAKE_CASE   )

  # convert model
  _UpperCamelCase       			=get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=task_specific_params[f'''summarization_{dataset}''']
  if dataset == "large":
    _UpperCamelCase       			=task_specific_params
  _UpperCamelCase       			=convert_pegasus(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  torch_model.save_pretrained(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=torch_model.state_dict()
  sd.pop('''model.decoder.embed_positions.weight'''   )
  sd.pop('''model.encoder.embed_positions.weight'''   )
  torch.save(__SCREAMING_SNAKE_CASE  ,      Path(__SCREAMING_SNAKE_CASE   ) / '''pytorch_model.bin'''   )


if __name__ == "__main__":
      __lowerCamelCase						: str		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
      parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
      __lowerCamelCase						: Optional[int]		=   parser.parse_args()
      if args.save_dir is None:
            __lowerCamelCase						: Any		=   Path(args.tf_ckpt_path).parent.name
            __lowerCamelCase						: str		=   os.path.join('pegasus', dataset)
      convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)













'''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()
__lowerCamelCase						: Any		=   logging.get_logger(__name__)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

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

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

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

  return config


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]

  # 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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=dct.pop(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=val


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   ():


  """simple docstring"""

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

  return im


@torch.no_grad()
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=get_deta_config(__SCREAMING_SNAKE_CASE   )

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

  _UpperCamelCase       			=torch.load(__SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''   )['''model''']

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

  # rename keys
  _UpperCamelCase       			=create_rename_keys(__SCREAMING_SNAKE_CASE   )
  for src, dest in rename_keys:
    rename_key(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  read_in_swin_q_k_v(__SCREAMING_SNAKE_CASE  ,      config.backbone_config   )
  read_in_decoder_q_k_v(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_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:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val
    if "input_proj" in key:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _UpperCamelCase       			=state_dict.pop(__SCREAMING_SNAKE_CASE   )
      _UpperCamelCase       			=val

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

  _UpperCamelCase       			='''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(__SCREAMING_SNAKE_CASE   )

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

  # verify our conversion on image
  _UpperCamelCase       			=prepare_img()
  _UpperCamelCase       			=processor(images=__SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''   )
  _UpperCamelCase       			=encoding['''pixel_values''']
  _UpperCamelCase       			=model(pixel_values.to(__SCREAMING_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":
    _UpperCamelCase       			=torch.tensor(
        [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]   )
    _UpperCamelCase       			=torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]   )
  elif model_name == "deta-swin-large-o365":
    _UpperCamelCase       			=torch.tensor(
        [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]   )
    _UpperCamelCase       			=torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]   )
  assert torch.allclose(outputs.logits[0, :3, :3]  ,      expected_logits.to(__SCREAMING_SNAKE_CASE   )  ,      atol=1E-4   )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,      expected_boxes.to(__SCREAMING_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(__SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE   )
    model.save_pretrained(__SCREAMING_SNAKE_CASE   )
    processor.save_pretrained(__SCREAMING_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__":
      __lowerCamelCase						: 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.'
      )
      __lowerCamelCase						: List[str]		=   parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






