





'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
      from ...onnx.config import PatchingSpec
      from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowerCamelCase						: int		=   logging.get_logger(__name__)

__lowerCamelCase						: int		=   {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				"""longformer"""


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					Union[List[int], int] = 512							,	UpperCamelCase__    :					int = 2							,	UpperCamelCase__    :					int = 1							,	UpperCamelCase__    :					int = 0							,	UpperCamelCase__    :					int = 2							,	UpperCamelCase__    :					int = 3_0522							,	UpperCamelCase__    :					int = 768							,	UpperCamelCase__    :					int = 12							,	UpperCamelCase__    :					int = 12							,	UpperCamelCase__    :					int = 3072							,	UpperCamelCase__    :					str = "gelu"							,	UpperCamelCase__    :					float = 0.1							,	UpperCamelCase__    :					float = 0.1							,	UpperCamelCase__    :					int = 512							,	UpperCamelCase__    :					int = 2							,	UpperCamelCase__    :					float = 0.02							,	UpperCamelCase__    :					float = 1E-12							,	UpperCamelCase__    :					bool = False							,	**UpperCamelCase__    :					Union[str, Any]							,	)     ->						List[Any]:
    super().__init__(pad_token_id=UpperCamelCase__							,	**UpperCamelCase__  )

    _UpperCamelCase       			=attention_window
    _UpperCamelCase       			=sep_token_id
    _UpperCamelCase       			=bos_token_id
    _UpperCamelCase       			=eos_token_id
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=hidden_act
    _UpperCamelCase       			=intermediate_size
    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=max_position_embeddings
    _UpperCamelCase       			=type_vocab_size
    _UpperCamelCase       			=initializer_range
    _UpperCamelCase       			=layer_norm_eps
    _UpperCamelCase       			=onnx_export







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					"PretrainedConfig"							,	UpperCamelCase__    :					str = "default"							,	UpperCamelCase__    :					"List[PatchingSpec]" = None  )     ->						List[str]:
    super().__init__(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=True


  @property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
      _UpperCamelCase       			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
    else:
      _UpperCamelCase       			={0: '''batch''', 1: '''sequence'''}
    return OrderedDict(
        [
            ('''input_ids''', dynamic_axis),
            ('''attention_mask''', dynamic_axis),
            ('''global_attention_mask''', dynamic_axis),
        ]  )


  @property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Mapping[str, Mapping[int, str]]:
    _UpperCamelCase       			=super().outputs
    if self.task == "default":
      _UpperCamelCase       			={0: '''batch'''}
    return outputs


  @property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						float:
    return 1E-4


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
    # needs to be >= 14 to support tril operator
    return max(super().default_onnx_opset							,	14  )




  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					"PreTrainedTokenizerBase"							,	UpperCamelCase__    :					int = -1							,	UpperCamelCase__    :					int = -1							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					Optional[TensorType] = None							,	)     ->						Mapping[str, Any]:
    _UpperCamelCase       			=super().generate_dummy_inputs(
        preprocessor=UpperCamelCase__							,	batch_size=UpperCamelCase__							,	seq_length=UpperCamelCase__							,	is_pair=UpperCamelCase__							,	framework=UpperCamelCase__  )
    import torch

    # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
    # makes the export fail randomly
    _UpperCamelCase       			=torch.zeros_like(inputs['''input_ids''']  )
    # make every second token global
    _UpperCamelCase       			=1

    return inputs













'''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)






