





'''simple docstring'''






import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowerCamelCase						: Dict		=   '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n'

_lowerCamelCase						: Tuple		=   '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

_lowerCamelCase						: List[Any]		=   '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: "c" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,     _KWARGS_DESCRIPTION)
class 		UpperCAmelCase   (				datasets.Metric):


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Dict  )     ->						Dict:
    return datasets.MetricInfo(
        description=_DESCRIPTION							,	citation=_CITATION							,	homepage='''https://github.com/krishnap25/mauve'''							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
            {
                '''predictions''': datasets.Value('''string'''							,	id='''sequence'''  ),
                '''references''': datasets.Value('''string'''							,	id='''sequence'''  ),
            }  )							,	codebase_urls=['''https://github.com/krishnap25/mauve''']							,	reference_urls=[
            '''https://arxiv.org/abs/2102.01454''',
            '''https://github.com/krishnap25/mauve''',
        ]							,	)




  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Tuple="auto"							,	UpperCamelCase__    :					Optional[int]=-1							,	UpperCamelCase__    :					List[str]=0.9							,	UpperCamelCase__    :					int=5							,	UpperCamelCase__    :					List[Any]=500							,	UpperCamelCase__    :					Optional[Any]="gpt2-large"							,	UpperCamelCase__    :					int=-1							,	UpperCamelCase__    :					Dict=1024							,	UpperCamelCase__    :					str=25							,	UpperCamelCase__    :					str=5							,	UpperCamelCase__    :					Optional[int]=True							,	UpperCamelCase__    :					List[str]=25							,	)     ->						Optional[int]:
    _UpperCamelCase       			=compute_mauve(
        p_text=UpperCamelCase__							,	q_text=UpperCamelCase__							,	p_features=UpperCamelCase__							,	q_features=UpperCamelCase__							,	p_tokens=UpperCamelCase__							,	q_tokens=UpperCamelCase__							,	num_buckets=UpperCamelCase__							,	pca_max_data=UpperCamelCase__							,	kmeans_explained_var=UpperCamelCase__							,	kmeans_num_redo=UpperCamelCase__							,	kmeans_max_iter=UpperCamelCase__							,	featurize_model_name=UpperCamelCase__							,	device_id=UpperCamelCase__							,	max_text_length=UpperCamelCase__							,	divergence_curve_discretization_size=UpperCamelCase__							,	mauve_scaling_factor=UpperCamelCase__							,	verbose=UpperCamelCase__							,	seed=UpperCamelCase__							,	)
    return out













'''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)






