






from sklearn.metrics import matthews_corrcoef

import datasets


__UpperCamelCase   :   Union[str, Any]      =    """
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

__UpperCamelCase   :   List[str]      =    """
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

__UpperCamelCase   :   Tuple      =    """\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""


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







	def 			_a (	self   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		return datasets.MetricInfo(
		    description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""int32"""      ),
		            """references""": datasets.Value("""int32"""      ),
		        }      )							,					reference_urls=[
		        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
		    ]							,					)







	def 			_a (	self   :				Union[str, Any]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Optional[Any]							,					_lowerCAmelCase   :				Optional[Any]=None      )		->			Optional[Any]:
		"""simple docstring"""







		return {
		    "matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase							,					_lowerCAmelCase							,					sample_weight=_lowerCAmelCase      )      ),
		}


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()
SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Optional[Any]  =		SwinConfig(
						    embed_dim=192		, depths=(2, 2, 18, 2)		, num_heads=(6, 12, 24, 48)		, window_size=12		, out_features=["""stage2""", """stage3""", """stage4"""]		, )

						UpperCamelCase_		:							Optional[Any]  =		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_		:							Dict  =		"""huggingface/label-files"""
						if "o365" in model_name:
												UpperCamelCase_		:							List[Any]  =		366
												UpperCamelCase_		:							str  =		"""object365-id2label.json"""
						else:
												UpperCamelCase_		:							Any  =		91
												UpperCamelCase_		:							Optional[int]  =		"""coco-detection-id2label.json"""

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

						return config






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[str]  =		[]

						# 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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		dct.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							str  =		val






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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Optional[int]			):
						# transformer decoder self-attention layers
						UpperCamelCase_		:							List[Any]  =		config.d_model
						for i in range(config.decoder_layers			):
												# read in weights + bias of input projection layer of self-attention
												UpperCamelCase_		:							str  =		state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
												UpperCamelCase_		:							Optional[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
												UpperCamelCase_		:							Optional[Any]  =		in_proj_weight[:hidden_size, :]
												UpperCamelCase_		:							int  =		in_proj_bias[:hidden_size]
												UpperCamelCase_		:							Dict  =		in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												UpperCamelCase_		:							Union[str, Any]  =		in_proj_bias[hidden_size : hidden_size * 2]
												UpperCamelCase_		:							Dict  =		in_proj_weight[-hidden_size:, :]
												UpperCamelCase_		:							Dict  =		in_proj_bias[-hidden_size:]






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Optional[Any]  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_		:							int  =		Image.open(requests.get(_SCREAMING_SNAKE_CASE		, stream=_SCREAMING_SNAKE_CASE			).raw			)

						return im






@torch.no_grad()
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Tuple  =		get_deta_config(_SCREAMING_SNAKE_CASE			)

						# load original state dict
						if model_name == "deta-swin-large":
												UpperCamelCase_		:							int  =		hf_hub_download(repo_id="""nielsr/deta-checkpoints"""		, filename="""adet_swin_ft.pth"""			)
						elif model_name == "deta-swin-large-o365":
												UpperCamelCase_		:							int  =		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_		:							Union[str, Any]  =		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_		:							List[str]  =		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_		:							Dict  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Optional[Any]  =		val
												if "input_proj" in key:
																		UpperCamelCase_		:							Tuple  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							int  =		val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		UpperCamelCase_		:							int  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							str  =		val

    # finally, create HuggingFace model and load state dict
						UpperCamelCase_		:							List[Any]  =		DetaForObjectDetection(_SCREAMING_SNAKE_CASE			)
						model.load_state_dict(_SCREAMING_SNAKE_CASE			)
						model.eval()

						UpperCamelCase_		:							Dict  =		"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_SCREAMING_SNAKE_CASE			)

						# load image processor
						UpperCamelCase_		:							Optional[Any]  =		DetaImageProcessor(format="""coco_detection"""			)

						# verify our conversion on image
						UpperCamelCase_		:							Optional[int]  =		prepare_img()
						UpperCamelCase_		:							Any  =		processor(images=_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""			)
						UpperCamelCase_		:							int  =		encoding["""pixel_values"""]
						UpperCamelCase_		:							Tuple  =		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_		:							Optional[Any]  =		torch.tensor(
												    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]			)
												UpperCamelCase_		:							Optional[Any]  =		torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]			)
						elif model_name == "deta-swin-large-o365":
												UpperCamelCase_		:							str  =		torch.tensor(
												    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]			)
												UpperCamelCase_		:							Union[str, Any]  =		torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]			)
						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__":
	SCREAMING_SNAKE_CASE		:     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."
	)
	SCREAMING_SNAKE_CASE		:     int         =	parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






