





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
from transformers.utils import logging


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





def   _lowercase       (    )    ->	List[Any]:
				lowerCamelCase									=DetaConfig(
				    num_queries=9_00      ,   encoder_ffn_dim=20_48      ,   decoder_ffn_dim=20_48      ,   num_feature_levels=5      ,   assign_first_stage=_UpperCAmelCase      ,   with_box_refine=_UpperCAmelCase      ,   two_stage=_UpperCAmelCase      ,   )

				# set labels
				lowerCamelCase									=91
				lowerCamelCase									="""huggingface/label-files"""
				lowerCamelCase									="""coco-detection-id2label.json"""
				lowerCamelCase									=json.load(open(cached_download(hf_hub_url(_UpperCAmelCase      ,   _UpperCAmelCase      ,   repo_type="""dataset"""					)					)      ,   """r"""					)					)
				lowerCamelCase									={int(_UpperCAmelCase					): v for k, v in idalabel.items()}
				lowerCamelCase									=idalabel
				lowerCamelCase									={v: k for k, v in idalabel.items()}

				return config





def   _lowercase       (    _UpperCAmelCase					)    ->	List[str]:
				lowerCamelCase									=[]

				# stem
				# fmt: off
				rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")					)
				rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")					)
				rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")					)
				rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")					)
				rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")					)
				# stages
				for stage_idx in range(len(config.backbone_config.depths					)					):
								for layer_idx in range(config.backbone_config.depths[stage_idx]					):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																    )					)
												# 3 convs
												for i in range(3					):
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																    )					)
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																        F"""model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																    )					)
    # 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   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Union[str, Any]:
				lowerCamelCase									=dct.pop(_UpperCAmelCase					)
				lowerCamelCase									=val





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Dict:
				# transformer decoder self-attention layers
				lowerCamelCase									=config.d_model
				for i in range(config.decoder_layers					):
								# read in weights + bias of input projection layer of self-attention
								lowerCamelCase									=state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""					)
								lowerCamelCase									=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
								lowerCamelCase									=in_proj_weight[:hidden_size, :]
								lowerCamelCase									=in_proj_bias[:hidden_size]
								lowerCamelCase									=in_proj_weight[
								    hidden_size : hidden_size * 2, :
								]
								lowerCamelCase									=in_proj_bias[hidden_size : hidden_size * 2]
								lowerCamelCase									=in_proj_weight[-hidden_size:, :]
								lowerCamelCase									=in_proj_bias[-hidden_size:]





def   _lowercase       (    )    ->	List[str]:
				lowerCamelCase									="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase									=Image.open(requests.get(_UpperCAmelCase      ,   stream=_UpperCAmelCase					).raw					)

				return im





@torch.no_grad()
def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Optional[int]:
				lowerCamelCase									=get_deta_config()

				# load original state dict
				if model_name == "deta-resnet-50":
								lowerCamelCase									="""adet_checkpoint0011.pth"""
				elif model_name == "deta-resnet-50-24-epochs":
								lowerCamelCase									="""adet_2x_checkpoint0023.pth"""
				else:
								raise ValueError(F"""Model name {model_name} not supported"""					)
				lowerCamelCase									=hf_hub_download(repo_id="""nielsr/deta-checkpoints"""      ,   filename=_UpperCAmelCase					)
				lowerCamelCase									=torch.load(_UpperCAmelCase      ,   map_location="""cpu"""					)["""model"""]

				# rename keys
				lowerCamelCase									=create_rename_keys(_UpperCAmelCase					)
				for src, dest in rename_keys:
								rename_key(_UpperCAmelCase      ,   _UpperCAmelCase      ,   _UpperCAmelCase					)
				read_in_decoder_q_k_v(_UpperCAmelCase      ,   _UpperCAmelCase					)

				# fix some prefixes
				for key in state_dict.copy().keys():
								if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val
								if "input_proj" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val
								if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
												lowerCamelCase									=state_dict.pop(_UpperCAmelCase					)
												lowerCamelCase									=val

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

				lowerCamelCase									="""cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(_UpperCAmelCase					)

				# load image processor
				lowerCamelCase									=DetaImageProcessor(format="""coco_detection"""					)

				# verify our conversion on image
				lowerCamelCase									=prepare_img()
				lowerCamelCase									=processor(images=_UpperCAmelCase      ,   return_tensors="""pt"""					)
				lowerCamelCase									=encoding["""pixel_values"""]
				lowerCamelCase									=model(pixel_values.to(_UpperCAmelCase					)					)

				# verify logits
				if model_name == "deta-resnet-50":
								lowerCamelCase									=torch.tensor(
								    [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]					)
								lowerCamelCase									=torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]					)
				elif model_name == "deta-resnet-50-24-epochs":
								lowerCamelCase									=torch.tensor(
								    [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]					)
								lowerCamelCase									=torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]					)

				assert torch.allclose(outputs.logits[0, :3, :3]      ,   expected_logits.to(_UpperCAmelCase					)      ,   atol=1e-4					)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,   expected_boxes.to(_UpperCAmelCase					)      ,   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(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
								model.save_pretrained(_UpperCAmelCase					)
								processor.save_pretrained(_UpperCAmelCase					)

				# Push to hub
				if push_to_hub:
								print("""Pushing model and processor to hub..."""					)
								model.push_to_hub(F"""jozhang97/{model_name}"""					)
								processor.push_to_hub(F"""jozhang97/{model_name}"""					)


if __name__ == "__main__":
							UpperCAmelCase__    :      Any       					=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    type=str,
							    default='''deta-resnet-50''',
							    choices=['''deta-resnet-50''', '''deta-resnet-50-24-epochs'''],
							    help='''Name of the model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    type=str,
							    help='''Path to the folder to output PyTorch model.''',
							)
							parser.add_argument(
							    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
							)
							UpperCAmelCase__    :      Dict       					=parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@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    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





