





from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase       = '''
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       = '''
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       = '''
@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 				_lowerCamelCase (    datasets.Metric    ):



						"""simple docstring"""






						def 						UpperCAmelCase		( self   )       ->   str:






								'''simple docstring'''






								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 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=True				,						UpperCAmelCase=None   )       ->   Optional[int]:






								'''simple docstring'''






								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase				,						UpperCAmelCase				,						normalize=UpperCAmelCase				,						sample_weight=UpperCAmelCase   )   )
								}








import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase       = logging.get_logger(__name__)







def       lowerCAmelCase__(	lowercase       :    Tuple				) ->    int:
  if "backbone.0.body" in orig_key:
    __snake_case   :				str        =       orig_key.replace("backbone.0.body"   ,     "backbone.conv_encoder.model"				)
  if "transformer" in orig_key:
    __snake_case   :				List[Any]        =       orig_key.replace("transformer."   ,     ""				)
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      __snake_case   :				List[str]        =       orig_key.replace("norm1"   ,     "self_attn_layer_norm"				)
    else:
      __snake_case   :				Optional[int]        =       orig_key.replace("norm1"   ,     "encoder_attn_layer_norm"				)
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      __snake_case   :				Optional[Any]        =       orig_key.replace("norm2"   ,     "final_layer_norm"				)
    else:
      __snake_case   :				List[str]        =       orig_key.replace("norm2"   ,     "self_attn_layer_norm"				)
  if "norm3" in orig_key:
    __snake_case   :				Dict        =       orig_key.replace("norm3"   ,     "final_layer_norm"				)
  if "linear1" in orig_key:
    __snake_case   :				Optional[Any]        =       orig_key.replace("linear1"   ,     "fc1"				)
  if "linear2" in orig_key:
    __snake_case   :				Dict        =       orig_key.replace("linear2"   ,     "fc2"				)
  if "query_embed" in orig_key:
    __snake_case   :				Optional[int]        =       orig_key.replace("query_embed"   ,     "query_position_embeddings"				)
  if "cross_attn" in orig_key:
    __snake_case   :				str        =       orig_key.replace("cross_attn"   ,     "encoder_attn"				)

  return orig_key







def       lowerCAmelCase__(	lowercase       :    Optional[int]				) ->    Tuple:
  # transformer decoder self-attention layers
  for i in range(6				):
    # read in weights + bias of input projection layer of self-attention
    __snake_case   :				str        =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
    __snake_case   :				Optional[Any]        =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_bias"""				)
    # next, add query, keys and values (in that order) to the state dict
    __snake_case   :				Tuple        =       in_proj_weight[:256, :]
    __snake_case   :				Dict        =       in_proj_bias[:256]
    __snake_case   :				List[str]        =       in_proj_weight[256:512, :]
    __snake_case   :				Optional[Any]        =       in_proj_bias[256:512]
    __snake_case   :				Optional[Any]        =       in_proj_weight[-256:, :]
    __snake_case   :				List[Any]        =       in_proj_bias[-256:]







def       lowerCAmelCase__(	) ->    str:
  __snake_case   :				Union[str, Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __snake_case   :				Optional[Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)

  return im







@torch.no_grad()
def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    Any   ,     lowercase       :    Optional[int]   ,     lowercase       :    List[Any]   ,     lowercase       :    str   ,     lowercase       :    int   ,     ) ->    Optional[Any]:
  __snake_case   :				Optional[int]        =       DeformableDetrConfig()
  # set config attributes
  if single_scale:
    __snake_case   :				List[str]        =       1
  __snake_case   :				Any        =       dilation
  __snake_case   :				List[str]        =       with_box_refine
  __snake_case   :				List[str]        =       two_stage
  # set labels
  __snake_case   :				int        =       91
  __snake_case   :				Any        =       "huggingface/label-files"
  __snake_case   :				List[Any]        =       "coco-detection-id2label.json"
  __snake_case   :				Tuple        =       json.load(open(cached_download(hf_hub_url(lowercase   ,     lowercase   ,     repo_type="dataset"				)				)   ,     "r"				)				)
  __snake_case   :				List[str]        =       {int(lowercase				): v for k, v in idalabel.items()}
  __snake_case   :				Optional[int]        =       idalabel
  __snake_case   :				str        =       {v: k for k, v in idalabel.items()}

  # load image processor
  __snake_case   :				Union[str, Any]        =       DeformableDetrImageProcessor(format="coco_detection"				)

  # prepare image
  __snake_case   :				List[Any]        =       prepare_img()
  __snake_case   :				Optional[int]        =       image_processor(images=lowercase   ,     return_tensors="pt"				)
  __snake_case   :				List[Any]        =       encoding["pixel_values"]

  logger.info("Converting model..."				)

  # load original state dict
  __snake_case   :				Optional[Any]        =       torch.load(lowercase   ,     map_location="cpu"				)["model"]
  # rename keys
  for key in state_dict.copy().keys():
    __snake_case   :				List[Any]        =       state_dict.pop(lowercase				)
    __snake_case   :				Dict        =       val
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowercase				)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  __snake_case   :				Union[str, Any]        =       "model."
  for key in state_dict.copy().keys():
    if not key.startswith("class_embed"				) and not key.startswith("bbox_embed"				):
      __snake_case   :				List[str]        =       state_dict.pop(lowercase				)
      __snake_case   :				int        =       val
    # finally, create HuggingFace model and load state dict
  __snake_case   :				int        =       DeformableDetrForObjectDetection(lowercase				)
  model.load_state_dict(lowercase				)
  model.eval()

  __snake_case   :				str        =       "cuda" if torch.cuda.is_available() else "cpu"
  model.to(lowercase				)
  # verify our conversion
  __snake_case   :				Any        =       model(pixel_values.to(lowercase				)				)

  __snake_case   :				Union[str, Any]        =       torch.tensor(
      [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]				)
  __snake_case   :				Any        =       torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]				)

  if single_scale:
    __snake_case   :				Union[str, Any]        =       torch.tensor(
        [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]				)
    __snake_case   :				Any        =       torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]				)

  if single_scale and dilation:
    __snake_case   :				Dict        =       torch.tensor(
        [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]				)
    __snake_case   :				List[str]        =       torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]				)

  if with_box_refine:
    __snake_case   :				List[str]        =       torch.tensor(
        [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]				)
    __snake_case   :				List[str]        =       torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]				)

  if with_box_refine and two_stage:
    __snake_case   :				Tuple        =       torch.tensor(
        [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]				)
    __snake_case   :				Any        =       torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]				)

  print("Logits:"   ,     outputs.logits[0, :3, :3]				)

  assert torch.allclose(outputs.logits[0, :3, :3]   ,     expected_logits.to(lowercase				)   ,     atol=1E-4				)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,     expected_boxes.to(lowercase				)   ,     atol=1E-4				)

  print("Everything ok!"				)

  # Save model and image processor
  logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
  Path(lowercase				).mkdir(exist_ok=lowercase				)
  model.save_pretrained(lowercase				)
  image_processor.save_pretrained(lowercase				)

  # Push to hub
  if push_to_hub:
    __snake_case   :				int        =       "deformable-detr"
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print("Pushing model to hub..."				)
    model.push_to_hub(repo_path_or_name=lowercase   ,     organization="nielsr"   ,     commit_message="Add model"				)


if __name__ == "__main__":
     _UpperCamelCase       = argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''',
         type=str,
         default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
         help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
     )
     parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
     parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
     parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
     parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=str,
         required=True,
         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       = parser.parse_args()
     convert_deformable_detr_checkpoint(
         args.checkpoint_path,
         args.single_scale,
         args.dilation,
         args.with_box_refine,
         args.two_stage,
         args.pytorch_dump_folder_path,
         args.push_to_hub,
     )

