



from sklearn.metrics import roc_auc_score

import datasets


_lowerCamelCase         =	"""
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

_lowerCamelCase         =	"""
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

_lowerCamelCase         =	"""\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@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    _SCREAMING_SNAKE_CASE						(datasets.Metric			):





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






     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Optional[int]="macro"  ,	UpperCamelCase			:				Union[str, Any]=None  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				str="raise"  ,	UpperCamelCase			:				int=None  ,	)->List[Any]:
      return {
          "roc_auc": roc_auc_score(
              UpperCamelCase  ,	UpperCamelCase  ,	average=UpperCamelCase  ,	sample_weight=UpperCamelCase  ,	max_fpr=UpperCamelCase  ,	multi_class=UpperCamelCase  ,	labels=UpperCamelCase  ,	)
      }











"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						:							Dict        =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:							Union[str, Any]        =      []
for i in range(6):
     # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
     # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
             f'decoder.layers.{i}.encoder_attn.out_proj.weight',
         )
     )
     rename_keys.append(
         (
             f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
             f'decoder.layers.{i}.encoder_attn.out_proj.bias',
         )
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
     )
     rename_keys.append(
         (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
     )
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
     rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							Union[str, Any]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]		)		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Tuple								=					OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      lowerCAmelCase_  :							List[str]								=					key.replace('backbone.0.body'       ,  'backbone.conv_encoder.model'		)
      lowerCAmelCase_  :							Optional[int]								=					value
    else:
      lowerCAmelCase_  :							Optional[Any]								=					value

  return new_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							List[Any]=False		)		->  Optional[int]:

  """simple docstring"""




  lowerCAmelCase_  :							Union[str, Any]								=					''
  if is_panoptic:
    lowerCAmelCase_  :							str								=					'detr.'

  # first: transformer encoder
  for i in range(6		):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Any								=					state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_  :							Optional[int]								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[:256]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Union[str, Any]								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							int								=					in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6		):
    # read in weights + bias of input projection layer of self-attention
    lowerCAmelCase_  :							List[str]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
    lowerCAmelCase_  :							str								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_  :							Any								=					in_proj_weight[:256, :]
    lowerCAmelCase_  :							List[Any]								=					in_proj_bias[:256]
    lowerCAmelCase_  :							List[Any]								=					in_proj_weight[256:512, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[256:512]
    lowerCAmelCase_  :							Any								=					in_proj_weight[-256:, :]
    lowerCAmelCase_  :							Tuple								=					in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    lowerCAmelCase_  :							Optional[Any]								=					state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
    lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"		)
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    lowerCAmelCase_  :							int								=					in_proj_weight_cross_attn[:256, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[:256]
    lowerCAmelCase_  :							Any								=					in_proj_weight_cross_attn[256:512, :]
    lowerCAmelCase_  :							int								=					in_proj_bias_cross_attn[256:512]
    lowerCAmelCase_  :							Dict								=					in_proj_weight_cross_attn[-256:, :]
    lowerCAmelCase_  :							Optional[Any]								=					in_proj_bias_cross_attn[-256:]

def 							UpperCamelCase_       (       )		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							List[str]								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					DetrConfig()
  # set backbone and dilation attributes
  if "resnet101" in model_name:
    lowerCAmelCase_  :							int								=					'resnet101'
  if "dc5" in model_name:
    lowerCAmelCase_  :							Any								=					True
  lowerCAmelCase_  :							Tuple								=					'panoptic' in model_name
  if is_panoptic:
    lowerCAmelCase_  :							int								=					250
  else:
    lowerCAmelCase_  :							Optional[int]								=					91
    lowerCAmelCase_  :							int								=					'huggingface/label-files'
    lowerCAmelCase_  :							Dict								=					'coco-detection-id2label.json'
    lowerCAmelCase_  :							List[str]								=					json.load(open(hf_hub_download(lowerCAmelCase__       ,  lowerCAmelCase__       ,  repo_type='dataset'		)       ,  'r'		)		)
    lowerCAmelCase_  :							str								=					{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
    lowerCAmelCase_  :							Union[str, Any]								=					idalabel
    lowerCAmelCase_  :							int								=					{v: k for k, v in idalabel.items()}

  # load image processor
  lowerCAmelCase_  :							Optional[int]								=					'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCAmelCase_  :							List[Any]								=					DetrImageProcessor(format=lowerCAmelCase__		)

  # prepare image
  lowerCAmelCase_  :							str								=					prepare_img()
  lowerCAmelCase_  :							Tuple								=					image_processor(images=lowerCAmelCase__       ,  return_tensors='pt'		)
  lowerCAmelCase_  :							int								=					encoding['pixel_values']

  logger.info(f"Converting model {model_name}..."		)

  # load original model from torch hub
  lowerCAmelCase_  :							int								=					torch.hub.load('facebookresearch/detr'       ,  lowerCAmelCase__       ,  pretrained=lowerCAmelCase__		).eval()
  lowerCAmelCase_  :							Optional[Any]								=					detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase_  :							List[Any]								=					'detr.' + src
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					rename_backbone_keys(lowerCAmelCase__		)
  # query, key and value matrices need special treatment
  read_in_q_k_v(lowerCAmelCase__       ,  is_panoptic=lowerCAmelCase__		)
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  lowerCAmelCase_  :							List[str]								=					'detr.model.' if is_panoptic else 'model.'
  for key in state_dict.copy().keys():
    if is_panoptic:
      if (
          key.startswith('detr'		)
          and not key.startswith('class_labels_classifier'		)
          and not key.startswith('bbox_predictor'		)
      ):
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Any								=					val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Dict								=					val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							List[str]								=					DetrForSegmentation(lowerCAmelCase__		) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()
  # verify our conversion
  lowerCAmelCase_  :							Tuple								=					detr(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[Any]								=					model(lowerCAmelCase__		)
  assert torch.allclose(outputs.logits       ,  original_outputs['pred_logits']       ,  atol=1e-4		)
  assert torch.allclose(outputs.pred_boxes       ,  original_outputs['pred_boxes']       ,  atol=1e-4		)
  if is_panoptic:
    assert torch.allclose(outputs.pred_masks       ,  original_outputs['pred_masks']       ,  atol=1e-4		)

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


if __name__ == "__main__":
     lowercase__						:							Tuple        =      argparse.ArgumentParser()

     parser.add_argument(
         """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR 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."""
     )
     lowercase__						:							Tuple        =      parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





