

from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowerCAmelCase     :   str						     =  '\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

lowerCAmelCase     :   Tuple						     =  '\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

lowerCAmelCase     :   int						     =  '\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'

def    A_  (							a					,   a  ):

      """simple docstring"""



      return float((preds == labels).mean()  )

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     simple_accuracy(lowerCamelCase_					,   lowerCamelCase_  )
      SCREAMING_SNAKE_CASE_				:  Any           =     float(fa_score(y_true=lowerCamelCase_					,   y_pred=lowerCamelCase_  )  )
      return {
          "accuracy": acc,
          "f1": fa,
      }

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     float(pearsonr(lowerCamelCase_					,   lowerCamelCase_  )[0]  )
      SCREAMING_SNAKE_CASE_				:  int           =     float(spearmanr(lowerCamelCase_					,   lowerCamelCase_  )[0]  )
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }



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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        if self.config_name not in [
            "sst2",
            "mnli",
            "mnli_mismatched",
            "mnli_matched",
            "cola",
            "stsb",
            "mrpc",
            "qqp",
            "qnli",
            "rte",
            "wnli",
            "hans",
        ]:
              raise KeyError(
                  'You should supply a configuration name selected in '
                  '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                  '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'					)
        return datasets.MetricInfo(
            description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
                {
                    'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'					),
                    'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'					),
                }					) ,					codebase_urls=[] ,					reference_urls=[] ,					format='numpy' ,					)



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.config_name == "cola":
              return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase ,					__lowerCamelCase					)}
        elif self.config_name == "stsb":
              return pearson_and_spearman(__lowerCamelCase ,					__lowerCamelCase					)
        elif self.config_name in ["mrpc", "qqp"]:
              return acc_and_fa(__lowerCamelCase ,					__lowerCamelCase					)
        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
              return {"accuracy": simple_accuracy(__lowerCamelCase ,					__lowerCamelCase					)}
        else:
              raise KeyError(
                  'You should supply a configuration name selected in '
                  '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                  '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'					)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase     :   Dict						     =  logging.get_logger(__name__)

lowerCAmelCase     :   str						     =  {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  '''table-transformer'''
  SCREAMING_SNAKE_CASE    :      Any       =  ['''past_key_values''']
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  {
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=3 ,					_SCREAMING_SNAKE_CASE=100 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=256 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.0 ,					_SCREAMING_SNAKE_CASE=0.02 ,					_SCREAMING_SNAKE_CASE=1.0 ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="sine" ,					_SCREAMING_SNAKE_CASE="resnet50" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=1 ,					_SCREAMING_SNAKE_CASE=5 ,					_SCREAMING_SNAKE_CASE=2 ,					_SCREAMING_SNAKE_CASE=0.1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        if backbone_config is not None and use_timm_backbone:
              raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'					)

        if not use_timm_backbone:
              if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     CONFIG_MAPPING['resnet'](out_features=['stage4']					)
              elif isinstance(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone_config.get('model_type'					)
                    SCREAMING_SNAKE_CASE_				:  str           =     CONFIG_MAPPING[backbone_model_type]
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     config_class.from_dict(_SCREAMING_SNAKE_CASE					)
              # set timm attributes to None
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None, None, None

        SCREAMING_SNAKE_CASE_				:  Any           =     use_timm_backbone
        SCREAMING_SNAKE_CASE_				:  int           =     backbone_config
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     num_channels
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_queries
        SCREAMING_SNAKE_CASE_				:  int           =     d_model
        SCREAMING_SNAKE_CASE_				:  int           =     encoder_ffn_dim
        SCREAMING_SNAKE_CASE_				:  Any           =     encoder_layers
        SCREAMING_SNAKE_CASE_				:  str           =     encoder_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[str]           =     decoder_ffn_dim
        SCREAMING_SNAKE_CASE_				:  str           =     decoder_layers
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     decoder_attention_heads
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dropout
        SCREAMING_SNAKE_CASE_				:  int           =     attention_dropout
        SCREAMING_SNAKE_CASE_				:  Dict           =     activation_dropout
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     activation_function
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     init_std
        SCREAMING_SNAKE_CASE_				:  int           =     init_xavier_std
        SCREAMING_SNAKE_CASE_				:  str           =     encoder_layerdrop
        SCREAMING_SNAKE_CASE_				:  Any           =     decoder_layerdrop
        SCREAMING_SNAKE_CASE_				:  Any           =     encoder_layers
        SCREAMING_SNAKE_CASE_				:  List[str]           =     auxiliary_loss
        SCREAMING_SNAKE_CASE_				:  List[str]           =     position_embedding_type
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     backbone
        SCREAMING_SNAKE_CASE_				:  Tuple           =     use_pretrained_backbone
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dilation
        # Hungarian matcher
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     class_cost
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     bbox_cost
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     giou_cost
        # Loss coefficients
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     mask_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  str           =     dice_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  Dict           =     bbox_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  int           =     giou_loss_coefficient
        SCREAMING_SNAKE_CASE_				:  Any           =     eos_coefficient
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.encoder_attention_heads



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.d_model

class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[Any]       =  version.parse('''1.11''')

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ('pixel_mask', {0: 'batch'}),
            ]					)

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 1e-5



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 12






