
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor

class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						int=				"pixel_values"
    _a		:						List[str]=				False
    _a		:						Union[str, Any]=				TimmBackboneConfig
    def __init__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           requires_backends(self		,"""timm"""      )
           super().__init__(snake_case      )
           lowercase							:     int       	=      config

           if config.backbone is None:
                  raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""      )

           if config.backbone not in timm.list_models():
                  raise ValueError(f"backbone {config.backbone} is not supported by timm."      )

           if hasattr(snake_case		,"""out_features"""      ) and config.out_features is not None:
                  raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""      )

           lowercase							:     str       	=      getattr(snake_case		,"""use_pretrained_backbone"""		,snake_case      )
           if pretrained is None:
                  raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""      )

           # We just take the final layer by default. This matches the default for the transformers models.
           lowercase							:     Optional[int]       	=      config.out_indices if getattr(snake_case		,"""out_indices"""		,snake_case      ) is not None else (-1,)

           lowercase							:     List[Any]       	=      timm.create_model(
               config.backbone		,pretrained=snake_case		,features_only=config.features_only		,in_chans=config.num_channels		,out_indices=snake_case		,**snake_case		,)
           # These are used to control the output of the model when called. If output_hidden_states is True, then
           # return_layers is modified to include all layers.
           lowercase							:     Optional[int]       	=      self._backbone.return_layers
           lowercase							:     Tuple       	=      {layer["""module"""]: str(snake_case      ) for i, layer in enumerate(self._backbone.feature_info.info      )}
           super()._init_backbone(snake_case      )
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           requires_backends(cls		,["""vision""", """timm"""]      )
           from ...models.timm_backbone import TimmBackboneConfig

           lowercase							:     Tuple       	=      kwargs.pop("""config"""		,TimmBackboneConfig()      )

           lowercase							:     int       	=      kwargs.pop("""use_timm_backbone"""		,snake_case      )
           if not use_timm:
                  raise ValueError("""use_timm_backbone must be True for timm backbones"""      )

           lowercase							:     Any       	=      kwargs.pop("""num_channels"""		,config.num_channels      )
           lowercase							:     List[Any]       	=      kwargs.pop("""features_only"""		,config.features_only      )
           lowercase							:     Any       	=      kwargs.pop("""use_pretrained_backbone"""		,config.use_pretrained_backbone      )
           lowercase							:     Dict       	=      kwargs.pop("""out_indices"""		,config.out_indices      )
           lowercase							:     Any       	=      TimmBackboneConfig(
               backbone=snake_case		,num_channels=snake_case		,features_only=snake_case		,use_pretrained_backbone=snake_case		,out_indices=snake_case		,)
           return super()._from_config(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,snake_case=None		,snake_case=None		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      return_dict if return_dict is not None else self.config.use_return_dict
           lowercase							:     Union[str, Any]       	=      (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:     Any       	=      output_attentions if output_attentions is not None else self.config.output_attentions

           if output_attentions:
                  raise ValueError("""Cannot output attentions for timm backbones at the moment"""      )

           if output_hidden_states:
                  # We modify the return layers to include all the stages of the backbone
                  lowercase							:     str       	=      self._all_layers
                  lowercase							:     Any       	=      self._backbone(snake_case		,**snake_case      )
                  lowercase							:     List[str]       	=      self._return_layers
                  lowercase							:     List[Any]       	=      tuple(hidden_states[i] for i in self.out_indices      )
           else:
                  lowercase							:     List[Any]       	=      self._backbone(snake_case		,**snake_case      )
                  lowercase							:     Tuple       	=      None

           lowercase							:     Any       	=      tuple(snake_case      )
           lowercase							:     Dict       	=      tuple(snake_case      ) if hidden_states is not None else None

           if not return_dict:
                  lowercase							:     Union[str, Any]       	=      (feature_maps,)
                  if output_hidden_states:
                         lowercase							:     Optional[Any]       	=      output + (hidden_states,)
                  return output

           return BackboneOutput(feature_maps=snake_case		,hidden_states=snake_case		,attentions=snake_case      )


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

import datasets


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

lowercase  :					Dict						    =							"""\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowercase  :					int						    =							"""
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(snake_case		,snake_case      )}
           elif self.config_name == "stsb":
                  return pearson_and_spearman(snake_case		,snake_case      )
           elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(snake_case		,snake_case      )
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(snake_case		,snake_case      )}
           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\"]"""      )
