






"""simple docstring"""


import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
   from scipy.stats import pearsonr, spearmanr
   from sklearn.metrics import fa_score, matthews_corrcoef


_UpperCamelCase    :   Tuple   			=						(
    'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
    'library. You can have a look at this example script for pointers: '
    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)




def       snake_case  (A_				:str     ,      A_				:List[Any]				):







  '''simple docstring'''

  warnings.warn(A_     ,      A_				)
  requires_backends(A_     ,      'sklearn'				)
  return (preds == labels).mean()




def       snake_case  (A_				:str     ,      A_				:str				):







  '''simple docstring'''

  warnings.warn(A_     ,      A_				)
  requires_backends(A_     ,      'sklearn'				)
  a					:      Any    =       simple_accuracy(A_     ,      A_				)
  a					:      List[Any]    =       fa_score(y_true=A_     ,      y_pred=A_				)
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def       snake_case  (A_				:Tuple     ,      A_				:int				):







  '''simple docstring'''

  warnings.warn(A_     ,      A_				)
  requires_backends(A_     ,      'sklearn'				)
  a					:      Union[str, Any]    =       pearsonr(A_     ,      A_				)[0]
  a					:      List[Any]    =       spearmanr(A_     ,      A_				)[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def       snake_case  (A_				:Any     ,      A_				:Dict     ,      A_				:int				):







  '''simple docstring'''

  warnings.warn(A_     ,      A_				)
  requires_backends(A_     ,      'sklearn'				)
  assert len(A_				) == len(A_				), f'''Predictions and labels have mismatched lengths {len(A_				)} and {len(A_				)}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(A_     ,      A_				)}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "mrpc":
    return acc_and_fa(A_     ,      A_				)
  elif task_name == "sts-b":
    return pearson_and_spearman(A_     ,      A_				)
  elif task_name == "qqp":
    return acc_and_fa(A_     ,      A_				)
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "rte":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  elif task_name == "hans":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  else:
    raise KeyError(A_				)






def       snake_case  (A_				:Tuple     ,      A_				:Optional[Any]     ,      A_				:List[Any]				):







  '''simple docstring'''

  warnings.warn(A_     ,      A_				)
  requires_backends(A_     ,      'sklearn'				)
  if len(A_				) != len(A_				):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(A_				)} and {len(A_				)}'''				)
  if task_name == "xnli":
    return {"acc": simple_accuracy(A_     ,      A_				)}
  else:
    raise KeyError(A_				)












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




