


"""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


lowerCamelCase_       :       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 _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    warnings.warn(lowercase	,   lowercase		)
    requires_backends(lowercase	,   '''sklearn'''		)
    return (preds == labels).mean()
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    warnings.warn(lowercase	,   lowercase		)
    requires_backends(lowercase	,   '''sklearn'''		)
    a					=simple_accuracy(lowercase	,   lowercase		)
    a					=fa_score(y_true=lowercase	,   y_pred=lowercase		)
    return {
        "acc": acc,
        "f1": fa,
        "acc_and_f1": (acc + fa) / 2,
    }
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    warnings.warn(lowercase	,   lowercase		)
    requires_backends(lowercase	,   '''sklearn'''		)
    a					=pearsonr(lowercase	,   lowercase		)[0]
    a					=spearmanr(lowercase	,   lowercase		)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }
def _A						(						lowercase	,   lowercase	,   lowercase		):







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





def _A						(						lowercase	,   lowercase	,   lowercase		):







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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)