


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      =   (
    """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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




     """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




     """simple docstring"""

     warnings.warn(__lowerCamelCase , __lowerCamelCase )
     requires_backends(__lowerCamelCase , '''sklearn''' )
     snake_case_	=				simple_accuracy(__lowerCamelCase , __lowerCamelCase )
     snake_case_	=				fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  str:




     """simple docstring"""

     warnings.warn(__lowerCamelCase , __lowerCamelCase )
     requires_backends(__lowerCamelCase , '''sklearn''' )
     snake_case_	=				pearsonr(__lowerCamelCase , __lowerCamelCase )[0]
     snake_case_	=				spearmanr(__lowerCamelCase , __lowerCamelCase )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




     """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[Any]:




     """simple docstring"""

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



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,
)


UpperCAmelCase      =   {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase      =   _LazyModule(__name__, globals()["""__file__"""], _import_structure)