




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	:						List[Any]			=     (
    "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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  warnings.warn(lowerCamelCase,			lowerCamelCase   )
  requires_backends(lowerCamelCase,			"sklearn"   )
  return (preds == labels).mean()



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  warnings.warn(lowerCamelCase,			lowerCamelCase   )
  requires_backends(lowerCamelCase,			"sklearn"   )
  lowercase    :Any      	=						simple_accuracy(lowerCamelCase,			lowerCamelCase   )
  lowercase    :Any      	=						fa_score(y_true=lowerCamelCase,			y_pred=lowerCamelCase   )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  warnings.warn(lowerCamelCase,			lowerCamelCase   )
  requires_backends(lowerCamelCase,			"sklearn"   )
  lowercase    :Tuple      	=						pearsonr(lowerCamelCase,			lowerCamelCase   )[0]
  lowercase    :List[str]      	=						spearmanr(lowerCamelCase,			lowerCamelCase   )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  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   )









import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_UpperCAmelCase	:						List[Any]			=     logging.get_logger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :int      	=						r"\w+[.]\d+"
  lowercase    :Tuple      	=						re.findall(lowerCamelCase,			lowerCamelCase   )
  for pat in pats:
    lowercase    :List[str]      	=						key.replace(lowerCamelCase,			"_".join(pat.split("."   )   )   )
  return key



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("scale",)
  if (
      any("norm" in str_ for str_ in pt_tuple_key   )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    lowercase    :Optional[int]      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    lowercase    :int      	=						pt_tuple_key[:-1] + ("scale",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    lowercase    :List[str]      	=						pt_tuple_key[:-1] + ("embedding",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowercase    :Union[str, Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    lowercase    :List[Any]      	=						pt_tensor.transpose(2,			3,			1,			0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowercase    :Optional[Any]      	=						pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight":
    lowercase    :Tuple      	=						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  lowercase    :Tuple      	=						pt_tuple_key[:-1] + ("weight",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  lowercase    :int      	=						pt_tuple_key[:-1] + ("bias",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase=42   ):
  # Step 1: Convert pytorch tensor to numpy
  lowercase    :Optional[int]      	=						{k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  lowercase    :str      	=						flax_model.init_weights(PRNGKey(lowerCamelCase   )   )

  lowercase    :Tuple      	=						flatten_dict(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    lowercase    :List[Any]      	=						rename_key(lowerCamelCase   )
    lowercase    :List[Any]      	=						tuple(renamed_pt_key.split("."   )   )

    # Correctly rename weight parameters
    lowercase	,    lowercase    :List[str]      	=						rename_key_and_reshape_tensor(lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # also add unexpected weight so that warning is thrown
    lowercase    :List[str]      	=						jnp.asarray(lowerCamelCase   )

  return unflatten_dict(lowerCamelCase   )



