

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


a_							=      logging.get_logger(__name__)




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : int					)    ->  Optional[Any]:

  try:
    with open(A_						,	"rb"					) as flax_state_f:
      __UpperCAmelCase						    =       from_bytes(A_						,	flax_state_f.read()					)
  except UnpicklingError as e:
    try:
      with open(A_					) as f:
        if f.read().startswith("version"					):
          raise OSError(
              "You seem to have cloned a repository without having git-lfs installed. Please"
              " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
              " folder you cloned."					)
        else:
          raise ValueError from e
    except (UnicodeDecodeError, ValueError):
      raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''					)

  return load_flax_weights_in_pytorch_model(A_						,	A_					)





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

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
        " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
        " instructions."					)
    raise

  # check if we have bf16 weights
  __UpperCAmelCase						    =       flatten_dict(jax.tree_util.tree_map(lambda A_					: x.dtype == jnp.bfloataa						,	A_					)					).values()
  if any(A_					):
    # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

    # and bf16 is not fully supported in PT yet.
    logger.warning(
        "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
        "before loading those in PyTorch model."					)
    __UpperCAmelCase						    =       jax.tree_util.tree_map(
        lambda A_					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params						,	A_					)

  __UpperCAmelCase						    =       ""

  __UpperCAmelCase						    =       flatten_dict(A_						,	sep="."					)
  __UpperCAmelCase						    =       pt_model.state_dict()

  # keep track of unexpected & missing keys
  __UpperCAmelCase						    =       []
  __UpperCAmelCase						    =       set(pt_model_dict.keys()					)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __UpperCAmelCase						    =       flax_key_tuple.split("."					)

    if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
      __UpperCAmelCase						    =       flax_key_tuple_array[:-1] + ["weight"]
      __UpperCAmelCase						    =       jnp.transpose(A_						,	(3, 2, 0, 1)					)
    elif flax_key_tuple_array[-1] == "kernel":
      __UpperCAmelCase						    =       flax_key_tuple_array[:-1] + ["weight"]
      __UpperCAmelCase						    =       flax_tensor.T
    elif flax_key_tuple_array[-1] == "scale":
      __UpperCAmelCase						    =       flax_key_tuple_array[:-1] + ["weight"]

    if "time_embedding" not in flax_key_tuple_array:
      for i, flax_key_tuple_string in enumerate(A_					):
        __UpperCAmelCase						    =       (
            flax_key_tuple_string.replace("_0"						,	".0"					)
            .replace("_1"						,	".1"					)
            .replace("_2"						,	".2"					)
            .replace("_3"						,	".3"					)
            .replace("_4"						,	".4"					)
            .replace("_5"						,	".5"					)
            .replace("_6"						,	".6"					)
            .replace("_7"						,	".7"					)
            .replace("_8"						,	".8"					)
            .replace("_9"						,	".9"					)
        )

    __UpperCAmelCase						    =       ".".join(A_					)

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
            F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)
      else:
        # add weight to pytorch dict
        __UpperCAmelCase						    =       np.asarray(A_					) if not isinstance(A_						,	np.ndarray					) else flax_tensor
        __UpperCAmelCase						    =       torch.from_numpy(A_					)
        # remove from missing keys
        missing_keys.remove(A_					)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(A_					)

  pt_model.load_state_dict(A_					)

  # re-transform missing_keys to list
  __UpperCAmelCase						    =       list(A_					)

  if len(A_					) > 0:
    logger.warning(
        "Some weights of the Flax model were not used when initializing the PyTorch model"
        F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
        F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
        " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
        F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
        " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
        " FlaxBertForSequenceClassification model)."					)
  if len(A_					) > 0:
    logger.warning(
        F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
        F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
        " use it for predictions and inference."					)

  return pt_model




from sklearn.metrics import fa_score

import datasets


a_							=      """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


a_							=      """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


a_							=      """
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION					)
class 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
            {
                "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                "references": datasets.Sequence(datasets.Value("int32"   )   ),
            }
            if self.config_name == "multilabel"
            else {
                "predictions": datasets.Value("int32"   ),
                "references": datasets.Value("int32"   ),
            }   )				,				reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       fa_score(
        __lowerCAmelCase				,				__lowerCAmelCase				,				labels=__lowerCAmelCase				,				pos_label=__lowerCAmelCase				,				average=__lowerCAmelCase				,				sample_weight=__lowerCAmelCase   )
    return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



