






'''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,
)


__SCREAMING_SNAKE_CASE	:					str					=  {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE	:					int					=  ["T5Tokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE	:					Optional[Any]					=  ["T5TokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE	:					int					=  [
      "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
      "T5EncoderModel",
      "T5ForConditionalGeneration",
      "T5Model",
      "T5PreTrainedModel",
      "load_tf_weights_in_t5",
      "T5ForQuestionAnswering",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE	:					Optional[Any]					=  [
      "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFT5EncoderModel",
      "TFT5ForConditionalGeneration",
      "TFT5Model",
      "TFT5PreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE	:					int					=  [
      "FlaxT5EncoderModel",
      "FlaxT5ForConditionalGeneration",
      "FlaxT5Model",
      "FlaxT5PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_ta import TaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_ta_fast import TaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ta import (
        T5_PRETRAINED_MODEL_ARCHIVE_LIST,
        TaEncoderModel,
        TaForConditionalGeneration,
        TaForQuestionAnswering,
        TaModel,
        TaPreTrainedModel,
        load_tf_weights_in_ta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ta import (
        TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTaEncoderModel,
        TFTaForConditionalGeneration,
        TFTaModel,
        TFTaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_ta import (
        FlaxTaEncoderModel,
        FlaxTaForConditionalGeneration,
        FlaxTaModel,
        FlaxTaPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE	:					Union[str, Any]					=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@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      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             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.accuracy_score.html"]						, )





      def 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

