


from sklearn.metrics import precision_score

import datasets


snake_case__     							= '\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n'


snake_case__     							= '\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`. If `average` is `None`, it should be the label order. 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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'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.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to \'warn\'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - \'warn\': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'precision\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'precision\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric("precision")\n        >>> results = precision_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])\n        >>> print(results)\n        {\'precision\': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'precision\': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'precision\': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results[\'precision\']])\n        [0.67, 0.0, 0.0]\n'


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



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION  )
class     snake_case__  (      datasets.Metric  ):
				def  UpperCamelCase_						(  self      ):




										"""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.precision_score.html']     ,      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=1     ,      _UpperCAmelCase="binary"     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase="warn"     ,      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      precision_score(
										    _UpperCAmelCase     ,      _UpperCAmelCase     ,      labels=_UpperCAmelCase     ,      pos_label=_UpperCAmelCase     ,      average=_UpperCAmelCase     ,      sample_weight=_UpperCAmelCase     ,      zero_division=_UpperCAmelCase     ,      )
										return {"precision": float(_UpperCAmelCase      ) if score.size == 1 else score}






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						def filter_log(lowercase__  :List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

						__magic_name__				:							Any				=      datasets.utils.logging.get_logger('transformers.modeling_utils'				)
						logger.addFilter(lowercase__				)
						try:
												yield
						finally:
												logger.removeFilter(lowercase__				)


snake_case__     							= '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

snake_case__     							= '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case__     							= '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION  )
class     snake_case__  (      datasets.Metric  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,      citation=_CITATION     ,      homepage='https://github.com/Tiiiger/bert_score'     ,      inputs_description=_KWARGS_DESCRIPTION     ,      features=datasets.Features(
										        {
										            'predictions': datasets.Value('string'     ,      id='sequence'      ),
										            'references': datasets.Sequence(datasets.Value('string'     ,      id='sequence'      )     ,      id='references'      ),
										        }      )     ,      codebase_urls=['https://github.com/Tiiiger/bert_score']     ,      reference_urls=[
										        'https://github.com/Tiiiger/bert_score',
										        'https://arxiv.org/abs/1904.09675',
										    ]     ,      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      bert_score.utils.get_hash
										__magic_name__				:							Dict				=      bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
																__magic_name__				:							Any				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
																__magic_name__				:							Tuple				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
										elif use_fast_tokenizer:
																raise ImportWarning(
																    'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
																    'You can install it with `pip install "bert-score>=0.3.10"`.'      )

										if model_type is None:
																assert lang is not None, "either lang or model_type should be specified"
																__magic_name__				:							List[Any]				=      bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																__magic_name__				:							Optional[Any]				=      bert_score.utils.modelalayers[model_type]

										__magic_name__				:							List[str]				=      get_hash(
										    model=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      use_custom_baseline=baseline_path is not None     ,      )

										with filter_logging_context():
																if not hasattr(self     ,      'cached_bertscorer'      ) or self.cached_bertscorer.hash != hashcode:
																						__magic_name__				:							Optional[int]				=      scorer(
																						    model_type=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      nthreads=_UpperCAmelCase     ,      all_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      device=_UpperCAmelCase     ,      lang=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      baseline_path=_UpperCAmelCase     ,      )

										((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      self.cached_bertscorer.score(
										    cands=_UpperCAmelCase     ,      refs=_UpperCAmelCase     ,      verbose=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      )
										__magic_name__				:							int				=      {
										    'precision': P.tolist(),
										    'recall': R.tolist(),
										    'f1': F.tolist(),
										    'hashcode': hashcode,
										}
										return output_dict
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if references is not None:
																__magic_name__				:							List[str]				=      [[ref] if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else ref for ref in references]
										super().add_batch(predictions=_UpperCAmelCase     ,      references=_UpperCAmelCase     ,      **_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																__magic_name__				:							List[Any]				=      [reference]
										super().add(prediction=_UpperCAmelCase     ,      reference=_UpperCAmelCase     ,      **_UpperCAmelCase      )


