


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Optional[Any]       )			->	List[str]:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       assert dataset.num_rows == 4
       assert dataset.num_columns == 1
       assert dataset.column_names == ["text"]
       for feature, expected_dtype in expected_features.items():
              assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]       )			->	str:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	Optional[Any]							=						{'text': 'string'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Dict							=						TextDatasetReader(__A						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_text_dataset(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Optional[Any]						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Optional[int]							=						tmp_path / 'cache'
       a_      :	Optional[int]							=						{'text': 'string'}
       a_      :	Any							=						features.copy() if features else default_expected_features
       a_      :	Dict							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	List[str]							=						TextDatasetReader(__A						,					features=__A						,					cache_dir=__A       ).read()
       _check_text_dataset(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    Dict       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	int							=						tmp_path / 'cache'
       a_      :	Any							=						{'text': 'string'}
       a_      :	List[Any]							=						TextDatasetReader(__A						,					cache_dir=__A						,					split=__A       ).read()
       _check_text_dataset(__A						,					__A       )
       assert dataset.split == split if split else "train"







@pytest.mark.parametrize('path_type'						,					[str, list]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[Any]						,					__A				:    Dict       )			->	int:

       """simple docstring"""

       if issubclass(__A						,					__A       ):
              a_      :	str							=						text_path
       elif issubclass(__A						,					__A       ):
              a_      :	Tuple							=						[text_path]
       a_      :	List[str]							=						tmp_path / 'cache'
       a_      :	str							=						{'text': 'string'}
       a_      :	Optional[int]							=						TextDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_text_dataset(__A						,					__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Optional[int]						,					__A				:    Optional[Any]=("train",)       )			->	Tuple:

       """simple docstring"""

       assert isinstance(__A						,					__A       )
       for split in splits:
              a_      :	int							=						dataset_dict[split]
              assert dataset.num_rows == 4
              assert dataset.num_columns == 1
              assert dataset.column_names == ["text"]
              for feature, expected_dtype in expected_features.items():
                     assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize('keep_in_memory'						,					[False, True]       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    Any						,					__A				:    Optional[int]       )			->	str:

       """simple docstring"""

       a_      :	List[Any]							=						tmp_path / 'cache'
       a_      :	int							=						{'text': 'string'}
       with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
              a_      :	Dict							=						TextDatasetReader({'train': text_path}						,					cache_dir=__A						,					keep_in_memory=__A       ).read()
       _check_text_datasetdict(__A						,					__A       )







@pytest.mark.parametrize(
    'features'						,					[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]						,					)
def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]       )			->	List[str]:

       """simple docstring"""

       a_      :	Optional[Any]							=						tmp_path / 'cache'
       # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
       a_      :	Optional[int]							=						{'text': 'string'}
       a_      :	Any							=						features.copy() if features else default_expected_features
       a_      :	Tuple							=						(
           Features({feature: Value(__A       ) for feature, dtype in features.items()}       ) if features is not None else None
       )
       a_      :	Union[str, Any]							=						TextDatasetReader({'train': text_path}						,					features=__A						,					cache_dir=__A       ).read()
       _check_text_datasetdict(__A						,					__A       )







@pytest.mark.parametrize('split'						,					[None, NamedSplit('train'       ), 'train', 'test']       )
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    int						,					__A				:    Optional[int]       )			->	Tuple:

       """simple docstring"""

       if split:
              a_      :	Dict							=						{split: text_path}
       else:
              a_      :	Dict							=						'train'
              a_      :	Any							=						{'train': text_path, 'test': text_path}
       a_      :	Optional[Any]							=						tmp_path / 'cache'
       a_      :	Dict							=						{'text': 'string'}
       a_      :	List[Any]							=						TextDatasetReader(__A						,					cache_dir=__A       ).read()
       _check_text_datasetdict(__A						,					__A						,					splits=list(path.keys()       )       )
       assert all(dataset[split].split == split for split in path.keys()       )






from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_   :				List[Any]						      = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_   :				Optional[Any]						      = '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\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\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> 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])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\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 = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_   :				Optional[Any]						      = '\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 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    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.f1_score.html']  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="binary"  ,							SCREAMING_SNAKE_CASE__       : Dict=None  )	->    Any:
             a_      :	List[Any]							=						fa_score(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  ,							pos_label=SCREAMING_SNAKE_CASE__  ,							average=SCREAMING_SNAKE_CASE__  ,							sample_weight=SCREAMING_SNAKE_CASE__  )
             return {"f1": float(SCREAMING_SNAKE_CASE__  ) if score.size == 1 else score}


