

"""simple docstring"""




from sklearn.metrics import fa_score

import datasets


A_							:			Dict											= """
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_							:			Any											= """
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_							:			Optional[Any]											= """
@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			):

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





"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob("""./metrics/*/"""    )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()			)
@for_all_test_methods(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									SCREAMING_SNAKE_CASE__								=      load_local_metric
									yield





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""    )  # handle pytest cli flags





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""    ) as mock_create_predictor:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case__    )    )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""    ), patch(
			    """bert_score.scorer.bert_cos_score_idf"""    ) as mock_bert_cos_score_idf:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""    ) as mock_download_model:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_metric(os.path.join("""metrics"""		,				"""seqeval"""    )    )
			SCREAMING_SNAKE_CASE__								=      """ERROR"""
			SCREAMING_SNAKE_CASE__								=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(snake_case__		,				match=re.escape(snake_case__    )    ):
						metric.compute(predictions=[]		,				references=[]		,				scheme=snake_case__    )


