





import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:


					class   A			:
								'''simple docstring'''




								@staticmethod
								def    a_       (			*__lowerCAmelCase   :    Union[str, Any]							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Tuple:







										"""simple docstring"""






										pass


@is_pipeline_test
@require_vision
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@require_torch
			def    a_       (			self   :    str   )	->	List[Any]:







					"""simple docstring"""






					A__        =   pipeline(
					    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""							,	)
					A__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
					A__        =   image_classifier(__lowerCAmelCase							,	candidate_labels=["""a""", """b""", """c"""]   )

					# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
					# python and torch versions.
					self.assertIn(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
					        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
					    ]							,	)

					A__        =   image_classifier([image] * 5							,	candidate_labels=["""A""", """B""", """C"""]							,	batch_size=2   )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					    ]							,	)






			@require_tf
			def    a_       (			self   :    Union[str, Any]   )	->	str:







					"""simple docstring"""






					A__        =   pipeline(
					    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""							,	framework="""tf"""   )
					A__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
					A__        =   image_classifier(__lowerCAmelCase							,	candidate_labels=["""a""", """b""", """c"""]   )

					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]							,	)

					A__        =   image_classifier([image] * 5							,	candidate_labels=["""A""", """B""", """C"""]							,	batch_size=2   )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					        [
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					            {"""score""": 0.3_3_3, """label""": ANY(__lowerCAmelCase   )},
					        ],
					    ]							,	)






			@slow
			@require_torch
			def    a_       (			self   :    Any   )	->	Any:







					"""simple docstring"""






					A__        =   pipeline(
					    task="""zero-shot-image-classification"""							,	model="""openai/clip-vit-base-patch32"""							,	)
					# This is an image of 2 cats with remotes and no planes
					A__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
					A__        =   image_classifier(__lowerCAmelCase							,	candidate_labels=["""cat""", """plane""", """remote"""]   )

					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        {"""score""": 0.5_1_1, """label""": """remote"""},
					        {"""score""": 0.4_8_5, """label""": """cat"""},
					        {"""score""": 0.0_0_4, """label""": """plane"""},
					    ]							,	)

					A__        =   image_classifier([image] * 5							,	candidate_labels=["""cat""", """plane""", """remote"""]							,	batch_size=2   )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        [
					            {"""score""": 0.5_1_1, """label""": """remote"""},
					            {"""score""": 0.4_8_5, """label""": """cat"""},
					            {"""score""": 0.0_0_4, """label""": """plane"""},
					        ],
					    ]
					    * 5							,	)







			@slow
			@require_tf
			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   pipeline(
					    task="""zero-shot-image-classification"""							,	model="""openai/clip-vit-base-patch32"""							,	framework="""tf"""   )
					# This is an image of 2 cats with remotes and no planes
					A__        =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
					A__        =   image_classifier(__lowerCAmelCase							,	candidate_labels=["""cat""", """plane""", """remote"""]   )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        {"""score""": 0.5_1_1, """label""": """remote"""},
					        {"""score""": 0.4_8_5, """label""": """cat"""},
					        {"""score""": 0.0_0_4, """label""": """plane"""},
					    ]							,	)

					A__        =   image_classifier([image] * 5							,	candidate_labels=["""cat""", """plane""", """remote"""]							,	batch_size=2   )
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase   )							,	[
					        [
					            {"""score""": 0.5_1_1, """label""": """remote"""},
					            {"""score""": 0.4_8_5, """label""": """cat"""},
					            {"""score""": 0.0_0_4, """label""": """plane"""},
					        ],
					    ]
					    * 5							,	)










from sklearn.metrics import fa_score

import datasets


A    :					Any =      '''
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    :					List[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    :					List[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   A			(datasets.Metric    ):
			'''simple docstring'''




			def    a_       (			self   :    Optional[int]   )	->	List[Any]:







					"""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    a_       (			self   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Dict=None							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    Any="binary"							,	__lowerCAmelCase   :    Optional[int]=None   )	->	List[Any]:







					"""simple docstring"""






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



