





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class 					A__   (       __snake_case			):
      # to overwrite at feature extractactor specific tests
      _UpperCAmelCase	:Any						    =   None
      _UpperCAmelCase	:Optional[Any]						    =   None





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.feat_extract_tester.prepare_feat_extract_dict()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       self.assertTrue(hasattr(A_       ,							"feature_size"   )   )
       self.assertTrue(hasattr(A_       ,							"sampling_rate"   )   )
       self.assertTrue(hasattr(A_       ,							"padding_value"   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.feat_extract_tester.prepare_inputs_for_common()
       UpperCamelCase	:					Dict       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Tuple       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Optional[int]       	=   BatchFeature({input_name: speech_inputs}   )

       self.assertTrue(all(len(A_   ) == len(A_   ) for x, y in zip(A_       ,							processed_features[input_name]   )   )   )

       UpperCamelCase	:					List[str]       	=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_   )
       UpperCamelCase	:					int       	=   BatchFeature({input_name: speech_inputs}       ,							tensor_type="np"   )

       UpperCamelCase	:					Optional[int]       	=   processed_features[input_name]

       if len(batch_features_input.shape   ) < 3:
        UpperCamelCase	:					int       	=   batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_   )
       UpperCamelCase	:					int       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Tuple       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Optional[Any]       	=   BatchFeature({input_name: speech_inputs}       ,							tensor_type="pt"   )

       UpperCamelCase	:					Union[str, Any]       	=   processed_features[input_name]

       if len(batch_features_input.shape   ) < 3:
        UpperCamelCase	:					List[str]       	=   batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )





      @require_tf
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_   )
       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					str       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Dict       	=   BatchFeature({input_name: speech_inputs}       ,							tensor_type="tf"   )

       UpperCamelCase	:					Dict       	=   processed_features[input_name]

       if len(batch_features_input.shape   ) < 3:
        UpperCamelCase	:					str       	=   batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.feature_size)   )





      def        __UpperCamelCase( self       ,							A_=False   ):
       '''simple docstring'''




       def _inputs_have_equal_length(A_   ):
        UpperCamelCase	:					List[Any]       	=   len(input[0]   )
        for input_slice in input[1:]:
         if len(A_   ) != length:
          return False
        return True

       def _inputs_are_equal(A_       ,							A_   ):
        if len(A_   ) != len(A_   ):
         return False

        for input_slice_a, input_slice_a in zip(A_       ,							A_   ):
         if not np.allclose(np.asarray(A_   )       ,							np.asarray(A_   )       ,							atol=1e-3   ):
          return False
        return True

       UpperCamelCase	:					int       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Optional[int]       	=   self.feat_extract_tester.prepare_inputs_for_common(numpify=A_   )
       UpperCamelCase	:					List[Any]       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					str       	=   BatchFeature({input_name: speech_inputs}   )

       UpperCamelCase	:					str       	=   self.feat_extract_tester.seq_length_diff
       UpperCamelCase	:					List[Any]       	=   self.feat_extract_tester.max_seq_length + pad_diff
       UpperCamelCase	:					Optional[Any]       	=   self.feat_extract_tester.min_seq_length
       UpperCamelCase	:					List[str]       	=   self.feat_extract_tester.batch_size
       UpperCamelCase	:					Tuple       	=   self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCamelCase	:					Optional[int]       	=   feat_extract.pad(A_       ,							padding=A_   )
       UpperCamelCase	:					Dict       	=   input_a[input_name]

       UpperCamelCase	:					Dict       	=   feat_extract.pad(A_       ,							padding="longest"   )
       UpperCamelCase	:					Any       	=   input_a[input_name]

       UpperCamelCase	:					List[Any]       	=   feat_extract.pad(A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[-1]   )   )
       UpperCamelCase	:					Optional[int]       	=   input_a[input_name]

       UpperCamelCase	:					Optional[Any]       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="np"   )
       UpperCamelCase	:					int       	=   input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(A_   ):
        feat_extract.pad(A_       ,							padding="max_length"   )[input_name]

       UpperCamelCase	:					str       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=A_       ,							return_tensors="np"   )
       UpperCamelCase	:					Optional[int]       	=   input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(A_   )   )
       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertTrue(_inputs_are_equal(A_       ,							A_   )   )
       self.assertTrue(len(input_a[0]   ) == pad_min_length   )
       self.assertTrue(len(input_a[1]   ) == pad_min_length + pad_diff   )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]   ))   )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)   )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size   )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCamelCase	:					Dict       	=   feat_extract.pad(A_       ,							pad_to_multiple_of=10   )
       UpperCamelCase	:					Optional[Any]       	=   input_a[input_name]

       UpperCamelCase	:					Optional[int]       	=   feat_extract.pad(A_       ,							padding="longest"       ,							pad_to_multiple_of=10   )
       UpperCamelCase	:					str       	=   input_a[input_name]

       UpperCamelCase	:					List[str]       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							pad_to_multiple_of=10       ,							max_length=A_   )
       UpperCamelCase	:					str       	=   input_a[input_name]

       UpperCamelCase	:					List[Any]       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							pad_to_multiple_of=10       ,							max_length=A_       ,							return_tensors="np"       ,							)
       UpperCamelCase	:					Tuple       	=   input_a[input_name]

       self.assertTrue(all(len(A_   ) % 10 == 0 for x in input_a   )   )
       self.assertTrue(_inputs_are_equal(A_       ,							A_   )   )

       UpperCamelCase	:					Dict       	=   pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(A_   ) == expected_mult_pad_length for x in input_a   )   )
       self.assertEqual(input_a.shape[:2]       ,							(batch_size, expected_mult_pad_length)   )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size   )

       # Check padding value is correct
       UpperCamelCase	:					Any       	=   (np.ones(self.feat_extract_tester.feature_size   ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]   )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   )
           < 1e-3   )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]   )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)   )
           < 1e-3   )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]   )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)   )
           < 1e-3   )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)   ) < 1e-3   )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)   )
           < 1e-3   )





      def        __UpperCamelCase( self       ,							A_=False   ):
       '''simple docstring'''




       def _inputs_have_equal_length(A_   ):
        UpperCamelCase	:					List[Any]       	=   len(input[0]   )
        for input_slice in input[1:]:
         if len(A_   ) != length:
          return False
        return True

       def _inputs_are_equal(A_       ,							A_   ):
        if len(A_   ) != len(A_   ):
         return False

        for input_slice_a, input_slice_a in zip(A_       ,							A_   ):
         if not np.allclose(np.asarray(A_   )       ,							np.asarray(A_   )       ,							atol=1e-3   ):
          return False
        return True

       UpperCamelCase	:					Union[str, Any]       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Tuple       	=   self.feat_extract_tester.prepare_inputs_for_common(numpify=A_   )
       UpperCamelCase	:					Tuple       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Optional[int]       	=   BatchFeature({input_name: speech_inputs}   )

       # truncate to smallest
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )       ,							truncation=A_   )
       UpperCamelCase	:					Union[str, Any]       	=   input_a[input_name]

       UpperCamelCase	:					Any       	=   feat_extract.pad(A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )   )
       UpperCamelCase	:					Union[str, Any]       	=   input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertFalse(_inputs_have_equal_length(A_   )   )

       # truncate to smallest with np
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )       ,							return_tensors="np"       ,							truncation=A_       ,							)
       UpperCamelCase	:					str       	=   input_a[input_name]

       UpperCamelCase	:					Dict       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )       ,							return_tensors="np"   )
       UpperCamelCase	:					str       	=   input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]   )   )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(A_   )   )

       # truncate to middle
       UpperCamelCase	:					Dict       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[1]   )       ,							truncation=A_       ,							return_tensors="np"       ,							)
       UpperCamelCase	:					Union[str, Any]       	=   input_a[input_name]

       UpperCamelCase	:					Tuple       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[1]   )       ,							truncation=A_   )
       UpperCamelCase	:					List[str]       	=   input_a[input_name]

       UpperCamelCase	:					int       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[1]   )       ,							return_tensors="np"   )
       UpperCamelCase	:					Optional[Any]       	=   input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]   )   )
       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertTrue(_inputs_are_equal(A_       ,							A_   )   )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(A_   )   )
       self.assertTrue(len(input_a[-1]   ) == len(speech_inputs[-1]   )   )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(A_   ):
        feat_extract.pad(A_       ,							truncation=A_   )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(A_   ):
        feat_extract.pad(A_       ,							padding="longest"       ,							truncation=A_   )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(A_   ):
        feat_extract.pad(A_       ,							padding="longest"       ,							truncation=A_   )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(A_   ):
        feat_extract.pad(A_       ,							padding="max_length"       ,							truncation=A_   )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCamelCase	:					Any       	=   12
       UpperCamelCase	:					str       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )       ,							pad_to_multiple_of=A_       ,							truncation=A_       ,							)
       UpperCamelCase	:					Tuple       	=   input_a[input_name]

       UpperCamelCase	:					Optional[int]       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=len(speech_inputs[0]   )       ,							pad_to_multiple_of=A_       ,							)
       UpperCamelCase	:					str       	=   input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCamelCase	:					List[str]       	=   len(speech_inputs[0]   )
       if expected_length % pad_to_multiple_of != 0:
        UpperCamelCase	:					Optional[Any]       	=   ((len(speech_inputs[0]   ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]   ) == expected_length   )
       self.assertTrue(_inputs_have_equal_length(A_   )   )
       self.assertFalse(_inputs_have_equal_length(A_   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._check_padding(numpify=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._check_padding(numpify=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._check_truncation(numpify=A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self._check_truncation(numpify=A_   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Dict       	=   self.feat_extract_tester.prepare_inputs_for_common()
       UpperCamelCase	:					Optional[Any]       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Tuple       	=   BatchFeature({input_name: speech_inputs}   )

       UpperCamelCase	:					Optional[int]       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="np"   )[input_name]
       UpperCamelCase	:					List[Any]       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="pt"   )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )





      @require_tf
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.feature_extraction_class(**self.feat_extract_dict   )
       UpperCamelCase	:					Optional[int]       	=   self.feat_extract_tester.prepare_inputs_for_common()
       UpperCamelCase	:					str       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Tuple       	=   BatchFeature({input_name: speech_inputs}   )

       UpperCamelCase	:					int       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="np"   )[input_name]
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="tf"   )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_tf.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.feat_extract_dict
       UpperCamelCase	:					Optional[Any]       	=   True
       UpperCamelCase	:					Union[str, Any]       	=   self.feature_extraction_class(**A_   )
       UpperCamelCase	:					List[Any]       	=   self.feat_extract_tester.prepare_inputs_for_common()
       UpperCamelCase	:					Union[str, Any]       	=   [len(A_   ) for x in speech_inputs]
       UpperCamelCase	:					List[Any]       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Optional[Any]       	=   BatchFeature({input_name: speech_inputs}   )

       UpperCamelCase	:					Tuple       	=   feat_extract.pad(A_       ,							padding="longest"       ,							return_tensors="np"   )
       self.assertIn("attention_mask"       ,							A_   )
       self.assertListEqual(list(processed.attention_mask.shape   )       ,							list(processed[input_name].shape[:2]   )   )
       self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.feat_extract_dict
       UpperCamelCase	:					Optional[int]       	=   True
       UpperCamelCase	:					Optional[int]       	=   self.feature_extraction_class(**A_   )
       UpperCamelCase	:					Dict       	=   self.feat_extract_tester.prepare_inputs_for_common()
       UpperCamelCase	:					Tuple       	=   [len(A_   ) for x in speech_inputs]
       UpperCamelCase	:					Optional[Any]       	=   feat_extract.model_input_names[0]

       UpperCamelCase	:					Union[str, Any]       	=   BatchFeature({input_name: speech_inputs}   )
       UpperCamelCase	:					Dict       	=   min(A_   )

       UpperCamelCase	:					str       	=   feat_extract.pad(
           A_       ,							padding="max_length"       ,							max_length=A_       ,							truncation=A_       ,							return_tensors="np"   )
       self.assertIn("attention_mask"       ,							A_   )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape   )       ,							[processed_pad[input_name].shape[0], max_length]   )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()       ,							[max_length for x in speech_inputs]   )







from sklearn.metrics import fa_score

import datasets


__lowerCamelCase       :     List[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)
"""


__lowerCamelCase       :     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. ])}
"""


__lowerCamelCase       :     str   =     """
@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			):





      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.f1_score.html"]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=None       ,							A_=1       ,							A_="binary"       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   fa_score(
           A_       ,							A_       ,							labels=A_       ,							pos_label=A_       ,							average=A_       ,							sample_weight=A_   )
       return {"f1": float(A_   ) if score.size == 1 else score}
