





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCamelCase       :     str   =     random.Random()

if is_torch_available():
    import torch



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase=1.0							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None     )						-> Optional[Any]:
 if rng is None:
  UpperCamelCase	:					Optional[int]       	=   global_rng

 UpperCamelCase	:					Optional[Any]       	=   []
 for batch_idx in range(shape[0]     ):
  values.append([]     )
  for _ in range(shape[1]     ):
   values[-1].append(rng.random() * scale     )

 return values



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=400       ,							A_=2000       ,							A_=1       ,							A_=0.0       ,							A_=1_6000       ,							A_=True       ,							A_=True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   parent
       UpperCamelCase	:					List[Any]       	=   batch_size
       UpperCamelCase	:					List[Any]       	=   min_seq_length
       UpperCamelCase	:					List[str]       	=   max_seq_length
       UpperCamelCase	:					int       	=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCamelCase	:					Union[str, Any]       	=   feature_size
       UpperCamelCase	:					List[str]       	=   padding_value
       UpperCamelCase	:					Optional[Any]       	=   sampling_rate
       UpperCamelCase	:					List[str]       	=   return_attention_mask
       UpperCamelCase	:					List[Any]       	=   do_normalize





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




       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }





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




       def _flatten(A_   ):
        return list(itertools.chain(*A_   )   )

       if equal_length:
        UpperCamelCase	:					List[str]       	=   floats_list((self.batch_size, self.max_seq_length)   )
       else:
        # make sure that inputs increase in size
        UpperCamelCase	:					Dict       	=   [
            _flatten(floats_list((x, self.feature_size)   )   )
            for x in range(self.min_seq_length       ,							self.max_seq_length       ,							self.seq_length_diff   )
        ]

       if numpify:
        UpperCamelCase	:					Union[str, Any]       	=   [np.asarray(A_   ) for x in speech_inputs]

       return speech_inputs



@require_torch
@require_torchaudio
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   ASTFeatureExtractor





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




       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractionTester(self   )





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




       UpperCamelCase	:					Optional[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase	:					Tuple       	=   [floats_list((1, x)   )[0] for x in range(800       ,							1400       ,							200   )]
       UpperCamelCase	:					Dict       	=   [np.asarray(A_   ) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase	:					Dict       	=   feat_extract(speech_inputs[0]       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract(np_speech_inputs[0]       ,							return_tensors="np"   ).input_values
       self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test batched
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test 2-D numpy arrays are batched.
       UpperCamelCase	:					Dict       	=   [floats_list((1, x)   )[0] for x in (800, 800, 800)]
       UpperCamelCase	:					int       	=   np.asarray(A_   )
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					List[str]       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       for enc_seq_a, enc_seq_a in zip(A_       ,							A_   ):
        self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )





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




       import torch

       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       UpperCamelCase	:					int       	=   np.random.rand(100   ).astype(np.floataa   )
       UpperCamelCase	:					str       	=   np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        UpperCamelCase	:					List[Any]       	=   feature_extractor.pad([{"input_values": inputs}]       ,							return_tensors="np"   )
        self.assertTrue(np_processed.input_values.dtype == np.floataa   )
        UpperCamelCase	:					List[str]       	=   feature_extractor.pad([{"input_values": inputs}]       ,							return_tensors="pt"   )
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )





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




       from datasets import load_dataset

       UpperCamelCase	:					Dict       	=   load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,							"clean"       ,							split="validation"   )
       # automatic decoding with librispeech
       UpperCamelCase	:					Any       	=   ds.sort("id"   ).select(range(A_   )   )[:num_samples]["audio"]

       return [x["array"] for x in speech_samples]





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




       UpperCamelCase	:					Any       	=   torch.tensor(
           [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
            -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
            -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
            -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]   )
       # fmt: on

       UpperCamelCase	:					List[Any]       	=   self._load_datasamples(1   )
       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractor()
       UpperCamelCase	:					str       	=   feature_extractor(A_       ,							return_tensors="pt"   ).input_values
       self.assertEquals(input_values.shape       ,							(1, 1024, 128)   )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,							A_       ,							atol=1e-4   )   )







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}
