'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case__					:      Dict				      =  logging.get_logger(__name__)






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		['''input_features''', '''is_longer''']


  def __init__(			self				,		snake_case_=6_4				,		snake_case_=4_8_0_0_0				,		snake_case_=4_8_0				,		snake_case_=1_0				,		snake_case_=1_0_2_4				,		snake_case_=0.0				,		snake_case_=False				,		snake_case_ = 0				,		snake_case_ = 1_4_0_0_0				,		snake_case_ = None				,		snake_case_ = "fusion"				,		snake_case_ = "repeatpad"				,		**snake_case_				,		):




       '''simple docstring'''

       super().__init__(
           feature_size=snake_case_				,		sampling_rate=snake_case_				,		padding_value=snake_case_				,		return_attention_mask=snake_case_				,		**snake_case_				,		)
       UpperCAmelCase_							:       Optional[Any]       =				top_db
       UpperCAmelCase_							:       Optional[int]       =				truncation
       UpperCAmelCase_							:       List[str]       =				padding
       UpperCAmelCase_							:       Optional[Any]       =				fft_window_size
       UpperCAmelCase_							:       Any       =				(fft_window_size >> 1) + 1
       UpperCAmelCase_							:       Optional[Any]       =				hop_length
       UpperCAmelCase_							:       int       =				max_length_s
       UpperCAmelCase_							:       List[Any]       =				max_length_s * sampling_rate
       UpperCAmelCase_							:       List[Any]       =				sampling_rate
       UpperCAmelCase_							:       Optional[Any]       =				frequency_min
       UpperCAmelCase_							:       Optional[int]       =				frequency_max
       UpperCAmelCase_							:       Any       =				mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=snake_case_				,		min_frequency=snake_case_				,		max_frequency=snake_case_				,		sampling_rate=snake_case_				,		norm=snake_case_				,		mel_scale='htk'				,		)
       UpperCAmelCase_							:       List[str]       =				mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=snake_case_				,		min_frequency=snake_case_				,		max_frequency=snake_case_				,		sampling_rate=snake_case_				,		norm='slaney'				,		mel_scale='slaney'				,		)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				copy.deepcopy(self.__dict__						)
       UpperCAmelCase_							:       List[Any]       =				self.__class__.__name__
       if "mel_filters" in output:
            del output["mel_filters"]
       if "mel_filters_slaney" in output:
            del output["mel_filters_slaney"]
       return output


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				spectrogram(
           snake_case_				,		window_function(self.fft_window_size				,		'hann'						)				,		frame_length=self.fft_window_size				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=snake_case_				,		log_mel='dB'				,		)
       return log_mel_spectrogram.T


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				np.array_split(list(range(0				,		total_frames - chunk_frames + 1						)						)				,		3						)
       if len(ranges[1]						) == 0:
            # if the audio is too short, we just use the first chunk
            UpperCAmelCase_							:       Tuple       =				[0]
       if len(ranges[2]						) == 0:
            # if the audio is too short, we just use the first chunk
            UpperCAmelCase_							:       Optional[int]       =				[0]
       # randomly choose index for each part
       UpperCAmelCase_							:       List[str]       =				np.random.choice(ranges[0]						)
       UpperCAmelCase_							:       Optional[int]       =				np.random.choice(ranges[1]						)
       UpperCAmelCase_							:       Tuple       =				np.random.choice(ranges[2]						)

       UpperCAmelCase_							:       str       =				mel[idx_front : idx_front + chunk_frames, :]
       UpperCAmelCase_							:       int       =				mel[idx_middle : idx_middle + chunk_frames, :]
       UpperCAmelCase_							:       Any       =				mel[idx_back : idx_back + chunk_frames, :]

       UpperCAmelCase_							:       int       =				torch.tensor(mel[None, None, :]						)
       UpperCAmelCase_							:       Dict       =				torch.nn.functional.interpolate(
           snake_case_				,		size=[chunk_frames, 6_4]				,		mode='bilinear'				,		align_corners=snake_case_						)
       UpperCAmelCase_							:       Dict       =				mel_shrink[0][0].numpy()
       UpperCAmelCase_							:       Union[str, Any]       =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,		axis=0						)
       return mel_fusion


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                 UpperCAmelCase_							:       int       =				True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 UpperCAmelCase_							:       Dict       =				len(snake_case_						) - max_length
                 UpperCAmelCase_							:       str       =				np.random.randint(0				,		overflow + 1						)
                 UpperCAmelCase_							:       str       =				waveform[idx : idx + max_length]
                 UpperCAmelCase_							:       Optional[int]       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters_slaney						)[None, :]
            elif truncation == "fusion":
                 UpperCAmelCase_							:       int       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters						)
                 UpperCAmelCase_							:       Tuple       =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 UpperCAmelCase_							:       Tuple       =				mel.shape[0]
                 if chunk_frames == total_frames:
                      # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                      # In this case, we just use the whole audio.
                      UpperCAmelCase_							:       List[Any]       =				np.stack([mel, mel, mel, mel]				,		axis=0						)
                      UpperCAmelCase_							:       Optional[int]       =				False
                 else:
                      UpperCAmelCase_							:       Dict       =				self._random_mel_fusion(snake_case_				,		snake_case_				,		snake_case_						)
                      UpperCAmelCase_							:       Any       =				True
            else:
                 raise NotImplementedError(F'''data_truncating {truncation} not implemented'''						)

       else:
            UpperCAmelCase_							:       List[Any]       =				False
            # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
            if waveform.shape[0] < max_length:
                 if padding == "repeat":
                      UpperCAmelCase_							:       List[Any]       =				int(max_length / len(snake_case_						)						)
                      UpperCAmelCase_							:       Any       =				np.stack(np.tile(snake_case_				,		n_repeat + 1						)						)[:max_length]
                 if padding == "repeatpad":
                      UpperCAmelCase_							:       List[Any]       =				int(max_length / len(snake_case_						)						)
                      UpperCAmelCase_							:       Tuple       =				np.stack(np.tile(snake_case_				,		snake_case_						)						)
                 UpperCAmelCase_							:       Any       =				np.pad(snake_case_				,		(0, max_length - waveform.shape[0])				,		mode='constant'				,		constant_values=0						)

            if truncation == "fusion":
                 UpperCAmelCase_							:       int       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters						)
                 UpperCAmelCase_							:       Tuple       =				np.stack([input_mel, input_mel, input_mel, input_mel]				,		axis=0						)
            else:
                 UpperCAmelCase_							:       str       =				self._np_extract_fbank_features(snake_case_				,		self.mel_filters_slaney						)[None, :]

       return input_mel, longer






  def __call__(			self				,		snake_case_				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = None				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				truncation if truncation is not None else self.truncation
       UpperCAmelCase_							:       Union[str, Any]       =				padding if padding else self.padding

       if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                     F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                     F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''						)
       else:
            logger.warning(
                'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                'Failing to do so can result in silent errors that might be hard to debug.'						)

       UpperCAmelCase_							:       int       =				isinstance(snake_case_				,		np.ndarray						) and len(raw_speech.shape						) > 1
       if is_batched_numpy and len(raw_speech.shape						) > 2:
            raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''						)
       UpperCAmelCase_							:       List[str]       =				is_batched_numpy or (
           isinstance(snake_case_				,		(list, tuple)						) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)						))
       )

       if is_batched:
            UpperCAmelCase_							:       Any       =				[np.asarray(snake_case_				,		dtype=np.floataa						) for speech in raw_speech]
       elif not is_batched and not isinstance(snake_case_				,		np.ndarray						):
            UpperCAmelCase_							:       Optional[int]       =				np.asarray(snake_case_				,		dtype=np.floataa						)
       elif isinstance(snake_case_				,		np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            UpperCAmelCase_							:       Any       =				raw_speech.astype(np.floataa						)

       # always return batch
       if not is_batched:
            UpperCAmelCase_							:       Union[str, Any]       =				[np.asarray(snake_case_						)]

       # convert to mel spectrogram, truncate and pad if needed.
       UpperCAmelCase_							:       Optional[Any]       =				[
           self._get_input_mel(snake_case_				,		max_length if max_length else self.nb_max_samples				,		snake_case_				,		snake_case_						)
           for waveform in raw_speech
       ]

       UpperCAmelCase_							:       Optional[Any]       =				[]
       UpperCAmelCase_							:       int       =				[]
       for mel, longer in padded_inputs:
            input_mel.append(snake_case_						)
            is_longer.append(snake_case_						)

       if truncation == "fusion" and sum(snake_case_						) == 0:
            # if no audio is longer than 10s, then randomly select one audio to be longer
            UpperCAmelCase_							:       int       =				np.random.randint(0				,		len(snake_case_						)						)
            UpperCAmelCase_							:       Union[str, Any]       =				True

       if isinstance(input_mel[0]				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				[np.asarray(snake_case_				,		dtype=np.floataa						) for feature in input_mel]

       # is_longer is a list of bool
       UpperCAmelCase_							:       Optional[Any]       =				[[longer] for longer in is_longer]

       UpperCAmelCase_							:       Dict       =				{'input_features': input_mel, 'is_longer': is_longer}
       UpperCAmelCase_							:       Optional[int]       =				BatchFeature(snake_case_						)

       if return_tensors is not None:
            UpperCAmelCase_							:       List[str]       =				input_features.convert_to_tensors(snake_case_						)

       return input_features







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


snake_case__					:      str				      =  '''
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)
'''


snake_case__					:      int				      =  '''
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. ])}
'''


snake_case__					:      Tuple				      =  '''
@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 					__SCREAMING_SNAKE_CASE						(	datasets.Metric	):







  '''simple docstring'''






  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				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=1				,		snake_case_="binary"				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				fa_score(
           snake_case_				,		snake_case_				,		labels=snake_case_				,		pos_label=snake_case_				,		average=snake_case_				,		sample_weight=snake_case_						)
       return {"f1": float(snake_case_						) if score.size == 1 else score}






