


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

import numpy as np

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


_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					_UpperCAmelCase				):

    """simple docstring"""







    lowerCAmelCase 		= ['input_features']

    def __init__(						self				,   a__=80				,   a__=1_6000				,   a__=160				,   a__=30				,   a__=400				,   a__=0.0				,   a__=False				,   **a__				,   )	->				List[str]:
     super().__init__(
         feature_size=lowercase__				,   sampling_rate=lowercase__				,   padding_value=lowercase__				,   return_attention_mask=lowercase__				,   **lowercase__				,   )
     A								=				n_fft
     A								=				hop_length
     A								=				chunk_length
     A								=				chunk_length * sampling_rate
     A								=				self.n_samples // hop_length
     A								=				sampling_rate
     A								=				mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2				,   num_mel_filters=lowercase__				,   min_frequency=0.0				,   max_frequency=80_00.0				,   sampling_rate=lowercase__				,   norm="""slaney"""				,   mel_scale="""slaney"""				,   )

    def      _UpperCAmelCase					(						self				,   a__   )	->				Dict:
     A								=				spectrogram(
         lowercase__				,   window_function(self.n_fft				,   """hann"""   )				,   frame_length=self.n_fft				,   hop_length=self.hop_length				,   power=2.0				,   mel_filters=self.mel_filters				,   log_mel="""log10"""				,   )
     A								=				log_spec[:, :-1]
     A								=				np.maximum(lowercase__				,   log_spec.max() - 8.0   )
     A								=				(log_spec + 4.0) / 4.0
     return log_spec

    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def      _UpperCAmelCase					(						a__				,   a__				,   a__ = 0.0   )	->				List[str]:






     if attention_mask is not None:
      A								=				np.array(lowercase__				,   np.intaa   )
      A								=				[]

      for vector, length in zip(lowercase__				,   attention_mask.sum(-1   )   ):
       A								=				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7   )
       if length < normed_slice.shape[0]:
        A								=				padding_value

       normed_input_values.append(lowercase__   )
     else:
      A								=				[(x - x.mean()) / np.sqrt(x.var() + 1e-7   ) for x in input_values]

     return normed_input_values

    def __call__(						self				,   a__				,   a__ = True				,   a__ = None				,   a__ = None				,   a__ = None				,   a__ = "max_length"				,   a__ = None				,   a__ = None				,   a__ = None				,   **a__				,   )	->				Dict:






     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."""   )

     A								=				isinstance(lowercase__				,   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}'   )
     A								=				is_batched_numpy or (
         isinstance(lowercase__				,   (list, tuple)   ) and (isinstance(raw_speech[0]				,   (np.ndarray, tuple, list)   ))
     )

     if is_batched:
      A								=				[np.asarray([speech]				,   dtype=np.floataa   ).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowercase__				,   np.ndarray   ):
      A								=				np.asarray(lowercase__				,   dtype=np.floataa   )
     elif isinstance(lowercase__				,   np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
      A								=				raw_speech.astype(np.floataa   )

     # always return batch
     if not is_batched:
      A								=				[np.asarray([raw_speech]   ).T]

     A								=				BatchFeature({"""input_features""": raw_speech}   )

     # convert into correct format for padding

     A								=				self.pad(
         lowercase__				,   padding=lowercase__				,   max_length=max_length if max_length else self.n_samples				,   truncation=lowercase__				,   pad_to_multiple_of=lowercase__				,   return_attention_mask=return_attention_mask or do_normalize				,   )

     # zero-mean and unit-variance normalization
     if do_normalize:
      A								=				self.zero_mean_unit_var_norm(
          padded_inputs["""input_features"""]				,   attention_mask=padded_inputs["""attention_mask"""]				,   padding_value=self.padding_value				,   )
      A								=				np.stack(padded_inputs["""input_features"""]				,   axis=0   )

     # make sure list is in array format
     A								=				padded_inputs.get("""input_features"""   ).transpose(2				,   0				,   1   )

     A								=				[self._np_extract_fbank_features(lowercase__   ) for waveform in input_features[0]]

     if isinstance(input_features[0]				,   lowercase__   ):
      A								=				[np.asarray(lowercase__				,   dtype=np.floataa   ) for feature in input_features]
     else:
      A								=				input_features

     if return_attention_mask:
      # rescale from sample (48000) to feature (3000)
      A								=				padded_inputs["attention_mask"][:, :: self.hop_length]

     if return_tensors is not None:
      A								=				padded_inputs.convert_to_tensors(lowercase__   )

     return padded_inputs

    def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
     A								=				copy.deepcopy(self.__dict__   )
     A								=				self.__class__.__name__
     if "mel_filters" in output:
      del output["mel_filters"]
     return output







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







	A								=				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
	A								=				requests.get(UpperCamelCase__						).json()[:max_stories]
	return [get_hackernews_story(UpperCamelCase__						) for story_id in story_ids]





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







	A								=				hackernews_top_stories(UpperCamelCase__						)
	return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase__						) for story in stories						)


if __name__ == "__main__":
				print(hackernews_top_stories_as_markdown())



