


from math import ceil
from typing import List, Optional, Union

import numpy as np

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


_lowercase       :      Any	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= ['audio_values', 'audio_mask']

				def __init__(						self				,   a__=2048				,   a__=1				,   a__=[16, 16]				,   a__=128				,   a__=4_4100				,   a__=86				,   a__=2048				,   a__=0.0				,   **a__				,   )	->				str:
					super().__init__(
					    feature_size=a__				,   sampling_rate=a__				,   padding_value=a__				,   **a__				,   )

					A								=				spectrogram_length
					A								=				num_channels
					A								=				patch_size
					A								=				feature_size // self.patch_size[1]
					A								=				n_fft
					A								=				sampling_rate // hop_length_to_sampling_rate
					A								=				sampling_rate
					A								=				padding_value
					A								=				mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2				,   num_mel_filters=a__				,   min_frequency=0.0				,   max_frequency=2_20_50.0				,   sampling_rate=a__				,   norm="""slaney"""				,   mel_scale="""slaney"""				,   ).T

				def      _UpperCAmelCase					(						self				,   a__   )	->				np.ndarray:
					A								=				spectrogram(
					    a__				,   window_function(self.n_fft				,   """hann"""   )				,   frame_length=self.n_fft				,   hop_length=self.hop_length				,   power=2.0				,   mel_filters=self.mel_filters.T				,   log_mel="""dB"""				,   db_range=80.0				,   )
					A								=				log_spec[:, :-1]
					A								=				log_spec - 20.0
					A								=				np.clip(log_spec / 40.0				,   -2.0				,   0.0   ) + 1.0
					return log_spec

				def __call__(						self				,   a__				,   a__ = None				,   a__ = True				,   a__ = None				,   a__ = False				,   a__ = False				,   **a__				,   )	->				BatchFeature:

					if sampling_rate is not None:
						if sampling_rate != self.sampling_rate:
							raise ValueError(
							    """This feature extractor is set to support sampling rate"""
							    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
							    f' 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(a__				,   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(a__				,   (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(a__				,   np.ndarray   ):
						A								=				np.asarray(a__				,   dtype=np.floataa   )
					elif isinstance(a__				,   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]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					A								=				[
					    self._np_extract_fbank_features(waveform.squeeze()   ).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]				,   a__   ):
						A								=				[np.asarray(a__				,   dtype=np.floataa   ) for feature in audio_features]

					# Create audio attention mask
					A								=				max(
					    [ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len for feature in audio_features]   )  # The maximum number of audio patches in a batch
					if return_attention_mask:
						A								=				[
						    (ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [1]
						    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]   ) * self.freq_len) * [0]
						    for feature in audio_features
						]
						A								=				np.array(a__   ).astype(np.floataa   )

					# convert into correct format for padding
					A								=				max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					A								=				np.ones([len(a__   ), 1, max_time_len, self.feature_size]   ).astype(np.floataa   )
					A								=				padded_audio_features * self.padding_value
					for i in range(len(a__   )   ):
						A								=				audio_features[i]
						A								=				feature

					# return as BatchFeature
					if return_attention_mask:
						A								=				{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
						A								=				{"""audio_values""": padded_audio_features}

					A								=				BatchFeature(data=a__				,   tensor_type=a__   )
					return encoded_inputs







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())



