



'''simple docstring'''



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


lowerCAmelCase	:		str				=   logging.get_logger(__name__)






class 					UpperCAmelCase__	(		_UpperCAmelCase ):
     a      :     Any        =							['''audio_values''', '''audio_mask''']



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

      __lowerCAmelCase												=  spectrogram_length
      __lowerCAmelCase												=  num_channels
      __lowerCAmelCase												=  patch_size
      __lowerCAmelCase												=  feature_size // self.patch_size[1]
      __lowerCAmelCase												=  n_fft
      __lowerCAmelCase												=  sampling_rate // hop_length_to_sampling_rate
      __lowerCAmelCase												=  sampling_rate
      __lowerCAmelCase												=  padding_value
      __lowerCAmelCase												=  mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2			,				num_mel_filters=A_			,				min_frequency=0.0			,				max_frequency=2_2050.0			,				sampling_rate=A_			,				norm="slaney"			,				mel_scale="slaney"			,				).T



     def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			np.ndarray:
      __lowerCAmelCase												=  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			,				)
      __lowerCAmelCase												=  log_spec[:, :-1]
      __lowerCAmelCase												=  log_spec - 20.0
      __lowerCAmelCase												=  np.clip(log_spec / 40.0			,				-2.0			,				0.0     ) + 1.0
      return log_spec



     def __call__(    self			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = True			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = False			,				**UpperCamelCase			,				)     ->			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."     )

      __lowerCAmelCase												=  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}'''     )
      __lowerCAmelCase												=  is_batched_numpy or (
          isinstance(A_			,				(list, tuple)     ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)     ))
      )
      if is_batched:
       __lowerCAmelCase												=  [np.asarray([speech]			,				dtype=np.floataa     ).T for speech in raw_speech]
      elif not is_batched and not isinstance(A_			,				np.ndarray     ):
       __lowerCAmelCase												=  np.asarray(A_			,				dtype=np.floataa     )
      elif isinstance(A_			,				np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
       __lowerCAmelCase												=  raw_speech.astype(np.floataa     )
      # always return batch
      if not is_batched:
       __lowerCAmelCase												=  [np.asarray([raw_speech]     ).T]

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

      # Create audio attention mask
      __lowerCAmelCase												=  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:
       __lowerCAmelCase												=  [
           (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
       ]
       __lowerCAmelCase												=  np.array(A_     ).astype(np.floataa     )

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

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

      __lowerCAmelCase												=  BatchFeature(data=A_			,				tensor_type=A_     )
      return encoded_inputs




'''simple docstring'''



import requests
from bsa import BeautifulSoup



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "https://www.worldometers.info/coronavirus"					):






	'''simple docstring'''




	__lowerCAmelCase												=  BeautifulSoup(requests.get(lowerCamelCase					).text				,		"html.parser"					)
	__lowerCAmelCase												=  soup.findAll("h1"					)
	__lowerCAmelCase												=  soup.findAll("div"				,		{"class": "maincounter-number"}					)
	keys += soup.findAll("span"				,		{"class": "panel-title"}					)
	values += soup.findAll("div"				,		{"class": "number-table-main"}					)
	return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase				,		lowerCamelCase					)}


if __name__ == "__main__":
	print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
	for key, value in world_covidaa_stats().items():
		print(f'{key}\n{value}\n')