


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


A   :					List[Any]	=      logging.get_logger(__name__)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["audio_values", "audio_mask"]





       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple=2048      ,   __lowerCamelCase							:    Union[str, Any]=1      ,   __lowerCamelCase							:    Optional[int]=[16, 16]      ,   __lowerCamelCase							:    List[Any]=128      ,   __lowerCamelCase							:    List[Any]=44100      ,   __lowerCamelCase							:    Any=86      ,   __lowerCamelCase							:    Dict=2048      ,   __lowerCamelCase							:    List[Any]=0.0      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__(
                 feature_size=__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   padding_value=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

             lowerCamelCase__	:       Dict					=						spectrogram_length
             lowerCamelCase__	:       List[str]					=						num_channels
             lowerCamelCase__	:       str					=						patch_size
             lowerCamelCase__	:       Dict					=						feature_size // self.patch_size[1]
             lowerCamelCase__	:       List[Any]					=						n_fft
             lowerCamelCase__	:       str					=						sampling_rate // hop_length_to_sampling_rate
             lowerCamelCase__	:       Optional[int]					=						sampling_rate
             lowerCamelCase__	:       Tuple					=						padding_value
             lowerCamelCase__	:       Dict					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2      ,   num_mel_filters=__lowerCamelCase      ,   min_frequency=0.0      ,   max_frequency=2_2_0_5_0.0      ,   sampling_rate=__lowerCamelCase      ,   norm="slaney"      ,   mel_scale="slaney"      ,   ).T





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    np.array ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						spectrogram(
                 __lowerCamelCase      ,   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=8_0.0      ,   )
             lowerCamelCase__	:       str					=						log_spec[:, :-1]
             lowerCamelCase__	:       str					=						log_spec - 2_0.0
             lowerCamelCase__	:       int					=						np.clip(log_spec / 4_0.0      ,   -2.0      ,   0.0 ) + 1.0
             return log_spec





       def __call__(     self							:    Optional[int]      ,   __lowerCamelCase							:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,   __lowerCamelCase							:    Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase							:    Optional[bool] = True      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    bool = False      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             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__	:       str					=						isinstance(__lowerCamelCase      ,   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__	:       Any					=						is_batched_numpy or (
                 isinstance(__lowerCamelCase      ,   (list, tuple) ) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list) ))
             )
             if is_batched:
                   lowerCamelCase__	:       List[str]					=						[np.asarray([speech]      ,   dtype=np.floataa ).T for speech in raw_speech]
             elif not is_batched and not isinstance(__lowerCamelCase      ,   np.ndarray ):
                   lowerCamelCase__	:       Optional[Any]					=						np.asarray(__lowerCamelCase      ,   dtype=np.floataa )
             elif isinstance(__lowerCamelCase      ,   np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
                   lowerCamelCase__	:       List[Any]					=						raw_speech.astype(np.floataa )
             # always return batch
             if not is_batched:
                   lowerCamelCase__	:       int					=						[np.asarray([raw_speech] ).T]

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

             # Create audio attention mask
             lowerCamelCase__	:       Any					=						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__	:       Optional[Any]					=						[
                       (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__	:       Tuple					=						np.array(__lowerCamelCase ).astype(np.floataa )

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

             # return as BatchFeature
             if return_attention_mask:
                   lowerCamelCase__	:       Any					=						{"audio_values": padded_audio_features, "audio_mask": audio_mask}
             else:
                   lowerCamelCase__	:       List[str]					=						{"audio_values": padded_audio_features}

             lowerCamelCase__	:       Any					=						BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )
             return encoded_inputs






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


