



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


SCREAMING_SNAKE_CASE_:Optional[int]					=						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[int] =				["audio_values", "audio_mask"]






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

        A			:	Optional[int]										=       spectrogram_length
        A			:	int										=       num_channels
        A			:	Tuple										=       patch_size
        A			:	str										=       feature_size // self.patch_size[1]
        A			:	Optional[int]										=       n_fft
        A			:	str										=       sampling_rate // hop_length_to_sampling_rate
        A			:	int										=       sampling_rate
        A			:	Any										=       padding_value
        A			:	Optional[Any]										=       mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,		num_mel_filters=lowerCamelCase__,		min_frequency=0.0,		max_frequency=2_2050.0,		sampling_rate=lowerCamelCase__,		norm="""slaney""",		mel_scale="""slaney""",		).T






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       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=80.0,		)
        A			:	List[str]										=       log_spec[:, :-1]
        A			:	Optional[int]										=       log_spec - 20.0
        A			:	List[Any]										=       np.clip(log_spec / 40.0,		-2.0,		0.0		) + 1.0
        return log_spec






    def __call__(	self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		**lowerCamelCase__,		):

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

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

        # Create audio attention mask
        A			:	int										=       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			:	Dict										=       [
                (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			:	Optional[Any]										=       np.array(lowerCamelCase__		).astype(np.floataa		)

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

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

        A			:	Optional[Any]										=       BatchFeature(data=lowerCamelCase__,		tensor_type=lowerCamelCase__		)
        return encoded_inputs







import requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """weather"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """forecast"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """onecall"""				,					params=locals()		).json()


if __name__ == "__main__":
     from pprint import pprint

     while True:
          SCREAMING_SNAKE_CASE_:int					=						input("""Enter a location:""").strip()
          if location:
               pprint(current_weather(location))
          else:
               break


