

import requests

UpperCAmelCase_							:					List[Any]		 =   ''''''  # <-- Put your OpenWeatherMap appid here!
UpperCAmelCase_							:					List[Any]		 =   '''https://api.openweathermap.org/data/2.5/'''
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str = "Chicago"			,a__    :   str = APPID       )					->				dict:
	return requests.get(URL_BASE + """weather"""			,params=locals()       ).json()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str = "Kolkata, India"			,a__    :   str = APPID       )					->				dict:
	return requests.get(URL_BASE + """forecast"""			,params=locals()       ).json()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   float = 55.68			,a__    :   float = 12.57			,a__    :   str = APPID       )					->				dict:
	return requests.get(URL_BASE + """onecall"""			,params=locals()       ).json()


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

			while True:
						UpperCAmelCase_							:					List[str]		 =   input('''Enter a location:''').strip()
						if location:
									pprint(current_weather(location))
						else:
									break













'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase      					=logging.get_logger(__name__)




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    List[Any]			  =    ['''input_features''', '''attention_mask''']
 def __init__(				self      ,  __lowerCAmelCase=80      ,  __lowerCAmelCase=1_60_00      ,  __lowerCAmelCase=80      ,  __lowerCAmelCase=0.0      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  **__lowerCAmelCase      ,  ):
       super().__init__(feature_size=__lowerCAmelCase      ,  sampling_rate=__lowerCAmelCase      ,  padding_value=__lowerCAmelCase      ,  **__lowerCAmelCase			)
       UpperCamelCase_					: int            =				num_mel_bins
       UpperCamelCase_					: Union[str, Any]            =				do_ceptral_normalize
       UpperCamelCase_					: Any            =				normalize_means
       UpperCamelCase_					: int            =				normalize_vars
       UpperCamelCase_					: int            =				True
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Optional[Any]            =				waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase_					: Optional[int]            =				torch.from_numpy(__lowerCAmelCase			).unsqueeze(0			)
       UpperCamelCase_					: Tuple            =				ta_kaldi.fbank(__lowerCAmelCase      ,  num_mel_bins=self.num_mel_bins      ,  sample_frequency=self.sampling_rate			)
       return features.numpy()
 @staticmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 0.0      ,  ):
       # make sure we normalize float32 arrays
       if normalize_means:
             UpperCamelCase_					: int            =				x[:input_length].mean(axis=0			)
             UpperCamelCase_					: str            =				np.subtract(__lowerCAmelCase      ,  __lowerCAmelCase			)
       if normalize_vars:
             UpperCamelCase_					: Optional[int]            =				x[:input_length].std(axis=0			)
             UpperCamelCase_					: Optional[Any]            =				np.divide(__lowerCAmelCase      ,  __lowerCAmelCase			)

       if input_length < x.shape[0]:
             UpperCamelCase_					: Any            =				padding_value

       # make sure array is in float32
       UpperCamelCase_					: str            =				x.astype(np.floataa			)

       return x
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: List[str]            =				attention_mask.sum(-1			) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(__lowerCAmelCase      ,  __lowerCAmelCase      ,  self.normalize_means      ,  self.normalize_vars      ,  self.padding_value			)
           for x, n in zip(__lowerCAmelCase      ,  __lowerCAmelCase			)
       ]





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = False      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase      ,  ):

       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                       F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                       F" {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."""			)

       UpperCamelCase_					: Optional[int]            =				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}"			)
       UpperCamelCase_					: List[str]            =				is_batched_numpy or (
           isinstance(__lowerCAmelCase      ,  (list, tuple)			) and (isinstance(raw_speech[0]      ,  (np.ndarray, tuple, list)			))
       )

       if is_batched:
             UpperCamelCase_					: str            =				[np.asarray(__lowerCAmelCase      ,  dtype=np.floataa			) for speech in raw_speech]
       elif not is_batched and not isinstance(__lowerCAmelCase      ,  np.ndarray			):
             UpperCamelCase_					: Optional[Any]            =				np.asarray(__lowerCAmelCase      ,  dtype=np.floataa			)
       elif isinstance(__lowerCAmelCase      ,  np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
             UpperCamelCase_					: int            =				raw_speech.astype(np.floataa			)

       # always return batch
       if not is_batched:
             UpperCamelCase_					: Tuple            =				[raw_speech]

       # extract fbank features
       UpperCamelCase_					: Any            =				[self._extract_fbank_features(__lowerCAmelCase			) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase_					: Tuple            =				BatchFeature({"""input_features""": features}			)

       UpperCamelCase_					: Tuple            =				self.pad(
           __lowerCAmelCase      ,  padding=__lowerCAmelCase      ,  max_length=__lowerCAmelCase      ,  truncation=__lowerCAmelCase      ,  pad_to_multiple_of=__lowerCAmelCase      ,  return_attention_mask=__lowerCAmelCase      ,  **__lowerCAmelCase      ,  )

       # make sure list is in array format
       UpperCamelCase_					: str            =				padded_inputs.get("""input_features"""			)
       if isinstance(input_features[0]      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Any            =				[np.asarray(__lowerCAmelCase      ,  dtype=np.floataa			) for feature in input_features]

       UpperCamelCase_					: Union[str, Any]            =				padded_inputs.get("""attention_mask"""			)
       if attention_mask is not None:
             UpperCamelCase_					: Union[str, Any]            =				[np.asarray(__lowerCAmelCase      ,  dtype=np.intaa			) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
             UpperCamelCase_					: List[str]            =				(
                 np.array(__lowerCAmelCase      ,  dtype=np.intaa			)
                 if self._get_padding_strategies(__lowerCAmelCase      ,  max_length=__lowerCAmelCase			) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase_					: Optional[int]            =				self.normalize(
                 padded_inputs["""input_features"""]      ,  attention_mask=__lowerCAmelCase			)

       if return_tensors is not None:
             UpperCamelCase_					: Optional[Any]            =				padded_inputs.convert_to_tensors(__lowerCAmelCase			)

       return padded_inputs






