


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 TensorType, logging


UpperCAmelCase__     			=							logging.get_logger(__name__)







class    lowerCamelCase__     ( lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    ['''input_values''', '''attention_mask''']





     def __init__(self	,  UpperCAmelCase=1	,  UpperCAmelCase=1_6_0_0_0	,  UpperCAmelCase=1_2_8	,  UpperCAmelCase=1_0_2_4	,  UpperCAmelCase=0.0	,  UpperCAmelCase=True	,  UpperCAmelCase=-4.267_7393	,  UpperCAmelCase=4.568_9974	,  UpperCAmelCase=False	,  **UpperCAmelCase	,  )  ->     Dict:
      super().__init__(feature_size=UpperCAmelCase	,  sampling_rate=UpperCAmelCase	,  padding_value=UpperCAmelCase	,  **UpperCAmelCase )
      _lowercase							   =num_mel_bins
      _lowercase							   =max_length
      _lowercase							   =do_normalize
      _lowercase							   =mean
      _lowercase							   =std
      _lowercase							   =return_attention_mask





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  )  ->     np.ndarray:
      _lowercase							   =torch.from_numpy(UpperCAmelCase ).unsqueeze(0 )
      _lowercase							   =ta_kaldi.fbank(
          UpperCAmelCase	,  htk_compat=UpperCAmelCase	,  sample_frequency=self.sampling_rate	,  use_energy=UpperCAmelCase	,  window_type='''hanning'''	,  num_mel_bins=self.num_mel_bins	,  dither=0.0	,  frame_shift=1_0	,  )

      _lowercase							   =fbank.shape[0]
      _lowercase							   =max_length - n_frames

      # pad or truncate, depending on difference
      if difference > 0:
       _lowercase							   =torch.nn.ZeroPadad((0, 0, 0, difference) )
       _lowercase							   =pad_module(UpperCAmelCase )
      elif difference < 0:
       _lowercase							   =fbank[0:max_length, :]

      _lowercase							   =fbank.numpy()

      return fbank





     def   __A      (self	,  UpperCAmelCase )  ->     np.ndarray:
      return (input_values - (self.mean)) / (self.std * 2)





     def __call__(self	,  UpperCAmelCase	,  UpperCAmelCase = None	,  UpperCAmelCase = None	,  **UpperCAmelCase	,  )  ->     BatchFeature:

      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.''' )

      _lowercase							   =isinstance(UpperCAmelCase	,  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}" )
      _lowercase							   =is_batched_numpy or (
          isinstance(UpperCAmelCase	,  (list, tuple) ) and (isinstance(raw_speech[0]	,  (np.ndarray, tuple, list) ))
      )

      if is_batched:
       _lowercase							   =[np.asarray(UpperCAmelCase	,  dtype=np.floataa ) for speech in raw_speech]
      elif not is_batched and not isinstance(UpperCAmelCase	,  np.ndarray ):
       _lowercase							   =np.asarray(UpperCAmelCase	,  dtype=np.floataa )
      elif isinstance(UpperCAmelCase	,  np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
       _lowercase							   =raw_speech.astype(np.floataa )

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

      # extract fbank features and pad/truncate to max_length
      _lowercase							   =[self._extract_fbank_features(UpperCAmelCase	,  max_length=self.max_length ) for waveform in raw_speech]

      # convert into BatchFeature
      _lowercase							   =BatchFeature({'''input_values''': features} )

      # make sure list is in array format
      _lowercase							   =padded_inputs.get('''input_values''' )
      if isinstance(input_values[0]	,  UpperCAmelCase ):
       _lowercase							   =[np.asarray(UpperCAmelCase	,  dtype=np.floataa ) for feature in input_values]

      # normalization
      if self.do_normalize:
       _lowercase							   =[self.normalize(UpperCAmelCase ) for feature in input_values]

      if return_tensors is not None:
       _lowercase							   =padded_inputs.convert_to_tensors(UpperCAmelCase )

      return padded_inputs





import requests

try:
  from django.contrib.auth import authenticate, login
  from django.shortcuts import redirect, render
except ImportError:
  UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							print
def        UpperCAmelCase_      (							__snake_case		)  ->    List[str]:







 """simple docstring"""


 _lowercase							   ='''secretKey'''
 _lowercase							   ='''https://www.google.com/recaptcha/api/siteverify'''

 # when method is not POST, direct user to login page
 if request.method != "POST":
  return render(__snake_case  ,		'''login.html'''		)

 # from the frontend, get username, password, and client_key
 _lowercase							   =request.POST.get('''username'''		)
 _lowercase							   =request.POST.get('''password'''		)
 _lowercase							   =request.POST.get('''g-recaptcha-response'''		)

 # post recaptcha response to Google's recaptcha api
 _lowercase							   =requests.post(__snake_case  ,		data={'''secret''': secret_key, '''response''': client_key}		)
 # if the recaptcha api verified our keys
 if response.json().get('''success'''  ,		__snake_case		):
  # authenticate the user
  _lowercase							   =authenticate(__snake_case  ,		username=__snake_case  ,		password=__snake_case		)
  if user_in_database:
   login(__snake_case  ,		__snake_case		)
   return redirect('''/your-webpage'''		)
 return render(__snake_case  ,		'''login.html'''		)

