"""simple docstring"""







from typing import List, Optional, Union

import numpy as np

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


_SCREAMING_SNAKE_CASE	:		str   =   logging.get_logger(__name__)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    ["input_values", "attention_mask"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=16000 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

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







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = 0.0	):


      '''simple docstring'''

      if attention_mask is not None:
         __lowerCamelCase    		=					np.array(_SCREAMING_SNAKE_CASE ,				np.intaa	)
         __lowerCamelCase    		=					[]

         for vector, length in zip(_SCREAMING_SNAKE_CASE ,				attention_mask.sum(-1	)	):
            __lowerCamelCase    		=					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7	)
            if length < normed_slice.shape[0]:
               __lowerCamelCase    		=					padding_value

            normed_input_values.append(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7	) for x in input_values]

      return normed_input_values







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


      '''simple docstring'''

      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.'	)

      __lowerCamelCase    		=					isinstance(_SCREAMING_SNAKE_CASE ,				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(_SCREAMING_SNAKE_CASE ,				(list, tuple)	) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)	))
      )

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

      # convert into correct format for padding
      __lowerCamelCase    		=					BatchFeature({'input_values': raw_speech}	)

      __lowerCamelCase    		=					self.pad(
          _SCREAMING_SNAKE_CASE ,				padding=_SCREAMING_SNAKE_CASE ,				max_length=_SCREAMING_SNAKE_CASE ,				truncation=_SCREAMING_SNAKE_CASE ,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,				return_attention_mask=_SCREAMING_SNAKE_CASE ,				)

      # convert input values to correct format
      __lowerCamelCase    		=					padded_inputs['input_values']
      if not isinstance(input_values[0] ,				np.ndarray	):
         __lowerCamelCase    		=					[np.asarray(_SCREAMING_SNAKE_CASE ,				dtype=np.floataa	) for array in input_values]
      elif (
          not isinstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	)
          and isinstance(input_values[0] ,				np.ndarray	)
          and input_values[0].dtype is np.dtype(np.floataa	)
      ):
         __lowerCamelCase    		=					[array.astype(np.floataa	) for array in input_values]
      elif isinstance(_SCREAMING_SNAKE_CASE ,				np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
         __lowerCamelCase    		=					input_values.astype(np.floataa	)

      # convert attention_mask to correct format
      __lowerCamelCase    		=					padded_inputs.get('attention_mask'	)
      if attention_mask is not None:
         __lowerCamelCase    		=					[np.asarray(_SCREAMING_SNAKE_CASE ,				dtype=np.intaa	) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if self.do_normalize:
         __lowerCamelCase    		=					(
             attention_mask
             if self._get_padding_strategies(_SCREAMING_SNAKE_CASE ,				max_length=_SCREAMING_SNAKE_CASE	) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         __lowerCamelCase    		=					self.zero_mean_unit_var_norm(
             padded_inputs['input_values'] ,				attention_mask=_SCREAMING_SNAKE_CASE ,				padding_value=self.padding_value	)

      if return_tensors is not None:
         __lowerCamelCase    		=					padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE	)

      return padded_inputs













import numpy as np
import requests


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''       )
       _UpperCamelCase														=    response.text.splitlines()
       _UpperCamelCase														=    []
       for item in lines:
              _UpperCamelCase														=    item.split(''','''       )
              data.append(lowerCAmelCase       )
       data.pop(0       )  # This is for removing the labels from the list
       _UpperCamelCase														=    np.matrix(lowerCAmelCase       )
       return dataset


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta - (alpha / n) * sum_grad
       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

       _UpperCamelCase														=    data_x.shape[1]
       _UpperCamelCase														=    data_x.shape[0] - 1

       _UpperCamelCase														=    np.zeros((1, no_features)       )

       for i in range(0     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    run_steep_gradient_descent(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              _UpperCamelCase														=    sum_of_square_error(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              print(f'''At Iteration {i + 1} - Error is {error:.5f}'''       )

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

       _UpperCamelCase														=    data.shape[0]
       _UpperCamelCase														=    np.c_[np.ones(lowerCAmelCase       ), data[:, :-1]].astype(lowerCAmelCase       )
       _UpperCamelCase														=    data[:, -1].astype(lowerCAmelCase       )

       _UpperCamelCase														=    run_linear_regression(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta.shape[1]
       print('''Resultant Feature vector : '''       )
       for i in range(0     ,							lowerCAmelCase       ):
              print(f'''{theta[0, i]:.5f}'''       )


if __name__ == "__main__":
       main()






