



'''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


SCREAMING_SNAKE_CASE__     :						int	=  logging.get_logger(__name__)

class 							a__( _a     ):
			a_	: List[Any]									=      ['''input_features''', '''attention_mask''']






			def __init__(     self					,   _UpperCAmelCase=80					,   _UpperCAmelCase=1_6000					,   _UpperCAmelCase=80					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   _UpperCAmelCase=True					,   **_UpperCAmelCase					,   )							->							List[str]:
					super().__init__(feature_size=__UpperCamelCase					,   sampling_rate=__UpperCamelCase					,   padding_value=__UpperCamelCase					,   **__UpperCamelCase						)
					snake_case__	     =num_mel_bins
					snake_case__	     =do_ceptral_normalize
					snake_case__	     =normalize_means
					snake_case__	     =normalize_vars
					snake_case__	     =True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   )							->							List[Any]:
					snake_case__	     =waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
					snake_case__	     =torch.from_numpy(__UpperCamelCase						).unsqueeze(0						)
					snake_case__	     =ta_kaldi.fbank(__UpperCamelCase					,   num_mel_bins=self.num_mel_bins					,   sample_frequency=self.sampling_rate						)
					return features.numpy()






			@staticmethod
			def 						_lowercase  (     _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = True					,   _UpperCAmelCase = True					,   _UpperCAmelCase = 0.0					,   )							->							Optional[int]:
					# make sure we normalize float32 arrays
					if normalize_means:
							snake_case__	     =x[:input_length].mean(axis=0						)
							snake_case__	     =np.subtract(__UpperCamelCase					,   __UpperCamelCase						)
					if normalize_vars:
							snake_case__	     =x[:input_length].std(axis=0						)
							snake_case__	     =np.divide(__UpperCamelCase					,   __UpperCamelCase						)

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

					# make sure array is in float32
					snake_case__	     =x.astype(np.floataa						)

					return x






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple:
					snake_case__	     =attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
					return [
					    self.utterance_cmvn(__UpperCamelCase					,   __UpperCamelCase					,   self.normalize_means					,   self.normalize_vars					,   self.padding_value						)
					    for x, n in zip(__UpperCamelCase					,   __UpperCamelCase						)
					]






			def __call__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase = False					,   _UpperCAmelCase = None					,   _UpperCAmelCase = False					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   _UpperCAmelCase = None					,   **_UpperCAmelCase					,   )							->							Union[str, Any]:

					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.'						)

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

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

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

					# extract fbank features
					snake_case__	     =[self._extract_fbank_features(__UpperCamelCase						) for waveform in raw_speech]

					# convert into correct format for padding
					snake_case__	     =BatchFeature({'input_features': features}						)

					snake_case__	     =self.pad(
					    __UpperCamelCase					,   padding=__UpperCamelCase					,   max_length=__UpperCamelCase					,   truncation=__UpperCamelCase					,   pad_to_multiple_of=__UpperCamelCase					,   return_attention_mask=__UpperCamelCase					,   **__UpperCamelCase					,   )

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

					snake_case__	     =padded_inputs.get('attention_mask'						)
					if attention_mask is not None:
							snake_case__	     =[np.asarray(__UpperCamelCase					,   dtype=np.intaa						) for array in attention_mask]

					# Utterance-level cepstral mean and variance normalization
					if self.do_ceptral_normalize:
							snake_case__	     =(
							    np.array(__UpperCamelCase					,   dtype=np.intaa						)
							    if self._get_padding_strategies(__UpperCamelCase					,   max_length=__UpperCamelCase						) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							snake_case__	     =self.normalize(
							    padded_inputs['input_features']					,   attention_mask=__UpperCamelCase						)

					if return_tensors is not None:
							snake_case__	     =padded_inputs.convert_to_tensors(__UpperCamelCase						)

					return padded_inputs






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A		: Union[str, Any]								=     pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
A		: Union[str, Any]								=     dataset.iloc[:, 1:2].values
A		: Dict								=     dataset.iloc[:, 2].values


A							,      A							,      A							,      A		: str								=     train_test_split(X, y, test_size=0.2, random_state=0)


A		: Union[str, Any]								=     PolynomialFeatures(degree=4)
A		: str								=     poly_reg.fit_transform(X)
A		: Union[str, Any]								=     LinearRegression()
pol_reg.fit(X_poly, y)




def 	UpperCamelCase__  (     )  ->     Any:
						plt.scatter(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   color="""red"""       )
						plt.plot(SCREAMING_SNAKE_CASE_		,   pol_reg.predict(poly_reg.fit_transform(SCREAMING_SNAKE_CASE_       )       )		,   color="""blue"""       )
						plt.title("""Truth or Bluff (Linear Regression)"""       )
						plt.xlabel("""Position level"""       )
						plt.ylabel("""Salary"""       )
						plt.show()


if __name__ == "__main__":
		viz_polymonial()

		# Predicting a new result with Polymonial Regression
		pol_reg.predict(poly_reg.fit_transform([[5.5]]))
		# output should be 132148.43750003