





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
UpperCamelCase_												=	pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
UpperCamelCase_												=	dataset.iloc[:, 1:2].values
UpperCamelCase_												=	dataset.iloc[:, 2].values


UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_												=	train_test_split(X, y, test_size=0.2, random_state=0)


UpperCamelCase_												=	PolynomialFeatures(degree=4)
UpperCamelCase_												=	poly_reg.fit_transform(X)
UpperCamelCase_												=	LinearRegression()
pol_reg.fit(X_poly, y)






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   plt.scatter(A				,							A				,							color="red"     )
   plt.plot(A				,							pol_reg.predict(poly_reg.fit_transform(A     )     )				,							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










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio is None and text is None:
       raise ValueError("You need to specify either an `audio` or `text` input to process."		)

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



