




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase      :  Tuple      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):



				def __init__(      self      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		float		,	**_UpperCAmelCase      :		int      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      feature_size
								_lowerCAmelCase    :    Optional[Any]										=      sampling_rate
								_lowerCAmelCase    :    str										=      padding_value

								_lowerCAmelCase    :    Optional[int]										=      kwargs.pop("""padding_side"""		,	"""right"""      )
								_lowerCAmelCase    :    List[str]										=      kwargs.pop("""return_attention_mask"""		,	_UpperCAmelCase      )

								super().__init__(**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Union[
				        BatchFeature,
				        List[BatchFeature],
				        Dict[str, BatchFeature],
				        Dict[str, List[BatchFeature]],
				        List[Dict[str, BatchFeature]],
				    ]		,	_UpperCAmelCase      :		Union[bool, str, PaddingStrategy] = True		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Union[str, TensorType]] = None		,	)  ->    BatchFeature:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase		,	(list, tuple)      ) and isinstance(processed_features[0]		,	(dict, BatchFeature)      ):
												_lowerCAmelCase    :    List[str]										=      {
												    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
												}

								# The model's main input name, usually `input_values`, has be passed for padding
								if self.model_input_names[0] not in processed_features:
												raise ValueError(
												    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
												    f" to this method that includes {self.model_input_names[0]}, but you provided"
												    f" {list(processed_features.keys()      )}"      )

								_lowerCAmelCase    :    Any										=      processed_features[self.model_input_names[0]]
								_lowerCAmelCase    :    Union[str, Any]										=      (
								    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
								)

								if len(_UpperCAmelCase      ) == 0:
												if return_attention_mask:
																_lowerCAmelCase    :    Optional[int]										=      []
												return processed_features

								# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
								# and rebuild them afterwards if no return_tensors is specified
								# Note that we lose the specific device the tensor may be on for PyTorch

								_lowerCAmelCase    :    str										=      required_input[0]
								if isinstance(_UpperCAmelCase		,	(list, tuple)      ):
												# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
												_lowerCAmelCase    :    str										=      0
												while len(required_input[index]      ) == 0:
																index += 1
												if index < len(_UpperCAmelCase      ):
																_lowerCAmelCase    :    Union[str, Any]										=      required_input[index][0]

								if return_tensors is None:
												if is_tf_tensor(_UpperCAmelCase      ):
																_lowerCAmelCase    :    Optional[Any]										=      """tf"""
												elif is_torch_tensor(_UpperCAmelCase      ):
																_lowerCAmelCase    :    List[str]										=      """pt"""
												elif isinstance(_UpperCAmelCase		,	(int, float, list, tuple, np.ndarray)      ):
																_lowerCAmelCase    :    Union[str, Any]										=      """np"""
												else:
																raise ValueError(
																    f"type of {first_element} unknown: {type(_UpperCAmelCase      )}. "
																    """Should be one of a python, numpy, pytorch or tensorflow object."""      )

								for key, value in processed_features.items():
												if isinstance(value[0]		,	(int, float)      ):
																_lowerCAmelCase    :    Any										=      to_numpy(_UpperCAmelCase      )
												else:
																_lowerCAmelCase    :    Any										=      [to_numpy(_UpperCAmelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
								_lowerCAmelCase    :    Optional[int]										=      self._get_padding_strategies(padding=_UpperCAmelCase		,	max_length=_UpperCAmelCase      )

								_lowerCAmelCase    :    Tuple										=      processed_features[self.model_input_names[0]]

								_lowerCAmelCase    :    Union[str, Any]										=      len(_UpperCAmelCase      )
								if not all(len(_UpperCAmelCase      ) == batch_size for v in processed_features.values()      ):
												raise ValueError("""Some items in the output dictionary have a different batch size than others."""      )

								_lowerCAmelCase    :    Optional[Any]										=      []
								for i in range(_UpperCAmelCase      ):
												_lowerCAmelCase    :    List[Any]										=      {k: v[i] for k, v in processed_features.items()}
												# truncation
												_lowerCAmelCase    :    List[Any]										=      self._truncate(
												    _UpperCAmelCase		,	max_length=_UpperCAmelCase		,	pad_to_multiple_of=_UpperCAmelCase		,	truncation=_UpperCAmelCase		,	)
												truncated_inputs.append(_UpperCAmelCase      )

								if padding_strategy == PaddingStrategy.LONGEST:
												# make sure that `max_length` cannot be longer than the longest truncated length
												_lowerCAmelCase    :    List[Any]										=      max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
												_lowerCAmelCase    :    Dict										=      PaddingStrategy.MAX_LENGTH

								_lowerCAmelCase    :    List[str]										=      {}
								for i in range(_UpperCAmelCase      ):
												# padding
												_lowerCAmelCase    :    Optional[Any]										=      self._pad(
												    truncated_inputs[i]		,	max_length=_UpperCAmelCase		,	padding_strategy=_UpperCAmelCase		,	pad_to_multiple_of=_UpperCAmelCase		,	return_attention_mask=_UpperCAmelCase		,	)

												for key, value in outputs.items():
																if key not in batch_outputs:
																				_lowerCAmelCase    :    Optional[Any]										=      []
																if value.dtype is np.dtype(np.floataa      ):
																				_lowerCAmelCase    :    Tuple										=      value.astype(np.floataa      )
																batch_outputs[key].append(_UpperCAmelCase      )

								return BatchFeature(_UpperCAmelCase		,	tensor_type=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Union[Dict[str, np.ndarray], BatchFeature]		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	)  ->    dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
												_lowerCAmelCase    :    str										=      len(_UpperCAmelCase      )

								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
												_lowerCAmelCase    :    Union[str, Any]										=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								_lowerCAmelCase    :    Any										=      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCAmelCase      ) < max_length

								if return_attention_mask and "attention_mask" not in processed_features:
												_lowerCAmelCase    :    List[str]										=      np.ones(len(_UpperCAmelCase      )		,	dtype=np.intaa      )

								if needs_to_be_padded:
												_lowerCAmelCase    :    Union[str, Any]										=      max_length - len(_UpperCAmelCase      )
												if self.padding_side == "right":
																if return_attention_mask:
																				_lowerCAmelCase    :    Any										=      np.pad(
																				    processed_features["""attention_mask"""]		,	(0, difference)      )
																_lowerCAmelCase    :    Optional[int]										=      ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																_lowerCAmelCase    :    Union[str, Any]										=      np.pad(
																    _UpperCAmelCase		,	_UpperCAmelCase		,	"""constant"""		,	constant_values=self.padding_value      )
												elif self.padding_side == "left":
																if return_attention_mask:
																				_lowerCAmelCase    :    List[str]										=      np.pad(
																				    processed_features["""attention_mask"""]		,	(difference, 0)      )
																_lowerCAmelCase    :    List[Any]										=      ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																_lowerCAmelCase    :    List[str]										=      np.pad(
																    _UpperCAmelCase		,	_UpperCAmelCase		,	"""constant"""		,	constant_values=self.padding_value      )
												else:
																raise ValueError("""Invalid padding strategy:""" + str(self.padding_side      )      )

								return processed_features



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Union[Dict[str, np.ndarray], BatchFeature]		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	)  ->    Optional[Any]:





								'''simple docstring'''




								if not truncation:
												return processed_features
								elif truncation and max_length is None:
												raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""      )

								_lowerCAmelCase    :    Union[str, Any]										=      processed_features[self.model_input_names[0]]

								# find `max_length` that fits `pad_to_multiple_of`
								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
												_lowerCAmelCase    :    Optional[Any]										=      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								_lowerCAmelCase    :    Optional[int]										=      len(_UpperCAmelCase      ) > max_length

								if needs_to_be_truncated:
												_lowerCAmelCase    :    Union[str, Any]										=      processed_features[self.model_input_names[0]][:max_length]
												if "attention_mask" in processed_features:
																_lowerCAmelCase    :    Optional[int]										=      processed_features["""attention_mask"""][:max_length]

								return processed_features



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any=False		,	_UpperCAmelCase      :		Optional[int]=None      )  ->    Tuple:





								'''simple docstring'''




								if padding is not False:
												if padding is True:
																_lowerCAmelCase    :    Union[str, Any]										=      PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
												elif not isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
																_lowerCAmelCase    :    int										=      PaddingStrategy(_UpperCAmelCase      )
												elif isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
																_lowerCAmelCase    :    Optional[int]										=      padding
								else:
												_lowerCAmelCase    :    Union[str, Any]										=      PaddingStrategy.DO_NOT_PAD

								# Set max length if needed
								if max_length is None:
												if padding_strategy == PaddingStrategy.MAX_LENGTH:
																raise ValueError(
																    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
								if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
												raise ValueError(
												    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
												    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""      )

								return padding_strategy







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				plt.scatter(UpperCamelCase_      ,    UpperCamelCase_      ,    color="""red"""       )
				plt.plot(UpperCamelCase_      ,    pol_reg.predict(poly_reg.fit_transform(UpperCamelCase_       )       )      ,    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

