


"""simple docstring"""





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


_UpperCamelCase     :		Optional[Any]											=					logging.get_logger(__name__)


class   UpperCAmelCase_			(					__a):


      def __init__(     self	,    a	,    a	,    a	,    **a							) ->      Union[str, Any]:
             lowercase__    :	Optional[Any]     					=			feature_size
             lowercase__    :	List[Any]     					=			sampling_rate
             lowercase__    :	Optional[int]     					=			padding_value

             lowercase__    :	Optional[Any]     					=			kwargs.pop('padding_side'	,    'right'							)
             lowercase__    :	str     					=			kwargs.pop('return_attention_mask'	,    A__							)

             super().__init__(**A__							)


      def 	_UpperCAmelCase (     self	,    a	,    a = True	,    a = None	,    a = False	,    a = None	,    a = None	,    a = None	,    ) ->      BatchFeature:
             # If we have a list of dicts, let's convert it in a dict of lists
             # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
             if isinstance(A__	,    (list, tuple)							) and isinstance(processed_features[0]	,    (dict, BatchFeature)							):
                    lowercase__    :	Any     					=			{
                        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()							)}"""							)

             lowercase__    :	List[str]     					=			processed_features[self.model_input_names[0]]
             lowercase__    :	str     					=			(
                 return_attention_mask if return_attention_mask is not None else self.return_attention_mask
             )

             if len(A__							) == 0:
                    if return_attention_mask:
                           lowercase__    :	Dict     					=			[]
                    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

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

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

             for key, value in processed_features.items():
                    if isinstance(value[0]	,    (int, float)							):
                           lowercase__    :	Optional[int]     					=			to_numpy(A__							)
                    else:
                           lowercase__    :	int     					=			[to_numpy(A__							) for v in value]

                           # Convert padding_strategy in PaddingStrategy
             lowercase__    :	int     					=			self._get_padding_strategies(padding=A__	,    max_length=A__							)

             lowercase__    :	int     					=			processed_features[self.model_input_names[0]]

             lowercase__    :	Any     					=			len(A__							)
             if not all(len(A__							) == batch_size for v in processed_features.values()							):
                    raise ValueError('Some items in the output dictionary have a different batch size than others.'							)

             lowercase__    :	int     					=			[]
             for i in range(A__							):
                    lowercase__    :	Any     					=			{k: v[i] for k, v in processed_features.items()}
                    # truncation
                    lowercase__    :	Dict     					=			self._truncate(
                        A__	,    max_length=A__	,    pad_to_multiple_of=A__	,    truncation=A__	,    )
                    truncated_inputs.append(A__							)

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

             lowercase__    :	Optional[int]     					=			{}
             for i in range(A__							):
                    # padding
                    lowercase__    :	Union[str, Any]     					=			self._pad(
                        truncated_inputs[i]	,    max_length=A__	,    padding_strategy=A__	,    pad_to_multiple_of=A__	,    return_attention_mask=A__	,    )

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

             return BatchFeature(A__	,    tensor_type=A__							)


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = PaddingStrategy.DO_NOT_PAD	,    a = None	,    a = None	,    ) ->      dict:
             lowercase__    :	List[str]     					=			processed_features[self.model_input_names[0]]

             if padding_strategy == PaddingStrategy.LONGEST:
                    lowercase__    :	Any     					=			len(A__							)

             if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                    lowercase__    :	Tuple     					=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

             lowercase__    :	Dict     					=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__							) < max_length

             if return_attention_mask and "attention_mask" not in processed_features:
                    lowercase__    :	int     					=			np.ones(len(A__							)	,    dtype=np.intaa							)

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

             return processed_features


      def 	_UpperCAmelCase (     self	,    a	,    a = None	,    a = None	,    a = None	,    ) ->      Union[str, Any]:
             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.'							)

             lowercase__    :	str     					=			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):
                    lowercase__    :	Any     					=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

             lowercase__    :	Union[str, Any]     					=			len(A__							) > max_length

             if needs_to_be_truncated:
                    lowercase__    :	List[str]     					=			processed_features[self.model_input_names[0]][:max_length]
                    if "attention_mask" in processed_features:
                           lowercase__    :	List[Any]     					=			processed_features['attention_mask'][:max_length]

             return processed_features


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

             # Get padding strategy
             if padding is not False:
                    if padding is True:
                           lowercase__    :	List[Any]     					=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                    elif not isinstance(A__	,    A__							):
                           lowercase__    :	Optional[Any]     					=			PaddingStrategy(A__							)
                    elif isinstance(A__	,    A__							):
                           lowercase__    :	List[Any]     					=			padding
             else:
                    lowercase__    :	List[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


"""simple docstring"""





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        a_  (   _lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       return (data["data"], data["target"])





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			xgboost(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Error printing
       print(f"""Mean Absolute Error : {mean_absolute_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )
       print(f"""Mean Square Error  : {mean_squared_error(_lowerCAmelCase     ,				_lowerCAmelCase  )}"""  )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

