

'''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 	lowerCamelCase__							(   a__)		->     tuple:



    """simple docstring"""







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

def 	lowerCamelCase__							(   a__      ,   a__      ,   a__)		->     np.ndarray:



    """simple docstring"""







    _snake_case			:			Any				       =		XGBRegressor(verbosity=0      ,   random_state=4_2)
    xgb.fit(a__      ,   a__)
    # Predict target for test data
    _snake_case			:			Dict				       =		xgb.predict(a__)
    _snake_case			:			Optional[int]				       =		predictions.reshape(len(a__)      ,   1)
    return predictions

def 	lowerCamelCase__							(   )		->     None:



    """simple docstring"""







    _snake_case			:			List[str]				       =		fetch_california_housing()
    _snake_case       ,				_snake_case			:			Optional[Any]				       =		data_handling(a__)
    _snake_case       ,				_snake_case       ,				_snake_case       ,				_snake_case			:			Optional[Any]				       =		train_test_split(
        a__      ,   a__      ,   test_size=0.2_5      ,   random_state=1)
    _snake_case			:			Optional[int]				       =		xgboost(a__      ,   a__      ,   a__)
    # Error printing
    print(F"""Mean Absolute Error : {mean_absolute_error(a__      ,   a__)}""")
    print(F"""Mean Square Error  : {mean_squared_error(a__      ,   a__)}""")


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()





"""simple docstring"""




import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a		:       int     						=       logging.get_logger(__name__)







class     lowercase(_lowercase       ):
     __snake_case:	Any						=     ['input_features', 'is_longer']



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE=6_4     ,				__SCREAMING_SNAKE_CASE=4_8_0_0_0     ,				__SCREAMING_SNAKE_CASE=4_8_0     ,				__SCREAMING_SNAKE_CASE=1_0     ,				__SCREAMING_SNAKE_CASE=1_0_2_4     ,				__SCREAMING_SNAKE_CASE=0.0     ,				__SCREAMING_SNAKE_CASE=False     ,				__SCREAMING_SNAKE_CASE = 0     ,				__SCREAMING_SNAKE_CASE = 1_4_0_0_0     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = "fusion"     ,				__SCREAMING_SNAKE_CASE = "repeatpad"     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__(
             feature_size=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				padding_value=__SCREAMING_SNAKE_CASE     ,				return_attention_mask=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			top_db
         a__		=			truncation
         a__		=			padding
         a__		=			fft_window_size
         a__		=			(fft_window_size >> 1) + 1
         a__		=			hop_length
         a__		=			max_length_s
         a__		=			max_length_s * sampling_rate
         a__		=			sampling_rate
         a__		=			frequency_min
         a__		=			frequency_max
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm=__SCREAMING_SNAKE_CASE     ,				mel_scale='htk'     ,				)
         a__		=			mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins     ,				num_mel_filters=__SCREAMING_SNAKE_CASE     ,				min_frequency=__SCREAMING_SNAKE_CASE     ,				max_frequency=__SCREAMING_SNAKE_CASE     ,				sampling_rate=__SCREAMING_SNAKE_CASE     ,				norm='slaney'     ,				mel_scale='slaney'     ,				)



     def 			lowercase__  (							self   )		->     Dict[str, Any]:







         """simple docstring"""



         a__		=			copy.deepcopy(self.__dict__   )
         a__		=			self.__class__.__name__
         if "mel_filters" in output:
             del output["mel_filters"]
         if "mel_filters_slaney" in output:
             del output["mel_filters_slaney"]
         return output



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None   )		->     np.ndarray:







         """simple docstring"""



         a__		=			spectrogram(
             __SCREAMING_SNAKE_CASE     ,				window_function(self.fft_window_size     ,				'hann'   )     ,				frame_length=self.fft_window_size     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=__SCREAMING_SNAKE_CASE     ,				log_mel='dB'     ,				)
         return log_mel_spectrogram.T



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			np.array_split(list(range(0     ,				total_frames - chunk_frames + 1   )   )     ,				3   )
         if len(ranges[1]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         if len(ranges[2]   ) == 0:
             # if the audio is too short, we just use the first chunk
             a__		=			[0]
         # randomly choose index for each part
         a__		=			np.random.choice(ranges[0]   )
         a__		=			np.random.choice(ranges[1]   )
         a__		=			np.random.choice(ranges[2]   )

         a__		=			mel[idx_front : idx_front + chunk_frames, :]
         a__		=			mel[idx_middle : idx_middle + chunk_frames, :]
         a__		=			mel[idx_back : idx_back + chunk_frames, :]

         a__		=			torch.tensor(mel[None, None, :]   )
         a__		=			torch.nn.functional.interpolate(
             __SCREAMING_SNAKE_CASE     ,				size=[chunk_frames, 6_4]     ,				mode='bilinear'     ,				align_corners=__SCREAMING_SNAKE_CASE   )
         a__		=			mel_shrink[0][0].numpy()
         a__		=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,				axis=0   )
         return mel_fusion



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     np.array:







         """simple docstring"""



         if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                 a__		=			True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 a__		=			len(__SCREAMING_SNAKE_CASE   ) - max_length
                 a__		=			np.random.randint(0     ,				overflow + 1   )
                 a__		=			waveform[idx : idx + max_length]
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]
             elif truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 a__		=			mel.shape[0]
                 if chunk_frames == total_frames:
                     # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                     # In this case, we just use the whole audio.
                     a__		=			np.stack([mel, mel, mel, mel]     ,				axis=0   )
                     a__		=			False
                 else:
                     a__		=			self._random_mel_fusion(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
                     a__		=			True
             else:
                 raise NotImplementedError(f'data_truncating {truncation} not implemented'   )

         else:
             a__		=			False
             # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
             if waveform.shape[0] < max_length:
                 if padding == "repeat":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				n_repeat + 1   )   )[:max_length]
                 if padding == "repeatpad":
                     a__		=			int(max_length / len(__SCREAMING_SNAKE_CASE   )   )
                     a__		=			np.stack(np.tile(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )   )
                 a__		=			np.pad(__SCREAMING_SNAKE_CASE     ,				(0, max_length - waveform.shape[0])     ,				mode='constant'     ,				constant_values=0   )

             if truncation == "fusion":
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters   )
                 a__		=			np.stack([input_mel, input_mel, input_mel, input_mel]     ,				axis=0   )
             else:
                 a__		=			self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE     ,				self.mel_filters_slaney   )[None, :]

         return input_mel, longer




     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     BatchFeature:







         """simple docstring"""



         a__		=			truncation if truncation is not None else self.truncation
         a__		=			padding if padding else self.padding

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                     f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                     f' was sampled with {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.'   )

         a__		=			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}'   )
         a__		=			is_batched_numpy or (
             isinstance(__SCREAMING_SNAKE_CASE     ,				(list, tuple)   ) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)   ))
         )

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

         # always return batch
         if not is_batched:
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE   )]

         # convert to mel spectrogram, truncate and pad if needed.
         a__		=			[
             self._get_input_mel(__SCREAMING_SNAKE_CASE     ,				max_length if max_length else self.nb_max_samples     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
             for waveform in raw_speech
         ]

         a__		=			[]
         a__		=			[]
         for mel, longer in padded_inputs:
             input_mel.append(__SCREAMING_SNAKE_CASE   )
             is_longer.append(__SCREAMING_SNAKE_CASE   )

         if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE   ) == 0:
             # if no audio is longer than 10s, then randomly select one audio to be longer
             a__		=			np.random.randint(0     ,				len(__SCREAMING_SNAKE_CASE   )   )
             a__		=			True

         if isinstance(input_mel[0]     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			[np.asarray(__SCREAMING_SNAKE_CASE     ,				dtype=np.floataa   ) for feature in input_mel]

         # is_longer is a list of bool
         a__		=			[[longer] for longer in is_longer]

         a__		=			{'input_features': input_mel, 'is_longer': is_longer}
         a__		=			BatchFeature(__SCREAMING_SNAKE_CASE   )

         if return_tensors is not None:
             a__		=			input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE   )

         return input_features


