
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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    tuple:
       return (data["data"], data["target"])







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    np.ndarray:
       lowercase							:     int       	=      XGBRegressor(verbosity=0					,	random_state=42				)
       xgb.fit(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       # Predict target for test data
       lowercase							:     List[str]       	=      xgb.predict(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      predictions.reshape(len(SCREAMING_SNAKE_CASE__				)					,	1				)
       return predictions







def 						_snake_case(  ) ->    None:
       lowercase							:     str       	=      fetch_california_housing()
       lowercase	,			lowercase							:     str       	=      data_handling(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      train_test_split(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	test_size=0.25					,	random_state=1				)
       lowercase							:     str       	=      xgboost(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       # Error printing
       print(f"Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)}"				)
       print(f"Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)}"				)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()


from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


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

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,**snake_case      ):



           '''simple docstring'''







           super().__init__(**snake_case      )

           if self.framework != "pt":
                  raise ValueError(f"The {self.__class__} is only available in PyTorch."      )
           # No specific FOR_XXX available yet
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      {}
           if "candidate_labels" in kwargs:
                  lowercase							:     List[str]       	=      kwargs["""candidate_labels"""]
           if "hypothesis_template" in kwargs:
                  lowercase							:     Dict       	=      kwargs["""hypothesis_template"""]

           return preprocess_params, {}, {}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,snake_case="This is a sound of {}."      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  if audio.startswith("""http://"""      ) or audio.startswith("""https://"""      ):
                         # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                         # like http_huggingface_co.png
                         lowercase							:     Optional[Any]       	=      requests.get(snake_case      ).content
                  else:
                         with open(snake_case		,"""rb"""      ) as f:
                                lowercase							:     Union[str, Any]       	=      f.read()

           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     int       	=      ffmpeg_read(snake_case		,self.feature_extractor.sampling_rate      )

           if not isinstance(snake_case		,np.ndarray      ):
                  raise ValueError("""We expect a numpy ndarray as input"""      )
           if len(audio.shape      ) != 1:
                  raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline"""      )

           lowercase							:     Dict       	=      self.feature_extractor(
               [audio]		,sampling_rate=self.feature_extractor.sampling_rate		,return_tensors="""pt"""      )
           lowercase							:     Tuple       	=      candidate_labels
           lowercase							:     Tuple       	=      [hypothesis_template.format(snake_case      ) for x in candidate_labels]
           lowercase							:     Optional[Any]       	=      self.tokenizer(snake_case		,return_tensors=self.framework		,padding=snake_case      )
           lowercase							:     Optional[Any]       	=      [text_inputs]
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      model_inputs.pop("""candidate_labels"""      )
           lowercase							:     Dict       	=      model_inputs.pop("""text_inputs"""      )
           if isinstance(text_inputs[0]		,snake_case      ):
                  lowercase							:     List[Any]       	=      text_inputs[0]
           else:
                  # Batching case.
                  lowercase							:     Dict       	=      text_inputs[0][0]

           lowercase							:     Optional[Any]       	=      self.model(**snake_case		,**snake_case      )

           lowercase							:     Any       	=      {
               """candidate_labels""": candidate_labels,
               """logits""": outputs.logits_per_audio,
           }
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      model_outputs.pop("""candidate_labels"""      )
           lowercase							:     Any       	=      model_outputs["""logits"""][0]

           if self.framework == "pt":
                  lowercase							:     Any       	=      logits.softmax(dim=0      )
                  lowercase							:     Tuple       	=      probs.tolist()
           else:
                  raise ValueError("""`tf` framework not supported."""      )

           lowercase							:     Tuple       	=      [
               {"""score""": score, """label""": candidate_label}
               for score, candidate_label in sorted(zip(snake_case		,snake_case      )		,key=lambda snake_case      : -x[0]      )
           ]
           return result
