





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='''%(message)s''')







def 					lowerCAmelCase__(		lowercase				:			np.ndarray )       ->			List[str]:
  return input_array.reshape((input_array.size, 1) )







def 					lowerCAmelCase__(		lowercase				:			np.ndarray      ,   lowercase				:			np.ndarray      ,   lowercase				:			int )       ->			int:
  __snake_case      : List[Any]						=				np.nan
  for i in range(_A ):
    __snake_case      : List[Any]						=				features[:, labels == i]
    __snake_case      : str						=				data.mean(1 )
    # Centralize the data of class i
    __snake_case      : List[str]						=				data - column_reshape(_A )
    if i > 0:
      # If covariance_sum is not None
      covariance_sum += np.dot(_A      ,   centered_data.T )
    else:
      # If covariance_sum is np.nan (i.e. first loop)
      __snake_case      : Tuple						=				np.dot(_A      ,   centered_data.T )

  return covariance_sum / features.shape[1]







def 					lowerCAmelCase__(		lowercase				:			np.ndarray      ,   lowercase				:			np.ndarray      ,   lowercase				:			int )       ->			str:
  __snake_case      : Optional[int]						=				features.mean(1 )
  __snake_case      : Optional[int]						=				np.nan
  for i in range(_A ):
    __snake_case      : str						=				features[:, labels == i]
    __snake_case      : List[str]						=				data.shape[1]
    __snake_case      : Any						=				data.mean(1 )
    if i > 0:
      # If covariance_sum is not None
      covariance_sum += device_data * np.dot(
          column_reshape(_A ) - column_reshape(_A )      ,   (column_reshape(_A ) - column_reshape(_A )).T      ,   )
    else:
      # If covariance_sum is np.nan (i.e. first loop)
      __snake_case      : Optional[int]						=				device_data * np.dot(
          column_reshape(_A ) - column_reshape(_A )      ,   (column_reshape(_A ) - column_reshape(_A )).T      ,   )

  return covariance_sum / features.shape[1]







def 					lowerCAmelCase__(		lowercase				:			np.ndarray      ,   lowercase				:			int )       ->			List[Any]:




  if features.any():
    __snake_case      : List[Any]						=				features.mean(1 )
    # Center the dataset
    __snake_case      : str						=				features - np.reshape(_A      ,   (data_mean.size, 1) )
    __snake_case      : Dict						=				np.dot(_A      ,   centered_data.T ) / features.shape[1]
    __snake_case      : List[Any]						=				np.linalg.eigh(_A )
    # Take all the columns in the reverse order (-1), and then takes only the first
    __snake_case      : List[Any]						=				eigenvectors[:, ::-1][:, 0:dimensions]
    # Project the database on the new space
    __snake_case      : Dict						=				np.dot(filtered_eigenvectors.T      ,   _A )
    logging.info("Principal Component Analysis computed" )

    return projected_data
  else:
    logging.basicConfig(level=logging.ERROR      ,   format="%(message)s"      ,   force=_A )
    logging.error("Dataset empty" )
    raise AssertionError







def 					lowerCAmelCase__(		lowercase				:			np.ndarray      ,   lowercase				:			np.ndarray      ,   lowercase				:			int      ,   lowercase				:			int )       ->			List[Any]:
  assert classes > dimensions

  # Check if features have been already loaded
  if features.any:
    __snake_case      : Dict						=				eigh(
        covariance_between_classes(_A      ,   _A      ,   _A )      ,   covariance_within_classes(_A      ,   _A      ,   _A )      ,   )
    __snake_case      : int						=				eigenvectors[:, ::-1][:, :dimensions]
    __snake_case      : Union[str, Any]						=				np.linalg.svd(_A )
    __snake_case      : Optional[int]						=				svd_matrix[:, 0:dimensions]
    __snake_case      : Dict						=				np.dot(filtered_svd_matrix.T      ,   _A )
    logging.info("Linear Discriminant Analysis computed" )

    return projected_data
  else:
    logging.basicConfig(level=logging.ERROR      ,   format="%(message)s"      ,   force=_A )
    logging.error("Dataset empty" )
    raise AssertionError







def 					lowerCAmelCase__(		)       ->			int:
  __snake_case      : int						=				np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
  __snake_case      : List[str]						=				np.array([0, 0, 0, 1, 1] )
  __snake_case      : str						=				2
  __snake_case      : Dict						=				2

  # Assert that the function raises an AssertionError if dimensions > classes
  with pytest.raises(_A ) as error_info:
    __snake_case      : Tuple						=				linear_discriminant_analysis(
        _A      ,   _A      ,   _A      ,   _A )
    if isinstance(_A      ,   np.ndarray ):
      raise AssertionError(
          "Did not raise AssertionError for dimensions > classes" )
    assert error_info.type is AssertionError







def 					lowerCAmelCase__(		)       ->			Tuple:
  __snake_case      : Tuple						=				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
  __snake_case      : str						=				2
  __snake_case      : int						=				np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]] )

  with pytest.raises(_A ) as error_info:
    __snake_case      : Optional[int]						=				principal_component_analysis(_A      ,   _A )
    if not np.allclose(_A      ,   _A ):
      raise AssertionError
    assert error_info.type is AssertionError


if __name__ == "__main__":
     import doctest

     doctest.testmod()





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase__)
class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			field(default="automatic-speech-recognition"							,  metadata={"include_in_asdict_even_if_is_default": True})
       A__											=			Features({"audio": Audio()})
       A__											=			Features({"transcription": Value("string")})
       A__											=			"audio"
       A__											=			"transcription"





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             if self.audio_column not in features:
                   raise ValueError(f"Column {self.audio_column} is not present in features."       )
             if not isinstance(features[self.audio_column]					,      __lowerCamelCase       ):
                   raise ValueError(f"Column {self.audio_column} is not an Audio type."       )
             lowerCamelCase__		:				Tuple    =  copy.deepcopy(self       )
             lowerCamelCase__		:				Tuple    =  self.input_schema.copy()
             lowerCamelCase__		:				Optional[int]    =  features[self.audio_column]
             lowerCamelCase__		:				int    =  input_schema
             return task_template





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return {self.audio_column: "audio", self.transcription_column: "transcription"}


