



"""simple docstring"""




import logging

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		np.ndarray							)    ->  np.ndarray:





     '''simple docstring'''

     return input_array.reshape((input_array.size, 1)							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  np.ndarray:





     '''simple docstring'''

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

     return covariance_sum / features.shape[1]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  np.ndarray:





     '''simple docstring'''

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

     return covariance_sum / features.shape[1]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  np.ndarray:





     '''simple docstring'''

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  np.ndarray:





     '''simple docstring'''

     assert classes > dimensions

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

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

def 		__snake_case		( )    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]							)
     _UpperCAmelCase     :      int														=       np.array([0, 0, 0, 1, 1]							)
     _UpperCAmelCase     :      Any														=       2
     _UpperCAmelCase     :      str														=       2

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

def 		__snake_case		( )    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]							)
     _UpperCAmelCase     :      Optional[Any]														=       2
     _UpperCAmelCase     :      Tuple														=       np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [3.0, 3.0, 3.0]]							)

     with pytest.raises(SCREAMING_SNAKE_CASE__							) as error_info:
          _UpperCAmelCase     :      str														=       principal_component_analysis(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if not np.allclose(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""simple docstring"""




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       test_file.split(os.path.sep							)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
              f'{test_file} instead.'							)
     _UpperCAmelCase     :      int														=       components[-1]
     if not test_fn.endswith("py"							):
          raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'							)
     if not test_fn.startswith("test_modeling_"							):
          raise ValueError(
              f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'							)

     _UpperCAmelCase     :      Any														=       components[:-1] + [test_fn.replace(".py"		,    ""							)]
     _UpperCAmelCase     :      List[str]														=       ".".join(SCREAMING_SNAKE_CASE__							)

     return test_module_path

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       get_module_path(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       importlib.import_module(SCREAMING_SNAKE_CASE__							)

     return test_module

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      str														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          if attr.endswith("ModelTester"							):
               tester_classes.append(getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      Tuple														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          _UpperCAmelCase     :      Optional[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    "all_model_classes"		,    []							)
          if len(SCREAMING_SNAKE_CASE__							) > 0:
               test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes							)

     # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       test_class()
     if hasattr(SCREAMING_SNAKE_CASE__		,    "setUp"							):
          test.setUp()

     _UpperCAmelCase     :      int														=       None
     if hasattr(SCREAMING_SNAKE_CASE__		,    "model_tester"							):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               _UpperCAmelCase     :      List[str]														=       test.model_tester.__class__

     return model_tester

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       []
     for test_class in test_classes:
          _UpperCAmelCase     :      Optional[Any]														=       get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							)
          if tester_class is not None:
               tester_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							) for test_class in test_classes}
     return test_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {
         model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_test_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       {
         model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_to_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  List[str]:





     '''simple docstring'''

     if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o.__name__
     elif isinstance(SCREAMING_SNAKE_CASE__		,    (list, tuple)							):
          return [to_json(SCREAMING_SNAKE_CASE__							) for x in o]
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return {to_json(SCREAMING_SNAKE_CASE__							): to_json(SCREAMING_SNAKE_CASE__							) for k, v in o.items()}
     else:
          return o
