


"""simple docstring"""



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			)     ->						np.ndarray:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  np.ndarray						,							lowercase  :  np.ndarray						,							lowercase  :  int			)     ->						np.ndarray:


    """simple docstring"""





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

    return covariance_sum / features.shape[1]







def   __lowerCAmelCase						(						lowercase  :  np.ndarray						,							lowercase  :  np.ndarray						,							lowercase  :  int			)     ->						np.ndarray:


    """simple docstring"""





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

    return covariance_sum / features.shape[1]







def   __lowerCAmelCase						(						lowercase  :  np.ndarray						,							lowercase  :  int			)     ->						np.ndarray:


    """simple docstring"""





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

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







def   __lowerCAmelCase						(						lowercase  :  np.ndarray						,							lowercase  :  np.ndarray						,							lowercase  :  int						,							lowercase  :  int			)     ->						np.ndarray:


    """simple docstring"""





    assert classes > dimensions

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

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







def   __lowerCAmelCase						(						)     ->						None:


    """simple docstring"""





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

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







def   __lowerCAmelCase						(						)     ->						None:


    """simple docstring"""





    snake_case   :				List[str]				 =   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			)
    snake_case   :				Any				 =   2
    snake_case   :				Any				 =   np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]			)

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()









"""simple docstring"""



import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer







@require_tf
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
       for a, b in zip(UpperCamelCase__						,     UpperCamelCase__						):
           self.assertAlmostEqual(UpperCamelCase__						,     UpperCamelCase__						,     delta=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(UpperCamelCase__						):
           accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step						,     3						)
       self.assertEqual(len(accumulator.gradients						)						,     1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [-2.0, 5.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,     [0.0, 0.0]						,     tol=1e-2						)







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   None
       ops.enable_eager_execution_internal()
       snake_case   :				str				 =   tf.config.list_physical_devices("CPU"						)
       if len(UpperCamelCase__						) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]						,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       snake_case   :				Optional[int]				 =   tf.config.list_logical_devices(device_type="CPU"						)
       snake_case   :				List[str]				 =   tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
           snake_case   :				int				 =   GradientAccumulator()
           snake_case   :				int				 =   tf.Variable([4.0, 3.0]						)
           snake_case ,snake_case   :				Any				 =   create_optimizer(5e-5						,     10						,     5						)
           snake_case   :				Tuple				 =   tf.Variable([0.0, 0.0]						,     trainable=UpperCamelCase__						)

       def accumulate_on_replica(UpperCamelCase__						):
           accumulator([gradient]						)

       def apply_on_replica():
           optimizer.apply_gradients(list(zip(accumulator.gradients						,     [variable]						)						)						)

       @tf.function
       def accumulate(UpperCamelCase__						,     UpperCamelCase__						):
           with strategy.scope():
               snake_case   :				Union[str, Any]				 =   strategy.experimental_local_results(UpperCamelCase__						)
               local_variables[0].assign(UpperCamelCase__						)
               local_variables[1].assign(UpperCamelCase__						)
               strategy.run(UpperCamelCase__						,     args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
           with strategy.scope():
               strategy.run(UpperCamelCase__						)

       def _check_local_values(UpperCamelCase__						,     UpperCamelCase__						):
           snake_case   :				List[Any]				 =   strategy.experimental_local_results(accumulator._gradients[0]						)
           self.assertListAlmostEqual(values[0].value()						,     UpperCamelCase__						,     tol=1e-2						)
           self.assertListAlmostEqual(values[1].value()						,     UpperCamelCase__						,     tol=1e-2						)

       accumulate([1.0, 2.0]						,     [-1.0, 1.0]						)
       accumulate([3.0, -1.0]						,     [-1.0, -1.0]						)
       accumulate([-2.0, 2.0]						,     [3.0, -2.0]						)
       self.assertEqual(accumulator.step						,     3						)
       _check_local_values([2.0, 3.0]						,     [1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,     [4.0, 3.0]						,     tol=1e-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step						,     0						)
       _check_local_values([0.0, 0.0]						,     [0.0, 0.0]						)





