



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



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			np.ndarray:
							UpperCAmelCase     :		int							     =		np.nan
							for i in range(_lowercase		):
														UpperCAmelCase     :		Optional[int]							     =		features[:, labels == i]
														UpperCAmelCase     :		List[str]							     =		data.mean(1		)
														# Centralize the data of class i
														UpperCAmelCase     :		Union[str, Any]							     =		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)
																					UpperCAmelCase     :		int							     =		np.dot(_lowercase	,				centered_data.T		)

							return covariance_sum / features.shape[1]



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			np.ndarray:
							UpperCAmelCase     :		Dict							     =		features.mean(1		)
							UpperCAmelCase     :		Optional[int]							     =		np.nan
							for i in range(_lowercase		):
														UpperCAmelCase     :		List[str]							     =		features[:, labels == i]
														UpperCAmelCase     :		int							     =		data.shape[1]
														UpperCAmelCase     :		Tuple							     =		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)
																					UpperCAmelCase     :		Optional[int]							     =		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	,				_lowercase		)      ->			np.ndarray:

							# Check if the features have been loaded
							if features.any():
														UpperCAmelCase     :		List[str]							     =		features.mean(1		)
														# Center the dataset
														UpperCAmelCase     :		List[Any]							     =		features - np.reshape(_lowercase	,				(data_mean.size, 1)		)
														UpperCAmelCase     :		Union[str, Any]							     =		np.dot(_lowercase	,				centered_data.T		) / features.shape[1]
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		np.linalg.eigh(_lowercase		)
														# Take all the columns in the reverse order (-1), and then takes only the first
														UpperCAmelCase     :		List[str]							     =		eigenvectors[:, ::-1][:, 0:dimensions]
														# Project the database on the new space
														UpperCAmelCase     :		Dict							     =		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	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			np.ndarray:
							assert classes > dimensions

							# Check if features have been already loaded
							if features.any:
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		eigh(
														    covariance_between_classes(_lowercase	,				_lowercase	,				_lowercase		)	,				covariance_within_classes(_lowercase	,				_lowercase	,				_lowercase		)	,				)
														UpperCAmelCase     :		Optional[int]							     =		eigenvectors[:, ::-1][:, :dimensions]
														UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		str							     =		np.linalg.svd(_lowercase		)
														UpperCAmelCase     :		Union[str, Any]							     =		svd_matrix[:, 0:dimensions]
														UpperCAmelCase     :		List[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:
							# Create dummy dataset with 2 classes and 3 features
							UpperCAmelCase     :		Dict							     =		np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]		)
							UpperCAmelCase     :		Optional[int]							     =		np.array([0, 0, 0, 1, 1]		)
							UpperCAmelCase     :		Dict							     =		2
							UpperCAmelCase     :		int							     =		2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowercase		) as error_info:
														UpperCAmelCase     :		Optional[int]							     =		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:
							UpperCAmelCase     :		Any							     =		np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
							UpperCAmelCase     :		Any							     =		2
							UpperCAmelCase     :		int							     =		np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]		)

							with pytest.raises(_lowercase		) as error_info:
														UpperCAmelCase     :		Dict							     =		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 time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a   :			List[Any]					  =    """__DUMMY_TRANSFORMERS_USER__"""
a   :			Tuple					  =    """Dummy User"""
a   :			Optional[Any]					  =    """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"""

a   :			Optional[Any]					  =    """https://hub-ci.huggingface.co"""
a   :			List[Any]					  =    CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
a   :			Tuple					  =    CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
a   :			str					  =    Path("""~/.huggingface/hub_ci_token""").expanduser()



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							monkeypatch.setattr(
							    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""	,				_lowercase		)
							monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							HfFolder.save_token(_lowercase		)
							yield
							HfFolder.delete_token()



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					)      ->			str:
							return HfApi(endpoint=_lowercase		)



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		str							     =		HfFolder.get_token()
							HfFolder.save_token(_lowercase		)
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							def _cleanup_repo(_lowercase		):
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)

							return _cleanup_repo



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							@contextmanager
							def _temporary_repo(_lowercase		):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(_lowercase		)

							return _temporary_repo



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		str							     =		F'''repo_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[Any]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data/text_data.txt"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[int]							     =		F'''repo_zipped_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		Optional[int]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		List[Any]							     =		F'''repo_zipped_img_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[str]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_zipped_img_data_

