


import logging

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

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







def        lowerCAmelCase_  (      __lowerCamelCase			):
							return input_array.reshape((input_array.size, 1)			)







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

							return covariance_sum / features.shape[1]







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

							return covariance_sum / features.shape[1]







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

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

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							assert classes > dimensions

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

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







def        lowerCAmelCase_  (      ):
							# Create dummy dataset with 2 classes and 3 features
							__snake_case	:    Optional[Any]											=     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	:    Optional[Any]											=     2
							__snake_case	:    Union[str, Any]											=     2

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







def        lowerCAmelCase_  (      ):
							__snake_case	:    str											=     np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]			)
							__snake_case	:    Union[str, Any]											=     2
							__snake_case	:    List[str]											=     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(__lowerCamelCase			) as error_info:
														__snake_case	:    Tuple											=     principal_component_analysis(__lowerCamelCase							,			__lowerCamelCase			)
														if not np.allclose(__lowerCamelCase							,			__lowerCamelCase			):
																					raise AssertionError
														assert error_info.type is AssertionError


if __name__ == "__main__":
			import doctest

			doctest.testmod()





import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
			from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if is_torch_version("<"							,			"2.0.0"			) or not hasattr(__lowerCamelCase							,			"_dynamo"			):
														return False
							return isinstance(__lowerCamelCase							,			torch._dynamo.eval_frame.OptimizedModule			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase = True			):
							__snake_case	:    Optional[Any]											=     (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							__snake_case	:    Tuple											=     is_compiled_module(__lowerCamelCase			)
							if is_compiled:
														__snake_case	:    Tuple											=     model
														__snake_case	:    List[str]											=     model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[Any]											=     model.module

							if not keep_fpaa_wrapper:
														__snake_case	:    Tuple											=     getattr(__lowerCamelCase							,			"forward"			)
														__snake_case	:    int											=     model.__dict__.pop("_original_forward"							,			__lowerCamelCase			)
														if original_forward is not None:
																					while hasattr(__lowerCamelCase							,			"__wrapped__"			):
																												__snake_case	:    str											=     forward.__wrapped__
																												if forward == original_forward:
																																			break
																					__snake_case	:    Dict											=     forward
														if getattr(__lowerCamelCase							,			"_converted_to_transformer_engine"							,			__lowerCamelCase			):
																					convert_model(__lowerCamelCase							,			to_transformer_engine=__lowerCamelCase			)

							if is_compiled:
														__snake_case	:    Tuple											=     model
														__snake_case	:    str											=     compiled_model

							return model







def        lowerCAmelCase_  (      ):
							PartialState().wait_for_everyone()







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(__lowerCamelCase							,			__lowerCamelCase			)
							elif PartialState().local_process_index == 0:
														torch.save(__lowerCamelCase							,			__lowerCamelCase			)







@contextmanager
def        lowerCAmelCase_  (      **__lowerCamelCase			):
							for key, value in kwargs.items():
														__snake_case	:    Union[str, Any]											=     str(__lowerCamelCase			)

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not hasattr(__lowerCamelCase							,			"__qualname__"			) and not hasattr(__lowerCamelCase							,			"__name__"			):
														__snake_case	:    Dict											=     getattr(__lowerCamelCase							,			"__class__"							,			__lowerCamelCase			)
							if hasattr(__lowerCamelCase							,			"__qualname__"			):
														return obj.__qualname__
							if hasattr(__lowerCamelCase							,			"__name__"			):
														return obj.__name__
							return str(__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							for key, value in source.items():
														if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Tuple											=     destination.setdefault(__lowerCamelCase							,			{}			)
																					merge_dicts(__lowerCamelCase							,			__lowerCamelCase			)
														else:
																					__snake_case	:    Union[str, Any]											=     value

							return destination







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if port is None:
														__snake_case	:    List[str]											=     2_9_5_0_0
							with socket.socket(socket.AF_INET							,			socket.SOCK_STREAM			) as s:
														return s.connect_ex(("localhost", port)			) == 0

