



"""simple docstring"""


import logging

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

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						np.ndarray				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    np.nan
				for i in range(_SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    features[:, labels == i]
								_UpperCAmelCase					=    data.mean(1				)
								# Centralize the data of class i
								_UpperCAmelCase					=    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					=    np.dot(_SCREAMING_SNAKE_CASE					,     centered_data.T				)

				return covariance_sum / features.shape[1]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    features.mean(1				)
				_UpperCAmelCase					=    np.nan
				for i in range(_SCREAMING_SNAKE_CASE				):
								_UpperCAmelCase					=    features[:, labels == i]
								_UpperCAmelCase					=    data.shape[1]
								_UpperCAmelCase					=    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					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if features.any():
								_UpperCAmelCase					=    features.mean(1				)
								# Center the dataset
								_UpperCAmelCase					=    features - np.reshape(_SCREAMING_SNAKE_CASE					,     (data_mean.size, 1)				)
								_UpperCAmelCase					=    np.dot(_SCREAMING_SNAKE_CASE					,     centered_data.T				) / features.shape[1]
								_UpperCAmelCase		,						_UpperCAmelCase					=    np.linalg.eigh(_SCREAMING_SNAKE_CASE				)
								# Take all the columns in the reverse order (-1), and then takes only the first
								_UpperCAmelCase					=    eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						np.ndarray					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								_UpperCAmelCase		,						_UpperCAmelCase					=    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					=    eigenvectors[:, ::-1][:, :dimensions]
								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    np.linalg.svd(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    svd_matrix[:, 0:dimensions]
								_UpperCAmelCase					=    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  lowercase	(      ):






				'''simple docstring'''



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

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(_SCREAMING_SNAKE_CASE				) as error_info:
								_UpperCAmelCase					=    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  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]				)
				_UpperCAmelCase					=    2
				_UpperCAmelCase					=    np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]				)

				with pytest.raises(_SCREAMING_SNAKE_CASE				) as error_info:
								_UpperCAmelCase					=    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 ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__A   :							str										=   [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    bool				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    list = None   )->int:
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''by_feature'''   )   )
							_UpperCAmelCase					=    os.path.abspath('''examples'''   )
							for item in os.listdir(__UpperCamelCase   ):
											if item not in EXCLUDE_EXAMPLES:
															_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )
															if os.path.isfile(__UpperCamelCase   ) and ".py" in item_path:
																			with self.subTest(
																			    tested_script=__UpperCamelCase				,   feature_script=__UpperCamelCase				,   tested_section='''main()''' if parser_only else '''training_function()'''				,   ):
																							_UpperCAmelCase					=    compare_against_test(
																							    os.path.join(__UpperCamelCase				,   __UpperCamelCase   )				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
																							_UpperCAmelCase					=    '''\n'''.join(__UpperCamelCase   )
																							if special_strings is not None:
																											for string in special_strings:
																															_UpperCAmelCase					=    diff.replace(__UpperCamelCase				,   ''''''   )
																							self.assertEqual(__UpperCamelCase				,   ''''''   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )
							self.one_complete_example('''complete_nlp_example.py'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    os.path.abspath(os.path.join('''examples'''				,   '''cv_example.py'''   )   )
							_UpperCAmelCase					=    [
							    ''' ''' * 1_6 + '''{\n\n''',
							    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
							    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
							    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
							    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
							    ''' ''' * 1_6 + '''},\n\n''',
							    ''' ''' * 1_6 + '''step=epoch,\n''',
							    ''' ''' * 1_2,
							    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
							]
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							self.one_complete_example('''complete_cv_example.py'''				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )



@mock.patch.dict(os.environ			,							{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	False



			@classmethod
			def  lowercase__						(		cls     :    Optional[int]   )->Optional[Any]:
							super().setUpClass()
							_UpperCAmelCase					=    tempfile.mkdtemp()
							_UpperCAmelCase					=    os.path.join(cls._tmpdir				,   '''default_config.yml'''   )

							write_basic_config(save_location=cls.configPath   )
							_UpperCAmelCase					=    ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



			@classmethod
			def  lowercase__						(		cls     :    Dict   )->Any:
							super().tearDownClass()
							shutil.rmtree(cls._tmpdir   )



			def  lowercase__						(		self     :    Optional[int]   )->Any:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
							run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''epoch_0'''   )   )   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs   )
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir				,   '''step_2'''   )   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "epoch_0"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
							self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir				,   "step_2"   )}\n        '.split()
							_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
							if torch.cuda.is_available():
											_UpperCAmelCase					=    torch.cuda.device_count()
							else:
											_UpperCAmelCase					=    1
							if num_processes > 1:
											self.assertNotIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )
							else:
											self.assertIn('''epoch 0:'''				,   __UpperCamelCase   )
											self.assertIn('''epoch 1:'''				,   __UpperCamelCase   )



			@slow
			def  lowercase__						(		self     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    '''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
							with mock.patch.dict(os.environ				,   {'''TESTING_MOCKED_DATALOADERS''': '''0'''}   ):
											_UpperCAmelCase					=    run_command(self._launch_args + testargs				,   return_stdout=__UpperCamelCase   )
											_UpperCAmelCase					=    re.findall('''({.+})'''				,   __UpperCamelCase   )
											_UpperCAmelCase					=    [r for r in results if '''accuracy''' in r][-1]
											_UpperCAmelCase					=    ast.literal_eval(__UpperCamelCase   )
											self.assertGreaterEqual(results['''accuracy''']				,   0.7_5   )



			def  lowercase__						(		self     :    Any   )->List[Any]:
							_UpperCAmelCase					=    ['''examples/by_feature/multi_process_metrics.py''']
							run_command(self._launch_args + testargs   )



			@require_trackers
			@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}   )
			def  lowercase__						(		self     :    Optional[int]   )->Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdir:
											_UpperCAmelCase					=    F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
											run_command(self._launch_args + testargs   )
											self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase				,   '''tracking'''   )   )   )



			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    ['''examples/by_feature/gradient_accumulation.py''']
							run_command(self._launch_args + testargs   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Tuple:
							_UpperCAmelCase					=    ['''examples/by_feature/local_sgd.py''']
							run_command(self._launch_args + testargs   )



