





import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
					from comet_ml import OfflineExperiment

if is_tensorboard_available():
					import struct

					import tensorboard.compat.proto.event_pba as event_pba

UpperCAmelCase_   				=  logging.getLogger(__name__)
@require_tensorboard
class       UpperCamelCase_		(	unittest.TestCase       ):




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					'test_project_with_config'
										with tempfile.TemporaryDirectory() as dirpath:
															_snake_case						=					Accelerator(log_with='tensorboard'					,    project_dir=lowerCAmelCase_	)
															_snake_case						=					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
															accelerator.init_trackers(lowerCAmelCase_					,    lowerCAmelCase_	)
															accelerator.end_training()
															for child in Path(F'''{dirpath}/{project_name}'''	).glob('*/**'	):
																				_snake_case						=					list(filter(lambda lowerCAmelCase_	: x.is_file()					,    child.iterdir()	)	)[0]
															self.assertNotEqual(str(lowerCAmelCase_	)					,    ''	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					'test_project_with_log'
										with tempfile.TemporaryDirectory() as dirpath:
															_snake_case						=					Accelerator(log_with='tensorboard'					,    project_dir=lowerCAmelCase_	)
															accelerator.init_trackers(lowerCAmelCase_	)
															_snake_case						=					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
															accelerator.log(lowerCAmelCase_					,    step=0	)
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															_snake_case						=					list(filter(lambda lowerCAmelCase_	: x.is_file()					,    Path(F'''{dirpath}/{project_name}'''	).iterdir()	)	)[0]
															self.assertNotEqual(str(lowerCAmelCase_	)					,    ''	)




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					'test_project_with_log'
										with tempfile.TemporaryDirectory() as dirpath:
															_snake_case						=					Accelerator(log_with='tensorboard'					,    project_dir=lowerCAmelCase_	)
															accelerator.init_trackers(lowerCAmelCase_	)
															_snake_case						=					{'tensor': torch.tensor(1	)}
															accelerator.log(lowerCAmelCase_					,    step=0	)
															accelerator.end_training()
															# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
															# Names are randomly generated each time
															_snake_case						=					list(filter(lambda lowerCAmelCase_	: x.is_file()					,    Path(F'''{dirpath}/{project_name}'''	).iterdir()	)	)[0]
															# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
															with open(lowerCAmelCase_					,    'rb'	) as f:
																				_snake_case						=					f.read()
															_snake_case						=					False
															while data:
																				_snake_case						=					struct.unpack('Q'					,    data[:8]	)

																				_snake_case						=					data[12 : 12 + int(header[0]	)]  # 8+4
																				_snake_case						=					data[12 + int(header[0]	) + 4 :]
																				_snake_case						=					event_pba.Event()

																				event.ParseFromString(lowerCAmelCase_	)
																				if event.HasField('summary'	):
																									for value in event.summary.value:
																														if value.simple_value == 1.0 and value.tag == "tensor":
																																			_snake_case						=					True
															self.assertTrue(lowerCAmelCase_					,    'Converted tensor was not found in the log file!'	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										with self.assertRaisesRegex(lowerCAmelCase_					,    'Logging with `tensorboard` requires a `logging_dir`'	):
															_snake_case						=					Accelerator(log_with='tensorboard'	)
										with tempfile.TemporaryDirectory() as dirpath:
															_snake_case						=					Accelerator(log_with='tensorboard'					,    project_dir=lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					ProjectConfiguration(total_limit=30	)
										with tempfile.TemporaryDirectory() as dirpath:
															_snake_case						=					Accelerator(log_with='tensorboard'					,    project_dir=lowerCAmelCase_					,    project_config=lowerCAmelCase_	)





@require_wandb
@mock.patch.dict(os.environ				,	{'''WANDB_MODE''': '''offline'''}       )
class       UpperCamelCase_		(	_lowerCamelCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										super().setUp()
										# wandb let's us override where logs are stored to via the WANDB_DIR env var
										self.add_mocks(mock.patch.dict(os.environ					,    {'WANDB_DIR': self.tmpdir}	)	)




					@staticmethod
					def 						lowerCAmelCase      (		lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = True	)		->				List[str]:
										_snake_case						=					rF'''{section} ([\S\s]*?)\n\n'''
										if record:
															_snake_case						=					rF'''Record: {pattern}'''
										_snake_case						=					re.findall(lowerCAmelCase_					,    lowerCAmelCase_	)[0]
										# A config
										if section == "config" or section == "history":
															_snake_case						=					re.findall(r'"([a-zA-Z0-9_.,]+)'					,    lowerCAmelCase_	)
															return {key: val for key, val in zip(cleaned_record[0::2]					,    cleaned_record[1::2]	)}
										# Everything else
										else:
															return dict(re.findall(r'(\w+): "([^\s]+)"'					,    lowerCAmelCase_	)	)






					@skip
					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					'test_project_with_config'
										_snake_case						=					Accelerator(log_with='wandb'	)
										_snake_case						=					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										_snake_case						=					{'wandb': {'tags': ['my_tag']}}
										accelerator.init_trackers(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										_snake_case						=					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
										accelerator.log(lowerCAmelCase_					,    step=0	)
										accelerator.end_training()
										# The latest offline log is stored at wandb/latest-run/*.wandb
										for child in Path(F'''{self.tmpdir}/wandb/latest-run'''	).glob('*'	):
															if child.is_file() and child.suffix == ".wandb":
																				_snake_case						=					subprocess.check_output(
																				    ['wandb', 'sync', '--view', '--verbose', str(lowerCAmelCase_	)]					,    env=os.environ.copy()	).decode('utf8'					,    'ignore'	)
																				break

        # Check HPS through careful parsing and cleaning
										_snake_case						=					self.parse_log(lowerCAmelCase_					,    'config'	)
										self.assertEqual(logged_items['num_iterations']					,    '12'	)
										self.assertEqual(logged_items['learning_rate']					,    '0.01'	)
										self.assertEqual(logged_items['some_boolean']					,    'false'	)
										self.assertEqual(logged_items['some_string']					,    'some_value'	)
										self.assertEqual(logged_items['some_string']					,    'some_value'	)

										# Run tags
										_snake_case						=					self.parse_log(lowerCAmelCase_					,    'run'					,    lowerCAmelCase_	)
										self.assertEqual(logged_items['tags']					,    'my_tag'	)

										# Actual logging
										_snake_case						=					self.parse_log(lowerCAmelCase_					,    'history'	)
										self.assertEqual(logged_items['total_loss']					,    '0.1'	)
										self.assertEqual(logged_items['iteration']					,    '1'	)
										self.assertEqual(logged_items['my_text']					,    'some_value'	)
										self.assertEqual(logged_items['_step']					,    '0'	)





def     lowerCamelCase__  (  self      :							int							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							str    )      ->			Union[str, Any]:



					'''simple docstring'''




					_snake_case						=					run_name
					_snake_case						=					OfflineExperiment(project_name=UpperCamelCase__							,     offline_directory=UpperCamelCase__    )
					logger.info(F'''Initialized offline CometML project {self.run_name}'''    )
					logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'    )
@require_comet_ml
@mock.patch.object(_lowerCamelCase				,	'''__init__'''				,	_lowerCamelCase       )
class       UpperCamelCase_		(	unittest.TestCase       ):




					@staticmethod
					def 						lowerCAmelCase      (		lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				List[str]:
										for log in log_list:
															_snake_case						=					json.loads(lowerCAmelCase_	)['payload']
															if is_param and "param" in j.keys():
																				if j["param"]["paramName"] == key:
																									return j["param"]["paramValue"]
															if "log_other" in j.keys():
																				if j["log_other"]["key"] == key:
																									return j["log_other"]["val"]
															if "metric" in j.keys():
																				if j["metric"]["metricName"] == key:
																									return j["metric"]["metricValue"]




					def 						lowerCAmelCase      (		self	)		->				Dict:
										with tempfile.TemporaryDirectory() as d:
															_snake_case						=					CometMLTracker('test_project_with_config'					,    lowerCAmelCase_	)
															_snake_case						=					Accelerator(log_with=lowerCAmelCase_	)
															_snake_case						=					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
															accelerator.init_trackers(lowerCAmelCase_					,    lowerCAmelCase_	)
															accelerator.end_training()
															_snake_case						=					os.listdir(lowerCAmelCase_	)[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															_snake_case						=					os.path.join(lowerCAmelCase_					,    lowerCAmelCase_	)
															_snake_case						=					zipfile.ZipFile(lowerCAmelCase_					,    'r'	)
															_snake_case						=					archive.open('messages.json'	).read().decode('utf-8'	)
										_snake_case						=					log.split('\n'	)[:-1]
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'num_iterations'					,    lowerCAmelCase_	)					,    12	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'learning_rate'					,    lowerCAmelCase_	)					,    0.01	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'some_boolean'					,    lowerCAmelCase_	)					,    lowerCAmelCase_	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'some_string'					,    lowerCAmelCase_	)					,    'some_value'	)






					def 						lowerCAmelCase      (		self	)		->				List[str]:
										with tempfile.TemporaryDirectory() as d:
															_snake_case						=					CometMLTracker('test_project_with_config'					,    lowerCAmelCase_	)
															_snake_case						=					Accelerator(log_with=lowerCAmelCase_	)
															accelerator.init_trackers(lowerCAmelCase_	)
															_snake_case						=					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
															accelerator.log(lowerCAmelCase_					,    step=0	)
															accelerator.end_training()
															_snake_case						=					os.listdir(lowerCAmelCase_	)[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															_snake_case						=					os.path.join(lowerCAmelCase_					,    lowerCAmelCase_	)
															_snake_case						=					zipfile.ZipFile(lowerCAmelCase_					,    'r'	)
															_snake_case						=					archive.open('messages.json'	).read().decode('utf-8'	)
										_snake_case						=					log.split('\n'	)[:-1]
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'curr_step'					,    lowerCAmelCase_	)					,    0	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'total_loss'	)					,    0.1	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'iteration'	)					,    1	)
										self.assertEqual(self.get_value_from_key(lowerCAmelCase_					,    'my_text'	)					,    'some_value'	)





class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						[
					    '''total_loss''',
					    '''iteration''',
					    '''my_text''',
					    '''learning_rate''',
					    '''num_iterations''',
					    '''some_boolean''',
					    '''some_string''',
					]

					lowerCAmelCase_				=						'''my_custom_tracker'''
					lowerCAmelCase_				=						False




					def __init__(		self					,    lowerCAmelCase_	)		->				Optional[int]:
										_snake_case						=					open(F'''{dir}/log.csv'''					,    'w+'	)
										_snake_case						=					csv.DictWriter(self.f					,    fieldnames=self._col_names	)
										self.writer.writeheader()




					@property
					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.writer




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										logger.info('Call init'	)
										self.writer.writerow(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[int]:
										logger.info('Call log'	)
										self.writer.writerow(lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				Dict:
										self.f.close()





class       UpperCamelCase_		(	unittest.TestCase       ):




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										with tempfile.TemporaryDirectory() as d:
															_snake_case						=					MyCustomTracker(lowerCAmelCase_	)
															_snake_case						=					Accelerator(log_with=lowerCAmelCase_	)
															_snake_case						=					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
															accelerator.init_trackers('Some name'					,    lowerCAmelCase_	)
															accelerator.end_training()
															with open(F'''{d}/log.csv'''					,    'r'	) as f:
																				_snake_case						=					csv.DictReader(lowerCAmelCase_	)
																				_snake_case						=					next(lowerCAmelCase_	)
																				_snake_case						=					{
																				    'total_loss': '',
																				    'iteration': '',
																				    'my_text': '',
																				    'learning_rate': '0.01',
																				    'num_iterations': '12',
																				    'some_boolean': 'False',
																				    'some_string': 'some_value',
																				}
																				self.assertDictEqual(lowerCAmelCase_					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				str:
										with tempfile.TemporaryDirectory() as d:
															_snake_case						=					MyCustomTracker(lowerCAmelCase_	)
															_snake_case						=					Accelerator(log_with=lowerCAmelCase_	)
															accelerator.init_trackers('Some name'	)
															_snake_case						=					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
															accelerator.log(lowerCAmelCase_					,    step=0	)
															accelerator.end_training()
															with open(F'''{d}/log.csv'''					,    'r'	) as f:
																				_snake_case						=					csv.DictReader(lowerCAmelCase_	)
																				_snake_case						=					next(lowerCAmelCase_	)
																				_snake_case						=					{
																				    'total_loss': '0.1',
																				    'iteration': '1',
																				    'my_text': 'some_value',
																				    'learning_rate': '',
																				    'num_iterations': '',
																				    'some_boolean': '',
																				    'some_string': '',
																				}
																				self.assertDictEqual(lowerCAmelCase_					,    lowerCAmelCase_	)










from typing import Any

import numpy as np





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray    )      ->			bool:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(UpperCamelCase__							,     matrix.T    ) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					_snake_case				,  _snake_case						=					np.linalg.eigh(UpperCamelCase__    )

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0    )    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			Any:



					'''simple docstring'''




					_snake_case						=					np.random.randn(UpperCamelCase__							,     UpperCamelCase__    )
					_snake_case						=					np.dot(UpperCamelCase__							,     random_matrix.T    )
					assert _is_matrix_spd(UpperCamelCase__    )
					return spd_matrix





def     lowerCamelCase__  (  UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							np.ndarray							,     UpperCamelCase__      :							int = 1_000							,     UpperCamelCase__      :							float = 1e-8							,     )      ->			Any:



					'''simple docstring'''




					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[1]
					assert np.shape(UpperCamelCase__    )[0] == np.shape(UpperCamelCase__    )[0]
					assert _is_matrix_spd(UpperCamelCase__    )

					# Initialize solution guess, residual, search direction.
					_snake_case						=					np.zeros((np.shape(UpperCamelCase__    )[0], 1)    )
					_snake_case						=					np.copy(UpperCamelCase__    )
					_snake_case						=					np.copy(UpperCamelCase__    )

					# Set initial errors in solution guess and residual.
					_snake_case						=					1e9
					_snake_case						=					1e9
					_snake_case						=					1e9

					# Set iteration counter to threshold number of iterations.
					_snake_case						=					0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

										# The main algorithm.

										# Update search direction magnitude.
										_snake_case						=					np.dot(ra.T							,     UpperCamelCase__    ) / np.dot(pa.T							,     UpperCamelCase__    )
										# Update solution guess.
										_snake_case						=					xa + alpha * pa
										# Calculate new residual.
										_snake_case						=					ra - alpha * w
										# Calculate new Krylov subspace scale.
										_snake_case						=					np.dot(r.T							,     UpperCamelCase__    ) / np.dot(ra.T							,     UpperCamelCase__    )
										# Calculate new A conjuage search direction.
										_snake_case						=					r + beta * pa

										# Calculate errors.
										_snake_case						=					np.linalg.norm(r - ra    )
										_snake_case						=					np.linalg.norm(x - xa    )
										_snake_case						=					np.maximum(UpperCamelCase__							,     UpperCamelCase__    )

										# Update variables.
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )
										_snake_case						=					np.copy(UpperCamelCase__    )

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					_snake_case						=					3
					_snake_case						=					_create_spd_matrix(UpperCamelCase__    )
					_snake_case						=					np.random.randn(UpperCamelCase__							,     1    )
					_snake_case						=					np.dot(UpperCamelCase__							,     UpperCamelCase__    )

					# Numpy solution.
					_snake_case						=					np.linalg.solve(UpperCamelCase__							,     UpperCamelCase__    )

					# Our implementation.
					_snake_case						=					conjugate_gradient(UpperCamelCase__							,     UpperCamelCase__    )

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true    ) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true    ) <= 1e-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_conjugate_gradient()



