





'''simple docstring'''







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

snake_case_       :    Union[str, Any]          =					logging.getLogger(__name__)


@require_tensorboard
class    __a (unittest.TestCase       ):

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Any         =					'''test_project_with_config'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase_     : Any         =					Accelerator(log_with='''tensorboard'''    ,       project_dir=__magic_name__				)
																			UpperCAmelCase_     : Optional[int]         =					{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(__magic_name__    ,       __magic_name__				)
																			accelerator.end_training()
																			for child in Path(F"""{dirpath}/{project_name}"""				).glob('''*/**'''				):
																									UpperCAmelCase_     : List[Any]         =					list(filter(lambda __magic_name__				: x.is_file()    ,       child.iterdir()				)				)[0]
																			self.assertNotEqual(str(__magic_name__				)    ,       ''''''				)

							def       UpperCAmelCase__		(							self							:       Any				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					'''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase_     : Dict         =					Accelerator(log_with='''tensorboard'''    ,       project_dir=__magic_name__				)
																			accelerator.init_trackers(__magic_name__				)
																			UpperCAmelCase_     : str         =					{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(__magic_name__    ,       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
																			UpperCAmelCase_     : Union[str, Any]         =					list(filter(lambda __magic_name__				: x.is_file()    ,       Path(F"""{dirpath}/{project_name}"""				).iterdir()				)				)[0]
																			self.assertNotEqual(str(__magic_name__				)    ,       ''''''				)

							def       UpperCAmelCase__		(							self							:       Dict				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Union[str, Any]         =					'''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase_     : Any         =					Accelerator(log_with='''tensorboard'''    ,       project_dir=__magic_name__				)
																			accelerator.init_trackers(__magic_name__				)
																			UpperCAmelCase_     : Union[str, Any]         =					{'''tensor''': torch.tensor(1				)}
																			accelerator.log(__magic_name__    ,       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
																			UpperCAmelCase_     : List[Any]         =					list(filter(lambda __magic_name__				: 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(__magic_name__    ,       '''rb'''				) as f:
																									UpperCAmelCase_     : Optional[Any]         =					f.read()
																			UpperCAmelCase_     : Optional[Any]         =					False
																			while data:
																									UpperCAmelCase_     : Optional[Any]         =					struct.unpack('''Q'''    ,       data[:8]				)

																									UpperCAmelCase_     : Optional[Any]         =					data[12 : 12 + int(header[0]				)]  # 8+4
																									UpperCAmelCase_     : Any         =					data[12 + int(header[0]				) + 4 :]
																									UpperCAmelCase_     : Tuple         =					event_pba.Event()

																									event.ParseFromString(__magic_name__				)
																									if event.HasField('''summary'''				):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											UpperCAmelCase_     : Union[str, Any]         =					True
																			self.assertTrue(__magic_name__    ,       '''Converted tensor was not found in the log file!'''				)

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Union[str, Any]:







													"""simple docstring"""
													with self.assertRaisesRegex(__magic_name__    ,       '''Logging with `tensorboard` requires a `logging_dir`'''				):
																			UpperCAmelCase_     : Optional[int]         =					Accelerator(log_with='''tensorboard'''				)
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase_     : Dict         =					Accelerator(log_with='''tensorboard'''    ,       project_dir=__magic_name__				)



							def       UpperCAmelCase__		(							self							:       Any				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					ProjectConfiguration(total_limit=30				)
													with tempfile.TemporaryDirectory() as dirpath:
																			UpperCAmelCase_     : Union[str, Any]         =					Accelerator(log_with='''tensorboard'''    ,       project_dir=__magic_name__    ,       project_config=__magic_name__				)





@require_wandb
@mock.patch.dict(os.environ    ,						{"WANDB_MODE": "offline"}       )
class    __a (lowerCamelCase    ,						lowerCamelCase       ):

							def       UpperCAmelCase__		(							self							:       Any				)   ->    str:







													"""simple docstring"""
													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       UpperCAmelCase__		(							__magic_name__							:       str    ,       __magic_name__							:       str    ,       __magic_name__							:       bool = True				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					RF"""{section} ([\S\s]*?)\n\n"""
													if record:
																			UpperCAmelCase_     : Any         =					RF"""Record: {pattern}"""
													UpperCAmelCase_     : List[str]         =					re.findall(__magic_name__    ,       __magic_name__				)[0]
													# A config
													if section == "config" or section == "history":
																			UpperCAmelCase_     : Tuple         =					re.findall(R'''"([a-zA-Z0-9_.,]+)'''    ,       __magic_name__				)
																			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]+)"'''    ,       __magic_name__				)				)



							@skip
							def       UpperCAmelCase__		(							self							:       int				)   ->    str:







													"""simple docstring"""
													UpperCAmelCase_     : int         =					'''test_project_with_config'''
													UpperCAmelCase_     : str         =					Accelerator(log_with='''wandb'''				)
													UpperCAmelCase_     : str         =					{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
													UpperCAmelCase_     : List[Any]         =					{'''wandb''': {'''tags''': ['''my_tag''']}}
													accelerator.init_trackers(__magic_name__    ,       __magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Optional[int]         =					{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
													accelerator.log(__magic_name__    ,       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":
																									UpperCAmelCase_     : Dict         =					subprocess.check_output(
																									    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(__magic_name__				)]    ,       env=os.environ.copy()				).decode('''utf8'''    ,       '''ignore'''				)
																									break

        # Check HPS through careful parsing and cleaning
													UpperCAmelCase_     : List[str]         =					self.parse_log(__magic_name__    ,       '''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
													UpperCAmelCase_     : str         =					self.parse_log(__magic_name__    ,       '''run'''    ,       __magic_name__				)
													self.assertEqual(logged_items['''tags''']    ,       '''my_tag'''				)

													# Actual logging
													UpperCAmelCase_     : Dict         =					self.parse_log(__magic_name__    ,       '''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 :     List[Any],     SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     str				)	->		Optional[Any]:
						UpperCAmelCase_     : Dict         =					run_name
						UpperCAmelCase_     : int         =					OfflineExperiment(project_name=SCREAMING_SNAKE_CASE__,     offline_directory=SCREAMING_SNAKE_CASE__				)
						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    __a (unittest.TestCase       ):

							@staticmethod
							def       UpperCAmelCase__		(							__magic_name__							:       Any    ,       __magic_name__							:       str    ,       __magic_name__							:       bool = False				)   ->    Any:







													"""simple docstring"""
													for log in log_list:
																			UpperCAmelCase_     : str         =					json.loads(__magic_name__				)['''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       UpperCAmelCase__		(							self							:       Any				)   ->    str:







													"""simple docstring"""
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase_     : List[str]         =					CometMLTracker('''test_project_with_config'''    ,       __magic_name__				)
																			UpperCAmelCase_     : List[Any]         =					Accelerator(log_with=__magic_name__				)
																			UpperCAmelCase_     : Optional[int]         =					{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
																			accelerator.init_trackers(__magic_name__    ,       __magic_name__				)
																			accelerator.end_training()
																			UpperCAmelCase_     : Union[str, Any]         =					os.listdir(__magic_name__				)[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			UpperCAmelCase_     : int         =					os.path.join(__magic_name__    ,       __magic_name__				)
																			UpperCAmelCase_     : str         =					zipfile.ZipFile(__magic_name__    ,       '''r'''				)
																			UpperCAmelCase_     : int         =					archive.open('''messages.json'''				).read().decode('''utf-8'''				)
													UpperCAmelCase_     : Optional[Any]         =					log.split('''\n'''				)[:-1]
													self.assertEqual(self.get_value_from_key(__magic_name__    ,       '''num_iterations'''    ,       __magic_name__				)    ,       12				)
													self.assertEqual(self.get_value_from_key(__magic_name__    ,       '''learning_rate'''    ,       __magic_name__				)    ,       0.0_1				)
													self.assertEqual(self.get_value_from_key(__magic_name__    ,       '''some_boolean'''    ,       __magic_name__				)    ,       __magic_name__				)
													self.assertEqual(self.get_value_from_key(__magic_name__    ,       '''some_string'''    ,       __magic_name__				)    ,       '''some_value'''				)



							def       UpperCAmelCase__		(							self							:       Dict				)   ->    Dict:







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





class    __a (lowerCamelCase       ):
							__a   :				Optional[Any]       =    [
							    "total_loss",
							    "iteration",
							    "my_text",
							    "learning_rate",
							    "num_iterations",
							    "some_boolean",
							    "some_string",
							]

							__a   :				Optional[int]       =    "my_custom_tracker"
							__a   :				List[Any]       =    False

							def __init__(							self							:       Any    ,       __magic_name__							:       str				)   ->    List[str]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[int]         =					open(F"""{dir}/log.csv"""    ,       '''w+'''				)
													UpperCAmelCase_     : Tuple         =					csv.DictWriter(self.f    ,       fieldnames=self._col_names				)
													self.writer.writeheader()

							@property
							def       UpperCAmelCase__		(							self							:       str				)   ->    str:







													"""simple docstring"""
													return self.writer

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       dict				)   ->    Optional[int]:







													"""simple docstring"""
													logger.info('''Call init'''				)
													self.writer.writerow(__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Dict    ,       __magic_name__							:       dict    ,       __magic_name__							:       Optional[int]				)   ->    List[Any]:







													"""simple docstring"""
													logger.info('''Call log'''				)
													self.writer.writerow(__magic_name__				)



							def       UpperCAmelCase__		(							self							:       Dict				)   ->    Dict:







													"""simple docstring"""
													self.f.close()





class    __a (unittest.TestCase       ):

							def       UpperCAmelCase__		(							self							:       Optional[Any]				)   ->    Optional[Any]:







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



							def       UpperCAmelCase__		(							self							:       str				)   ->    Any:







													"""simple docstring"""
													with tempfile.TemporaryDirectory() as d:
																			UpperCAmelCase_     : List[Any]         =					MyCustomTracker(__magic_name__				)
																			UpperCAmelCase_     : Optional[int]         =					Accelerator(log_with=__magic_name__				)
																			accelerator.init_trackers('''Some name'''				)
																			UpperCAmelCase_     : int         =					{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(__magic_name__    ,       step=0				)
																			accelerator.end_training()
																			with open(F"""{d}/log.csv"""    ,       '''r'''				) as f:
																									UpperCAmelCase_     : Tuple         =					csv.DictReader(__magic_name__				)
																									UpperCAmelCase_     : Dict         =					next(__magic_name__				)
																									UpperCAmelCase_     : Tuple         =					{
																									    '''total_loss''': '''0.1''',
																									    '''iteration''': '''1''',
																									    '''my_text''': '''some_value''',
																									    '''learning_rate''': '''''',
																									    '''num_iterations''': '''''',
																									    '''some_boolean''': '''''',
																									    '''some_string''': '''''',
																									}
																									self.assertDictEqual(__magic_name__    ,       __magic_name__				)












'''simple docstring'''







import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					np.shape(SCREAMING_SNAKE_CASE__				)

						UpperCAmelCase_     : NDArray[floataa]         =					np.zeros((rows, 1),     dtype=SCREAMING_SNAKE_CASE__				)
						for row in reversed(range(SCREAMING_SNAKE_CASE__				)				):
												UpperCAmelCase_     : Union[str, Any]         =					0
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		total += coefficients[row, col] * x[col]

												UpperCAmelCase_     : str         =					(vector[row] - total) / coefficients[row, row]

						return x





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     NDArray[floataa],     SCREAMING_SNAKE_CASE__ :     NDArray[floataa]				)	->		NDArray[floataa]:
						UpperCAmelCase_  ,    UpperCAmelCase_     : List[Any]         =					np.shape(SCREAMING_SNAKE_CASE__				)
						if rows != columns:
												return np.array((),     dtype=SCREAMING_SNAKE_CASE__				)

						# augmented matrix
						UpperCAmelCase_     : NDArray[floataa]         =					np.concatenate((coefficients, vector),     axis=1				)
						UpperCAmelCase_     : Optional[Any]         =					augmented_mat.astype('''float64'''				)

						# scale the matrix leaving it triangular
						for row in range(rows - 1				):
												UpperCAmelCase_     : Optional[int]         =					augmented_mat[row, row]
												for col in range(row + 1,     SCREAMING_SNAKE_CASE__				):
																		UpperCAmelCase_     : Optional[Any]         =					augmented_mat[col, row] / pivot
																		augmented_mat[col, :] -= factor * augmented_mat[row, :]

						UpperCAmelCase_     : Optional[Any]         =					retroactive_resolution(
						    augmented_mat[:, 0:columns],     augmented_mat[:, columns : columns + 1]				)

						return x


if __name__ == "__main__":
					import doctest

					doctest.testmod()





