code: 
  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_	:    str  					=       logging.getLogger(__name__)
  
  
  
  
  @require_tensorboard
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> int:
  							_lowercase					:  str    			=      "test_project_with_config"
  							with tempfile.TemporaryDirectory() as dirpath:
  													_lowercase					:  Tuple    			=      Accelerator(log_with="tensorboard"			,  project_dir=snake_case						)
  													_lowercase					:  Any    			=      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  													accelerator.init_trackers(snake_case			,  snake_case						)
  													accelerator.end_training()
  													for child in Path(F"""{dirpath}/{project_name}"""						).glob("*/**"						):
  																			_lowercase					:  str    			=      list(filter(lambda snake_case						: x.is_file()			,  child.iterdir()						)						)[0]
  													self.assertNotEqual(str(snake_case						)			,  ""						)
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> Tuple:
  							_lowercase					:  Optional[int]    			=      "test_project_with_log"
  							with tempfile.TemporaryDirectory() as dirpath:
  													_lowercase					:  Optional[int]    			=      Accelerator(log_with="tensorboard"			,  project_dir=snake_case						)
  													accelerator.init_trackers(snake_case						)
  													_lowercase					:  Optional[Any]    			=      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  													accelerator.log(snake_case			,  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
  													_lowercase					:  Tuple    			=      list(filter(lambda snake_case						: x.is_file()			,  Path(F"""{dirpath}/{project_name}"""						).iterdir()						)						)[0]
  													self.assertNotEqual(str(snake_case						)			,  ""						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Any:
  							_lowercase					:  Optional[Any]    			=      "test_project_with_log"
  							with tempfile.TemporaryDirectory() as dirpath:
  													_lowercase					:  List[Any]    			=      Accelerator(log_with="tensorboard"			,  project_dir=snake_case						)
  													accelerator.init_trackers(snake_case						)
  													_lowercase					:  List[Any]    			=      {"tensor": torch.tensor(1						)}
  													accelerator.log(snake_case			,  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
  													_lowercase					:  List[str]    			=      list(filter(lambda snake_case						: 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(snake_case			,  "rb"						) as f:
  																			_lowercase					:  str    			=      f.read()
  													_lowercase					:  str    			=      False
  													while data:
  																			_lowercase					:  Any    			=      struct.unpack("Q"			,  data[:8]						)
  
  																			_lowercase					:  List[Any]    			=      data[12 : 12 + int(header[0]						)]  # 8+4
  																			_lowercase					:  List[Any]    			=      data[12 + int(header[0]						) + 4 :]
  																			_lowercase					:  int    			=      event_pba.Event()
  
  																			event.ParseFromString(snake_case						)
  																			if event.HasField("summary"						):
  																									for value in event.summary.value:
  																															if value.simple_value == 1.0 and value.tag == "tensor":
  																																					_lowercase					:  Dict    			=      True
  													self.assertTrue(snake_case			,  "Converted tensor was not found in the log file!"						)
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> str:
  							with self.assertRaisesRegex(snake_case			,  "Logging with `tensorboard` requires a `logging_dir`"						):
  													_lowercase					:  List[Any]    			=      Accelerator(log_with="tensorboard"						)
  							with tempfile.TemporaryDirectory() as dirpath:
  													_lowercase					:  int    			=      Accelerator(log_with="tensorboard"			,  project_dir=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Any:
  							_lowercase					:  Tuple    			=      ProjectConfiguration(total_limit=30						)
  							with tempfile.TemporaryDirectory() as dirpath:
  													_lowercase					:  Optional[Any]    			=      Accelerator(log_with="tensorboard"			,  project_dir=snake_case			,  project_config=snake_case						)
  
  
  
  
  @require_wandb
  @mock.patch.dict(os.environ      ,   {"WANDB_MODE": "offline"}					)
  class      __lowercase	(	__snake_case      ,   __snake_case					):
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]						)   -> Optional[int]:
  							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 			_a(snake_case							:   str			,  snake_case							:   str			,  snake_case							:   bool = True						)   -> str:
  							_lowercase					:  Union[str, Any]    			=      RF"""{section} ([\S\s]*?)\n\n"""
  							if record:
  													_lowercase					:  List[str]    			=      RF"""Record: {pattern}"""
  							_lowercase					:  Tuple    			=      re.findall(snake_case			,  snake_case						)[0]
  							# A config
  							if section == "config" or section == "history":
  													_lowercase					:  Union[str, Any]    			=      re.findall(R"\"([a-zA-Z0-9_.,]+)"			,  snake_case						)
  													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]+)\""			,  snake_case						)						)
  
  
  
  
  
  
  	@skip
  	def 			_a(self							:   List[Any]						)   -> List[str]:
  							_lowercase					:  Optional[int]    			=      "test_project_with_config"
  							_lowercase					:  Optional[Any]    			=      Accelerator(log_with="wandb"						)
  							_lowercase					:  Dict    			=      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  							_lowercase					:  str    			=      {"wandb": {"tags": ["my_tag"]}}
  							accelerator.init_trackers(snake_case			,  snake_case			,  snake_case						)
  							_lowercase					:  List[str]    			=      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  							accelerator.log(snake_case			,  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":
  																			_lowercase					:  Optional[Any]    			=      subprocess.check_output(
  																			    ["wandb", "sync", "--view", "--verbose", str(snake_case						)]			,  env=os.environ.copy()						).decode("utf8"			,  "ignore"						)
  																			break
  
          # Check HPS through careful parsing and cleaning
  							_lowercase					:  Any    			=      self.parse_log(snake_case			,  "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
  							_lowercase					:  str    			=      self.parse_log(snake_case			,  "run"			,  snake_case						)
  							self.assertEqual(logged_items["tags"]			,  "my_tag"						)
  
  							# Actual logging
  							_lowercase					:  Dict    			=      self.parse_log(snake_case			,  "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 		UpperCamelCase    (					self     :      List[str]   , _UpperCAmelCase     :      str   , _UpperCAmelCase     :      str     )      -> List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  str    			=      run_name
  						_lowercase					:  Tuple    			=      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(__snake_case      ,   "__init__"      ,   __snake_case					)
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@staticmethod
  	def 			_a(snake_case							:   Union[str, Any]			,  snake_case							:   str			,  snake_case							:   bool = False						)   -> Tuple:
  							for log in log_list:
  													_lowercase					:  List[Any]    			=      json.loads(snake_case						)["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 			_a(self							:   Dict						)   -> Tuple:
  							with tempfile.TemporaryDirectory() as d:
  													_lowercase					:  Tuple    			=      CometMLTracker("test_project_with_config"			,  snake_case						)
  													_lowercase					:  List[Any]    			=      Accelerator(log_with=snake_case						)
  													_lowercase					:  Dict    			=      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  													accelerator.init_trackers(snake_case			,  snake_case						)
  													accelerator.end_training()
  													_lowercase					:  List[Any]    			=      os.listdir(snake_case						)[0]  # Comet is nice, it's just a zip file here
  													# We parse the raw logs
  													_lowercase					:  Optional[Any]    			=      os.path.join(snake_case			,  snake_case						)
  													_lowercase					:  List[Any]    			=      zipfile.ZipFile(snake_case			,  "r"						)
  													_lowercase					:  int    			=      archive.open("messages.json"						).read().decode("utf-8"						)
  							_lowercase					:  Dict    			=      log.split("\n"						)[:-1]
  							self.assertEqual(self.get_value_from_key(snake_case			,  "num_iterations"			,  snake_case						)			,  12						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "learning_rate"			,  snake_case						)			,  0.01						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "some_boolean"			,  snake_case						)			,  snake_case						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "some_string"			,  snake_case						)			,  "some_value"						)
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> List[str]:
  							with tempfile.TemporaryDirectory() as d:
  													_lowercase					:  Optional[Any]    			=      CometMLTracker("test_project_with_config"			,  snake_case						)
  													_lowercase					:  Any    			=      Accelerator(log_with=snake_case						)
  													accelerator.init_trackers(snake_case						)
  													_lowercase					:  int    			=      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  													accelerator.log(snake_case			,  step=0						)
  													accelerator.end_training()
  													_lowercase					:  List[Any]    			=      os.listdir(snake_case						)[0]  # Comet is nice, it's just a zip file here
  													# We parse the raw logs
  													_lowercase					:  Optional[int]    			=      os.path.join(snake_case			,  snake_case						)
  													_lowercase					:  Union[str, Any]    			=      zipfile.ZipFile(snake_case			,  "r"						)
  													_lowercase					:  Optional[int]    			=      archive.open("messages.json"						).read().decode("utf-8"						)
  							_lowercase					:  Any    			=      log.split("\n"						)[:-1]
  							self.assertEqual(self.get_value_from_key(snake_case			,  "curr_step"			,  snake_case						)			,  0						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "total_loss"						)			,  0.1						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "iteration"						)			,  1						)
  							self.assertEqual(self.get_value_from_key(snake_case			,  "my_text"						)			,  "some_value"						)
  
  
  
  
  class      __lowercase	(	__snake_case					):
  	_A   			=      [
  	    "total_loss",
  	    "iteration",
  	    "my_text",
  	    "learning_rate",
  	    "num_iterations",
  	    "some_boolean",
  	    "some_string",
  	]
  
  	_A   			=      "my_custom_tracker"
  	_A   			=      False
  
  
  
  
  
  
  	def __init__(self							:   Tuple			,  snake_case							:   str						)   -> List[str]:
  							_lowercase					:  List[Any]    			=      open(F"""{dir}/log.csv"""			,  "w+"						)
  							_lowercase					:  Any    			=      csv.DictWriter(self.f			,  fieldnames=self._col_names						)
  							self.writer.writeheader()
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   str						)   -> List[Any]:
  							return self.writer
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]			,  snake_case							:   dict						)   -> Any:
  							logger.info("Call init"						)
  							self.writer.writerow(snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   dict			,  snake_case							:   Optional[int]						)   -> Any:
  							logger.info("Call log"						)
  							self.writer.writerow(snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Dict:
  							self.f.close()
  
  
  
  
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Optional[int]:
  							with tempfile.TemporaryDirectory() as d:
  													_lowercase					:  List[str]    			=      MyCustomTracker(snake_case						)
  													_lowercase					:  Any    			=      Accelerator(log_with=snake_case						)
  													_lowercase					:  Tuple    			=      {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  													accelerator.init_trackers("Some name"			,  snake_case						)
  													accelerator.end_training()
  													with open(F"""{d}/log.csv"""			,  "r"						) as f:
  																			_lowercase					:  Union[str, Any]    			=      csv.DictReader(snake_case						)
  																			_lowercase					:  Optional[Any]    			=      next(snake_case						)
  																			_lowercase					:  Tuple    			=      {
  																			    "total_loss": "",
  																			    "iteration": "",
  																			    "my_text": "",
  																			    "learning_rate": "0.01",
  																			    "num_iterations": "12",
  																			    "some_boolean": "False",
  																			    "some_string": "some_value",
  																			}
  																			self.assertDictEqual(snake_case			,  snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> List[str]:
  							with tempfile.TemporaryDirectory() as d:
  													_lowercase					:  List[str]    			=      MyCustomTracker(snake_case						)
  													_lowercase					:  List[str]    			=      Accelerator(log_with=snake_case						)
  													accelerator.init_trackers("Some name"						)
  													_lowercase					:  List[Any]    			=      {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  													accelerator.log(snake_case			,  step=0						)
  													accelerator.end_training()
  													with open(F"""{d}/log.csv"""			,  "r"						) as f:
  																			_lowercase					:  Optional[Any]    			=      csv.DictReader(snake_case						)
  																			_lowercase					:  int    			=      next(snake_case						)
  																			_lowercase					:  List[str]    			=      {
  																			    "total_loss": "0.1",
  																			    "iteration": "1",
  																			    "my_text": "some_value",
  																			    "learning_rate": "",
  																			    "num_iterations": "",
  																			    "some_boolean": "",
  																			    "some_string": "",
  																			}
  																			self.assertDictEqual(snake_case			,  snake_case						)
  
  
  
  
  
style_context: 
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> bool:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if not isinstance(_UpperCAmelCase   , _UpperCAmelCase     ):
  												_lowercase					:  Optional[int]    			=      f"""Input value of [number={number}] must be an integer"""
  												raise TypeError(_UpperCAmelCase     )
  						if number < 0 or number % 2 == 1:
  												return False
  						_lowercase					:  List[str]    			=      int(number**0.5     )
  						return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  