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
  
  _lowerCAmelCase     :			Any    =logging.getLogger(__name__)
  
  @require_tensorboard
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   "test_project_with_config"
  						with tempfile.TemporaryDirectory() as dirpath:
  							UpperCAmelCase__:  Optional[int]		       =   Accelerator(log_with="tensorboard"							,			project_dir=lowerCamelCase__	)
  							UpperCAmelCase__:  Optional[Any]		       =   {"num_iterations": 1_2, "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("*/**"	):
  								UpperCAmelCase__:  List[Any]		       =   list(filter(lambda lowerCamelCase__	: x.is_file()							,			child.iterdir()	)	)[0]
  							self.assertNotEqual(str(lowerCamelCase__	)							,			""	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   "test_project_with_log"
  						with tempfile.TemporaryDirectory() as dirpath:
  							UpperCAmelCase__:  Any		       =   Accelerator(log_with="tensorboard"							,			project_dir=lowerCamelCase__	)
  							accelerator.init_trackers(lowerCamelCase__	)
  							UpperCAmelCase__:  Tuple		       =   {"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
  							UpperCAmelCase__:  int		       =   list(filter(lambda lowerCamelCase__	: x.is_file()							,			Path(F"{dirpath}/{project_name}"	).iterdir()	)	)[0]
  							self.assertNotEqual(str(lowerCamelCase__	)							,			""	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   "test_project_with_log"
  						with tempfile.TemporaryDirectory() as dirpath:
  							UpperCAmelCase__:  List[str]		       =   Accelerator(log_with="tensorboard"							,			project_dir=lowerCamelCase__	)
  							accelerator.init_trackers(lowerCamelCase__	)
  							UpperCAmelCase__:  Dict		       =   {"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
  							UpperCAmelCase__:  Tuple		       =   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:
  								UpperCAmelCase__:  Optional[Any]		       =   f.read()
  							UpperCAmelCase__:  List[str]		       =   False
  							while data:
  								UpperCAmelCase__:  List[Any]		       =   struct.unpack("Q"							,			data[:8]	)
  
  								UpperCAmelCase__:  Any		       =   data[1_2 : 1_2 + int(header[0]	)]  # 8+4
  								UpperCAmelCase__:  Optional[Any]		       =   data[1_2 + int(header[0]	) + 4 :]
  								UpperCAmelCase__:  Dict		       =   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":
  											UpperCAmelCase__:  List[Any]		       =   True
  							self.assertTrue(lowerCamelCase__							,			"Converted tensor was not found in the log file!"	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						with self.assertRaisesRegex(lowerCamelCase__							,			"Logging with `tensorboard` requires a `logging_dir`"	):
  							UpperCAmelCase__:  str		       =   Accelerator(log_with="tensorboard"	)
  						with tempfile.TemporaryDirectory() as dirpath:
  							UpperCAmelCase__:  int		       =   Accelerator(log_with="tensorboard"							,			project_dir=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   ProjectConfiguration(total_limit=3_0	)
  						with tempfile.TemporaryDirectory() as dirpath:
  							UpperCAmelCase__:  Dict		       =   Accelerator(log_with="tensorboard"							,			project_dir=lowerCamelCase__							,			project_config=lowerCamelCase__	)
  
  @require_wandb
  @mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}		)
  class __UpperCamelCase    (							_a				,_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						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						(							lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = True	):
  						UpperCAmelCase__:  Any		       =   RF"{section} ([\S\s]*?)\n\n"
  						if record:
  							UpperCAmelCase__:  Any		       =   RF"Record: {pattern}"
  						UpperCAmelCase__:  Dict		       =   re.findall(lowerCamelCase__							,			lowerCamelCase__	)[0]
  						# A config
  						if section == "config" or section == "history":
  							UpperCAmelCase__:  Optional[Any]		       =   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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   "test_project_with_config"
  						UpperCAmelCase__:  Optional[int]		       =   Accelerator(log_with="wandb"	)
  						UpperCAmelCase__:  int		       =   {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  						UpperCAmelCase__:  Optional[int]		       =   {"wandb": {"tags": ["my_tag"]}}
  						accelerator.init_trackers(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   {"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":
  								UpperCAmelCase__:  Any		       =   subprocess.check_output(
  								    ["wandb", "sync", "--view", "--verbose", str(lowerCamelCase__	)]							,			env=os.environ.copy()	).decode("utf8"							,			"ignore"	)
  								break
  
          # Check HPS through careful parsing and cleaning
  						UpperCAmelCase__:  int		       =   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
  						UpperCAmelCase__:  Optional[Any]		       =   self.parse_log(lowerCamelCase__							,			"run"							,			lowerCamelCase__	)
  						self.assertEqual(logged_items["tags"]							,			"my_tag"	)
  
  						# Actual logging
  						UpperCAmelCase__:  Union[str, Any]		       =   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  _A  (			self  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Dict		       =   run_name
  	UpperCAmelCase__:  Union[str, Any]		       =   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(_a				,"__init__"				,_a		)
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@staticmethod
  					def       _UpperCAmelCase						(							lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = False	):
  						for log in log_list:
  							UpperCAmelCase__:  Optional[int]		       =   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       _UpperCAmelCase						(							self	):
  						with tempfile.TemporaryDirectory() as d:
  							UpperCAmelCase__:  Optional[Any]		       =   CometMLTracker("test_project_with_config"							,			lowerCamelCase__	)
  							UpperCAmelCase__:  List[str]		       =   Accelerator(log_with=lowerCamelCase__	)
  							UpperCAmelCase__:  List[Any]		       =   {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
  							accelerator.init_trackers(lowerCamelCase__							,			lowerCamelCase__	)
  							accelerator.end_training()
  							UpperCAmelCase__:  Optional[Any]		       =   os.listdir(lowerCamelCase__	)[0]  # Comet is nice, it's just a zip file here
  							# We parse the raw logs
  							UpperCAmelCase__:  List[Any]		       =   os.path.join(lowerCamelCase__							,			lowerCamelCase__	)
  							UpperCAmelCase__:  Tuple		       =   zipfile.ZipFile(lowerCamelCase__							,			"r"	)
  							UpperCAmelCase__:  Tuple		       =   archive.open("messages.json"	).read().decode("utf-8"	)
  						UpperCAmelCase__:  List[Any]		       =   log.split("\n"	)[:-1]
  						self.assertEqual(self.get_value_from_key(lowerCamelCase__							,			"num_iterations"							,			lowerCamelCase__	)							,			1_2	)
  						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       _UpperCAmelCase						(							self	):
  						with tempfile.TemporaryDirectory() as d:
  							UpperCAmelCase__:  int		       =   CometMLTracker("test_project_with_config"							,			lowerCamelCase__	)
  							UpperCAmelCase__:  Union[str, Any]		       =   Accelerator(log_with=lowerCamelCase__	)
  							accelerator.init_trackers(lowerCamelCase__	)
  							UpperCAmelCase__:  List[str]		       =   {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
  							accelerator.log(lowerCamelCase__							,			step=0	)
  							accelerator.end_training()
  							UpperCAmelCase__:  Optional[Any]		       =   os.listdir(lowerCamelCase__	)[0]  # Comet is nice, it's just a zip file here
  							# We parse the raw logs
  							UpperCAmelCase__:  Dict		       =   os.path.join(lowerCamelCase__							,			lowerCamelCase__	)
  							UpperCAmelCase__:  List[Any]		       =   zipfile.ZipFile(lowerCamelCase__							,			"r"	)
  							UpperCAmelCase__:  List[str]		       =   archive.open("messages.json"	).read().decode("utf-8"	)
  						UpperCAmelCase__:  Dict		       =   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    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      [
  					    "total_loss",
  					    "iteration",
  					    "my_text",
  					    "learning_rate",
  					    "num_iterations",
  					    "some_boolean",
  					    "some_string",
  					]
  
  					__magic_name__     			=      "my_custom_tracker"
  					__magic_name__     			=      False
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   open(F"{dir}/log.csv"							,			"w+"	)
  						UpperCAmelCase__:  Union[str, Any]		       =   csv.DictWriter(self.f							,			fieldnames=self._col_names	)
  						self.writer.writeheader()
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return self.writer
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__	):
  						logger.info("Call init"	)
  						self.writer.writerow(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
  						logger.info("Call log"	)
  						self.writer.writerow(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.f.close()
  
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						with tempfile.TemporaryDirectory() as d:
  							UpperCAmelCase__:  Optional[int]		       =   MyCustomTracker(lowerCamelCase__	)
  							UpperCAmelCase__:  Optional[int]		       =   Accelerator(log_with=lowerCamelCase__	)
  							UpperCAmelCase__:  List[str]		       =   {"num_iterations": 1_2, "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:
  								UpperCAmelCase__:  Dict		       =   csv.DictReader(lowerCamelCase__	)
  								UpperCAmelCase__:  Tuple		       =   next(lowerCamelCase__	)
  								UpperCAmelCase__:  Union[str, Any]		       =   {
  								    "total_loss": "",
  								    "iteration": "",
  								    "my_text": "",
  								    "learning_rate": "0.01",
  								    "num_iterations": "12",
  								    "some_boolean": "False",
  								    "some_string": "some_value",
  								}
  								self.assertDictEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						with tempfile.TemporaryDirectory() as d:
  							UpperCAmelCase__:  str		       =   MyCustomTracker(lowerCamelCase__	)
  							UpperCAmelCase__:  List[str]		       =   Accelerator(log_with=lowerCamelCase__	)
  							accelerator.init_trackers("Some name"	)
  							UpperCAmelCase__:  Optional[Any]		       =   {"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:
  								UpperCAmelCase__:  Dict		       =   csv.DictReader(lowerCamelCase__	)
  								UpperCAmelCase__:  Tuple		       =   next(lowerCamelCase__	)
  								UpperCAmelCase__:  List[Any]		       =   {
  								    "total_loss": "0.1",
  								    "iteration": "1",
  								    "my_text": "some_value",
  								    "learning_rate": "",
  								    "num_iterations": "",
  								    "some_boolean": "",
  								    "some_string": "",
  								}
  								self.assertDictEqual(lowerCamelCase__							,			lowerCamelCase__	)
style_context: 
  
  _lowerCAmelCase     :			Optional[int]    ={
      """+""": lambda x, y: x + y,
      """-""": lambda x, y: x - y,
      """*""": lambda x, y: x * y,
      """/""": lambda x, y: x / y,
  }
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	return c.isdigit()
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  str		       =   []
  
  	# iterate over the string in reverse order
  	for c in expression.split()[::-1]:
  		# push operand to stack
  		if is_operand(SCREAMING_SNAKE_CASE			):
  			stack.append(int(SCREAMING_SNAKE_CASE			)			)
  
  		else:
  			# pop values from stack can calculate the result
  			# push the result onto the stack again
  			UpperCAmelCase__:  Any		       =   stack.pop()
  			UpperCAmelCase__:  int		       =   stack.pop()
  			stack.append(calc[c](SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)			)
  
  	return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
  					_lowerCAmelCase     :			Optional[int]    ="""+ 9 * 2 6"""
  					print(evaluate(test_expression))
  
  					_lowerCAmelCase     :			Optional[Any]    ="""/ * 10 2 + 4 1 """
  					print(evaluate(test_expression))