

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__	)


_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))