


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__						=			logging.getLogger(__name__)







@require_tensorboard
class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            _A				:	Optional[int]       =			"test_project_with_config"
            with tempfile.TemporaryDirectory() as dirpath:
                   _A				:	Optional[int]       =			Accelerator(log_with="tensorboard"			,						project_dir=UpperCamelCase__)
                   _A				:	Union[str, Any]       =			{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
                   accelerator.init_trackers(UpperCamelCase__			,						UpperCamelCase__)
                   accelerator.end_training()
                   for child in Path(F"{dirpath}/{project_name}").glob("*/**"):
                          _A				:	str       =			list(filter(lambda __lowerCamelCase: x.is_file()			,						child.iterdir()))[0]
                   self.assertNotEqual(str(UpperCamelCase__)			,						"")


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Optional[Any]       =			"test_project_with_log"
            with tempfile.TemporaryDirectory() as dirpath:
                   _A				:	str       =			Accelerator(log_with="tensorboard"			,						project_dir=UpperCamelCase__)
                   accelerator.init_trackers(UpperCamelCase__)
                   _A				:	Tuple       =			{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
                   accelerator.log(UpperCamelCase__			,						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
                   _A				:	List[Any]       =			list(filter(lambda __lowerCamelCase: x.is_file()			,						Path(F"{dirpath}/{project_name}").iterdir()))[0]
                   self.assertNotEqual(str(UpperCamelCase__)			,						"")


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	List[Any]       =			"test_project_with_log"
            with tempfile.TemporaryDirectory() as dirpath:
                   _A				:	Optional[int]       =			Accelerator(log_with="tensorboard"			,						project_dir=UpperCamelCase__)
                   accelerator.init_trackers(UpperCamelCase__)
                   _A				:	Any       =			{"tensor": torch.tensor(1)}
                   accelerator.log(UpperCamelCase__			,						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
                   _A				:	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(UpperCamelCase__			,						"rb") as f:
                          _A				:	List[str]       =			f.read()
                   _A				:	List[Any]       =			False
                   while data:
                          _A				:	Dict       =			struct.unpack("Q"			,						data[:8])

                          _A				:	Union[str, Any]       =			data[1_2 : 1_2 + int(header[0])]  # 8+4
                          _A				:	List[str]       =			data[1_2 + int(header[0]) + 4 :]
                          _A				:	int       =			event_pba.Event()

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


     def 			_lowerCamelCase			(						self)      ->    List[str]:






            with self.assertRaisesRegex(UpperCamelCase__			,						"Logging with `tensorboard` requires a `logging_dir`"):
                   _A				:	Any       =			Accelerator(log_with="tensorboard")
            with tempfile.TemporaryDirectory() as dirpath:
                   _A				:	Dict       =			Accelerator(log_with="tensorboard"			,						project_dir=UpperCamelCase__)


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            _A				:	Optional[Any]       =			ProjectConfiguration(total_limit=3_0)
            with tempfile.TemporaryDirectory() as dirpath:
                   _A				:	Union[str, Any]       =			Accelerator(log_with="tensorboard"			,						project_dir=UpperCamelCase__			,						project_config=UpperCamelCase__)







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




     '''simple docstring'''


     def 			_lowerCamelCase			(						self)      ->    Dict:
            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)      ->    Optional[int]:
            _A				:	List[Any]       =			rF"{section} ([\S\s]*?)\n\n"
            if record:
                   _A				:	Optional[Any]       =			rF"Record: {pattern}"
            _A				:	Any       =			re.findall(UpperCamelCase__			,						UpperCamelCase__)[0]
            # A config
            if section == "config" or section == "history":
                   _A				:	Optional[Any]       =			re.findall(r"\"([a-zA-Z0-9_.,]+)"			,						UpperCamelCase__)
                   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]+)\""			,						UpperCamelCase__))


     @skip
     def 			_lowerCamelCase			(						self)      ->    str:
            _A				:	Optional[int]       =			"test_project_with_config"
            _A				:	List[str]       =			Accelerator(log_with="wandb")
            _A				:	int       =			{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
            _A				:	int       =			{"wandb": {"tags": ["my_tag"]}}
            accelerator.init_trackers(UpperCamelCase__			,						UpperCamelCase__			,						UpperCamelCase__)
            _A				:	Optional[int]       =			{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
            accelerator.log(UpperCamelCase__			,						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":
                          _A				:	Any       =			subprocess.check_output(
                              ["wandb", "sync", "--view", "--verbose", str(UpperCamelCase__)]			,						env=os.environ.copy()).decode("utf8"			,						"ignore")
                          break

                          # Check HPS through careful parsing and cleaning
            _A				:	Optional[int]       =			self.parse_log(UpperCamelCase__			,						"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
            _A				:	Tuple       =			self.parse_log(UpperCamelCase__			,						"run"			,						UpperCamelCase__)
            self.assertEqual(logged_items["tags"]			,						"my_tag")

            # Actual logging
            _A				:	Tuple       =			self.parse_log(UpperCamelCase__			,						"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					:  Any    ,							UpperCamelCase__					:  str    ,							UpperCamelCase__					:  str						):
       _A				:	Tuple       =			run_name
       _A				:	Union[str, Any]       =			OfflineExperiment(project_name=A_    ,							offline_directory=A_						)
       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 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     @staticmethod
     def 			_lowerCamelCase			(						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = False)      ->    Optional[Any]:






            for log in log_list:
                   _A				:	List[str]       =			json.loads(UpperCamelCase__)["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)      ->    Union[str, Any]:






            with tempfile.TemporaryDirectory() as d:
                   _A				:	Tuple       =			CometMLTracker("test_project_with_config"			,						UpperCamelCase__)
                   _A				:	Optional[Any]       =			Accelerator(log_with=UpperCamelCase__)
                   _A				:	Union[str, Any]       =			{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
                   accelerator.init_trackers(UpperCamelCase__			,						UpperCamelCase__)
                   accelerator.end_training()
                   _A				:	Dict       =			os.listdir(UpperCamelCase__)[0]  # Comet is nice, it's just a zip file here
                   # We parse the raw logs
                   _A				:	Union[str, Any]       =			os.path.join(UpperCamelCase__			,						UpperCamelCase__)
                   _A				:	Union[str, Any]       =			zipfile.ZipFile(UpperCamelCase__			,						"r")
                   _A				:	Dict       =			archive.open("messages.json").read().decode("utf-8")
            _A				:	Optional[int]       =			log.split("\n")[:-1]
            self.assertEqual(self.get_value_from_key(UpperCamelCase__			,						"num_iterations"			,						UpperCamelCase__)			,						1_2)
            self.assertEqual(self.get_value_from_key(UpperCamelCase__			,						"learning_rate"			,						UpperCamelCase__)			,						0.0_1)
            self.assertEqual(self.get_value_from_key(UpperCamelCase__			,						"some_boolean"			,						UpperCamelCase__)			,						UpperCamelCase__)
            self.assertEqual(self.get_value_from_key(UpperCamelCase__			,						"some_string"			,						UpperCamelCase__)			,						"some_value")


     def 			_lowerCamelCase			(						self)      ->    int:






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







class 					lowerCAmelCase__	(    _A):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			[
         "total_loss",
         "iteration",
         "my_text",
         "learning_rate",
         "num_iterations",
         "some_boolean",
         "some_string",
     ]

     __SCREAMING_SNAKE_CASE        =			"my_custom_tracker"
     __SCREAMING_SNAKE_CASE        =			False


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


     @property
     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            return self.writer


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Any:
            logger.info("Call init")
            self.writer.writerow(UpperCamelCase__)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    str:
            logger.info("Call log")
            self.writer.writerow(UpperCamelCase__)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            self.f.close()







class 					lowerCAmelCase__	(    unittest.TestCase):




     '''simple docstring'''


     def 			_lowerCamelCase			(						self)      ->    Any:






            with tempfile.TemporaryDirectory() as d:
                   _A				:	Tuple       =			MyCustomTracker(UpperCamelCase__)
                   _A				:	str       =			Accelerator(log_with=UpperCamelCase__)
                   _A				:	str       =			{"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
                   accelerator.init_trackers("Some name"			,						UpperCamelCase__)
                   accelerator.end_training()
                   with open(F"{d}/log.csv"			,						"r") as f:
                          _A				:	Optional[Any]       =			csv.DictReader(UpperCamelCase__)
                          _A				:	Dict       =			next(UpperCamelCase__)
                          _A				:	Optional[Any]       =			{
                              "total_loss": "",
                              "iteration": "",
                              "my_text": "",
                              "learning_rate": "0.01",
                              "num_iterations": "12",
                              "some_boolean": "False",
                              "some_string": "some_value",
                          }
                          self.assertDictEqual(UpperCamelCase__			,						UpperCamelCase__)


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






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




from itertools import permutations
def 							_UpperCAmelCase    ():
       _A				:	List[Any]       =			list(map("".join    ,							permutations("0123456789"						)						)						)
       return result[999999]


if __name__ == "__main__":
    print(solution())
