

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

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






@require_tensorboard
class 		__magic_name__ (				unittest.TestCase							):






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






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






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         UpperCamelCase_          =		"test_project_with_log"
         with tempfile.TemporaryDirectory() as dirpath:
             UpperCamelCase_          =		Accelerator(log_with="tensorboard"						,		project_dir=_lowercase     )
             accelerator.init_trackers(_lowercase     )
             UpperCamelCase_          =		{"tensor": torch.tensor(1     )}
             accelerator.log(_lowercase						,		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(filter(lambda _lowercase     : 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(_lowercase						,		"rb"     ) as f:
                 UpperCamelCase_          =		f.read()
             UpperCamelCase_          =		False
             while data:
                 UpperCamelCase_          =		struct.unpack("Q"						,		data[:8]     )

                 UpperCamelCase_          =		data[12 : 12 + int(header[0]     )]  # 8+4
                 UpperCamelCase_          =		data[12 + int(header[0]     ) + 4 :]
                 UpperCamelCase_          =		event_pba.Event()

                 event.ParseFromString(_lowercase     )
                 if event.HasField("summary"     ):
                     for value in event.summary.value:
                         if value.simple_value == 1.0 and value.tag == "tensor":
                             UpperCamelCase_          =		True
             self.assertTrue(_lowercase						,		"Converted tensor was not found in the log file!"     )






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         with self.assertRaisesRegex(_lowercase						,		"Logging with `tensorboard` requires a `logging_dir`"     ):
             UpperCamelCase_          =		Accelerator(log_with="tensorboard"     )
         with tempfile.TemporaryDirectory() as dirpath:
             UpperCamelCase_          =		Accelerator(log_with="tensorboard"						,		project_dir=_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		ProjectConfiguration(total_limit=30     )
         with tempfile.TemporaryDirectory() as dirpath:
             UpperCamelCase_          =		Accelerator(log_with="tensorboard"						,		project_dir=_lowercase						,		project_config=_lowercase     )






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






     def      UpperCAmelCase_							(							self     )->			str:
         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_							(							_lowercase						,		_lowercase						,		_lowercase = True     )->			int:
         UpperCamelCase_          =		rF"{section} ([\S\s]*?)\n\n"
         if record:
             UpperCamelCase_          =		rF"Record: {pattern}"
         UpperCamelCase_          =		re.findall(_lowercase						,		_lowercase     )[0]
         # A config
         if section == "config" or section == "history":
             UpperCamelCase_          =		re.findall(r"\"([a-zA-Z0-9_.,]+)"						,		_lowercase     )
             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]+)\""						,		_lowercase     )     )






     @skip
     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		"test_project_with_config"
         UpperCamelCase_          =		Accelerator(log_with="wandb"     )
         UpperCamelCase_          =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         UpperCamelCase_          =		{"wandb": {"tags": ["my_tag"]}}
         accelerator.init_trackers(_lowercase						,		_lowercase						,		_lowercase     )
         UpperCamelCase_          =		{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(_lowercase						,		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_          =		subprocess.check_output(
                     ["wandb", "sync", "--view", "--verbose", str(_lowercase     )]						,		env=os.environ.copy()     ).decode("utf8"						,		"ignore"     )
                 break

        # Check HPS through careful parsing and cleaning
         UpperCamelCase_          =		self.parse_log(_lowercase						,		"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_          =		self.parse_log(_lowercase						,		"run"						,		_lowercase     )
         self.assertEqual(logged_items["tags"]						,		"my_tag"     )

         # Actual logging
         UpperCamelCase_          =		self.parse_log(_lowercase						,		"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       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    UpperCamelCase_          =		run_name
    UpperCamelCase_          =		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(snake_case       ,	"""__init__"""       ,	snake_case							)
class 		__magic_name__ (				unittest.TestCase							):






     @staticmethod
     def      UpperCAmelCase_							(							_lowercase						,		_lowercase						,		_lowercase = False     )->			Union[str, Any]:
         for log in log_list:
             UpperCamelCase_          =		json.loads(_lowercase     )["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     )->			Dict:
         with tempfile.TemporaryDirectory() as d:
             UpperCamelCase_          =		CometMLTracker("test_project_with_config"						,		_lowercase     )
             UpperCamelCase_          =		Accelerator(log_with=_lowercase     )
             UpperCamelCase_          =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
             accelerator.init_trackers(_lowercase						,		_lowercase     )
             accelerator.end_training()
             UpperCamelCase_          =		os.listdir(_lowercase     )[0]  # Comet is nice, it's just a zip file here
             # We parse the raw logs
             UpperCamelCase_          =		os.path.join(_lowercase						,		_lowercase     )
             UpperCamelCase_          =		zipfile.ZipFile(_lowercase						,		"r"     )
             UpperCamelCase_          =		archive.open("messages.json"     ).read().decode("utf-8"     )
         UpperCamelCase_          =		log.split("\n"     )[:-1]
         self.assertEqual(self.get_value_from_key(_lowercase						,		"num_iterations"						,		_lowercase     )						,		12     )
         self.assertEqual(self.get_value_from_key(_lowercase						,		"learning_rate"						,		_lowercase     )						,		0.01     )
         self.assertEqual(self.get_value_from_key(_lowercase						,		"some_boolean"						,		_lowercase     )						,		_lowercase     )
         self.assertEqual(self.get_value_from_key(_lowercase						,		"some_string"						,		_lowercase     )						,		"some_value"     )






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






class 		__magic_name__ (				snake_case							):
     UpperCamelCase_     :Any									=	[
         """total_loss""",
         """iteration""",
         """my_text""",
         """learning_rate""",
         """num_iterations""",
         """some_boolean""",
         """some_string""",
     ]

     UpperCamelCase_     :Dict									=	"""my_custom_tracker"""
     UpperCamelCase_     :Dict									=	False






     def __init__(							self						,		_lowercase     )->			Any:
         UpperCamelCase_          =		open(F"{dir}/log.csv"						,		"w+"     )
         UpperCamelCase_          =		csv.DictWriter(self.f						,		fieldnames=self._col_names     )
         self.writer.writeheader()






     @property
     def      UpperCAmelCase_							(							self     )->			List[Any]:
         return self.writer






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			int:
         logger.info("Call init"     )
         self.writer.writerow(_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         logger.info("Call log"     )
         self.writer.writerow(_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         self.f.close()






class 		__magic_name__ (				unittest.TestCase							):






     def      UpperCAmelCase_							(							self     )->			int:
         with tempfile.TemporaryDirectory() as d:
             UpperCamelCase_          =		MyCustomTracker(_lowercase     )
             UpperCamelCase_          =		Accelerator(log_with=_lowercase     )
             UpperCamelCase_          =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
             accelerator.init_trackers("Some name"						,		_lowercase     )
             accelerator.end_training()
             with open(F"{d}/log.csv"						,		"r"     ) as f:
                 UpperCamelCase_          =		csv.DictReader(_lowercase     )
                 UpperCamelCase_          =		next(_lowercase     )
                 UpperCamelCase_          =		{
                     "total_loss": "",
                     "iteration": "",
                     "my_text": "",
                     "learning_rate": "0.01",
                     "num_iterations": "12",
                     "some_boolean": "False",
                     "some_string": "some_value",
                 }
                 self.assertDictEqual(_lowercase						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         with tempfile.TemporaryDirectory() as d:
             UpperCamelCase_          =		MyCustomTracker(_lowercase     )
             UpperCamelCase_          =		Accelerator(log_with=_lowercase     )
             accelerator.init_trackers("Some name"     )
             UpperCamelCase_          =		{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
             accelerator.log(_lowercase						,		step=0     )
             accelerator.end_training()
             with open(F"{d}/log.csv"						,		"r"     ) as f:
                 UpperCamelCase_          =		csv.DictReader(_lowercase     )
                 UpperCamelCase_          =		next(_lowercase     )
                 UpperCamelCase_          =		{
                     "total_loss": "0.1",
                     "iteration": "1",
                     "my_text": "some_value",
                     "learning_rate": "",
                     "num_iterations": "",
                     "some_boolean": "",
                     "some_string": "",
                 }
                 self.assertDictEqual(_lowercase						,		_lowercase     )



from typing import Callable




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		List[Any]:



    """simple docstring"""






    def wrapper(SCREAMING_SNAKE_CASE_       ):
        UpperCamelCase_          =		function_with_docstring.__doc__
        return target_function

    return wrapper
