
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						)






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()




