






"""simple docstring"""







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

a :				Optional[Any]      					=		logging.getLogger(__name__)


@require_tensorboard
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       "test_project_with_config"
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      int=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__			)
																		lowercase__				:      List[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__				:      List[str]=       list(filter(lambda snake_case__			: x.is_file()       ,  child.iterdir()			)			)[0]
																		self.assertNotEqual(str(snake_case__			)       ,  ""			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       "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[str]=       {"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__				:      Optional[int]=       list(filter(lambda snake_case__			: x.is_file()       ,  Path(F'''{dirpath}/{project_name}'''			).iterdir()			)			)[0]
																		self.assertNotEqual(str(snake_case__			)       ,  ""			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[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__				:      str=       {"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__				:      Dict=       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__				:      Union[str, Any]=       f.read()
																		lowercase__				:      List[str]=       False
																		while data:
																								lowercase__				:      List[Any]=       struct.unpack("Q"       ,  data[:8]			)

																								lowercase__				:      Union[str, Any]=       data[12 : 12 + int(header[0]			)]  # 8+4
																								lowercase__				:      Union[str, Any]=       data[12 + int(header[0]			) + 4 :]
																								lowercase__				:      Optional[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__				:      Optional[Any]=       True
																		self.assertTrue(snake_case__       ,  "Converted tensor was not found in the log file!"			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with self.assertRaisesRegex(snake_case__       ,  "Logging with `tensorboard` requires a `logging_dir`"			):
																		lowercase__				:      Union[str, Any]=       Accelerator(log_with="tensorboard"			)
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      Optional[int]=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       ProjectConfiguration(total_limit=30			)
												with tempfile.TemporaryDirectory() as dirpath:
																		lowercase__				:      Any=       Accelerator(log_with="tensorboard"       ,  project_dir=snake_case__       ,  project_config=snake_case__			)


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




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												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_       (   snake_case__       ,  snake_case__       ,  snake_case__ = True			):
												'''simple docstring'''
												lowercase__				:      str=       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__				:      Dict=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       "test_project_with_config"
												lowercase__				:      List[str]=       Accelerator(log_with="wandb"			)
												lowercase__				:      Optional[int]=       {"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
												lowercase__				:      List[Any]=       {"wandb": {"tags": ["my_tag"]}}
												accelerator.init_trackers(snake_case__       ,  snake_case__       ,  snake_case__			)
												lowercase__				:      int=       {"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__				:      str=       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__				:      Tuple=       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__				:      List[str]=       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 						lowercase__(self					,		A					,		A						)				->Optional[int]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       run_name
						lowercase__				:      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(SCREAMING_SNAKE_CASE__  ,  "__init__"  ,  SCREAMING_SNAKE_CASE__							)
class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      Optional[Any]=       CometMLTracker("test_project_with_config"       ,  snake_case__			)
																		lowercase__				:      Any=       Accelerator(log_with=snake_case__			)
																		lowercase__				:      Union[str, 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()
																		lowercase__				:      int=       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__				:      Optional[int]=       zipfile.ZipFile(snake_case__       ,  "r"			)
																		lowercase__				:      Dict=       archive.open("messages.json"			).read().decode("utf-8"			)
												lowercase__				:      Any=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      Tuple=       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__				:      Union[str, 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__				:      int=       zipfile.ZipFile(snake_case__       ,  "r"			)
																		lowercase__				:      Union[str, Any]=       archive.open("messages.json"			).read().decode("utf-8"			)
												lowercase__				:      List[str]=       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     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					[
						    "total_loss",
						    "iteration",
						    "my_text",
						    "learning_rate",
						    "num_iterations",
						    "some_boolean",
						    "some_string",
						]

						__lowerCamelCase				       =					"my_custom_tracker"
						__lowerCamelCase				       =					False




						def __init__(   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      str=       open(F'''{dir}/log.csv'''       ,  "w+"			)
												lowercase__				:      str=       csv.DictWriter(self.f       ,  fieldnames=self._col_names			)
												self.writer.writeheader()




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.writer




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												logger.info("Call init"			)
												self.writer.writerow(snake_case__			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												logger.info("Call log"			)
												self.writer.writerow(snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												self.f.close()


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      List[str]=       MyCustomTracker(snake_case__			)
																		lowercase__				:      Tuple=       Accelerator(log_with=snake_case__			)
																		lowercase__				:      str=       {"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__				:      str=       csv.DictReader(snake_case__			)
																								lowercase__				:      Dict=       next(snake_case__			)
																								lowercase__				:      List[Any]=       {
																								    "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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												with tempfile.TemporaryDirectory() as d:
																		lowercase__				:      Optional[int]=       MyCustomTracker(snake_case__			)
																		lowercase__				:      Any=       Accelerator(log_with=snake_case__			)
																		accelerator.init_trackers("Some name"			)
																		lowercase__				:      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__				:      Union[str, Any]=       csv.DictReader(snake_case__			)
																								lowercase__				:      List[Any]=       next(snake_case__			)
																								lowercase__				:      Dict=       {
																								    "total_loss": "0.1",
																								    "iteration": "1",
																								    "my_text": "some_value",
																								    "learning_rate": "",
																								    "num_iterations": "",
																								    "some_boolean": "",
																								    "some_string": "",
																								}
																								self.assertDictEqual(snake_case__       ,  snake_case__			)














from doctest import testmod
from math import sqrt
def      lowerCamelCase					(						a_	)	->			list:
							lowerCAmelCase_										=     []
							if num < 1:
														return facs
							facs.append(1	)
							if num == 1:
														return facs
							facs.append(a_	)
							for i in range(2 ,						int(sqrt(a_	)	) + 1	):
														if num % i == 0:  # If i is a factor of num
																					facs.append(a_	)
																					lowerCAmelCase_										=     num // i  # num//i is the other factor of num
																					if d != i:  # If d and i are distinct
																												facs.append(a_	)  # we have found another factor
							facs.sort()
							return facs


if __name__ == "__main__":
		testmod(name="""factors_of_a_number""", verbose=True)



