




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					:     List[Any]				       =    logging.getLogger(__name__)

@require_tensorboard
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						'test_project_with_config'
					with tempfile.TemporaryDirectory() as dirpath:
						A_					:			Tuple						=						Accelerator(log_with='''tensorboard''' ,       project_dir=__UpperCamelCase    )
						A_					:			int						=						{'num_iterations': 12, '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_					:			Dict						=						list(filter(lambda _lowerCamelCase    : x.is_file() ,       child.iterdir()    )    )[0]
						self.assertNotEqual(str(__UpperCamelCase    ) ,       ''''''    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						'test_project_with_log'
					with tempfile.TemporaryDirectory() as dirpath:
						A_					:			List[str]						=						Accelerator(log_with='''tensorboard''' ,       project_dir=__UpperCamelCase    )
						accelerator.init_trackers(__UpperCamelCase    )
						A_					:			int						=						{'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_					:			Optional[Any]						=						list(filter(lambda _lowerCamelCase    : x.is_file() ,       Path(F"""{dirpath}/{project_name}"""    ).iterdir()    )    )[0]
						self.assertNotEqual(str(__UpperCamelCase    ) ,       ''''''    )







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_					:			int						=						'test_project_with_log'
					with tempfile.TemporaryDirectory() as dirpath:
						A_					:			Dict						=						Accelerator(log_with='''tensorboard''' ,       project_dir=__UpperCamelCase    )
						accelerator.init_trackers(__UpperCamelCase    )
						A_					:			List[str]						=						{'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_					:			Union[str, Any]						=						f.read()
						A_					:			Dict						=						False
						while data:
							A_					:			Optional[Any]						=						struct.unpack('''Q''' ,       data[:8]    )

							A_					:			List[str]						=						data[12 : 12 + int(header[0]    )]  # 8+4
							A_					:			List[str]						=						data[12 + int(header[0]    ) + 4 :]
							A_					:			Optional[Any]						=						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_					:			Optional[int]						=						True
						self.assertTrue(__UpperCamelCase ,       '''Converted tensor was not found in the log file!'''    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					with self.assertRaisesRegex(__UpperCamelCase ,       '''Logging with `tensorboard` requires a `logging_dir`'''    ):
						A_					:			Any						=						Accelerator(log_with='''tensorboard'''    )
					with tempfile.TemporaryDirectory() as dirpath:
						A_					:			Any						=						Accelerator(log_with='''tensorboard''' ,       project_dir=__UpperCamelCase    )







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			List[Any]						=						ProjectConfiguration(total_limit=30    )
					with tempfile.TemporaryDirectory() as dirpath:
						A_					:			Optional[int]						=						Accelerator(log_with='''tensorboard''' ,       project_dir=__UpperCamelCase ,       project_config=__UpperCamelCase    )

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







				def    a_					(							self				:   Optional[int]    ):



					"""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    a_					(							_lowerCamelCase				:   Any ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   Tuple = True    ):



					"""simple docstring"""

					A_					:			int						=						rF"""{section} ([\S\s]*?)\n\n"""
					if record:
						A_					:			Optional[int]						=						rF"""Record: {pattern}"""
					A_					:			int						=						re.findall(__UpperCamelCase ,       __UpperCamelCase    )[0]
					# A config
					if section == "config" or section == "history":
						A_					:			str						=						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    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						'test_project_with_config'
					A_					:			Union[str, Any]						=						Accelerator(log_with='''wandb'''    )
					A_					:			int						=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
					A_					:			int						=						{'wandb': {'tags': ['my_tag']}}
					accelerator.init_trackers(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase    )
					A_					:			List[Any]						=						{'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_					:			str						=						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_					:			List[Any]						=						self.parse_log(__UpperCamelCase ,       '''run''' ,       __UpperCamelCase    )
					self.assertEqual(logged_items['''tags'''] ,       '''my_tag'''    )

					# Actual logging
					A_					:			Dict						=						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 						lowercase_ (						self					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Union[str, Any]						=						run_name
	A_					:			List[str]						=						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(_lowerCamelCase       ,		"""__init__"""       ,		_lowerCamelCase)
class     lowercase (      unittest.TestCase):







				@staticmethod
				def    a_					(							_lowerCamelCase				:   str ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Dict = False    ):



					"""simple docstring"""

					for log in log_list:
						A_					:			List[Any]						=						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    a_					(							self				:   str    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as d:
						A_					:			Dict						=						CometMLTracker('''test_project_with_config''' ,       __UpperCamelCase    )
						A_					:			int						=						Accelerator(log_with=__UpperCamelCase    )
						A_					:			str						=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
						accelerator.init_trackers(__UpperCamelCase ,       __UpperCamelCase    )
						accelerator.end_training()
						A_					:			Tuple						=						os.listdir(__UpperCamelCase    )[0]  # Comet is nice, it's just a zip file here
						# We parse the raw logs
						A_					:			int						=						os.path.join(__UpperCamelCase ,       __UpperCamelCase    )
						A_					:			Union[str, Any]						=						zipfile.ZipFile(__UpperCamelCase ,       '''r'''    )
						A_					:			List[str]						=						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    ) ,       12    )
					self.assertEqual(self.get_value_from_key(__UpperCamelCase ,       '''learning_rate''' ,       __UpperCamelCase    ) ,       0.01    )
					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    a_					(							self				:   Any    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as d:
						A_					:			List[Any]						=						CometMLTracker('''test_project_with_config''' ,       __UpperCamelCase    )
						A_					:			List[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_					:			Union[str, Any]						=						os.listdir(__UpperCamelCase    )[0]  # Comet is nice, it's just a zip file here
						# We parse the raw logs
						A_					:			Optional[Any]						=						os.path.join(__UpperCamelCase ,       __UpperCamelCase    )
						A_					:			Dict						=						zipfile.ZipFile(__UpperCamelCase ,       '''r'''    )
						A_					:			Tuple						=						archive.open('''messages.json'''    ).read().decode('''utf-8'''    )
					A_					:			int						=						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     lowercase (      _lowerCamelCase):
				__lowerCAmelCase      :      Dict					    =				[
				    """total_loss""",
				    """iteration""",
				    """my_text""",
				    """learning_rate""",
				    """num_iterations""",
				    """some_boolean""",
				    """some_string""",
				]

				__lowerCAmelCase      :      Optional[Any]					    =				"""my_custom_tracker"""
				__lowerCAmelCase      :      int					    =				False







				def __init__(							self				:   Optional[Any] ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						open(F"""{dir}/log.csv""" ,       '''w+'''    )
					A_					:			Dict						=						csv.DictWriter(self.f ,       fieldnames=self._col_names    )
					self.writer.writeheader()







				@property
				def    a_					(							self				:   int    ):



					"""simple docstring"""

					return self.writer







				def    a_					(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					logger.info('''Call init'''    )
					self.writer.writerow(__UpperCamelCase    )







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Union[str, Any] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					logger.info('''Call log'''    )
					self.writer.writerow(__UpperCamelCase    )







				def    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					self.f.close()

class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as d:
						A_					:			Optional[int]						=						MyCustomTracker(__UpperCamelCase    )
						A_					:			str						=						Accelerator(log_with=__UpperCamelCase    )
						A_					:			Dict						=						{'num_iterations': 12, '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_					:			str						=						csv.DictReader(__UpperCamelCase    )
							A_					:			str						=						next(__UpperCamelCase    )
							A_					:			List[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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					with tempfile.TemporaryDirectory() as d:
						A_					:			Any						=						MyCustomTracker(__UpperCamelCase    )
						A_					:			Optional[Any]						=						Accelerator(log_with=__UpperCamelCase    )
						accelerator.init_trackers('''Some name'''    )
						A_					:			List[str]						=						{'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_					:			List[Any]						=						csv.DictReader(__UpperCamelCase    )
							A_					:			int						=						next(__UpperCamelCase    )
							A_					:			int						=						{
							    'total_loss': '0.1',
							    'iteration': '1',
							    'my_text': 'some_value',
							    'learning_rate': '',
							    'num_iterations': '',
							    'some_boolean': '',
							    'some_string': '',
							}
							self.assertDictEqual(__UpperCamelCase ,       __UpperCamelCase    )











"""simple docstring"""

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						9

	while num > 0:
		if num % 10 != digit:
			return False
		num //= 100
		digit -= 1

	return True

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Dict						=						1_3890_2663

	while not is_square_form(num * num		):
		if num % 10 == 3:
			num -= 6  # (3 - 6) % 10 = 7
		else:
			num -= 4  # (7 - 4) % 10 = 3

	return num * 10


if __name__ == "__main__":
		print(f'{solution() = }')





