


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    _a				(							unittest.TestCase):




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




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




	def 				UpperCAmelCase__(   self		:						Optional[int]						)->			int:
			lowerCAmelCase__  :     Union[str, Any]       	=   '''test_project_with_log'''
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Tuple       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=_SCREAMING_SNAKE_CASE						)
					accelerator.init_trackers(_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     List[Any]       	=   {'''tensor''': torch.tensor(1						)}
					accelerator.log(_SCREAMING_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
					lowerCAmelCase__  :     Tuple       	=   list(filter(lambda _SCREAMING_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(_SCREAMING_SNAKE_CASE						,   '''rb'''						) as f:
							lowerCAmelCase__  :     Tuple       	=   f.read()
					lowerCAmelCase__  :     List[str]       	=   False
					while data:
							lowerCAmelCase__  :     Any       	=   struct.unpack('''Q'''						,   data[:8]						)

							lowerCAmelCase__  :     List[Any]       	=   data[12 : 12 + int(header[0]						)]  # 8+4
							lowerCAmelCase__  :     int       	=   data[12 + int(header[0]						) + 4 :]
							lowerCAmelCase__  :     Dict       	=   event_pba.Event()

							event.ParseFromString(_SCREAMING_SNAKE_CASE						)
							if event.HasField('''summary'''						):
									for value in event.summary.value:
											if value.simple_value == 1.0 and value.tag == "tensor":
													lowerCAmelCase__  :     Optional[int]       	=   True
					self.assertTrue(_SCREAMING_SNAKE_CASE						,   '''Converted tensor was not found in the log file!'''						)




	def 				UpperCAmelCase__(   self		:						int						)->			Tuple:
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE						,   '''Logging with `tensorboard` requires a `logging_dir`'''						):
					lowerCAmelCase__  :     Optional[int]       	=   Accelerator(log_with='''tensorboard'''						)
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     Tuple       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						int						)->			List[Any]:
			lowerCAmelCase__  :     str       	=   ProjectConfiguration(total_limit=30						)
			with tempfile.TemporaryDirectory() as dirpath:
					lowerCAmelCase__  :     int       	=   Accelerator(log_with='''tensorboard'''						,   project_dir=_SCREAMING_SNAKE_CASE						,   project_config=_SCREAMING_SNAKE_CASE						)





@require_wandb
@mock.patch.dict(os.environ	,		{'''WANDB_MODE''': '''offline'''})
class    _a				(							_lowercase	,		_lowercase):




	def 				UpperCAmelCase__(   self		:						Any						)->			Any:
			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__(   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						bool = True						)->			Optional[Any]:
			lowerCAmelCase__  :     List[str]       	=   rF'{section} ([\S\s]*?)\n\n'
			if record:
					lowerCAmelCase__  :     Optional[Any]       	=   rF'Record: {pattern}'
			lowerCAmelCase__  :     int       	=   re.findall(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)[0]
			# A config
			if section == "config" or section == "history":
					lowerCAmelCase__  :     List[str]       	=   re.findall(r'''"([a-zA-Z0-9_.,]+)'''						,   _SCREAMING_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]+)"'''						,   _SCREAMING_SNAKE_CASE						)						)




	@skip
	def 				UpperCAmelCase__(   self		:						int						)->			Dict:
			lowerCAmelCase__  :     Tuple       	=   '''test_project_with_config'''
			lowerCAmelCase__  :     Dict       	=   Accelerator(log_with='''wandb'''						)
			lowerCAmelCase__  :     List[Any]       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
			lowerCAmelCase__  :     Union[str, Any]       	=   {'''wandb''': {'''tags''': ['''my_tag''']}}
			accelerator.init_trackers(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Any       	=   {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
			accelerator.log(_SCREAMING_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":
							lowerCAmelCase__  :     List[str]       	=   subprocess.check_output(
							    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(_SCREAMING_SNAKE_CASE						)]						,   env=os.environ.copy()						).decode('''utf8'''						,   '''ignore'''						)
							break

        # Check HPS through careful parsing and cleaning
			lowerCAmelCase__  :     List[Any]       	=   self.parse_log(_SCREAMING_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
			lowerCAmelCase__  :     Any       	=   self.parse_log(_SCREAMING_SNAKE_CASE						,   '''run'''						,   _SCREAMING_SNAKE_CASE						)
			self.assertEqual(logged_items['''tags''']						,   '''my_tag'''						)

			# Actual logging
			lowerCAmelCase__  :     Any       	=   self.parse_log(_SCREAMING_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 	lowerCamelCase_       (			self      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   run_name
		lowerCAmelCase__  :     Optional[int]       	=   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(_lowercase	,		'''__init__'''	,		_lowercase)
class    _a				(							unittest.TestCase):




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




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





class    _a				(							_lowercase):
	_a    :    Optional[Any]        =				[
	    '''total_loss''',
	    '''iteration''',
	    '''my_text''',
	    '''learning_rate''',
	    '''num_iterations''',
	    '''some_boolean''',
	    '''some_string''',
	]

	_a    :    int        =				'''my_custom_tracker'''
	_a    :    int        =				False




	def __init__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						str						)->			Union[str, Any]:
			lowerCAmelCase__  :     str       	=   open(F'{dir}/log.csv'						,   '''w+'''						)
			lowerCAmelCase__  :     Tuple       	=   csv.DictWriter(self.f						,   fieldnames=self._col_names						)
			self.writer.writeheader()




	@property
	def 				UpperCAmelCase__(   self		:						Dict						)->			Any:
			return self.writer




	def 				UpperCAmelCase__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						dict						)->			Tuple:
			logger.info('''Call init'''						)
			self.writer.writerow(_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						dict						,   _SCREAMING_SNAKE_CASE		:						Optional[int]						)->			Optional[int]:
			logger.info('''Call log'''						)
			self.writer.writerow(_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						str						)->			Dict:
			self.f.close()





class    _a				(							unittest.TestCase):




	def 				UpperCAmelCase__(   self		:						int						)->			Optional[Any]:
			with tempfile.TemporaryDirectory() as d:
					lowerCAmelCase__  :     List[Any]       	=   MyCustomTracker(_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     Optional[int]       	=   Accelerator(log_with=_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     Any       	=   {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
					accelerator.init_trackers('''Some name'''						,   _SCREAMING_SNAKE_CASE						)
					accelerator.end_training()
					with open(F'{d}/log.csv'						,   '''r'''						) as f:
							lowerCAmelCase__  :     Any       	=   csv.DictReader(_SCREAMING_SNAKE_CASE						)
							lowerCAmelCase__  :     List[Any]       	=   next(_SCREAMING_SNAKE_CASE						)
							lowerCAmelCase__  :     List[str]       	=   {
							    '''total_loss''': '''''',
							    '''iteration''': '''''',
							    '''my_text''': '''''',
							    '''learning_rate''': '''0.01''',
							    '''num_iterations''': '''12''',
							    '''some_boolean''': '''False''',
							    '''some_string''': '''some_value''',
							}
							self.assertDictEqual(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)




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








import math






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5      ,  int(math.sqrt(_a			) + 1			)      ,  6			):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 	lowerCamelCase_       (			_a = 1_000      ,  _a = 1_000			):




		"""simple docstring"""






		lowerCAmelCase__  :     Union[str, Any]       	=   [0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1      ,  _a			):
				for b in range(2      ,  _a			):
						if is_prime(_a			):
								lowerCAmelCase__  :     str       	=   0
								lowerCAmelCase__  :     int       	=   0
								while is_prime((n**2) + (a * n) + b			):
										count += 1
										n += 1
								if count > longest[0]:
										lowerCAmelCase__  :     Dict       	=   [count, a, b]
		lowerCAmelCase__  :     int       	=   longest[1] * longest[2]
		return ans


if __name__ == "__main__":
		print(solution(1000, 1000))




