
def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Tuple	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						assert b != 0, "This cannot accept modulo that is == 0"
						if n == 0:
												return 1

						if n % 2 == 1:
												return (bin_exp_mod(snake_case	, n - 1	, snake_case	) * a) % b

						__UpperCamelCase       					=     bin_exp_mod(snake_case	, n / 2	, snake_case	)
						return (r * r) % b


if __name__ == "__main__":
				try:
								lowercase__							:							Dict													=     int(input("Enter Base : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Power : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Modulo : ").strip())
				except ValueError:
								print("Invalid literal for integer")

				print(bin_exp_mod(BASE, POWER, MODULO))






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

lowercase__							:							Optional[int]													=     logging.getLogger(__name__)






@require_tensorboard
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_config'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''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('''*/**'''     ):
																									__UpperCamelCase       					=     list(filter(lambda SCREAMING_SNAKE_CASE_     : x.is_file()       ,		child.iterdir()     )     )[0]
																			self.assertNotEqual(str(SCREAMING_SNAKE_CASE_     )       ,		''''''     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






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



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     '''test_project_with_log'''
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     Accelerator(log_with='''tensorboard'''       ,		project_dir=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''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
																			__UpperCamelCase       					=     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:
																									__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(SCREAMING_SNAKE_CASE_     )
																									if event.HasField('''summary'''     ):
																															for value in event.summary.value:
																																					if value.simple_value == 1.0 and value.tag == "tensor":
																																											__UpperCamelCase       					=     True
																			self.assertTrue(SCREAMING_SNAKE_CASE_       ,		'''Converted tensor was not found in the log file!'''     )



							def       A__						(					self     )->				Any:





													'''simple docstring'''






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



							def       A__						(					self     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     ProjectConfiguration(total_limit=30     )
													with tempfile.TemporaryDirectory() as dirpath:
																			__UpperCamelCase       					=     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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							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__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = True     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     rF"{section} ([\S\s]*?)\n\n"
													if record:
																			__UpperCamelCase       					=     rF"Record: {pattern}"
													__UpperCamelCase       					=     re.findall(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )[0]
													# A config
													if section == "config" or section == "history":
																			__UpperCamelCase       					=     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       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__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(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {'''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":
																									__UpperCamelCase       					=     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
													__UpperCamelCase       					=     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
													__UpperCamelCase       					=     self.parse_log(SCREAMING_SNAKE_CASE_       ,		'''run'''       ,		SCREAMING_SNAKE_CASE_     )
													self.assertEqual(logged_items['''tags''']       ,		'''my_tag'''     )

													# Actual logging
													__UpperCamelCase       					=     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 		A_	(							self							:				str	, snake_case							:				str	, snake_case							:				str	) ->      Any:



						'''simple docstring'''

						__UpperCamelCase       					=     run_name
						__UpperCamelCase       					=     OfflineExperiment(project_name=snake_case	, offline_directory=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(SCREAMING_SNAKE_CASE_				,						'__init__'				,						SCREAMING_SNAKE_CASE_      )
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							@staticmethod
							def       A__						(					SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[str]:





													'''simple docstring'''






													for log in log_list:
																			__UpperCamelCase       					=     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       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''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()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     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_     )       ,		0.0_1     )
													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       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     CometMLTracker('''test_project_with_config'''       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
																			accelerator.log(SCREAMING_SNAKE_CASE_       ,		step=0     )
																			accelerator.end_training()
																			__UpperCamelCase       					=     os.listdir(SCREAMING_SNAKE_CASE_     )[0]  # Comet is nice, it's just a zip file here
																			# We parse the raw logs
																			__UpperCamelCase       					=     os.path.join(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     zipfile.ZipFile(SCREAMING_SNAKE_CASE_       ,		'''r'''     )
																			__UpperCamelCase       					=     archive.open('''messages.json'''     ).read().decode('''utf-8'''     )
													__UpperCamelCase       					=     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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					[
							    'total_loss',
							    'iteration',
							    'my_text',
							    'learning_rate',
							    'num_iterations',
							    'some_boolean',
							    'some_string',
							]

							_snake_case       						=					'my_custom_tracker'
							_snake_case       						=					False



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     open(F"{dir}/log.csv"       ,		'''w+'''     )
													__UpperCamelCase       					=     csv.DictWriter(self.f       ,		fieldnames=self._col_names     )
													self.writer.writeheader()



							@property
							def       A__						(					self     )->				Tuple:





													'''simple docstring'''






													return self.writer



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				Optional[int]:





													'''simple docstring'''






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



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													self.f.close()






class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     {'''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:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''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       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													with tempfile.TemporaryDirectory() as d:
																			__UpperCamelCase       					=     MyCustomTracker(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     Accelerator(log_with=SCREAMING_SNAKE_CASE_     )
																			accelerator.init_trackers('''Some name'''     )
																			__UpperCamelCase       					=     {'''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:
																									__UpperCamelCase       					=     csv.DictReader(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     next(SCREAMING_SNAKE_CASE_     )
																									__UpperCamelCase       					=     {
																									    '''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_     )




