





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

snake_case_				=      logging.getLogger(__name__)
@require_tensorboard
class 							snake_case_		(      unittest.TestCase):







		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Optional[Any]    		='''test_project_with_config'''
				with tempfile.TemporaryDirectory() as dirpath:
						lowerCamelCase :			List[str]    		=Accelerator(log_with='''tensorboard'''      ,  project_dir=__lowercase )
						lowerCamelCase :			str    		={'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
						accelerator.init_trackers(__lowercase      ,  __lowercase )
						accelerator.end_training()
						for child in Path(F"{dirpath}/{project_name}" ).glob('''*/**''' ):
								lowerCamelCase :			Union[str, Any]    		=list(filter(lambda __lowercase : x.is_file()      ,  child.iterdir() ) )[0]
						self.assertNotEqual(str(__lowercase )      ,  '''''' )







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







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

								lowerCamelCase :			int    		=data[1_2 : 1_2 + int(header[0] )]  # 8+4
								lowerCamelCase :			Optional[Any]    		=data[1_2 + int(header[0] ) + 4 :]
								lowerCamelCase :			Union[str, Any]    		=event_pba.Event()

								event.ParseFromString(__lowercase )
								if event.HasField('''summary''' ):
										for value in event.summary.value:
												if value.simple_value == 1.0 and value.tag == "tensor":
														lowerCamelCase :			Tuple    		=True
						self.assertTrue(__lowercase      ,  '''Converted tensor was not found in the log file!''' )







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







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Tuple    		=ProjectConfiguration(total_limit=3_0 )
				with tempfile.TemporaryDirectory() as dirpath:
						lowerCamelCase :			Optional[int]    		=Accelerator(log_with='''tensorboard'''      ,  project_dir=__lowercase      ,  project_config=__lowercase )

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







		def       __lowercase (		self )					-> 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       __lowercase (		__lowercase      ,  __lowercase      ,  __lowercase = True )					-> str:
				lowerCamelCase :			int    		=RF"{section} ([\S\s]*?)\n\n"
				if record:
						lowerCamelCase :			List[str]    		=RF"Record: {pattern}"
				lowerCamelCase :			Dict    		=re.findall(__lowercase      ,  __lowercase )[0]
				# A config
				if section == "config" or section == "history":
						lowerCamelCase :			List[Any]    		=re.findall(R'''"([a-zA-Z0-9_.,]+)'''      ,  __lowercase )
						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]+)"'''      ,  __lowercase ) )







		@skip
		def       __lowercase (		self )					-> int:
				lowerCamelCase :			str    		='''test_project_with_config'''
				lowerCamelCase :			List[Any]    		=Accelerator(log_with='''wandb''' )
				lowerCamelCase :			Tuple    		={'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
				lowerCamelCase :			Union[str, Any]    		={'''wandb''': {'''tags''': ['''my_tag''']}}
				accelerator.init_trackers(__lowercase      ,  __lowercase      ,  __lowercase )
				lowerCamelCase :			Optional[int]    		={'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
				accelerator.log(__lowercase      ,  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 :			Optional[Any]    		=subprocess.check_output(
								    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(__lowercase )]      ,  env=os.environ.copy() ).decode('''utf8'''      ,  '''ignore''' )
								break

        # Check HPS through careful parsing and cleaning
				lowerCamelCase :			Any    		=self.parse_log(__lowercase      ,  '''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 :			Tuple    		=self.parse_log(__lowercase      ,  '''run'''      ,  __lowercase )
				self.assertEqual(logged_items['''tags''']      ,  '''my_tag''' )

				# Actual logging
				lowerCamelCase :			Tuple    		=self.parse_log(__lowercase      ,  '''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				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		lowerCamelCase :			str    		=run_name
		lowerCamelCase :			int    		=OfflineExperiment(project_name=SCREAMING_SNAKE_CASE_				,							offline_directory=SCREAMING_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(_A    ,       "__init__"    ,       _A)
class 							snake_case_		(      unittest.TestCase):







		@staticmethod
		def       __lowercase (		__lowercase      ,  __lowercase      ,  __lowercase = False )					-> Any:
				for log in log_list:
						lowerCamelCase :			Union[str, Any]    		=json.loads(__lowercase )['''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       __lowercase (		self )					-> List[str]:
				with tempfile.TemporaryDirectory() as d:
						lowerCamelCase :			List[Any]    		=CometMLTracker('''test_project_with_config'''      ,  __lowercase )
						lowerCamelCase :			List[str]    		=Accelerator(log_with=__lowercase )
						lowerCamelCase :			List[Any]    		={'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
						accelerator.init_trackers(__lowercase      ,  __lowercase )
						accelerator.end_training()
						lowerCamelCase :			List[Any]    		=os.listdir(__lowercase )[0]  # Comet is nice, it's just a zip file here
						# We parse the raw logs
						lowerCamelCase :			int    		=os.path.join(__lowercase      ,  __lowercase )
						lowerCamelCase :			Any    		=zipfile.ZipFile(__lowercase      ,  '''r''' )
						lowerCamelCase :			int    		=archive.open('''messages.json''' ).read().decode('''utf-8''' )
				lowerCamelCase :			Optional[int]    		=log.split('''\n''' )[:-1]
				self.assertEqual(self.get_value_from_key(__lowercase      ,  '''num_iterations'''      ,  __lowercase )      ,  1_2 )
				self.assertEqual(self.get_value_from_key(__lowercase      ,  '''learning_rate'''      ,  __lowercase )      ,  0.0_1 )
				self.assertEqual(self.get_value_from_key(__lowercase      ,  '''some_boolean'''      ,  __lowercase )      ,  __lowercase )
				self.assertEqual(self.get_value_from_key(__lowercase      ,  '''some_string'''      ,  __lowercase )      ,  '''some_value''' )







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

class 							snake_case_		(      _A):
		lowerCamelCase     :Optional[Any]							=  [
		    "total_loss",
		    "iteration",
		    "my_text",
		    "learning_rate",
		    "num_iterations",
		    "some_boolean",
		    "some_string",
		]

		lowerCamelCase     :Tuple							=  "my_custom_tracker"
		lowerCamelCase     :int							=  False







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







		@property
		def       __lowercase (		self )					-> Tuple:
				return self.writer







		def       __lowercase (		self      ,  __lowercase )					-> Optional[int]:
				logger.info('''Call init''' )
				self.writer.writerow(__lowercase )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> Any:
				logger.info('''Call log''' )
				self.writer.writerow(__lowercase )







		def       __lowercase (		self )					-> Optional[Any]:
				self.f.close()

class 							snake_case_		(      unittest.TestCase):







		def       __lowercase (		self )					-> Optional[int]:
				with tempfile.TemporaryDirectory() as d:
						lowerCamelCase :			int    		=MyCustomTracker(__lowercase )
						lowerCamelCase :			Dict    		=Accelerator(log_with=__lowercase )
						lowerCamelCase :			Optional[int]    		={'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
						accelerator.init_trackers('''Some name'''      ,  __lowercase )
						accelerator.end_training()
						with open(F"{d}/log.csv"      ,  '''r''' ) as f:
								lowerCamelCase :			Any    		=csv.DictReader(__lowercase )
								lowerCamelCase :			Any    		=next(__lowercase )
								lowerCamelCase :			List[str]    		={
								    '''total_loss''': '''''',
								    '''iteration''': '''''',
								    '''my_text''': '''''',
								    '''learning_rate''': '''0.01''',
								    '''num_iterations''': '''12''',
								    '''some_boolean''': '''False''',
								    '''some_string''': '''some_value''',
								}
								self.assertDictEqual(__lowercase      ,  __lowercase )







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








def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

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

		lowerCamelCase :			Tuple    		=bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n / 2				,							SCREAMING_SNAKE_CASE_		)
		return (r * r) % b


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

				print(bin_exp_mod(BASE, POWER, MODULO))

