






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



@require_tensorboard
class 						a_ (      unittest.TestCase					):







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



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




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		'test_project_with_log'
       with tempfile.TemporaryDirectory() as dirpath:
        lowerCamelCase__	:     Optional[Any]								=		Accelerator(log_with='tensorboard',	project_dir=lowerCamelCase__   )
        accelerator.init_trackers(lowerCamelCase__   )
        lowerCamelCase__	:     Tuple								=		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
        accelerator.log(lowerCamelCase__,	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 lowerCamelCase_   : x.is_file(),	Path(f'''{dirpath}/{project_name}'''   ).iterdir()   )   )[0]
        self.assertNotEqual(str(lowerCamelCase__   ),	''   )




      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		'test_project_with_log'
       with tempfile.TemporaryDirectory() as dirpath:
        lowerCamelCase__	:     Tuple								=		Accelerator(log_with='tensorboard',	project_dir=lowerCamelCase__   )
        accelerator.init_trackers(lowerCamelCase__   )
        lowerCamelCase__	:     int								=		{'tensor': torch.tensor(1   )}
        accelerator.log(lowerCamelCase__,	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__	:     List[Any]								=		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(lowerCamelCase__,	'rb'   ) as f:
         lowerCamelCase__	:     List[str]								=		f.read()
        lowerCamelCase__	:     Optional[int]								=		False
        while data:
         lowerCamelCase__	:     Any								=		struct.unpack('Q',	data[:8]   )

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

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




      def      a__				(self   ):

       '''simple docstring'''



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





      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		ProjectConfiguration(total_limit=3_0   )
       with tempfile.TemporaryDirectory() as dirpath:
        lowerCamelCase__	:     Optional[Any]								=		Accelerator(log_with='tensorboard',	project_dir=lowerCamelCase__,	project_config=lowerCamelCase__   )



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







      '''simple docstring'''

      def      a__				(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      a__				(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = True   ):

       '''simple docstring'''



       lowerCamelCase__	:     str								=		rf'''{section} ([\S\s]*?)\n\n'''
       if record:
        lowerCamelCase__	:     int								=		rf'''Record: {pattern}'''
       lowerCamelCase__	:     Optional[Any]								=		re.findall(lowerCamelCase__,	lowerCamelCase__   )[0]
       # A config
       if section == "config" or section == "history":
        lowerCamelCase__	:     List[str]								=		re.findall(r'"([a-zA-Z0-9_.,]+)',	lowerCamelCase__   )
        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]+)"',	lowerCamelCase__   )   )





      @skip
      def      a__				(self   ):

       '''simple docstring'''



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

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

       # Actual logging
       lowerCamelCase__	:     Tuple								=		self.parse_log(lowerCamelCase__,	'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     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 lowerCamelCase__	:     str								=		run_name
 lowerCamelCase__	:     Optional[Any]								=		OfflineExperiment(project_name=_lowerCamelCase     ,  offline_directory=_lowerCamelCase							)
 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(__UpperCAmelCase    ,		'__init__'    ,		__UpperCAmelCase					)
class 						a_ (      unittest.TestCase					):







      '''simple docstring'''

      @staticmethod
      def      a__				(lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_ = False   ):

       '''simple docstring'''



       for log in log_list:
        lowerCamelCase__	:     Optional[Any]								=		json.loads(lowerCamelCase__   )['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   ):

       '''simple docstring'''



       with tempfile.TemporaryDirectory() as d:
        lowerCamelCase__	:     Dict								=		CometMLTracker('test_project_with_config',	lowerCamelCase__   )
        lowerCamelCase__	:     List[Any]								=		Accelerator(log_with=lowerCamelCase__   )
        lowerCamelCase__	:     List[Any]								=		{'num_iterations': 1_2, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
        accelerator.init_trackers(lowerCamelCase__,	lowerCamelCase__   )
        accelerator.end_training()
        lowerCamelCase__	:     List[str]								=		os.listdir(lowerCamelCase__   )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        lowerCamelCase__	:     str								=		os.path.join(lowerCamelCase__,	lowerCamelCase__   )
        lowerCamelCase__	:     Dict								=		zipfile.ZipFile(lowerCamelCase__,	'r'   )
        lowerCamelCase__	:     Union[str, Any]								=		archive.open('messages.json'   ).read().decode('utf-8'   )
       lowerCamelCase__	:     Union[str, Any]								=		log.split('\n'   )[:-1]
       self.assertEqual(self.get_value_from_key(lowerCamelCase__,	'num_iterations',	lowerCamelCase__   ),	1_2   )
       self.assertEqual(self.get_value_from_key(lowerCamelCase__,	'learning_rate',	lowerCamelCase__   ),	0.01   )
       self.assertEqual(self.get_value_from_key(lowerCamelCase__,	'some_boolean',	lowerCamelCase__   ),	lowerCamelCase__   )
       self.assertEqual(self.get_value_from_key(lowerCamelCase__,	'some_string',	lowerCamelCase__   ),	'some_value'   )





      def      a__				(self   ):

       '''simple docstring'''



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



class 						a_ (      __UpperCAmelCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Any					=      [
          'total_loss',
          'iteration',
          'my_text',
          'learning_rate',
          'num_iterations',
          'some_boolean',
          'some_string',
      ]

      lowerCamelCase__			:							List[Any]					=      'my_custom_tracker'
      lowerCamelCase__			:							List[str]					=      False




      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Any								=		open(f'''{dir}/log.csv''',	'w+'   )
       lowerCamelCase__	:     Optional[int]								=		csv.DictWriter(self.f,	fieldnames=self._col_names   )
       self.writer.writeheader()




      @property
      def      a__				(self   ):

       '''simple docstring'''



       return self.writer




      def      a__				(self,	lowerCamelCase_   ):

       '''simple docstring'''



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




      def      a__				(self,	lowerCamelCase_,	lowerCamelCase_   ):

       '''simple docstring'''



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





      def      a__				(self   ):

       '''simple docstring'''



       self.f.close()



class 						a_ (      unittest.TestCase					):







      '''simple docstring'''

      def      a__				(self   ):

       '''simple docstring'''



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





      def      a__				(self   ):

       '''simple docstring'''



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


"""simple docstring"""






from __future__ import annotations



def     a_		(   lowerCamelCase			):
				for r in range(1   ,     lowerCamelCase			):
								UpperCAmelCase__			 =				[]
								for x in range(n - 1			):
												UpperCAmelCase__			 =				pow(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			)
												if val in li:
																break
												li.append(lowerCamelCase			)
								else:
												return r
				return None


if __name__ == "__main__":
						lowerCAmelCase__    :    Any			=      int(input('Enter a prime number q: '))
						lowerCAmelCase__    :    List[str]			=      find_primitive(q)
						if a is None:
												print(F"""Cannot find the primitive for the value: {a!r}""")
						else:
												lowerCAmelCase__    :    str			=      int(input('Enter private key of A: '))
												lowerCAmelCase__    :    List[Any]			=      pow(a, a_private, q)
												lowerCAmelCase__    :    List[str]			=      int(input('Enter private key of B: '))
												lowerCAmelCase__    :    Any			=      pow(a, b_private, q)

												lowerCAmelCase__    :    Union[str, Any]			=      pow(b_public, a_private, q)
												lowerCAmelCase__    :    Tuple			=      pow(a_public, b_private, q)

												print('The key value generated by A is: ', a_secret)
												print('The key value generated by B is: ', b_secret)



