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		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__	      =   'test_project_with_config'
        with tempfile.TemporaryDirectory() as dirpath:
               lowerCAmelCase__	      =   Accelerator(log_with='tensorboard'     ,       project_dir=lowercase__)
               lowerCAmelCase__	      =   {'num_iterations': 12, '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__	      =   list(filter(lambda lowercase__: x.is_file()     ,       child.iterdir()))[0]
               self.assertNotEqual(str(lowercase__)     ,       '')




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'test_project_with_log'
        with tempfile.TemporaryDirectory() as dirpath:
               lowerCAmelCase__	      =   Accelerator(log_with='tensorboard'     ,       project_dir=lowercase__)
               accelerator.init_trackers(lowercase__)
               lowerCAmelCase__	      =   {'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__	      =   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__	      =   f.read()
               lowerCAmelCase__	      =   False
               while data:
                      lowerCAmelCase__	      =   struct.unpack('Q'     ,       data[:8])

                      lowerCAmelCase__	      =   data[12 : 12 + int(header[0])]  # 8+4
                      lowerCAmelCase__	      =   data[12 + int(header[0]) + 4 :]
                      lowerCAmelCase__	      =   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__	      =   True
               self.assertTrue(lowercase__     ,       'Converted tensor was not found in the log file!')




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   ProjectConfiguration(total_limit=30)
        with tempfile.TemporaryDirectory() as dirpath:
               lowerCAmelCase__	      =   Accelerator(log_with='tensorboard'     ,       project_dir=lowercase__     ,       project_config=lowercase__)






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






 '''simple docstring'''






 def 						__snake_case		(     self   :    Tuple):







        '''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 						__snake_case		(     lowercase__   :    str     ,       lowercase__   :    str     ,       lowercase__   :    bool = True):







        '''simple docstring'''



        lowerCAmelCase__	      =   rF"""{section} ([\S\s]*?)\n\n"""
        if record:
               lowerCAmelCase__	      =   rF"""Record: {pattern}"""
        lowerCAmelCase__	      =   re.findall(lowercase__     ,       lowercase__)[0]
        # A config
        if section == "config" or section == "history":
               lowerCAmelCase__	      =   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 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   'test_project_with_config'
        lowerCAmelCase__	      =   Accelerator(log_with='wandb')
        lowerCAmelCase__	      =   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
        lowerCAmelCase__	      =   {'wandb': {'tags': ['my_tag']}}
        accelerator.init_trackers(lowercase__     ,       lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   {'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__	      =   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__	      =   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__	      =   self.parse_log(lowercase__     ,       'run'     ,       lowercase__)
        self.assertEqual(logged_items['tags']     ,       'my_tag')

        # Actual logging
        lowerCAmelCase__	      =   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  __lowerCamelCase						(		self      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   run_name
       lowerCAmelCase__	      =   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(_snake_case			,	'__init__'			,	_snake_case		)
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 @staticmethod
 def 						__snake_case		(     lowercase__   :    List[Any]     ,       lowercase__   :    str     ,       lowercase__   :    bool = False):







        '''simple docstring'''



        for log in log_list:
               lowerCAmelCase__	      =   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 						__snake_case		(     self   :    int):







        '''simple docstring'''



        with tempfile.TemporaryDirectory() as d:
               lowerCAmelCase__	      =   CometMLTracker('test_project_with_config'     ,       lowercase__)
               lowerCAmelCase__	      =   Accelerator(log_with=lowercase__)
               lowerCAmelCase__	      =   {'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
               accelerator.init_trackers(lowercase__     ,       lowercase__)
               accelerator.end_training()
               lowerCAmelCase__	      =   os.listdir(lowercase__)[0]  # Comet is nice, it's just a zip file here
               # We parse the raw logs
               lowerCAmelCase__	      =   os.path.join(lowercase__     ,       lowercase__)
               lowerCAmelCase__	      =   zipfile.ZipFile(lowercase__     ,       'r')
               lowerCAmelCase__	      =   archive.open('messages.json').read().decode('utf-8')
        lowerCAmelCase__	      =   log.split('\n')[:-1]
        self.assertEqual(self.get_value_from_key(lowercase__     ,       'num_iterations'     ,       lowercase__)     ,       12)
        self.assertEqual(self.get_value_from_key(lowercase__     ,       'learning_rate'     ,       lowercase__)     ,       0.01)
        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 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



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






 '''simple docstring'''






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

 UpperCAmelCase_      					=   'my_custom_tracker'
 UpperCAmelCase_      					=   False




 def __init__(     self   :    Any     ,       lowercase__   :    str):







        '''simple docstring'''



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




 @property
 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        return self.writer




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    dict):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    dict     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        self.f.close()






class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        with tempfile.TemporaryDirectory() as d:
               lowerCAmelCase__	      =   MyCustomTracker(lowercase__)
               lowerCAmelCase__	      =   Accelerator(log_with=lowercase__)
               lowerCAmelCase__	      =   {'num_iterations': 12, '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__	      =   csv.DictReader(lowercase__)
                      lowerCAmelCase__	      =   next(lowercase__)
                      lowerCAmelCase__	      =   {
                          'total_loss': '',
                          'iteration': '',
                          'my_text': '',
                          'learning_rate': '0.01',
                          'num_iterations': '12',
                          'some_boolean': 'False',
                          'some_string': 'some_value',
                      }
                      self.assertDictEqual(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        with tempfile.TemporaryDirectory() as d:
               lowerCAmelCase__	      =   MyCustomTracker(lowercase__)
               lowerCAmelCase__	      =   Accelerator(log_with=lowercase__)
               accelerator.init_trackers('Some name')
               lowerCAmelCase__	      =   {'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__	      =   csv.DictReader(lowercase__)
                      lowerCAmelCase__	      =   next(lowercase__)
                      lowerCAmelCase__	      =   {
                          'total_loss': '0.1',
                          'iteration': '1',
                          'my_text': 'some_value',
                          'learning_rate': '',
                          'num_iterations': '',
                          'some_boolean': '',
                          'some_string': '',
                      }
                      self.assertDictEqual(lowercase__     ,       lowercase__)



'''simple docstring'''







from __future__ import annotations

import math
import random






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     d * e - 1
	_lowercase							=     0
	_lowercase							=     0
	while p == 0:
		_lowercase							=     random.randint(2							,       n - 1	)
		_lowercase							=     k
		while True:
			if t % 2 == 0:
				_lowercase							=     t // 2
				_lowercase							=     (g**t) % n
				_lowercase							=     math.gcd(x - 1							,       snake_case_	)
				if x > 1 and y > 1:
					_lowercase							=     y
					_lowercase							=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




