






"""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

lowercase_								=	logging.getLogger(__name__)


@require_tensorboard
class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					'''test_project_with_config'''
     with tempfile.TemporaryDirectory() as dirpath:
       __A		     =					Accelerator(log_with='''tensorboard''',			project_dir=_lowerCamelCase    )
       __A		     =					{'''num_iterations''': 12, '''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('''*/**'''    ):
         __A		     =					list(filter(lambda _lowerCamelCase    : x.is_file(),			child.iterdir()    )    )[0]
       self.assertNotEqual(str(_lowerCamelCase    ),			''''''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

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

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


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

     '''simple docstring'''
     with self.assertRaisesRegex(_lowerCamelCase,			'''Logging with `tensorboard` requires a `logging_dir`'''    ):
       __A		     =					Accelerator(log_with='''tensorboard'''    )
     with tempfile.TemporaryDirectory() as dirpath:
       __A		     =					Accelerator(log_with='''tensorboard''',			project_dir=_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					ProjectConfiguration(total_limit=30    )
     with tempfile.TemporaryDirectory() as dirpath:
       __A		     =					Accelerator(log_with='''tensorboard''',			project_dir=_lowerCamelCase,			project_config=_lowerCamelCase    )





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


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	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 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	List[str] = True    ):

     '''simple docstring'''
     __A		     =					Rf'{section} ([\S\s]*?)\n\n'
     if record:
       __A		     =					Rf'Record: {pattern}'
     __A		     =					re.findall(_lowerCamelCase,			_lowerCamelCase    )[0]
     # A config
     if section == "config" or section == "history":
       __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					'''test_project_with_config'''
     __A		     =					Accelerator(log_with='''wandb'''    )
     __A		     =					{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
     __A		     =					{'''wandb''': {'''tags''': ['''my_tag''']}}
     accelerator.init_trackers(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )
     __A		     =					{'''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":
         __A		     =					subprocess.check_output(
             ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(_lowerCamelCase    )],			env=os.environ.copy()    ).decode('''utf8''',			'''ignore'''    )
         break

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

     # Actual logging
     __A		     =					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						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					run_name
  __A		     =					OfflineExperiment(project_name=__UpperCamelCase						,			offline_directory=__UpperCamelCase					)
  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    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   @staticmethod
   def 							_SCREAMING_SNAKE_CASE							(      _lowerCamelCase					:	List[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[int] = False    ):

     '''simple docstring'''
     for log in log_list:
       __A		     =					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 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

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

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


   '''simple docstring'''

   A_		:      Optional[Any]							=					[
       """total_loss""",
       """iteration""",
       """my_text""",
       """learning_rate""",
       """num_iterations""",
       """some_boolean""",
       """some_string""",
   ]

   A_		:      Any							=					"""my_custom_tracker"""
   A_		:      Union[str, Any]							=					False


   def __init__(      self					:	Optional[int],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					open(f'{dir}/log.csv',			'''w+'''    )
     __A		     =					csv.DictWriter(self.f,			fieldnames=self._col_names    )
     self.writer.writeheader()


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

     '''simple docstring'''
     return self.writer


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     logger.info('''Call init'''    )
     self.writer.writerow(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     logger.info('''Call log'''    )
     self.writer.writerow(_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     self.f.close()





class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

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






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

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









'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()