



"""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

lowerCamelCase				:     Tuple								=  logging.getLogger(__name__)




@require_tensorboard
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''test_project_with_config'''
      with tempfile.TemporaryDirectory() as dirpath:
        _SCREAMING_SNAKE_CASE        =			Accelerator(log_with='''tensorboard'''    ,							project_dir=A_			)
        _SCREAMING_SNAKE_CASE        =			{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
        accelerator.init_trackers(A_    ,							A_			)
        accelerator.end_training()
        for child in Path(F'''{dirpath}/{project_name}'''			).glob('''*/**'''			):
          _SCREAMING_SNAKE_CASE        =			list(filter(lambda A_			: x.is_file()    ,							child.iterdir()			)			)[0]
        self.assertNotEqual(str(A_			)    ,							''''''			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
        _SCREAMING_SNAKE_CASE        =			Accelerator(log_with='''tensorboard'''    ,							project_dir=A_			)
        accelerator.init_trackers(A_			)
        _SCREAMING_SNAKE_CASE        =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
        accelerator.log(A_    ,							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
        _SCREAMING_SNAKE_CASE        =			list(filter(lambda A_			: x.is_file()    ,							Path(F'''{dirpath}/{project_name}'''			).iterdir()			)			)[0]
        self.assertNotEqual(str(A_			)    ,							''''''			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
        _SCREAMING_SNAKE_CASE        =			Accelerator(log_with='''tensorboard'''    ,							project_dir=A_			)
        accelerator.init_trackers(A_			)
        _SCREAMING_SNAKE_CASE        =			{'''tensor''': torch.tensor(1			)}
        accelerator.log(A_    ,							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
        _SCREAMING_SNAKE_CASE        =			list(filter(lambda A_			: 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(A_    ,							'''rb'''			) as f:
          _SCREAMING_SNAKE_CASE        =			f.read()
        _SCREAMING_SNAKE_CASE        =			False
        while data:
          _SCREAMING_SNAKE_CASE        =			struct.unpack('''Q'''    ,							data[:8]			)

          _SCREAMING_SNAKE_CASE        =			data[12 : 12 + int(header[0]			)]  # 8+4
          _SCREAMING_SNAKE_CASE        =			data[12 + int(header[0]			) + 4 :]
          _SCREAMING_SNAKE_CASE        =			event_pba.Event()

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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



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




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




    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   (   A_    ,							A_    ,							A_ = True			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			rF'''{section} ([\S\s]*?)\n\n'''
      if record:
        _SCREAMING_SNAKE_CASE        =			rF'''Record: {pattern}'''
      _SCREAMING_SNAKE_CASE        =			re.findall(A_    ,							A_			)[0]
      # A config
      if section == "config" or section == "history":
        _SCREAMING_SNAKE_CASE        =			re.findall(r'''"([a-zA-Z0-9_.,]+)'''    ,							A_			)
        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]+)"'''    ,							A_			)			)




    @skip
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''test_project_with_config'''
      _SCREAMING_SNAKE_CASE        =			Accelerator(log_with='''wandb'''			)
      _SCREAMING_SNAKE_CASE        =			{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
      _SCREAMING_SNAKE_CASE        =			{'''wandb''': {'''tags''': ['''my_tag''']}}
      accelerator.init_trackers(A_    ,							A_    ,							A_			)
      _SCREAMING_SNAKE_CASE        =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
      accelerator.log(A_    ,							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":
          _SCREAMING_SNAKE_CASE        =			subprocess.check_output(
              ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(A_			)]    ,							env=os.environ.copy()			).decode('''utf8'''    ,							'''ignore'''			)
          break

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

      # Actual logging
      _SCREAMING_SNAKE_CASE        =			self.parse_log(A_    ,							'''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       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			run_name
  _SCREAMING_SNAKE_CASE        =			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(__A			,			'''__init__'''			,			__A       )
class    __snake_case(					unittest.TestCase       ):




    @staticmethod
    def   A   (   A_    ,							A_    ,							A_ = False			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



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




class    __snake_case(					__A       ):
    _A       = [
        '''total_loss''',
        '''iteration''',
        '''my_text''',
        '''learning_rate''',
        '''num_iterations''',
        '''some_boolean''',
        '''some_string''',
    ]

    _A       = '''my_custom_tracker'''
    _A       = False




    def __init__(   self    ,							A_			):





      '''simple docstring'''



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




    @property
    def   A   (   self			):





      '''simple docstring'''



      return self.writer




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      self.f.close()




class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      with tempfile.TemporaryDirectory() as d:
        _SCREAMING_SNAKE_CASE        =			MyCustomTracker(A_			)
        _SCREAMING_SNAKE_CASE        =			Accelerator(log_with=A_			)
        _SCREAMING_SNAKE_CASE        =			{'''num_iterations''': 12, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
        accelerator.init_trackers('''Some name'''    ,							A_			)
        accelerator.end_training()
        with open(F'''{d}/log.csv'''    ,							'''r'''			) as f:
          _SCREAMING_SNAKE_CASE        =			csv.DictReader(A_			)
          _SCREAMING_SNAKE_CASE        =			next(A_			)
          _SCREAMING_SNAKE_CASE        =			{
              '''total_loss''': '''''',
              '''iteration''': '''''',
              '''my_text''': '''''',
              '''learning_rate''': '''0.01''',
              '''num_iterations''': '''12''',
              '''some_boolean''': '''False''',
              '''some_string''': '''some_value''',
          }
          self.assertDictEqual(A_    ,							A_			)




    def   A   (   self			):





      '''simple docstring'''



      with tempfile.TemporaryDirectory() as d:
        _SCREAMING_SNAKE_CASE        =			MyCustomTracker(A_			)
        _SCREAMING_SNAKE_CASE        =			Accelerator(log_with=A_			)
        accelerator.init_trackers('''Some name'''			)
        _SCREAMING_SNAKE_CASE        =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
        accelerator.log(A_    ,							step=0			)
        accelerator.end_training()
        with open(F'''{d}/log.csv'''    ,							'''r'''			) as f:
          _SCREAMING_SNAKE_CASE        =			csv.DictReader(A_			)
          _SCREAMING_SNAKE_CASE        =			next(A_			)
          _SCREAMING_SNAKE_CASE        =			{
              '''total_loss''': '''0.1''',
              '''iteration''': '''1''',
              '''my_text''': '''some_value''',
              '''learning_rate''': '''''',
              '''num_iterations''': '''''',
              '''some_boolean''': '''''',
              '''some_string''': '''''',
          }
          self.assertDictEqual(A_    ,							A_			)





"""simple docstring"""






import math


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


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


if __name__ == "__main__":
    import doctest

    doctest.testmod()
