




'''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_ 			=		logging.getLogger(__name__)






@require_tensorboard
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				Optional[Any] ):
           snake_case__					:      int  			=						'test_project_with_config'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[str]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 snake_case__					:      Dict  			=						{'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('*/**' ):
                       snake_case__					:      List[Any]  			=						list(filter(lambda __lowerCamelCase : x.is_file()    ,       child.iterdir() ) )[0]
                 self.assertNotEqual(str(__lowerCamelCase )    ,       '' )





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      Optional[Any]  			=						'test_project_with_log'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      Union[str, Any]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      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
                 snake_case__					:      Any  			=						list(filter(lambda __lowerCamelCase : x.is_file()    ,       Path(F"{dirpath}/{project_name}" ).iterdir() ) )[0]
                 self.assertNotEqual(str(__lowerCamelCase )    ,       '' )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      str  			=						'test_project_with_log'
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      Optional[int]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      Union[str, Any]  			=						{'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
                 snake_case__					:      Tuple  			=						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:
                       snake_case__					:      List[str]  			=						f.read()
                 snake_case__					:      Optional[Any]  			=						False
                 while data:
                       snake_case__					:      Dict  			=						struct.unpack('Q'    ,       data[:8] )

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





     def  _lowerCAmelCase			(    self    :				Dict ):
           with self.assertRaisesRegex(__lowerCamelCase    ,       'Logging with `tensorboard` requires a `logging_dir`' ):
                 snake_case__					:      Any  			=						Accelerator(log_with='tensorboard' )
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[Any]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      List[str]  			=						ProjectConfiguration(total_limit=30 )
           with tempfile.TemporaryDirectory() as dirpath:
                 snake_case__					:      List[str]  			=						Accelerator(log_with='tensorboard'    ,       project_dir=__lowerCamelCase    ,       project_config=__lowerCamelCase )






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





     def  _lowerCAmelCase			(    self    :				Optional[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  _lowerCAmelCase			(    __lowerCamelCase    :				str    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				bool = True ):
           snake_case__					:      Dict  			=						rF"{section} ([\S\s]*?)\n\n"
           if record:
                 snake_case__					:      str  			=						rF"Record: {pattern}"
           snake_case__					:      str  			=						re.findall(__lowerCamelCase    ,       __lowerCamelCase )[0]
           # A config
           if section == "config" or section == "history":
                 snake_case__					:      int  			=						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  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      Any  			=						'test_project_with_config'
           snake_case__					:      List[str]  			=						Accelerator(log_with='wandb' )
           snake_case__					:      List[str]  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
           snake_case__					:      Dict  			=						{'wandb': {'tags': ['my_tag']}}
           accelerator.init_trackers(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase )
           snake_case__					:      List[Any]  			=						{'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":
                       snake_case__					:      Dict  			=						subprocess.check_output(
                           ['wandb', 'sync', '--view', '--verbose', str(__lowerCamelCase )]    ,       env=os.environ.copy() ).decode('utf8'    ,       'ignore' )
                       break

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

           # Actual logging
           snake_case__					:      Union[str, Any]  			=						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 		UpperCamelCase__			(		self							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      List[Any]  			=						run_name
      snake_case__					:      Optional[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(lowerCAmelCase_	,					"__init__"	,					lowerCAmelCase_       )
class 					lowercase_			(	unittest.TestCase       ):





     @staticmethod
     def  _lowerCAmelCase			(    __lowerCamelCase    :				List[str]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				bool = False ):
           for log in log_list:
                 snake_case__					:      List[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  _lowerCAmelCase			(    self    :				str ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      List[str]  			=						CometMLTracker('test_project_with_config'    ,       __lowerCamelCase )
                 snake_case__					:      Tuple  			=						Accelerator(log_with=__lowerCamelCase )
                 snake_case__					:      Optional[int]  			=						{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowerCamelCase    ,       __lowerCamelCase )
                 accelerator.end_training()
                 snake_case__					:      Any  			=						os.listdir(__lowerCamelCase )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case__					:      Optional[int]  			=						os.path.join(__lowerCamelCase    ,       __lowerCamelCase )
                 snake_case__					:      str  			=						zipfile.ZipFile(__lowerCamelCase    ,       'r' )
                 snake_case__					:      Any  			=						archive.open('messages.json' ).read().decode('utf-8' )
           snake_case__					:      Optional[int]  			=						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 )    ,       0.0_1 )
           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  _lowerCAmelCase			(    self    :				Tuple ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      List[str]  			=						CometMLTracker('test_project_with_config'    ,       __lowerCamelCase )
                 snake_case__					:      Optional[Any]  			=						Accelerator(log_with=__lowerCamelCase )
                 accelerator.init_trackers(__lowerCamelCase )
                 snake_case__					:      List[Any]  			=						{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowerCamelCase    ,       step=0 )
                 accelerator.end_training()
                 snake_case__					:      Union[str, Any]  			=						os.listdir(__lowerCamelCase )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 snake_case__					:      Tuple  			=						os.path.join(__lowerCamelCase    ,       __lowerCamelCase )
                 snake_case__					:      List[str]  			=						zipfile.ZipFile(__lowerCamelCase    ,       'r' )
                 snake_case__					:      Optional[Any]  			=						archive.open('messages.json' ).read().decode('utf-8' )
           snake_case__					:      Optional[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 					lowercase_			(	lowerCAmelCase_       ):
     A_       							= [
         "total_loss",
         "iteration",
         "my_text",
         "learning_rate",
         "num_iterations",
         "some_boolean",
         "some_string",
     ]

     A_       							= "my_custom_tracker"
     A_       							= False





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





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.writer





     def  _lowerCAmelCase			(    self    :				Optional[int]    ,       __lowerCamelCase    :				dict ):
           logger.info('Call init' )
           self.writer.writerow(__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				dict    ,       __lowerCamelCase    :				Optional[int] ):
           logger.info('Call log' )
           self.writer.writerow(__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           self.f.close()






class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      Tuple  			=						MyCustomTracker(__lowerCamelCase )
                 snake_case__					:      List[str]  			=						Accelerator(log_with=__lowerCamelCase )
                 snake_case__					:      List[Any]  			=						{'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:
                       snake_case__					:      Optional[int]  			=						csv.DictReader(__lowerCamelCase )
                       snake_case__					:      Tuple  			=						next(__lowerCamelCase )
                       snake_case__					:      List[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  _lowerCAmelCase			(    self    :				Tuple ):
           with tempfile.TemporaryDirectory() as d:
                 snake_case__					:      Optional[Any]  			=						MyCustomTracker(__lowerCamelCase )
                 snake_case__					:      Tuple  			=						Accelerator(log_with=__lowerCamelCase )
                 accelerator.init_trackers('Some name' )
                 snake_case__					:      int  			=						{'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:
                       snake_case__					:      Dict  			=						csv.DictReader(__lowerCamelCase )
                       snake_case__					:      Optional[Any]  			=						next(__lowerCamelCase )
                       snake_case__					:      Union[str, 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'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
