"""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

_SCREAMING_SNAKE_CASE  :		str        =      logging.getLogger(__name__)






@require_tensorboard
class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				'test_project_with_log'
      with tempfile.TemporaryDirectory() as dirpath:
         __lowerCamelCase								=				Accelerator(log_with='tensorboard'   ,							project_dir=_SCREAMING_SNAKE_CASE  )
         accelerator.init_trackers(_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				{'tensor': torch.tensor(1  )}
         accelerator.log(_SCREAMING_SNAKE_CASE   ,							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 _SCREAMING_SNAKE_CASE  : 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(_SCREAMING_SNAKE_CASE   ,							'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(_SCREAMING_SNAKE_CASE  )
            if event.HasField('summary'  ):
               for value in event.summary.value:
                  if value.simple_value == 1.0 and value.tag == "tensor":
                     __lowerCamelCase								=				True
         self.assertTrue(_SCREAMING_SNAKE_CASE   ,							'Converted tensor was not found in the log file!'  )







   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

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






@require_wandb
@mock.patch.dict(os.environ						,							{"WANDB_MODE": "offline"}							)
class     _SCREAMING_SNAKE_CASE       ( 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							(  _SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = True  ):


      '''simple docstring'''

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







   @skip
   def       _A							(  self  ):


      '''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(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(_SCREAMING_SNAKE_CASE   ,							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(_SCREAMING_SNAKE_CASE  )]   ,							env=os.environ.copy()  ).decode('utf8'   ,							'ignore'  )
            break

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

      # Actual logging
      __lowerCamelCase								=				self.parse_log(_SCREAMING_SNAKE_CASE   ,							'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    :  int			,   lowercase_    :  str			,   lowercase_    :  str	)     ->       Optional[Any]:
   __lowerCamelCase								=				run_name
   __lowerCamelCase								=				OfflineExperiment(project_name=lowercase_			,   offline_directory=lowercase_	)
   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     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   @staticmethod
   def       _A							(  _SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = False  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

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






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Tuple						=						[
       "total_loss",
       "iteration",
       "my_text",
       "learning_rate",
       "num_iterations",
       "some_boolean",
       "some_string",
   ]

   __UpperCAmelCase  :       int						=						"my_custom_tracker"
   __UpperCAmelCase  :       Any						=						False







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return self.writer







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

      self.f.close()






class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

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







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
     from .tokenization_phobert import PhobertTokenizer

else:
     import sys

     _SCREAMING_SNAKE_CASE  :		List[Any]        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






