"""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      : Any       =logging.getLogger(__name__)






@require_tensorboard
class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



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




         '''simple docstring'''



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

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




         '''simple docstring'''



         with self.assertRaisesRegex(__lowerCamelCase					,       'Logging with `tensorboard` requires a `logging_dir`'	):
              __A					:					List[str]								=		Accelerator(log_with='tensorboard'	)
         with tempfile.TemporaryDirectory() as dirpath:
              __A					:					Optional[int]								=		Accelerator(log_with='tensorboard'					,       project_dir=__lowerCamelCase	)

    def 						_a							(  self	):




         '''simple docstring'''



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







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


    '''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							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase = True	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		RF'{section} ([\S\s]*?)\n\n'
         if record:
              __A					:					Union[str, Any]								=		RF'Record: {pattern}'
         __A					:					Union[str, Any]								=		re.findall(__lowerCamelCase					,       __lowerCamelCase	)[0]
         # A config
         if section == "config" or section == "history":
              __A					:					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 						_a							(  self	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		'test_project_with_config'
         __A					:					Optional[int]								=		Accelerator(log_with='wandb'	)
         __A					:					int								=		{'num_iterations': 12, 'learning_rate': 1e-2, 'some_boolean': False, 'some_string': 'some_value'}
         __A					:					Union[str, Any]								=		{'wandb': {'tags': ['my_tag']}}
         accelerator.init_trackers(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
         __A					:					Optional[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":
                   __A					:					int								=		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					:					Union[str, Any]								=		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					:					Union[str, Any]								=		self.parse_log(__lowerCamelCase					,       'run'					,       __lowerCamelCase	)
         self.assertEqual(logged_items['tags']					,       'my_tag'	)

         # Actual logging
         __A					:					str								=		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 						_lowercase				(  self				:				Any  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				str						)    ->     Dict:







     '''simple docstring'''


     __A					:					Any								=		run_name
     __A					:					Dict								=		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(A_				,	"__init__"				,	A_	)
class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase = False	):




         '''simple docstring'''



         for log in log_list:
              __A					:					List[str]								=		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 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



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


    '''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					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					int								=		open(F'{dir}/log.csv'					,       'w+'	)
         __A					:					Optional[int]								=		csv.DictWriter(self.f					,       fieldnames=self._col_names	)
         self.writer.writeheader()
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self.writer
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         logger.info('Call init'	)
         self.writer.writerow(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



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

    def 						_a							(  self	):




         '''simple docstring'''



         self.f.close()







class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         with tempfile.TemporaryDirectory() as d:
              __A					:					Optional[Any]								=		MyCustomTracker(__lowerCamelCase	)
              __A					:					Dict								=		Accelerator(log_with=__lowerCamelCase	)
              __A					:					List[str]								=		{'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					:					Optional[Any]								=		csv.DictReader(__lowerCamelCase	)
                   __A					:					List[str]								=		next(__lowerCamelCase	)
                   __A					:					Optional[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 						_a							(  self	):




         '''simple docstring'''



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

from datasets.utils.version import Version




@pytest.mark.parametrize(
    'other, expected_equality'  ,       [
        (Version('1.0.0'						), True),
        ('1.0.0', True),
        (Version('2.0.0'						), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]  ,       )
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Tuple								=		Version('1.0.0'						)
     assert (version == other) is expected_equality
     assert (version != other) is not expected_equality
     assert (hash(_SCREAMING_SNAKE_CASE						) == hash(_SCREAMING_SNAKE_CASE						)) is expected_equality



