

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      :    Optional[int]						       =    logging.getLogger(__name__)






@require_tensorboard
class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				List[Any]:




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





			def 	__magic_name__					(	self      )     ->				List[Any]:




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





			def 	__magic_name__					(	self      )     ->				str:




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





			def 	__magic_name__					(	self      )     ->				str:




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





			def 	__magic_name__					(	self      )     ->				Any:




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






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





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''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 	__magic_name__					(	__snake_case      ,						__snake_case      ,						__snake_case = True      )     ->				Any:




						'''simple docstring'''
						__a						   =Rf'{section} ([\S\s]*?)\n\n'
						if record:
									__a						   =Rf'Record: {pattern}'
						__a						   =re.findall(__snake_case      ,						__snake_case      )[0]
						# A config
						if section == "config" or section == "history":
									__a						   =re.findall(R'"([a-zA-Z0-9_.,]+)'      ,						__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]+)"'      ,						__snake_case      )      )





			@skip
			def 	__magic_name__					(	self      )     ->				List[str]:




						'''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(__snake_case      ,						__snake_case      ,						__snake_case      )
						__a						   ={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
						accelerator.log(__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":
												__a						   =subprocess.check_output(
												    ['wandb', 'sync', '--view', '--verbose', str(__snake_case      )]      ,						env=os.environ.copy()      ).decode('utf8'      ,						'ignore'      )
												break

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

						# Actual logging
						__a						   =self.parse_log(__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  UpperCamelCase_(							self     :						Union[str, Any] ,  _snake_case     :						str ,  _snake_case     :						str			):




			"""simple docstring"""






			__a						   =run_name
			__a						   =OfflineExperiment(project_name=_snake_case ,  offline_directory=_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 			__magic_name__					(    unittest.TestCase   ):





			@staticmethod
			def 	__magic_name__					(	__snake_case      ,						__snake_case      ,						__snake_case = False      )     ->				Optional[int]:




						'''simple docstring'''
						for log in log_list:
									__a						   =json.loads(__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 	__magic_name__					(	self      )     ->				int:




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





			def 	__magic_name__					(	self      )     ->				Dict:




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






class 			__magic_name__					(    lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		[
			    'total_loss',
			    'iteration',
			    'my_text',
			    'learning_rate',
			    'num_iterations',
			    'some_boolean',
			    'some_string',
			]

			SCREAMING_SNAKE_CASE							     =		'my_custom_tracker'
			SCREAMING_SNAKE_CASE							     =		False





			def __init__(	self      ,						__snake_case      )     ->				int:




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





			@property
			def 	__magic_name__					(	self      )     ->				Dict:




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





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				str:




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





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      )     ->				Any:




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





			def 	__magic_name__					(	self      )     ->				List[str]:




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






class 			__magic_name__					(    unittest.TestCase   ):





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




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





			def 	__magic_name__					(	self      )     ->				List[str]:




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









from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCAmelCase      :    Union[str, Any]						       =    {
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    str						       =    ["ErnieMTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase      :    Optional[Any]						       =    [
						    "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ErnieMForMultipleChoice",
						    "ErnieMForQuestionAnswering",
						    "ErnieMForSequenceClassification",
						    "ErnieMForTokenClassification",
						    "ErnieMModel",
						    "ErnieMPreTrainedModel",
						    "ErnieMForInformationExtraction",
						]


if TYPE_CHECKING:
						from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_ernie_m import ErnieMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ernie_m import (
												    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ErnieMForInformationExtraction,
												    ErnieMForMultipleChoice,
												    ErnieMForQuestionAnswering,
												    ErnieMForSequenceClassification,
												    ErnieMForTokenClassification,
												    ErnieMModel,
												    ErnieMPreTrainedModel,
												)


else:
						import sys

						_lowerCAmelCase      :    Tuple						       =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






