



'''simple docstring'''






def 			snake_case_					( _lowerCAmelCase  :						str  )				->       str:
							return " ".join(sorted(set(sentence.split()  )  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''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

UpperCamelCase__:       Optional[Any]							     =      logging.getLogger(__name__)

@require_tensorboard
class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					Union[str, Any]		)      ->		List[str]:
								UpperCAmelCase			:	Dict										=		'''test_project_with_config'''
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		)
															UpperCAmelCase			:	Tuple										=		{'''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('''*/**'''		):
																						UpperCAmelCase			:	Dict										=		list(filter(lambda __snake_case		: x.is_file()		,      child.iterdir()		)		)[0]
															self.assertNotEqual(str(__snake_case		)		,      ''''''		)







	def 					A     (						self      :					List[Any]		)      ->		List[str]:
								UpperCAmelCase			:	Any										=		'''test_project_with_log'''
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	str										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		)
															accelerator.init_trackers(__snake_case		)
															UpperCAmelCase			:	Union[str, Any]										=		{'''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
															UpperCAmelCase			:	str										=		list(filter(lambda __snake_case		: x.is_file()		,      Path(F"""{dirpath}/{project_name}"""		).iterdir()		)		)[0]
															self.assertNotEqual(str(__snake_case		)		,      ''''''		)







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

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







	def 					A     (						self      :					List[str]		)      ->		Optional[Any]:
								with self.assertRaisesRegex(__snake_case		,      '''Logging with `tensorboard` requires a `logging_dir`'''		):
															UpperCAmelCase			:	List[str]										=		Accelerator(log_with='''tensorboard'''		)
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	str										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		)







	def 					A     (						self      :					Dict		)      ->		Union[str, Any]:
								UpperCAmelCase			:	int										=		ProjectConfiguration(total_limit=30		)
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		,      project_config=__snake_case		)

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







	"""simple docstring"""






	def 					A     (						self      :					Dict		)      ->		str:
								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     (						__snake_case      :					str		,      __snake_case      :					str		,      __snake_case      :					bool = True		)      ->		Optional[int]:
								UpperCAmelCase			:	Optional[int]										=		rF"""{section} ([\S\s]*?)\n\n"""
								if record:
															UpperCAmelCase			:	Optional[int]										=		rF"""Record: {pattern}"""
								UpperCAmelCase			:	Optional[int]										=		re.findall(__snake_case		,      __snake_case		)[0]
								# A config
								if section == "config" or section == "history":
															UpperCAmelCase			:	Any										=		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 					A     (						self      :					int		)      ->		str:
								UpperCAmelCase			:	Any										=		'''test_project_with_config'''
								UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''wandb'''		)
								UpperCAmelCase			:	List[str]										=		{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
								UpperCAmelCase			:	Dict										=		{'''wandb''': {'''tags''': ['''my_tag''']}}
								accelerator.init_trackers(__snake_case		,      __snake_case		,      __snake_case		)
								UpperCAmelCase			:	int										=		{'''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":
																						UpperCAmelCase			:	str										=		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
								UpperCAmelCase			:	Optional[int]										=		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
								UpperCAmelCase			:	List[str]										=		self.parse_log(__snake_case		,      '''run'''		,      __snake_case		)
								self.assertEqual(logged_items['''tags''']		,      '''my_tag'''		)

								# Actual logging
								UpperCAmelCase			:	List[str]										=		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 			snake_case_					( self  :						Union[str, Any]       ,				_lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						str  )				->       Optional[Any]:
							UpperCAmelCase			:	Dict										=		run_name
							UpperCAmelCase			:	List[str]										=		OfflineExperiment(project_name=_lowerCAmelCase       ,				offline_directory=_lowerCAmelCase  )
							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     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






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







	"""simple docstring"""






	lowerCamelCase__      			=	[
	    """total_loss""",
	    """iteration""",
	    """my_text""",
	    """learning_rate""",
	    """num_iterations""",
	    """some_boolean""",
	    """some_string""",
	]

	lowerCamelCase__      			=	"""my_custom_tracker"""
	lowerCamelCase__      			=	False







	def __init__(						self      :					Optional[int]		,      __snake_case      :					str		)      ->		Dict:
								UpperCAmelCase			:	Optional[Any]										=		open(F"""{dir}/log.csv"""		,      '''w+'''		)
								UpperCAmelCase			:	List[str]										=		csv.DictWriter(self.f		,      fieldnames=self._col_names		)
								self.writer.writeheader()







	@property
	def 					A     (						self      :					List[Any]		)      ->		str:
								return self.writer







	def 					A     (						self      :					int		,      __snake_case      :					dict		)      ->		List[Any]:
								logger.info('''Call init'''		)
								self.writer.writerow(__snake_case		)







	def 					A     (						self      :					Dict		,      __snake_case      :					dict		,      __snake_case      :					Optional[int]		)      ->		List[Any]:
								logger.info('''Call log'''		)
								self.writer.writerow(__snake_case		)







	def 					A     (						self      :					Optional[Any]		)      ->		int:
								self.f.close()

class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					List[str]		)      ->		Tuple:
								with tempfile.TemporaryDirectory() as d:
															UpperCAmelCase			:	Any										=		MyCustomTracker(__snake_case		)
															UpperCAmelCase			:	Optional[Any]										=		Accelerator(log_with=__snake_case		)
															UpperCAmelCase			:	Any										=		{'''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:
																						UpperCAmelCase			:	str										=		csv.DictReader(__snake_case		)
																						UpperCAmelCase			:	Tuple										=		next(__snake_case		)
																						UpperCAmelCase			:	Any										=		{
																						    '''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 					A     (						self      :					Optional[Any]		)      ->		Any:
								with tempfile.TemporaryDirectory() as d:
															UpperCAmelCase			:	str										=		MyCustomTracker(__snake_case		)
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with=__snake_case		)
															accelerator.init_trackers('''Some name'''		)
															UpperCAmelCase			:	Optional[Any]										=		{'''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:
																						UpperCAmelCase			:	Any										=		csv.DictReader(__snake_case		)
																						UpperCAmelCase			:	str										=		next(__snake_case		)
																						UpperCAmelCase			:	List[str]										=		{
																						    '''total_loss''': '''0.1''',
																						    '''iteration''': '''1''',
																						    '''my_text''': '''some_value''',
																						    '''learning_rate''': '''''',
																						    '''num_iterations''': '''''',
																						    '''some_boolean''': '''''',
																						    '''some_string''': '''''',
																						}
																						self.assertDictEqual(__snake_case		,      __snake_case		)





