



'''simple docstring'''







from __future__ import annotations

import random
from collections.abc import Iterable




class 				__lowerCAmelCase:






			def __init__(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							list[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:dict[str, bool | None]										=      {literal: None for literal in literals}






			def __str__(   self  :							Optional[int]	):




					"""simple docstring"""







					return "{" + " , ".join(self.literals	) + "}"






			def __len__(   self  :							Optional[int]	):




					"""simple docstring"""







					return len(self.literals	)






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							dict[str, bool | None]	):




					"""simple docstring"""







					for literal in self.literals:
							SCREAMING_SNAKE_CASE_						:Tuple										=      literal[:2]
							if symbol in model:
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      model[symbol]
							else:
									continue
							if value is not None:
									# Complement assignment if literal is in complemented form
									if literal.endswith('\''	):
											SCREAMING_SNAKE_CASE_						:int										=      not value
							SCREAMING_SNAKE_CASE_						:List[str]										=      value






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							dict[str, bool | None]	):




					"""simple docstring"""







					for literal in self.literals:
							SCREAMING_SNAKE_CASE_						:Any										=      literal.rstrip('\''	) if literal.endswith('\''	) else literal + '\''
							if symbol in self.literals:
									return True

					self.assign(SCREAMING_SNAKE_CASE	)
					for value in self.literals.values():
							if value in (True, None):
									return value
					return any(self.literals.values()	)




class 				__lowerCAmelCase:






			def __init__(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Iterable[Clause]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      list(SCREAMING_SNAKE_CASE	)






			def __str__(   self  :							Optional[Any]	):




					"""simple docstring"""







					return "{" + " , ".join(str(SCREAMING_SNAKE_CASE	) for clause in self.clauses	) + "}"







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:str										=      []
		SCREAMING_SNAKE_CASE_						:Tuple										=      random.randint(1 ,		5     )
		SCREAMING_SNAKE_CASE_						:str										=      'A'
		SCREAMING_SNAKE_CASE_						:Any										=      0
		while i < no_of_literals:
				SCREAMING_SNAKE_CASE_						:Dict										=      random.randint(1 ,		5     )
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      base_var + str(SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      random.randint(0 ,		1     )
				if var_complement == 1:
						var_name += "'"
				if var_name in literals:
						i -= 1
				else:
						literals.append(SCREAMING_SNAKE_CASE     )
				i += 1
		return Clause(SCREAMING_SNAKE_CASE     )







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:set[Clause]										=      set()
		SCREAMING_SNAKE_CASE_						:str										=      random.randint(1 ,		10     )
		while len(SCREAMING_SNAKE_CASE     ) < no_of_clauses:
				clauses.add(generate_clause()     )
		return Formula(SCREAMING_SNAKE_CASE     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      formula.clauses
		SCREAMING_SNAKE_CASE_						:int										=      []
		for clause in formula.clauses:
				for literal in clause.literals:
						SCREAMING_SNAKE_CASE_						:Tuple										=      literal[:2]
						if symbol not in symbols_set:
								symbols_set.append(SCREAMING_SNAKE_CASE     )
		return clauses, symbols_set







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      []
		SCREAMING_SNAKE_CASE_						:dict[str, bool | None]										=      {}
		SCREAMING_SNAKE_CASE_						:Tuple										=      []

		for clause in clauses:
				if clause.evaluate(SCREAMING_SNAKE_CASE     ):
						continue
				for literal in clause.literals:
						literals.append(SCREAMING_SNAKE_CASE     )

		for s in symbols:
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      s + '\''
				if (s in literals and sym not in literals) or (
				    s not in literals and sym in literals
				):
						pure_symbols.append(SCREAMING_SNAKE_CASE     )
		for p in pure_symbols:
				SCREAMING_SNAKE_CASE_						:str										=      None
		for s in pure_symbols:
				SCREAMING_SNAKE_CASE_						:List[str]										=      s + '\''
				if s in literals:
						SCREAMING_SNAKE_CASE_						:Any										=      True
				elif sym in literals:
						SCREAMING_SNAKE_CASE_						:str										=      False
		return pure_symbols, assignment







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      []
		for clause in clauses:
				if len(SCREAMING_SNAKE_CASE     ) == 1:
						unit_symbols.append(list(clause.literals.keys()     )[0]     )
				else:
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      0, 0
						for literal, value in clause.literals.items():
								if value is False:
										f_count += 1
								elif value is None:
										SCREAMING_SNAKE_CASE_						:Tuple										=      literal
										n_count += 1
						if f_count == len(SCREAMING_SNAKE_CASE     ) - 1 and n_count == 1:
								unit_symbols.append(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:dict[str, bool | None]										=      {}
		for i in unit_symbols:
				SCREAMING_SNAKE_CASE_						:Dict										=      i[:2]
				SCREAMING_SNAKE_CASE_						:str										=      len(SCREAMING_SNAKE_CASE     ) == 2
		SCREAMING_SNAKE_CASE_						:List[str]										=      [i[:2] for i in unit_symbols]

		return unit_symbols, assignment







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      True
		for clause in clauses:
				SCREAMING_SNAKE_CASE_						:List[str]										=      clause.evaluate(SCREAMING_SNAKE_CASE     )
				if clause_check is False:
						return False, None
				elif clause_check is None:
						SCREAMING_SNAKE_CASE_						:Dict										=      False
						continue

		if check_clause_all_true:
				return True, model

		try:
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      find_pure_symbols(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		except RecursionError:
				print('raises a RecursionError and is'     )
				return None, {}
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      None
		if len(SCREAMING_SNAKE_CASE     ) > 0:
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pure_symbols[0], assignment[pure_symbols[0]]

		if p:
				SCREAMING_SNAKE_CASE_						:Dict										=      model
				SCREAMING_SNAKE_CASE_						:Tuple										=      value
				SCREAMING_SNAKE_CASE_						:List[Any]										=      list(SCREAMING_SNAKE_CASE     )
				if p in tmp_symbols:
						tmp_symbols.remove(SCREAMING_SNAKE_CASE     )
				return dpll_algorithm(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      find_unit_clauses(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Dict										=      None
		if len(SCREAMING_SNAKE_CASE     ) > 0:
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      unit_symbols[0], assignment[unit_symbols[0]]
		if p:
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      model
				SCREAMING_SNAKE_CASE_						:Dict										=      value
				SCREAMING_SNAKE_CASE_						:Dict										=      list(SCREAMING_SNAKE_CASE     )
				if p in tmp_symbols:
						tmp_symbols.remove(SCREAMING_SNAKE_CASE     )
				return dpll_algorithm(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      symbols[0]
		SCREAMING_SNAKE_CASE_						:List[Any]										=      symbols[1:]
		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      model, model
		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      True, False

		return dpll_algorithm(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) or dpll_algorithm(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   generate_formula()
							print(f"""The formula {formula} is""", end=""" """)

							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   generate_parameters(formula)
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   dpll_algorithm(clauses, symbols, {})

							if solution:
														print(f"""satisfiable with the assignment {model}.""")
							else:
														print("""not satisfiable.""")








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_     =			logging.getLogger(__name__)
@require_tensorboard
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> int:


						'''simple docstring'''
						A									=   'test_project_with_config'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, '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('*/**'		):
														A									=   list(filter(lambda _SCREAMING_SNAKE_CASE		: x.is_file()    ,child.iterdir()		)		)[0]
										self.assertNotEqual(str(_SCREAMING_SNAKE_CASE		)    ,''		)
		def    A(	self		:							Any		)     -> Dict:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'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
										A									=   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		:							int		)     -> Any:


						'''simple docstring'''
						A									=   'test_project_with_log'
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   Accelerator(log_with='tensorboard'    ,project_dir=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE		)
										A									=   {'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
										A									=   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:
														A									=   f.read()
										A									=   False
										while data:
														A									=   struct.unpack('Q'    ,data[:8]		)

														A									=   data[1_2 : 1_2 + int(header[0]		)]  # 8+4
														A									=   data[1_2 + int(header[0]		) + 4 :]
														A									=   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":
																										A									=   True
										self.assertTrue(_SCREAMING_SNAKE_CASE    ,'Converted tensor was not found in the log file!'		)
		def    A(	self		:							List[str]		)     -> Union[str, Any]:


						'''simple docstring'''
						with self.assertRaisesRegex(_SCREAMING_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=_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Optional[Any]		)     -> Tuple:


						'''simple docstring'''
						A									=   ProjectConfiguration(total_limit=3_0		)
						with tempfile.TemporaryDirectory() as dirpath:
										A									=   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   UpperCamelCase			(					snake_case__      ,							snake_case__			):







		"""simple docstring"""



		def    A(	self		:							Dict		)     -> Optional[int]:


						'''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		:							str    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = True		)     -> str:


						'''simple docstring'''
						A									=   Rf'{section} ([\S\s]*?)\n\n'
						if record:
										A									=   Rf'Record: {pattern}'
						A									=   re.findall(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)[0]
						# A config
						if section == "config" or section == "history":
										A									=   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		:							List[Any]		)     -> Optional[Any]:


						'''simple docstring'''
						A									=   'test_project_with_config'
						A									=   Accelerator(log_with='wandb'		)
						A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
						A									=   {'wandb': {'tags': ['my_tag']}}
						accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
						A									=   {'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":
														A									=   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
						A									=   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
						A									=   self.parse_log(_SCREAMING_SNAKE_CASE    ,'run'    ,_SCREAMING_SNAKE_CASE		)
						self.assertEqual(logged_items['tags']    ,'my_tag'		)

						# Actual logging
						A									=   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   snake_case		(				self						:							Union[str, Any],    UpperCAmelCase						:							str,    UpperCAmelCase						:							str ):
				A									=   run_name
				A									=   OfflineExperiment(project_name=UpperCAmelCase,    offline_directory=UpperCAmelCase )
				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(snake_case__      ,							"__init__"      ,							snake_case__			)
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		@staticmethod
		def    A(	_SCREAMING_SNAKE_CASE		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							bool = False		)     -> List[Any]:


						'''simple docstring'''
						for log in log_list:
										A									=   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		:							int		)     -> int:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   CometMLTracker('test_project_with_config'    ,_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
										accelerator.init_trackers(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										accelerator.end_training()
										A									=   os.listdir(_SCREAMING_SNAKE_CASE		)[0]  # Comet is nice, it's just a zip file here
										# We parse the raw logs
										A									=   os.path.join(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
										A									=   zipfile.ZipFile(_SCREAMING_SNAKE_CASE    ,'r'		)
										A									=   archive.open('messages.json'		).read().decode('utf-8'		)
						A									=   log.split('\n'		)[:-1]
						self.assertEqual(self.get_value_from_key(_SCREAMING_SNAKE_CASE    ,'num_iterations'    ,_SCREAMING_SNAKE_CASE		)    ,1_2		)
						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		:							Optional[int]		)     -> Optional[int]:


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







		"""simple docstring"""



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

		snake_case			 = "my_custom_tracker"
		snake_case			 = False
		def __init__(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						A									=   open(f'{dir}/log.csv'    ,'w+'		)
						A									=   csv.DictWriter(self.f    ,fieldnames=self._col_names		)
						self.writer.writeheader()
		@property
		def    A(	self		:							int		)     -> Union[str, Any]:


						'''simple docstring'''
						return self.writer
		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							dict		)     -> Any:


						'''simple docstring'''
						logger.info('Call init'		)
						self.writer.writerow(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							dict    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> List[Any]:


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







		def    A(	self		:							Union[str, Any]		)     -> Tuple:


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


class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def    A(	self		:							Union[str, Any]		)     -> int:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   MyCustomTracker(_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										A									=   {'num_iterations': 1_2, '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:
														A									=   csv.DictReader(_SCREAMING_SNAKE_CASE		)
														A									=   next(_SCREAMING_SNAKE_CASE		)
														A									=   {
														    '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		:							List[Any]		)     -> Tuple:


						'''simple docstring'''
						with tempfile.TemporaryDirectory() as d:
										A									=   MyCustomTracker(_SCREAMING_SNAKE_CASE		)
										A									=   Accelerator(log_with=_SCREAMING_SNAKE_CASE		)
										accelerator.init_trackers('Some name'		)
										A									=   {'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:
														A									=   csv.DictReader(_SCREAMING_SNAKE_CASE		)
														A									=   next(_SCREAMING_SNAKE_CASE		)
														A									=   {
														    '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		)





