


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     :			int      =logging.getLogger(__name__)
@require_tensorboard
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



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


				def 		__magic_name__(	self      :Dict     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		'''test_project_with_log'''
										with tempfile.TemporaryDirectory() as dirpath:
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		Accelerator(log_with='''tensorboard'''      ,      project_dir=lowerCAmelCase__     )
																accelerator.init_trackers(lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		{'''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
																__SCREAMING_SNAKE_CASE						:					str		      =		list(filter(lambda lowerCAmelCase__     : x.is_file()      ,      Path(f'''{dirpath}/{project_name}'''     ).iterdir()     )     )[0]
																self.assertNotEqual(str(lowerCAmelCase__     )      ,      ''''''     )


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

																						__SCREAMING_SNAKE_CASE						:					int		      =		data[12 : 12 + int(header[0]     )]  # 8+4
																						__SCREAMING_SNAKE_CASE						:					Tuple		      =		data[12 + int(header[0]     ) + 4 :]
																						__SCREAMING_SNAKE_CASE						:					List[str]		      =		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":
																																								__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		True
																self.assertTrue(lowerCAmelCase__      ,      '''Converted tensor was not found in the log file!'''     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Any:
										with self.assertRaisesRegex(lowerCAmelCase__      ,      '''Logging with `tensorboard` requires a `logging_dir`'''     ):
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		Accelerator(log_with='''tensorboard'''     )
										with tempfile.TemporaryDirectory() as dirpath:
																__SCREAMING_SNAKE_CASE						:					int		      =		Accelerator(log_with='''tensorboard'''      ,      project_dir=lowerCAmelCase__     )



				def 		__magic_name__(	self      :int     )		->      str:
										__SCREAMING_SNAKE_CASE						:					int		      =		ProjectConfiguration(total_limit=30     )
										with tempfile.TemporaryDirectory() as dirpath:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		Accelerator(log_with='''tensorboard'''      ,      project_dir=lowerCAmelCase__      ,      project_config=lowerCAmelCase__     )




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






				'''simple docstring'''



				def 		__magic_name__(	self      :Optional[int]     )		->      List[Any]:
										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__(	lowerCAmelCase__      :str      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :bool = True     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					int		      =		rf'''{section} ([\S\s]*?)\n\n'''
										if record:
																__SCREAMING_SNAKE_CASE						:					int		      =		rf'''Record: {pattern}'''
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		re.findall(lowerCAmelCase__      ,      lowerCAmelCase__     )[0]
										# A config
										if section == "config" or section == "history":
																__SCREAMING_SNAKE_CASE						:					Any		      =		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 		__magic_name__(	self      :List[Any]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		'''test_project_with_config'''
										__SCREAMING_SNAKE_CASE						:					Dict		      =		Accelerator(log_with='''wandb'''     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		{'''wandb''': {'''tags''': ['''my_tag''']}}
										accelerator.init_trackers(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					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":
																						__SCREAMING_SNAKE_CASE						:					str		      =		subprocess.check_output(
																						    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCAmelCase__     )]      ,      env=os.environ.copy()     ).decode('''utf8'''      ,      '''ignore'''     )
																						break

        # Check HPS through careful parsing and cleaning
										__SCREAMING_SNAKE_CASE						:					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
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.parse_log(lowerCAmelCase__      ,      '''run'''      ,      lowerCAmelCase__     )
										self.assertEqual(logged_items['''tags''']      ,      '''my_tag'''     )

										# Actual logging
										__SCREAMING_SNAKE_CASE						:					Any		      =		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 	_UpperCamelCase							(				self   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		run_name
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		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(A__						,      '''__init__'''						,      A__	)
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



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






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      str      =		[
				    '''total_loss''',
				    '''iteration''',
				    '''my_text''',
				    '''learning_rate''',
				    '''num_iterations''',
				    '''some_boolean''',
				    '''some_string''',
				]

				SCREAMING_SNAKE_CASE__    :      int      =		'''my_custom_tracker'''
				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		False


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


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


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :dict     )		->      List[str]:
										logger.info('''Call init'''     )
										self.writer.writerow(lowerCAmelCase__     )


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



				def 		__magic_name__(	self      :List[str]     )		->      int:
										self.f.close()




class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



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




from __future__ import annotations


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sorted(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					str		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	_UpperCamelCase							(				):
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()
