
'''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

__A       =      logging.getLogger(__name__)





@require_tensorboard
class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    """test_project_with_config"""
									with tempfile.TemporaryDirectory() as dirpath:
															lowercase__								=    Accelerator(log_with="""tensorboard"""    ,       project_dir=lowerCamelCase__    )
															lowercase__								=    {"""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("""*/**"""    ):
																					lowercase__								=    list(filter(lambda lowerCamelCase__    : x.is_file()    ,       child.iterdir()    )    )[0]
															self.assertNotEqual(str(lowerCamelCase__    )    ,       """"""    )



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




									'''simple docstring'''



									lowercase__								=    """test_project_with_log"""
									with tempfile.TemporaryDirectory() as dirpath:
															lowercase__								=    Accelerator(log_with="""tensorboard"""    ,       project_dir=lowerCamelCase__    )
															accelerator.init_trackers(lowerCamelCase__    )
															lowercase__								=    {"""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
															lowercase__								=    list(filter(lambda lowerCamelCase__    : x.is_file()    ,       Path(F'''{dirpath}/{project_name}'''    ).iterdir()    )    )[0]
															self.assertNotEqual(str(lowerCamelCase__    )    ,       """"""    )



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    """test_project_with_log"""
									with tempfile.TemporaryDirectory() as dirpath:
															lowercase__								=    Accelerator(log_with="""tensorboard"""    ,       project_dir=lowerCamelCase__    )
															accelerator.init_trackers(lowerCamelCase__    )
															lowercase__								=    {"""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
															lowercase__								=    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:
																					lowercase__								=    f.read()
															lowercase__								=    False
															while data:
																					lowercase__								=    struct.unpack("""Q"""    ,       data[:8]    )

																					lowercase__								=    data[12 : 12 + int(header[0]    )]  # 8+4
																					lowercase__								=    data[12 + int(header[0]    ) + 4 :]
																					lowercase__								=    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":
																																							lowercase__								=    True
															self.assertTrue(lowerCamelCase__    ,       """Converted tensor was not found in the log file!"""    )



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




									'''simple docstring'''



									with self.assertRaisesRegex(lowerCamelCase__    ,       """Logging with `tensorboard` requires a `logging_dir`"""    ):
															lowercase__								=    Accelerator(log_with="""tensorboard"""    )
									with tempfile.TemporaryDirectory() as dirpath:
															lowercase__								=    Accelerator(log_with="""tensorboard"""    ,       project_dir=lowerCamelCase__    )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    ProjectConfiguration(total_limit=30    )
									with tempfile.TemporaryDirectory() as dirpath:
															lowercase__								=    Accelerator(log_with="""tensorboard"""    ,       project_dir=lowerCamelCase__    ,       project_config=lowerCamelCase__    )





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



			def A__	(   self    )     ->						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__	(   lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__ = True    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    RF'''{section} ([\S\s]*?)\n\n'''
									if record:
															lowercase__								=    RF'''Record: {pattern}'''
									lowercase__								=    re.findall(lowerCamelCase__    ,       lowerCamelCase__    )[0]
									# A config
									if section == "config" or section == "history":
															lowercase__								=    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    )     ->						int:




									'''simple docstring'''



									lowercase__								=    """test_project_with_config"""
									lowercase__								=    Accelerator(log_with="""wandb"""    )
									lowercase__								=    {"""num_iterations""": 12, """learning_rate""": 1e-2, """some_boolean""": False, """some_string""": """some_value"""}
									lowercase__								=    {"""wandb""": {"""tags""": ["""my_tag"""]}}
									accelerator.init_trackers(lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    )
									lowercase__								=    {"""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":
																					lowercase__								=    subprocess.check_output(
																					    ["""wandb""", """sync""", """--view""", """--verbose""", str(lowerCamelCase__    )]    ,       env=os.environ.copy()    ).decode("""utf8"""    ,       """ignore"""    )
																					break

        # Check HPS through careful parsing and cleaning
									lowercase__								=    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
									lowercase__								=    self.parse_log(lowerCamelCase__    ,       """run"""    ,       lowerCamelCase__    )
									self.assertEqual(logged_items["""tags"""]    ,       """my_tag"""    )

									# Actual logging
									lowercase__								=    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 	_A						(							self						,						lowercase__						,						lowercase__		):
						lowercase__								=    run_name
						lowercase__								=    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(__UpperCAmelCase						,  """__init__"""						,  __UpperCAmelCase			)
class      A							(    unittest.TestCase			):



			@staticmethod
			def A__	(   lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__ = False    )     ->						Optional[Any]:




									'''simple docstring'''



									for log in log_list:
															lowercase__								=    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    )     ->						str:




									'''simple docstring'''



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




									'''simple docstring'''



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

			lowerCamelCase							:							int    						=					"""my_custom_tracker"""
			lowerCamelCase							:							Tuple    						=					False



			def __init__(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    open(F'''{dir}/log.csv'''    ,       """w+"""    )
									lowercase__								=    csv.DictWriter(self.f    ,       fieldnames=self._col_names    )
									self.writer.writeheader()



			@property
			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									return self.writer



			def A__	(   self    ,       lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									logger.info("""Call init"""    )
									self.writer.writerow(lowerCamelCase__    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									logger.info("""Call log"""    )
									self.writer.writerow(lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									self.f.close()





class      A							(    unittest.TestCase			):



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




									'''simple docstring'''



									with tempfile.TemporaryDirectory() as d:
															lowercase__								=    MyCustomTracker(lowerCamelCase__    )
															lowercase__								=    Accelerator(log_with=lowerCamelCase__    )
															lowercase__								=    {"""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:
																					lowercase__								=    csv.DictReader(lowerCamelCase__    )
																					lowercase__								=    next(lowerCamelCase__    )
																					lowercase__								=    {
																					    """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    )     ->						Union[str, Any]:




									'''simple docstring'''



									with tempfile.TemporaryDirectory() as d:
															lowercase__								=    MyCustomTracker(lowerCamelCase__    )
															lowercase__								=    Accelerator(log_with=lowerCamelCase__    )
															accelerator.init_trackers("""Some name"""    )
															lowercase__								=    {"""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:
																					lowercase__								=    csv.DictReader(lowerCamelCase__    )
																					lowercase__								=    next(lowerCamelCase__    )
																					lowercase__								=    {
																					    """total_loss""": """0.1""",
																					    """iteration""": """1""",
																					    """my_text""": """some_value""",
																					    """learning_rate""": """""",
																					    """num_iterations""": """""",
																					    """some_boolean""": """""",
																					    """some_string""": """""",
																					}
																					self.assertDictEqual(lowerCamelCase__    ,       lowerCamelCase__    )








'''simple docstring'''



def 	_A						(							lowercase__		):
						if not isinstance(lowercase__						,						lowercase__		):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""		)
						if len(lowercase__		) == 0:
												raise ValueError("""Input list must be a non empty list"""		)
						if len(lowercase__		) == 1 and series[0] != 0:
												return True
						lowercase__								=    []
						lowercase__								=    len(lowercase__		)
						for i in range(0						,						lowercase__		):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""		)
												rec_series.append(1 / series[i]		)
						lowercase__								=    rec_series[1] - rec_series[0]
						for index in range(2						,						lowercase__		):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True





def 	_A						(							lowercase__		):
						if not isinstance(lowercase__						,						lowercase__		):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""		)
						if len(lowercase__		) == 0:
												raise ValueError("""Input list must be a non empty list"""		)
						lowercase__								=    0
						for val in series:
												answer += 1 / val
						return len(lowercase__		) / answer


if __name__ == "__main__":
				import doctest

				doctest.testmod()






