



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  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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




@require_wandb
@mock.patch.dict(os.environ ,				{"""WANDB_MODE""": """offline"""} )
class  _lowercase				(a_ ,				a_ ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''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  _lowerCamelCase   (	snake_case__       ,	snake_case__       ,	snake_case__ = True      ):

									'''simple docstring'''


									UpperCamelCase_											=				RF"""{section} ([\S\s]*?)\n\n"""
									if record:
														UpperCamelCase_											=				RF"""Record: {pattern}"""
									UpperCamelCase_											=				re.findall(snake_case__       ,	snake_case__      )[0]
									# A config
									if section == "config" or section == "history":
														UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				"test_project_with_config"
									UpperCamelCase_											=				Accelerator(log_with="wandb"      )
									UpperCamelCase_											=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
									UpperCamelCase_											=				{"wandb": {"tags": ["my_tag"]}}
									accelerator.init_trackers(snake_case__       ,	snake_case__       ,	snake_case__      )
									UpperCamelCase_											=				{"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_											=				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_											=				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_											=				self.parse_log(snake_case__       ,	"run"       ,	snake_case__      )
									self.assertEqual(logged_items["tags"]       ,	"my_tag"      )

									# Actual logging
									UpperCamelCase_											=				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 				_lowerCAmelCase      (self				,		_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				run_name
					UpperCamelCase_											=				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  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				@staticmethod
				def  _lowerCamelCase   (	snake_case__       ,	snake_case__       ,	snake_case__ = False      ):

									'''simple docstring'''


									for log in log_list:
														UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									with tempfile.TemporaryDirectory() as d:
														UpperCamelCase_											=				CometMLTracker("test_project_with_config"       ,	snake_case__      )
														UpperCamelCase_											=				Accelerator(log_with=snake_case__      )
														UpperCamelCase_											=				{"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_											=				os.listdir(snake_case__      )[0]  # Comet is nice, it's just a zip file here
														# We parse the raw logs
														UpperCamelCase_											=				os.path.join(snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				zipfile.ZipFile(snake_case__       ,	"r"      )
														UpperCamelCase_											=				archive.open("messages.json"      ).read().decode("utf-8"      )
									UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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







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

				lowercase__				  =  """my_custom_tracker"""
				lowercase__				  =  False

				def __init__(	self       ,	snake_case__      ):

									'''simple docstring'''


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

				@property
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									return self.writer

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									self.f.close()




class  _lowercase				(unittest.TestCase ):
				'''simple docstring'''







				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									with tempfile.TemporaryDirectory() as d:
														UpperCamelCase_											=				MyCustomTracker(snake_case__      )
														UpperCamelCase_											=				Accelerator(log_with=snake_case__      )
														UpperCamelCase_											=				{"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_											=				csv.DictReader(snake_case__      )
																			UpperCamelCase_											=				next(snake_case__      )
																			UpperCamelCase_											=				{
																			    "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  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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





def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					UpperCamelCase_											=				0
					for ele in arr:
										total_sum += product_sum(_lowerCAmelCase				,		depth + 1) if isinstance(_lowerCAmelCase				,		_lowerCAmelCase) else ele
					return total_sum * depth



def 				_lowerCAmelCase      (_lowerCAmelCase):
					return product_sum(_lowerCAmelCase				,		1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
