

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 							a__							( unittest.TestCase							):







  """simple docstring"""




  def 			_lowercase   (      self     :			Union[str, Any]		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = "test_project_with_config"
        with tempfile.TemporaryDirectory() as dirpath:
              SCREAMING_SNAKE_CASE			:   int	  = Accelerator(log_with="""tensorboard"""					,					project_dir=lowercase_		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
              accelerator.init_trackers(lowercase_					,					lowercase_		)
              accelerator.end_training()
              for child in Path(f"{dirpath}/{project_name}"		).glob("""*/**"""		):
                    SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = list(filter(lambda UpperCAmelCase__		: x.is_file()					,					child.iterdir()		)		)[0]
              self.assertNotEqual(str(lowercase_		)					,					""""""		)


  def 			_lowercase   (      self     :			Optional[int]		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = "test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Accelerator(log_with="""tensorboard"""					,					project_dir=lowercase_		)
              accelerator.init_trackers(lowercase_		)
              SCREAMING_SNAKE_CASE			:   int	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
              accelerator.log(lowercase_					,					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			:   int	  = list(filter(lambda UpperCAmelCase__		: x.is_file()					,					Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
              self.assertNotEqual(str(lowercase_		)					,					""""""		)


  def 			_lowercase   (      self     :			Optional[int]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Tuple	  = "test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
              SCREAMING_SNAKE_CASE			:   str	  = Accelerator(log_with="""tensorboard"""					,					project_dir=lowercase_		)
              accelerator.init_trackers(lowercase_		)
              SCREAMING_SNAKE_CASE			:   Any	  = {"tensor": torch.tensor(1		)}
              accelerator.log(lowercase_					,					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[Any]	  = list(filter(lambda UpperCAmelCase__		: 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(lowercase_					,					"""rb"""		) as f:
                    SCREAMING_SNAKE_CASE			:   Any	  = f.read()
              SCREAMING_SNAKE_CASE			:   List[Any]	  = False
              while data:
                    SCREAMING_SNAKE_CASE			:   Dict	  = struct.unpack("""Q"""					,					data[:8]		)

                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = data[1_2 : 1_2 + int(header[0]		)]  # 8+4
                    SCREAMING_SNAKE_CASE			:   int	  = data[1_2 + int(header[0]		) + 4 :]
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = event_pba.Event()

                    event.ParseFromString(lowercase_		)
                    if event.HasField("""summary"""		):
                          for value in event.summary.value:
                                if value.simple_value == 1.0 and value.tag == "tensor":
                                      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = True
              self.assertTrue(lowercase_					,					"""Converted tensor was not found in the log file!"""		)


  def 			_lowercase   (      self     :			Optional[int]		)       ->List[Any]:
        """simple docstring"""







        with self.assertRaisesRegex(lowercase_					,					"""Logging with `tensorboard` requires a `logging_dir`"""		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = Accelerator(log_with="""tensorboard"""		)
        with tempfile.TemporaryDirectory() as dirpath:
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Accelerator(log_with="""tensorboard"""					,					project_dir=lowercase_		)




  def 			_lowercase   (      self     :			int		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   str	  = ProjectConfiguration(total_limit=3_0		)
        with tempfile.TemporaryDirectory() as dirpath:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = Accelerator(log_with="""tensorboard"""					,					project_dir=lowercase_					,					project_config=lowercase_		)







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







  """simple docstring"""




  def 			_lowercase   (      self     :			Union[str, Any]		)       ->Tuple:
        """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 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			List[Any] = True		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = Rf"{section} ([\S\s]*?)\n\n"
        if record:
              SCREAMING_SNAKE_CASE			:   Dict	  = Rf"Record: {pattern}"
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = re.findall(lowercase_					,					lowercase_		)[0]
        # A config
        if section == "config" or section == "history":
              SCREAMING_SNAKE_CASE			:   Any	  = re.findall(R"""\"([a-zA-Z0-9_.,]+)"""					,					lowercase_		)
              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]+)\""""					,					lowercase_		)		)




  @skip
  def 			_lowercase   (      self     :			Optional[Any]		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = "test_project_with_config"
        SCREAMING_SNAKE_CASE			:   List[Any]	  = Accelerator(log_with="""wandb"""		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        SCREAMING_SNAKE_CASE			:   List[str]	  = {"wandb": {"tags": ["my_tag"]}}
        accelerator.init_trackers(lowercase_					,					lowercase_					,					lowercase_		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(lowercase_					,					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			:   Optional[int]	  = subprocess.check_output(
                        ["""wandb""", """sync""", """--view""", """--verbose""", str(lowercase_		)]					,					env=os.environ.copy()		).decode("""utf8"""					,					"""ignore"""		)
                    break

              # Check HPS through careful parsing and cleaning
        SCREAMING_SNAKE_CASE			:   List[str]	  = self.parse_log(lowercase_					,					"""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(lowercase_					,					"""run"""					,					lowercase_		)
        self.assertEqual(logged_items["""tags"""]					,					"""my_tag"""		)

        # Actual logging
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.parse_log(lowercase_					,					"""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        __lowercase   (  self    ,     _A    ,     _A      )  ->    Optional[int]:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = run_name
      SCREAMING_SNAKE_CASE			:   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(lowercase__						,      """__init__"""						,      lowercase__							)
class 							a__							( unittest.TestCase							):







  """simple docstring"""




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			Tuple = False		)       ->Optional[int]:
        """simple docstring"""







        for log in log_list:
              SCREAMING_SNAKE_CASE			:   str	  = json.loads(lowercase_		)["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 			_lowercase   (      self     :			List[str]		)       ->Union[str, Any]:
        """simple docstring"""







        with tempfile.TemporaryDirectory() as d:
              SCREAMING_SNAKE_CASE			:   str	  = CometMLTracker("""test_project_with_config"""					,					lowercase_		)
              SCREAMING_SNAKE_CASE			:   Dict	  = Accelerator(log_with=lowercase_		)
              SCREAMING_SNAKE_CASE			:   List[Any]	  = {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
              accelerator.init_trackers(lowercase_					,					lowercase_		)
              accelerator.end_training()
              SCREAMING_SNAKE_CASE			:   Any	  = os.listdir(lowercase_		)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              SCREAMING_SNAKE_CASE			:   List[Any]	  = os.path.join(lowercase_					,					lowercase_		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = zipfile.ZipFile(lowercase_					,					"""r"""		)
              SCREAMING_SNAKE_CASE			:   List[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(lowercase_					,					"""num_iterations"""					,					lowercase_		)					,					1_2		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""learning_rate"""					,					lowercase_		)					,					0.01		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""some_boolean"""					,					lowercase_		)					,					lowercase_		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""some_string"""					,					lowercase_		)					,					"""some_value"""		)




  def 			_lowercase   (      self     :			str		)       ->str:
        """simple docstring"""







        with tempfile.TemporaryDirectory() as d:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = CometMLTracker("""test_project_with_config"""					,					lowercase_		)
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = Accelerator(log_with=lowercase_		)
              accelerator.init_trackers(lowercase_		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
              accelerator.log(lowercase_					,					step=0		)
              accelerator.end_training()
              SCREAMING_SNAKE_CASE			:   Tuple	  = os.listdir(lowercase_		)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              SCREAMING_SNAKE_CASE			:   Dict	  = os.path.join(lowercase_					,					lowercase_		)
              SCREAMING_SNAKE_CASE			:   str	  = zipfile.ZipFile(lowercase_					,					"""r"""		)
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = archive.open("""messages.json"""		).read().decode("""utf-8"""		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = log.split("""\n"""		)[:-1]
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""curr_step"""					,					lowercase_		)					,					0		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""total_loss"""		)					,					0.1		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""iteration"""		)					,					1		)
        self.assertEqual(self.get_value_from_key(lowercase_					,					"""my_text"""		)					,					"""some_value"""		)







class 							a__							( lowercase__							):







  """simple docstring"""




  UpperCAmelCase__   :			Dict      							=[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  UpperCAmelCase__   :			Dict      							="""my_custom_tracker"""
  UpperCAmelCase__   :			List[str]      							=False


  def __init__(      self     :			Optional[Any]					,					UpperCAmelCase__     :			Optional[Any]		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = open(f"{dir}/log.csv"					,					"""w+"""		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = csv.DictWriter(self.f					,					fieldnames=self._col_names		)
        self.writer.writeheader()


  @property
  def 			_lowercase   (      self     :			Any		)       ->List[Any]:
        """simple docstring"""







        return self.writer


  def 			_lowercase   (      self     :			Dict					,					UpperCAmelCase__     :			Any		)       ->Union[str, Any]:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			List[str]		)       ->Union[str, Any]:
        """simple docstring"""







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




  def 			_lowercase   (      self     :			int		)       ->Optional[Any]:
        """simple docstring"""







        self.f.close()







class 							a__							( unittest.TestCase							):







  """simple docstring"""




  def 			_lowercase   (      self     :			Dict		)       ->List[str]:
        """simple docstring"""







        with tempfile.TemporaryDirectory() as d:
              SCREAMING_SNAKE_CASE			:   Any	  = MyCustomTracker(lowercase_		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = Accelerator(log_with=lowercase_		)
              SCREAMING_SNAKE_CASE			:   Any	  = {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
              accelerator.init_trackers("""Some name"""					,					lowercase_		)
              accelerator.end_training()
              with open(f"{d}/log.csv"					,					"""r"""		) as f:
                    SCREAMING_SNAKE_CASE			:   Dict	  = csv.DictReader(lowercase_		)
                    SCREAMING_SNAKE_CASE			:   List[Any]	  = next(lowercase_		)
                    SCREAMING_SNAKE_CASE			:   int	  = {
                        "total_loss": "",
                        "iteration": "",
                        "my_text": "",
                        "learning_rate": "0.01",
                        "num_iterations": "12",
                        "some_boolean": "False",
                        "some_string": "some_value",
                    }
                    self.assertDictEqual(lowercase_					,					lowercase_		)




  def 			_lowercase   (      self     :			List[Any]		)       ->Optional[Any]:
        """simple docstring"""







        with tempfile.TemporaryDirectory() as d:
              SCREAMING_SNAKE_CASE			:   Any	  = MyCustomTracker(lowercase_		)
              SCREAMING_SNAKE_CASE			:   Dict	  = Accelerator(log_with=lowercase_		)
              accelerator.init_trackers("""Some name"""		)
              SCREAMING_SNAKE_CASE			:   Any	  = {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
              accelerator.log(lowercase_					,					step=0		)
              accelerator.end_training()
              with open(f"{d}/log.csv"					,					"""r"""		) as f:
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = csv.DictReader(lowercase_		)
                    SCREAMING_SNAKE_CASE			:   Any	  = next(lowercase_		)
                    SCREAMING_SNAKE_CASE			:   Tuple	  = {
                        "total_loss": "0.1",
                        "iteration": "1",
                        "my_text": "some_value",
                        "learning_rate": "",
                        "num_iterations": "",
                        "some_boolean": "",
                        "some_string": "",
                    }
                    self.assertDictEqual(lowercase_					,					lowercase_		)






"""simple docstring"""





import math
from timeit import timeit







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Any     	=       abs(__lowerCamelCase    )
   while True:
      UpperCAmelCase_   :   Optional[int]     	=       n // 10
      digits += 1
      if n == 0:
         break
   return digits







def       __a	(						__lowerCamelCase    ):
   return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase    ),						10    ) + 1    )







def       __a	(						__lowerCamelCase    ):
   return len(str(abs(__lowerCamelCase    )    )    )







def       __a	(						):
   from collections.abc import Callable

   def benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    ) -> None:
      UpperCAmelCase_   :   Optional[Any]     	=       f"""{func.__name__}({value})"""
      UpperCAmelCase_   :   Optional[int]     	=       timeit(f"""__main__.{call}""",						setup="import __main__"    )
      print(f"""{call}: {func(__lowerCamelCase    )} -- {timing} seconds"""    )

   for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
      for func in (num_digits, num_digits_fast, num_digits_faster):
         benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    )
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


