

'''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       :      int  	=			logging.getLogger(__name__)







@require_tensorboard
class   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Any        =       "test_project_with_config"
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase		:							Optional[int]        =       Accelerator(log_with="tensorboard"   ,      project_dir=snake_case      )
												UpperCAmelCase		:							Any        =       {"num_iterations": 1_2, "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		:							Any        =       list(filter(lambda snake_case      : x.is_file()   ,      child.iterdir()      )      )[0]
												self.assertNotEqual(str(snake_case      )   ,      ""      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase		:							str        =       Accelerator(log_with="tensorboard"   ,      project_dir=snake_case      )
												accelerator.init_trackers(snake_case      )
												UpperCAmelCase		:							Dict        =       {"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		:							Tuple        =       list(filter(lambda snake_case      : x.is_file()   ,      Path(f"{dirpath}/{project_name}"      ).iterdir()      )      )[0]
												self.assertNotEqual(str(snake_case      )   ,      ""      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase		:							Optional[int]        =       Accelerator(log_with="tensorboard"   ,      project_dir=snake_case      )
												accelerator.init_trackers(snake_case      )
												UpperCAmelCase		:							Any        =       {"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[str]        =       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		:							Dict        =       f.read()
												UpperCAmelCase		:							Optional[int]        =       False
												while data:
																	UpperCAmelCase		:							Any        =       struct.unpack("Q"   ,      data[:8]      )

																	UpperCAmelCase		:							Any        =       data[1_2 : 1_2 + int(header[0]      )]  # 8+4
																	UpperCAmelCase		:							Optional[Any]        =       data[1_2 + int(header[0]      ) + 4 :]
																	UpperCAmelCase		:							str        =       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		:							int        =       True
												self.assertTrue(snake_case   ,      "Converted tensor was not found in the log file!"      )







		def 				A_	(			self      ):


							'''simple docstring'''

							with self.assertRaisesRegex(snake_case   ,      "Logging with `tensorboard` requires a `logging_dir`"      ):
												UpperCAmelCase		:							Optional[int]        =       Accelerator(log_with="tensorboard"      )
							with tempfile.TemporaryDirectory() as dirpath:
												UpperCAmelCase		:							Dict        =       Accelerator(log_with="tensorboard"   ,      project_dir=snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

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







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


		"""simple docstring"""

		def 				A_	(			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 				A_	(			snake_case   ,      snake_case   ,      snake_case = True      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       rf"{section} ([\S\s]*?)\n\n"
							if record:
												UpperCAmelCase		:							Union[str, Any]        =       rf"Record: {pattern}"
							UpperCAmelCase		:							Dict        =       re.findall(snake_case   ,      snake_case      )[0]
							# A config
							if section == "config" or section == "history":
												UpperCAmelCase		:							Union[str, Any]        =       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 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple        =       "test_project_with_config"
							UpperCAmelCase		:							List[Any]        =       Accelerator(log_with="wandb"      )
							UpperCAmelCase		:							Optional[Any]        =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
							UpperCAmelCase		:							Optional[Any]        =       {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(snake_case   ,      snake_case   ,      snake_case      )
							UpperCAmelCase		:							List[str]        =       {"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		:							int        =       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		:							str        =       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		:							Optional[Any]        =       self.parse_log(snake_case   ,      "run"   ,      snake_case      )
							self.assertEqual(logged_items["tags"]   ,      "my_tag"      )

							# Actual logging
							UpperCAmelCase		:							Dict        =       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 				lowercase (					self			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[int]        =       run_name
					UpperCAmelCase		:							Optional[int]        =       OfflineExperiment(project_name=__magic_name__			,   offline_directory=__magic_name__							)
					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   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		@staticmethod
		def 				A_	(			snake_case   ,      snake_case   ,      snake_case = False      ):


							'''simple docstring'''

							for log in log_list:
												UpperCAmelCase		:							List[Any]        =       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 				A_	(			self      ):


							'''simple docstring'''

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


							'''simple docstring'''

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


		"""simple docstring"""

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

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							"my_custom_tracker"
		SCREAMING_SNAKE_CASE__  :     Dict       =							False







		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       open(f"{dir}/log.csv"   ,      "w+"      )
							UpperCAmelCase		:							str        =       csv.DictWriter(self.f   ,      fieldnames=self._col_names      )
							self.writer.writeheader()







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return self.writer







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

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







		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''simple docstring'''

							self.f.close()







class   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		def 				A_	(			self      ):


							'''simple docstring'''

							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase		:							Optional[int]        =       MyCustomTracker(snake_case      )
												UpperCAmelCase		:							Tuple        =       Accelerator(log_with=snake_case      )
												UpperCAmelCase		:							List[Any]        =       {"num_iterations": 1_2, "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		:							Union[str, Any]        =       csv.DictReader(snake_case      )
																	UpperCAmelCase		:							str        =       next(snake_case      )
																	UpperCAmelCase		:							Union[str, Any]        =       {
																	    "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 				A_	(			self      ):


							'''simple docstring'''

							with tempfile.TemporaryDirectory() as d:
												UpperCAmelCase		:							int        =       MyCustomTracker(snake_case      )
												UpperCAmelCase		:							str        =       Accelerator(log_with=snake_case      )
												accelerator.init_trackers("Some name"      )
												UpperCAmelCase		:							List[Any]        =       {"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		:							Union[str, Any]        =       csv.DictReader(snake_case      )
																	UpperCAmelCase		:							List[str]        =       next(snake_case      )
																	UpperCAmelCase		:							str        =       {
																	    "total_loss": "0.1",
																	    "iteration": "1",
																	    "my_text": "some_value",
																	    "learning_rate": "",
																	    "num_iterations": "",
																	    "some_boolean": "",
																	    "some_string": "",
																	}
																	self.assertDictEqual(snake_case   ,      snake_case      )



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
