

'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class   lowerCAmelCase     :







							def __init__(							self			: Dict						,      __lowercase			: Any      ):







												"""simple docstring"""







												__lowercase					=data
												__lowercase					=None







							def __str__(							self			: List[str]      ):







												"""simple docstring"""







												return f'''{self.data}'''






class   lowerCAmelCase     :







							def __init__(							self			: Any      ):







												"""simple docstring"""







												__lowercase					=None
												__lowercase					=None







							def __iter__(							self			: List[Any]      ):







												"""simple docstring"""







												__lowercase					=self.front
												while node:
																	yield node.data
																	__lowercase					=node.next







							def __len__(							self			: List[Any]      ):







												"""simple docstring"""







												return len(tuple(iter(self      )      )      )







							def __str__(							self			: Tuple      ):







												"""simple docstring"""







												return " <- ".join(str(__lowercase      ) for item in self      )







							def 			snake_case   (							self			: Optional[int]      ):







												"""simple docstring"""







												return len(self      ) == 0







							def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: Any      ):







												"""simple docstring"""







												__lowercase					=Node(__lowercase      )
												if self.is_empty():
																	__lowercase					=__lowercase					=node
												else:
																	assert isinstance(self.rear						,      __lowercase      )
																	__lowercase					=node
																	__lowercase					=node







							def 			snake_case   (							self			: List[Any]      ):







												"""simple docstring"""







												if self.is_empty():
																	raise IndexError('dequeue from empty queue'      )
												assert isinstance(self.front						,      __lowercase      )
												__lowercase					=self.front
												__lowercase					=self.front.next
												if self.front is None:
																	__lowercase					=None
												return node.data







							def 			snake_case   (							self			: List[Any]      ):







												"""simple docstring"""







												__lowercase					=__lowercase					=None


if __name__ == "__main__":
							from doctest import testmod

							testmod()





'''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

UpperCAmelCase				   =  logging.getLogger(__name__)


@require_tensorboard
class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					='test_project_with_config'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 __lowercase					={'num_iterations': 12, '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('*/**'      ):
                      __lowercase					=list(filter(lambda __lowercase      : x.is_file()						,      child.iterdir()      )      )[0]
                 self.assertNotEqual(str(__lowercase      )						,      ''      )







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					='test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'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
                 __lowercase					=list(filter(lambda __lowercase      : 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:
                      __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(__lowercase      )
                      if event.HasField('summary'      ):
                           for value in event.summary.value:
                                if value.simple_value == 1.0 and value.tag == "tensor":
                                     __lowercase					=True
                 self.assertTrue(__lowercase						,      'Converted tensor was not found in the log file!'      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=ProjectConfiguration(total_limit=30      )
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase						,      project_config=__lowercase      )






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







       def 			snake_case   (							self			: Optional[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 			snake_case   (							__lowercase			: str						,      __lowercase			: str						,      __lowercase			: bool = True      ):







            """simple docstring"""







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







            """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(__lowercase						,      __lowercase						,      __lowercase      )
            __lowercase					={'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":
                      __lowercase					=subprocess.check_output(
                          ['wandb', 'sync', '--view', '--verbose', str(__lowercase      )]						,      env=os.environ.copy()      ).decode('utf8'						,      'ignore'      )
                      break

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

            # Actual logging
            __lowercase					=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 				__UpperCamelCase							(	self						:  Union[str, Any],					lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __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(A   ,			"__init__"   ,			A   )
class   lowerCAmelCase     (      unittest.TestCase   ):







       @staticmethod
       def 			snake_case   (							__lowercase			: List[Any]						,      __lowercase			: str						,      __lowercase			: bool = False      ):







            """simple docstring"""







            for log in log_list:
                 __lowercase					=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 			snake_case   (							self			: int      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=CometMLTracker('test_project_with_config'						,      __lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowercase						,      __lowercase      )
                 accelerator.end_training()
                 __lowercase					=os.listdir(__lowercase      )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 __lowercase					=os.path.join(__lowercase						,      __lowercase      )
                 __lowercase					=zipfile.ZipFile(__lowercase						,      'r'      )
                 __lowercase					=archive.open('messages.json'      ).read().decode('utf-8'      )
            __lowercase					=log.split('\n'      )[:-1]
            self.assertEqual(self.get_value_from_key(__lowercase						,      'num_iterations'						,      __lowercase      )						,      12      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'learning_rate'						,      __lowercase      )						,      0.0_1      )
            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 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







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

       lowerCAmelCase_		= "my_custom_tracker"
       lowerCAmelCase_		= False







       def __init__(							self			: List[str]						,      __lowercase			: str      ):







            """simple docstring"""







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







       @property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return self.writer







       def 			snake_case   (							self			: Tuple						,      __lowercase			: dict      ):







            """simple docstring"""







            logger.info('Call init'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: List[Any]						,      __lowercase			: dict						,      __lowercase			: Optional[int]      ):







            """simple docstring"""







            logger.info('Call log'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            self.f.close()






class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=MyCustomTracker(__lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 __lowercase					={'num_iterations': 12, '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:
                      __lowercase					=csv.DictReader(__lowercase      )
                      __lowercase					=next(__lowercase      )
                      __lowercase					={
                          'total_loss': '',
                          'iteration': '',
                          'my_text': '',
                          'learning_rate': '0.01',
                          'num_iterations': '12',
                          'some_boolean': 'False',
                          'some_string': 'some_value',
                      }
                      self.assertDictEqual(__lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







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


