

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   UpperCAmelCase  (							self      :		str )							->     Any:
      __lowerCAmelCase:       str            =				'test_project_with_config'
      with tempfile.TemporaryDirectory() as dirpath:
       __lowerCAmelCase:       str            =				Accelerator(log_with='tensorboard'      ,  project_dir=UpperCAmelCase )
       __lowerCAmelCase:       Any            =				{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
       accelerator.init_trackers(UpperCAmelCase      ,  UpperCAmelCase )
       accelerator.end_training()
       for child in Path(F'''{dirpath}/{project_name}''' ).glob('*/**' ):
        __lowerCAmelCase:       Union[str, Any]            =				list(filter(lambda UpperCAmelCase : x.is_file()      ,  child.iterdir() ) )[0]
       self.assertNotEqual(str(UpperCAmelCase )      ,  '' )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     List[str]:
      __lowerCAmelCase:       List[str]            =				'test_project_with_log'
      with tempfile.TemporaryDirectory() as dirpath:
       __lowerCAmelCase:       Union[str, Any]            =				Accelerator(log_with='tensorboard'      ,  project_dir=UpperCAmelCase )
       accelerator.init_trackers(UpperCAmelCase )
       __lowerCAmelCase:       int            =				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
       accelerator.log(UpperCAmelCase      ,  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
       __lowerCAmelCase:       str            =				list(filter(lambda UpperCAmelCase : x.is_file()      ,  Path(F'''{dirpath}/{project_name}''' ).iterdir() ) )[0]
       self.assertNotEqual(str(UpperCAmelCase )      ,  '' )


     def   UpperCAmelCase  (							self      :		List[str] )							->     Tuple:
      __lowerCAmelCase:       Optional[Any]            =				'test_project_with_log'
      with tempfile.TemporaryDirectory() as dirpath:
       __lowerCAmelCase:       Optional[Any]            =				Accelerator(log_with='tensorboard'      ,  project_dir=UpperCAmelCase )
       accelerator.init_trackers(UpperCAmelCase )
       __lowerCAmelCase:       str            =				{'tensor': torch.tensor(1 )}
       accelerator.log(UpperCAmelCase      ,  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
       __lowerCAmelCase:       List[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(UpperCAmelCase      ,  'rb' ) as f:
        __lowerCAmelCase:       Union[str, Any]            =				f.read()
       __lowerCAmelCase:       Optional[Any]            =				False
       while data:
        __lowerCAmelCase:       List[str]            =				struct.unpack('Q'      ,  data[:8] )

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

        event.ParseFromString(UpperCAmelCase )
        if event.HasField('summary' ):
         for value in event.summary.value:
          if value.simple_value == 1.0 and value.tag == "tensor":
           __lowerCAmelCase:       Tuple            =				True
       self.assertTrue(UpperCAmelCase      ,  'Converted tensor was not found in the log file!' )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Any:
      with self.assertRaisesRegex(UpperCAmelCase      ,  'Logging with `tensorboard` requires a `logging_dir`' ):
       __lowerCAmelCase:       Union[str, Any]            =				Accelerator(log_with='tensorboard' )
      with tempfile.TemporaryDirectory() as dirpath:
       __lowerCAmelCase:       Any            =				Accelerator(log_with='tensorboard'      ,  project_dir=UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Tuple:
      __lowerCAmelCase:       Optional[int]            =				ProjectConfiguration(total_limit=3_0 )
      with tempfile.TemporaryDirectory() as dirpath:
       __lowerCAmelCase:       List[str]            =				Accelerator(log_with='tensorboard'      ,  project_dir=UpperCAmelCase      ,  project_config=UpperCAmelCase )







@require_wandb
@mock.patch.dict(os.environ							,     {'WANDB_MODE': 'offline'} )
class 							A_			(		snake_case__							,     snake_case__ ):


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Union[str, Any]:
      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   UpperCAmelCase  (							UpperCAmelCase      :		str      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		bool = True )							->     Any:
      __lowerCAmelCase:       List[Any]            =				RF'''{section} ([\S\s]*?)\n\n'''
      if record:
       __lowerCAmelCase:       int            =				RF'''Record: {pattern}'''
      __lowerCAmelCase:       Optional[int]            =				re.findall(UpperCAmelCase      ,  UpperCAmelCase )[0]
      # A config
      if section == "config" or section == "history":
       __lowerCAmelCase:       int            =				re.findall(R'"([a-zA-Z0-9_.,]+)'      ,  UpperCAmelCase )
       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]+)"'      ,  UpperCAmelCase ) )


     @skip
     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     List[str]:
      __lowerCAmelCase:       List[Any]            =				'test_project_with_config'
      __lowerCAmelCase:       List[Any]            =				Accelerator(log_with='wandb' )
      __lowerCAmelCase:       Any            =				{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      __lowerCAmelCase:       str            =				{'wandb': {'tags': ['my_tag']}}
      accelerator.init_trackers(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      __lowerCAmelCase:       List[str]            =				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(UpperCAmelCase      ,  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":
        __lowerCAmelCase:       Optional[Any]            =				subprocess.check_output(
            ['wandb', 'sync', '--view', '--verbose', str(UpperCAmelCase )]      ,  env=os.environ.copy() ).decode('utf8'      ,  'ignore' )
        break

        # Check HPS through careful parsing and cleaning
      __lowerCAmelCase:       Any            =				self.parse_log(UpperCAmelCase      ,  '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
      __lowerCAmelCase:       Dict            =				self.parse_log(UpperCAmelCase      ,  'run'      ,  UpperCAmelCase )
      self.assertEqual(logged_items['tags']      ,  'my_tag' )

      # Actual logging
      __lowerCAmelCase:       str            =				self.parse_log(UpperCAmelCase      ,  '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		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str		)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				run_name
 __lowerCAmelCase:       Optional[int]            =				OfflineExperiment(project_name=SCREAMING_SNAKE_CASE   , offline_directory=SCREAMING_SNAKE_CASE		)
 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(snake_case__							,     '__init__'							,     snake_case__ )
class 							A_			(		unittest.TestCase ):


     @staticmethod
     def   UpperCAmelCase  (							UpperCAmelCase      :		int      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		bool = False )							->     List[Any]:
      for log in log_list:
       __lowerCAmelCase:       int            =				json.loads(UpperCAmelCase )['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   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
      with tempfile.TemporaryDirectory() as d:
       __lowerCAmelCase:       Tuple            =				CometMLTracker('test_project_with_config'      ,  UpperCAmelCase )
       __lowerCAmelCase:       str            =				Accelerator(log_with=UpperCAmelCase )
       __lowerCAmelCase:       Optional[int]            =				{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
       accelerator.init_trackers(UpperCAmelCase      ,  UpperCAmelCase )
       accelerator.end_training()
       __lowerCAmelCase:       Optional[int]            =				os.listdir(UpperCAmelCase )[0]  # Comet is nice, it's just a zip file here
       # We parse the raw logs
       __lowerCAmelCase:       Any            =				os.path.join(UpperCAmelCase      ,  UpperCAmelCase )
       __lowerCAmelCase:       str            =				zipfile.ZipFile(UpperCAmelCase      ,  'r' )
       __lowerCAmelCase:       Tuple            =				archive.open('messages.json' ).read().decode('utf-8' )
      __lowerCAmelCase:       Any            =				log.split('\n' )[:-1]
      self.assertEqual(self.get_value_from_key(UpperCAmelCase      ,  'num_iterations'      ,  UpperCAmelCase )      ,  1_2 )
      self.assertEqual(self.get_value_from_key(UpperCAmelCase      ,  'learning_rate'      ,  UpperCAmelCase )      ,  0.01 )
      self.assertEqual(self.get_value_from_key(UpperCAmelCase      ,  'some_boolean'      ,  UpperCAmelCase )      ,  UpperCAmelCase )
      self.assertEqual(self.get_value_from_key(UpperCAmelCase      ,  'some_string'      ,  UpperCAmelCase )      ,  'some_value' )


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







class 							A_			(		snake_case__ ):
     _lowercase      :					Optional[Any]       =     [
         'total_loss',
         'iteration',
         'my_text',
         'learning_rate',
         'num_iterations',
         'some_boolean',
         'some_string',
     ]

     _lowercase      :					List[Any]       =     'my_custom_tracker'
     _lowercase      :					List[Any]       =     False


     def __init__(							self      :		List[str]      ,  UpperCAmelCase      :		str )							->     Union[str, Any]:
      __lowerCAmelCase:       Any            =				open(F'''{dir}/log.csv'''      ,  'w+' )
      __lowerCAmelCase:       Optional[int]            =				csv.DictWriter(self.f      ,  fieldnames=self._col_names )
      self.writer.writeheader()


     @property
     def   UpperCAmelCase  (							self      :		List[str] )							->     Tuple:
      return self.writer


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		dict )							->     str:
      logger.info('Call init' )
      self.writer.writerow(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		dict      ,  UpperCAmelCase      :		Optional[int] )							->     str:
      logger.info('Call log' )
      self.writer.writerow(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     List[Any]:
      self.f.close()







class 							A_			(		unittest.TestCase ):


     def   UpperCAmelCase  (							self      :		Tuple )							->     Optional[int]:
      with tempfile.TemporaryDirectory() as d:
       __lowerCAmelCase:       Optional[int]            =				MyCustomTracker(UpperCAmelCase )
       __lowerCAmelCase:       Tuple            =				Accelerator(log_with=UpperCAmelCase )
       __lowerCAmelCase:       Optional[int]            =				{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
       accelerator.init_trackers('Some name'      ,  UpperCAmelCase )
       accelerator.end_training()
       with open(F'''{d}/log.csv'''      ,  'r' ) as f:
        __lowerCAmelCase:       int            =				csv.DictReader(UpperCAmelCase )
        __lowerCAmelCase:       List[str]            =				next(UpperCAmelCase )
        __lowerCAmelCase:       List[Any]            =				{
            'total_loss': '',
            'iteration': '',
            'my_text': '',
            'learning_rate': '0.01',
            'num_iterations': '12',
            'some_boolean': 'False',
            'some_string': 'some_value',
        }
        self.assertDictEqual(UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Any:
      with tempfile.TemporaryDirectory() as d:
       __lowerCAmelCase:       List[str]            =				MyCustomTracker(UpperCAmelCase )
       __lowerCAmelCase:       Dict            =				Accelerator(log_with=UpperCAmelCase )
       accelerator.init_trackers('Some name' )
       __lowerCAmelCase:       Union[str, Any]            =				{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
       accelerator.log(UpperCAmelCase      ,  step=0 )
       accelerator.end_training()
       with open(F'''{d}/log.csv'''      ,  'r' ) as f:
        __lowerCAmelCase:       Union[str, Any]            =				csv.DictReader(UpperCAmelCase )
        __lowerCAmelCase:       Union[str, Any]            =				next(UpperCAmelCase )
        __lowerCAmelCase:       Optional[Any]            =				{
            'total_loss': '0.1',
            'iteration': '1',
            'my_text': 'some_value',
            'learning_rate': '',
            'num_iterations': '',
            'some_boolean': '',
            'some_string': '',
        }
        self.assertDictEqual(UpperCAmelCase      ,  UpperCAmelCase )








from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any






class 							A_			:
     _lowercase      :					Any       =     ('_front', '_back', '_len')






     @dataclass
     class 							A_			:
          _lowercase      :					Any       =     None
          _lowercase      :					Deque._Node | None       =     None
          _lowercase      :					Deque._Node | None       =     None







     class 							A_			:
          _lowercase      :					Optional[Any]       =     ('_cur',)


          def __init__(							self      :		Any      ,  UpperCAmelCase      :		Deque._Node | None )							->     None:
           __lowerCAmelCase:       int            =				cur


          def __iter__(							self      :		int )							->     Deque._Iterator:
           return self


          def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Any:
           if self._cur is None:
            # finished iterating
            raise StopIteration
           __lowerCAmelCase:       Optional[Any]            =				self._cur.val
           __lowerCAmelCase:       List[Any]            =				self._cur.next_node

           return val







     def __init__(							self      :		Any      ,  UpperCAmelCase      :		Iterable[Any] | None = None )							->     None:
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       Any            =				None
      __lowerCAmelCase:       int            =				0

      if iterable is not None:
       # append every value to the deque
       for val in iterable:
        self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       Union[str, Any]            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       int            =				node
       __lowerCAmelCase:       List[Any]            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Optional[Any]            =				node
       __lowerCAmelCase:       str            =				self._back
       __lowerCAmelCase:       Optional[int]            =				node  # assign new back to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Any )							->     None:
      __lowerCAmelCase:       int            =				self._Node(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      if self.is_empty():
       # front = back
       __lowerCAmelCase:       Union[str, Any]            =				node
       __lowerCAmelCase:       str            =				1
      else:
       # connect nodes
       __lowerCAmelCase:       Dict            =				self._front
       __lowerCAmelCase:       Optional[int]            =				node
       __lowerCAmelCase:       Any            =				node  # assign new front to the new node

       self._len += 1

       # make sure there were no errors
       assert not self.is_empty(), "Error on appending value."


     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.append(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Iterable[Any] )							->     None:
      for val in iterable:
       self.appendleft(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[int]            =				self._back
      __lowerCAmelCase:       Optional[Any]            =				self._back.prev_node  # set new back
      # drop the last node - python will deallocate memory automatically
      __lowerCAmelCase:       Optional[int]            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     Any:
      assert not self.is_empty(), "Deque is empty."

      __lowerCAmelCase:       Optional[Any]            =				self._front
      __lowerCAmelCase:       Optional[Any]            =				self._front.next_node  # set new front and drop the first node
      __lowerCAmelCase:       int            =				None

      self._len -= 1

      return topop.val


     def   UpperCAmelCase  (							self      :		Dict )							->     bool:
      return self._front is None


     def __len__(							self      :		List[Any] )							->     int:
      return self._len


     def __eq__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		object )							->     bool:

      if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
       return NotImplemented

      __lowerCAmelCase:       List[Any]            =				self._front
      __lowerCAmelCase:       Dict            =				other._front

      # if the length of the dequeues are not the same, they are not equal
      if len(self ) != len(UpperCAmelCase ):
       return False

      while me is not None and oth is not None:
       # compare every value
       if me.val != oth.val:
        return False
       __lowerCAmelCase:       Optional[Any]            =				me.next_node
       __lowerCAmelCase:       Optional[Any]            =				oth.next_node

      return True


     def __iter__(							self      :		Tuple )							->     Deque._Iterator:
      return Deque._Iterator(self._front )


     def __repr__(							self      :		Optional[Any] )							->     str:
      __lowerCAmelCase:       Union[str, Any]            =				[]
      __lowerCAmelCase:       Optional[int]            =				self._front
      while aux is not None:
       # append the values in a list to display
       values_list.append(aux.val )
       __lowerCAmelCase:       Any            =				aux.next_node

      return F'''[{', '.join(repr(UpperCAmelCase ) for val in values_list )}]'''


if __name__ == "__main__":
  import doctest

  doctest.testmod()





