

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



@require_tensorboard
class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


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


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_log'
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )
      accelerator.init_trackers(a_     )
      UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(a_   , 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 a_     : x.is_file()   , Path(F'''{dirpath}/{project_name}'''     ).iterdir()     )     )[0]
      self.assertNotEqual(str(a_     )   , ''     )
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_log'
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )
      accelerator.init_trackers(a_     )
      UpperCAmelCase													=	{'tensor': torch.tensor(1     )}
      accelerator.log(a_   , 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 a_     : 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(a_   , 'rb'     ) as f:
        UpperCAmelCase													=	f.read()
      UpperCAmelCase													=	False
      while data:
        UpperCAmelCase													=	struct.unpack('Q'   , data[:8]     )

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

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


    """simple docstring"""
    with self.assertRaisesRegex(a_   , 'Logging with `tensorboard` requires a `logging_dir`'     ):
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'     )
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )





  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	ProjectConfiguration(total_limit=3_0     )
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_   , project_config=a_     )




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

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[Any]:


    """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_			(     a_   , a_   , a_ = True     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	rF'''{section} ([\S\s]*?)\n\n'''
    if record:
      UpperCAmelCase													=	rF'''Record: {pattern}'''
    UpperCAmelCase													=	re.findall(a_   , a_     )[0]
    # A config
    if section == "config" or section == "history":
      UpperCAmelCase													=	re.findall(r'"([a-zA-Z0-9_.,]+)'   , a_     )
      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]+)"'   , a_     )     )





  @skip
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_config'
    UpperCAmelCase													=	Accelerator(log_with='wandb'     )
    UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
    UpperCAmelCase													=	{'wandb': {'tags': ['my_tag']}}
    accelerator.init_trackers(a_   , a_   , a_     )
    UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
    accelerator.log(a_   , 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(a_     )]   , env=os.environ.copy()     ).decode('utf8'   , 'ignore'     )
        break

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

    # Actual logging
    UpperCAmelCase													=	self.parse_log(a_   , '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      :   Union[str, Any]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	run_name
  UpperCAmelCase													=	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(a ,       "__init__" ,       a )
class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  @staticmethod
  def 						snake_case_			(     a_   , a_   , a_ = False     )							->				Optional[int]:


    """simple docstring"""
    for log in log_list:
      UpperCAmelCase													=	json.loads(a_     )['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     )							->				List[str]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	CometMLTracker('test_project_with_config'   , a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      accelerator.init_trackers(a_   , a_     )
      accelerator.end_training()
      UpperCAmelCase													=	os.listdir(a_     )[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      UpperCAmelCase													=	os.path.join(a_   , a_     )
      UpperCAmelCase													=	zipfile.ZipFile(a_   , 'r'     )
      UpperCAmelCase													=	archive.open('messages.json'     ).read().decode('utf-8'     )
    UpperCAmelCase													=	log.split('\n'     )[:-1]
    self.assertEqual(self.get_value_from_key(a_   , 'num_iterations'   , a_     )   , 1_2     )
    self.assertEqual(self.get_value_from_key(a_   , 'learning_rate'   , a_     )   , 0.01     )
    self.assertEqual(self.get_value_from_key(a_   , 'some_boolean'   , a_     )   , a_     )
    self.assertEqual(self.get_value_from_key(a_   , 'some_string'   , a_     )   , 'some_value'     )





  def 						snake_case_			(     self     )							->				Union[str, Any]:


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




class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: int      =   [
      "total_loss",
      "iteration",
      "my_text",
      "learning_rate",
      "num_iterations",
      "some_boolean",
      "some_string",
  ]

  __lowerCAmelCase					: Optional[Any]      =   "my_custom_tracker"
  __lowerCAmelCase					: Optional[Any]      =   False
  def __init__(     self   , a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	open(F'''{dir}/log.csv'''   , 'w+'     )
    UpperCAmelCase													=	csv.DictWriter(self.f   , fieldnames=self._col_names     )
    self.writer.writeheader()
  @property
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return self.writer
  def 						snake_case_			(     self   , a_     )							->				Any:


    """simple docstring"""
    logger.info('Call init'     )
    self.writer.writerow(a_     )
  def 						snake_case_			(     self   , a_   , a_     )							->				Union[str, Any]:


    """simple docstring"""
    logger.info('Call log'     )
    self.writer.writerow(a_     )





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    self.f.close()




class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	MyCustomTracker(a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      accelerator.init_trackers('Some name'   , a_     )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''   , 'r'     ) as f:
        UpperCAmelCase													=	csv.DictReader(a_     )
        UpperCAmelCase													=	next(a_     )
        UpperCAmelCase													=	{
            'total_loss': '',
            'iteration': '',
            'my_text': '',
            'learning_rate': '0.01',
            'num_iterations': '12',
            'some_boolean': 'False',
            'some_string': 'some_value',
        }
        self.assertDictEqual(a_   , a_     )





  def 						snake_case_			(     self     )							->				Optional[Any]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	MyCustomTracker(a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      accelerator.init_trackers('Some name'     )
      UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(a_   , step=0     )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''   , 'r'     ) as f:
        UpperCAmelCase													=	csv.DictReader(a_     )
        UpperCAmelCase													=	next(a_     )
        UpperCAmelCase													=	{
            'total_loss': '0.1',
            'iteration': '1',
            'my_text': 'some_value',
            'learning_rate': '',
            'num_iterations': '',
            'some_boolean': '',
            'some_string': '',
        }
        self.assertDictEqual(a_   , a_     )





'''simple docstring'''




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

_a				: List[Any] 							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


  def       lowerCamelCase__		(						cls      :   Dict						):
    return cls


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Union[Tuple, List]       ,   SCREAMING_SNAKE_CASE      :   Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.device"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.dtype"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.layout"] = None       ,   ):
  UpperCAmelCase													=	device
  UpperCAmelCase													=	shape[0]

  UpperCAmelCase													=	layout or torch.strided
  UpperCAmelCase													=	device or torch.device('cpu'						)

  if generator is not None:
    UpperCAmelCase													=	generator.device.type if not isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) else generator[0].device.type
    if gen_device_type != device.type and gen_device_type == "cpu":
      UpperCAmelCase													=	'cpu'
      if device != "mps":
        logger.info(
            f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
            f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
            f''' slighly speed up this function by passing a generator that was created on the {device} device.'''						)
    elif gen_device_type != device.type and gen_device_type == "cuda":
      raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''						)

  if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
    UpperCAmelCase													=	(1,) + shape[1:]
    UpperCAmelCase													=	[
        torch.randn(SCREAMING_SNAKE_CASE       ,   generator=generator[i]       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						)
        for i in range(SCREAMING_SNAKE_CASE						)
    ]
    UpperCAmelCase													=	torch.cat(SCREAMING_SNAKE_CASE       ,   dim=0						).to(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	torch.randn(SCREAMING_SNAKE_CASE       ,   generator=SCREAMING_SNAKE_CASE       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)

  return latents



def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Tuple						):
  if is_torch_version('<'       ,   '2.0.0'						) or not hasattr(SCREAMING_SNAKE_CASE       ,   '_dynamo'						):
    return False
  return isinstance(SCREAMING_SNAKE_CASE       ,   torch._dynamo.eval_frame.OptimizedModule						)


