





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





@require_tensorboard
class lowerCamelCase    (unittest.TestCase       ):







  def 					UpperCAmelCase_					(		self		)		->    str:






        """simple docstring"""





        _snake_case		:						Optional[int]         =  '''test_project_with_config'''
        with tempfile.TemporaryDirectory() as dirpath:
              _snake_case		:						int         =  Accelerator(log_with='''tensorboard'''  ,  project_dir=lowercase__		)
              _snake_case		:						List[Any]         =  {'''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('''*/**'''		):
                    _snake_case		:						Optional[Any]         =  list(filter(lambda lowercase__		: x.is_file()  ,  child.iterdir()		)		)[0]
              self.assertNotEqual(str(lowercase__		)  ,  ''''''		)







  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





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







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






        """simple docstring"""





        _snake_case		:						Union[str, Any]         =  '''test_project_with_log'''
        with tempfile.TemporaryDirectory() as dirpath:
              _snake_case		:						Any         =  Accelerator(log_with='''tensorboard'''  ,  project_dir=lowercase__		)
              accelerator.init_trackers(lowercase__		)
              _snake_case		:						str         =  {'''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
              _snake_case		:						List[str]         =  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:
                    _snake_case		:						List[str]         =  f.read()
              _snake_case		:						Tuple         =  False
              while data:
                    _snake_case		:						str         =  struct.unpack('''Q'''  ,  data[:8]		)

                    _snake_case		:						List[Any]         =  data[12 : 12 + int(header[0]		)]  # 8+4
                    _snake_case		:						List[str]         =  data[12 + int(header[0]		) + 4 :]
                    _snake_case		:						Tuple         =  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":
                                      _snake_case		:						Any         =  True
              self.assertTrue(lowercase__  ,  '''Converted tensor was not found in the log file!'''		)







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        with self.assertRaisesRegex(lowercase__  ,  '''Logging with `tensorboard` requires a `logging_dir`'''		):
              _snake_case		:						Tuple         =  Accelerator(log_with='''tensorboard'''		)
        with tempfile.TemporaryDirectory() as dirpath:
              _snake_case		:						Optional[int]         =  Accelerator(log_with='''tensorboard'''  ,  project_dir=lowercase__		)







  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





        _snake_case		:						List[str]         =  ProjectConfiguration(total_limit=30		)
        with tempfile.TemporaryDirectory() as dirpath:
              _snake_case		:						Tuple         =  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 					UpperCAmelCase_					(		self		)		->    List[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 					UpperCAmelCase_					(		lowercase__  ,  lowercase__  ,  lowercase__ = True		)		->    Dict:






        """simple docstring"""





        _snake_case		:						Tuple         =  rF'''{section} ([\S\s]*?)\n\n'''
        if record:
              _snake_case		:						Optional[Any]         =  rF'''Record: {pattern}'''
        _snake_case		:						List[str]         =  re.findall(lowercase__  ,  lowercase__		)[0]
        # A config
        if section == "config" or section == "history":
              _snake_case		:						str         =  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 					UpperCAmelCase_					(		self		)		->    List[Any]:






        """simple docstring"""





        _snake_case		:						Optional[int]         =  '''test_project_with_config'''
        _snake_case		:						Optional[int]         =  Accelerator(log_with='''wandb'''		)
        _snake_case		:						Union[str, Any]         =  {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
        _snake_case		:						Optional[Any]         =  {'''wandb''': {'''tags''': ['''my_tag''']}}
        accelerator.init_trackers(lowercase__  ,  lowercase__  ,  lowercase__		)
        _snake_case		:						Tuple         =  {'''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":
                    _snake_case		:						List[str]         =  subprocess.check_output(
                        ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowercase__		)]  ,  env=os.environ.copy()		).decode('''utf8'''  ,  '''ignore'''		)
                    break

        # Check HPS through careful parsing and cleaning
        _snake_case		:						Optional[Any]         =  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
        _snake_case		:						Tuple         =  self.parse_log(lowercase__  ,  '''run'''  ,  lowercase__		)
        self.assertEqual(logged_items['''tags''']  ,  '''my_tag'''		)

        # Actual logging
        _snake_case		:						Optional[int]         =  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  _a     (				self      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Optional[Any]         =  run_name
      _snake_case		:						List[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(a__    ,   """__init__"""    ,   a__       )
class lowerCamelCase    (unittest.TestCase       ):







  @staticmethod
  def 					UpperCAmelCase_					(		lowercase__  ,  lowercase__  ,  lowercase__ = False		)		->    List[Any]:






        """simple docstring"""





        for log in log_list:
              _snake_case		:						Any         =  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 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





        with tempfile.TemporaryDirectory() as d:
              _snake_case		:						str         =  CometMLTracker('''test_project_with_config'''  ,  lowercase__		)
              _snake_case		:						List[str]         =  Accelerator(log_with=lowercase__		)
              _snake_case		:						List[str]         =  {'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
              accelerator.init_trackers(lowercase__  ,  lowercase__		)
              accelerator.end_training()
              _snake_case		:						Tuple         =  os.listdir(lowercase__		)[0]  # Comet is nice, it's just a zip file here
              # We parse the raw logs
              _snake_case		:						Tuple         =  os.path.join(lowercase__  ,  lowercase__		)
              _snake_case		:						Any         =  zipfile.ZipFile(lowercase__  ,  '''r'''		)
              _snake_case		:						Union[str, Any]         =  archive.open('''messages.json'''		).read().decode('''utf-8'''		)
        _snake_case		:						List[Any]         =  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__		)  ,  0.01		)
        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 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





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

  _lowercase	:						Any							       =					"""my_custom_tracker"""
  _lowercase	:						str							       =					False







  def __init__(		self  ,  lowercase__		)		->    Any:






        """simple docstring"""





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







  @property
  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        return self.writer







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    Tuple:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__		)		->    int:






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        self.f.close()





class lowerCamelCase    (unittest.TestCase       ):







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        with tempfile.TemporaryDirectory() as d:
              _snake_case		:						Union[str, Any]         =  MyCustomTracker(lowercase__		)
              _snake_case		:						Optional[int]         =  Accelerator(log_with=lowercase__		)
              _snake_case		:						str         =  {'''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:
                    _snake_case		:						str         =  csv.DictReader(lowercase__		)
                    _snake_case		:						str         =  next(lowercase__		)
                    _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 					UpperCAmelCase_					(		self		)		->    Optional[int]:






        """simple docstring"""





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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase       :		Dict		=				{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Tuple		=				['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		List[str]		=				[
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Any		=				['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Optional[Any]		=				['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

   UpperCAmelCase       :		int		=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


