
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase__						:	int	     = '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

UpperCAmelCase__						:	Optional[Any]	     = '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


UpperCAmelCase__						:	str	     = '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'






def     lowerCamelCase__				(   a						,						a						,						a						,						a ) ->  List[str]:
   _A:    Dict						     =			[rgram for rgrams in rgramslist for rgram in rgrams]
   _A:    List[Any]						     =			Counter(a )

   _A:    int						     =			Counter(a )
   _A:    Optional[int]						     =			Counter()
   for sgram, scount in sgramcounter.items():
      _A:    Dict						     =			scount * numref

   _A:    Any						     =			Counter(a )
   _A:    List[str]						     =			Counter()
   for cgram, ccount in cgramcounter.items():
      _A:    Optional[Any]						     =			ccount * numref

   # KEEP
   _A:    Union[str, Any]						     =			sgramcounter_rep & cgramcounter_rep
   _A:    Tuple						     =			keepgramcounter_rep & rgramcounter
   _A:    Dict						     =			sgramcounter_rep & rgramcounter

   _A:    Union[str, Any]						     =			0
   _A:    Union[str, Any]						     =			0
   for keepgram in keepgramcountergood_rep:
      keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
      # Fix an alleged bug [2] in the keep score computation.
      # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
      keeptmpscorea += keepgramcountergood_rep[keepgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   #      a target exactly.
   _A:    str						     =			1
   _A:    List[str]						     =			1
   if len(a ) > 0:
      _A:    str						     =			keeptmpscorea / len(a )
   if len(a ) > 0:
      # Fix an alleged bug [2] in the keep score computation.
      # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
      _A:    str						     =			keeptmpscorea / sum(keepgramcounterall_rep.values() )
   _A:    int						     =			0
   if keepscore_precision > 0 or keepscore_recall > 0:
      _A:    List[str]						     =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

   # DELETION
   _A:    str						     =			sgramcounter_rep - cgramcounter_rep
   _A:    Tuple						     =			delgramcounter_rep - rgramcounter
   _A:    Optional[Any]						     =			sgramcounter_rep - rgramcounter
   _A:    Optional[int]						     =			0
   _A:    Union[str, Any]						     =			0
   for delgram in delgramcountergood_rep:
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   _A:    Optional[int]						     =			1
   if len(a ) > 0:
      _A:    int						     =			deltmpscorea / len(a )

   # ADDITION
   _A:    str						     =			set(a ) - set(a )
   _A:    Dict						     =			set(a ) & set(a )
   _A:    List[str]						     =			set(a ) - set(a )

   _A:    str						     =			0
   for addgram in addgramcountergood:
      addtmpscore += 1

   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   _A:    Optional[Any]						     =			1
   _A:    List[str]						     =			1
   if len(a ) > 0:
      _A:    Union[str, Any]						     =			addtmpscore / len(a )
   if len(a ) > 0:
      _A:    Dict						     =			addtmpscore / len(a )
   _A:    List[str]						     =			0
   if addscore_precision > 0 or addscore_recall > 0:
      _A:    str						     =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)






def     lowerCamelCase__				(   a						,						a						,						a ) ->  Tuple:
   _A:    List[str]						     =			len(a )

   _A:    Dict						     =			ssent.split(''' ''' )
   _A:    str						     =			csent.split(''' ''' )
   _A:    Any						     =			[]
   _A:    Any						     =			[]
   _A:    int						     =			[]
   _A:    List[str]						     =			[]
   _A:    List[Any]						     =			[]
   _A:    str						     =			[]

   _A:    Optional[int]						     =			[]
   _A:    Dict						     =			[]
   _A:    Tuple						     =			[]
   _A:    List[str]						     =			[]
   for rsent in rsents:
      _A:    Union[str, Any]						     =			rsent.split(''' ''' )
      _A:    List[Any]						     =			[]
      _A:    Optional[Any]						     =			[]
      _A:    Optional[int]						     =			[]
      ragramslist.append(a )
      for i in range(0						,						len(a ) - 1 ):
         if i < len(a ) - 1:
            _A:    Optional[Any]						     =			ragrams[i] + ''' ''' + ragrams[i + 1]
            ragrams.append(a )
         if i < len(a ) - 2:
            _A:    Any						     =			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
            ragrams.append(a )
         if i < len(a ) - 3:
            _A:    Any						     =			ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
            ragrams.append(a )
      ragramslist.append(a )
      ragramslist.append(a )
      ragramslist.append(a )

   for i in range(0						,						len(a ) - 1 ):
      if i < len(a ) - 1:
         _A:    Any						     =			sagrams[i] + ''' ''' + sagrams[i + 1]
         sagrams.append(a )
      if i < len(a ) - 2:
         _A:    Optional[int]						     =			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
         sagrams.append(a )
      if i < len(a ) - 3:
         _A:    Optional[Any]						     =			sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
         sagrams.append(a )

   for i in range(0						,						len(a ) - 1 ):
      if i < len(a ) - 1:
         _A:    List[str]						     =			cagrams[i] + ''' ''' + cagrams[i + 1]
         cagrams.append(a )
      if i < len(a ) - 2:
         _A:    Dict						     =			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
         cagrams.append(a )
      if i < len(a ) - 3:
         _A:    int						     =			cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
         cagrams.append(a )

   ((_A)     ,    (_A)     ,    (_A)):    List[Any]						     =			SARIngram(a						,						a						,						a						,						a )
   ((_A)     ,    (_A)     ,    (_A)):    Tuple						     =			SARIngram(a						,						a						,						a						,						a )
   ((_A)     ,    (_A)     ,    (_A)):    str						     =			SARIngram(a						,						a						,						a						,						a )
   ((_A)     ,    (_A)     ,    (_A)):    int						     =			SARIngram(a						,						a						,						a						,						a )
   _A:    str						     =			sum([keepascore, keepascore, keepascore, keepascore] ) / 4
   _A:    Union[str, Any]						     =			sum([delascore, delascore, delascore, delascore] ) / 4
   _A:    Any						     =			sum([addascore, addascore, addascore, addascore] ) / 4
   _A:    Optional[int]						     =			(avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore






def     lowerCamelCase__				(   a						,						a = True						,						a = "13a"						,						a = True ) ->  Union[str, Any]:
   # Normalization is requried for the ASSET dataset (one of the primary
   # datasets in sentence simplification) to allow using space
   # to split the sentence. Even though Wiki-Auto and TURK datasets,
   # do not require normalization, we do it for consistency.
   # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
   # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

   if lowercase:
      _A:    Tuple						     =			sentence.lower()

   if tokenizer in ["13a", "intl"]:
      if version.parse(sacrebleu.__version__ ).major >= 2:
         _A:    List[str]						     =			sacrebleu.metrics.bleu._get_tokenizer(a )()(a )
      else:
         _A:    Optional[int]						     =			sacrebleu.TOKENIZERS[tokenizer]()(a )
   elif tokenizer == "moses":
      _A:    List[str]						     =			sacremoses.MosesTokenizer().tokenize(a						,						return_str=a						,						escape=a )
   elif tokenizer == "penn":
      _A:    Optional[Any]						     =			sacremoses.MosesTokenizer().penn_tokenize(a						,						return_str=a )
   else:
      _A:    Tuple						     =			sentence

   if not return_str:
      _A:    Optional[int]						     =			normalized_sent.split()

   return normalized_sent






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION  )
class UpperCAmelCase  (		datasets.Metric  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION     ,     citation=_CITATION     ,     inputs_description=_KWARGS_DESCRIPTION     ,     features=datasets.Features(
              {
                  '''sources''': datasets.Value('''string'''     ,     id='''sequence'''	),
                  '''predictions''': datasets.Value('''string'''     ,     id='''sequence'''	),
                  '''references''': datasets.Sequence(datasets.Value('''string'''     ,     id='''sequence'''	)     ,     id='''references'''	),
              }	)     ,     codebase_urls=[
              '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
              '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
          ]     ,     reference_urls=['''https://www.aclweb.org/anthology/Q16-1029.pdf''']     ,     )


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Any	):

      """simple docstring"""
      if not (len(lowerCAmelCase_	) == len(lowerCAmelCase_	) == len(lowerCAmelCase_	)):
         raise ValueError('''Sources length must match predictions and references lengths.'''	)
      _A:    Any						     =			0
      for src, pred, refs in zip(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	):
         sari_score += SARIsent(normalize(lowerCAmelCase_	)     ,     normalize(lowerCAmelCase_	)     ,     [normalize(lowerCAmelCase_	) for sent in refs]	)
      _A:    Any						     =			sari_score / len(lowerCAmelCase_	)
      return {"sari": 1_0_0 * sari_score}





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__						:	int	     = logging.getLogger(__name__)






@require_tensorboard
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Any	):

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


   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    Tuple						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    str						     =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
         accelerator.log(lowerCAmelCase_     ,     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
         _A:    Optional[int]						     =			list(filter(lambda lowerCAmelCase_	: x.is_file()     ,     Path(F"""{dirpath}/{project_name}"""	).iterdir()	)	)[0]
         self.assertNotEqual(str(lowerCAmelCase_	)     ,     ''''''	)


   def 					__magic_name__							(   self	:				Union[str, Any]	):

      """simple docstring"""
      _A:    Tuple						     =			'''test_project_with_log'''
      with tempfile.TemporaryDirectory() as dirpath:
         _A:    int						     =			Accelerator(log_with='''tensorboard'''     ,     project_dir=lowerCAmelCase_	)
         accelerator.init_trackers(lowerCAmelCase_	)
         _A:    Optional[int]						     =			{'''tensor''': torch.tensor(1	)}
         accelerator.log(lowerCAmelCase_     ,     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
         _A:    Optional[int]						     =			list(filter(lambda lowerCAmelCase_	: 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(lowerCAmelCase_     ,     '''rb'''	) as f:
            _A:    Tuple						     =			f.read()
         _A:    Optional[Any]						     =			False
         while data:
            _A:    str						     =			struct.unpack('''Q'''     ,     data[:8]	)

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

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


   def 					__magic_name__							(   self	:				Union[str, Any]	):

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


   def 					__magic_name__							(   self	:				str	):

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






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





   '''simple docstring'''


   def 					__magic_name__							(   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 					__magic_name__							(   lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = True	):

      """simple docstring"""
      _A:    List[Any]						     =			RF"""{section} ([\S\s]*?)\n\n"""
      if record:
         _A:    Tuple						     =			RF"""Record: {pattern}"""
      _A:    int						     =			re.findall(lowerCAmelCase_     ,     lowerCAmelCase_	)[0]
      # A config
      if section == "config" or section == "history":
         _A:    Dict						     =			re.findall(R'''"([a-zA-Z0-9_.,]+)'''     ,     lowerCAmelCase_	)
         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]+)"'''     ,     lowerCAmelCase_	)	)


   @skip
   def 					__magic_name__							(   self	:				Dict	):

      """simple docstring"""
      _A:    int						     =			'''test_project_with_config'''
      _A:    List[str]						     =			Accelerator(log_with='''wandb'''	)
      _A:    List[str]						     =			{'''num_iterations''': 1_2, '''learning_rate''': 1e-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
      _A:    Optional[Any]						     =			{'''wandb''': {'''tags''': ['''my_tag''']}}
      accelerator.init_trackers(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)
      _A:    Union[str, Any]						     =			{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
      accelerator.log(lowerCAmelCase_     ,     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":
            _A:    Optional[Any]						     =			subprocess.check_output(
                ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(lowerCAmelCase_	)]     ,     env=os.environ.copy()	).decode('''utf8'''     ,     '''ignore'''	)
            break

        # Check HPS through careful parsing and cleaning
      _A:    Optional[int]						     =			self.parse_log(lowerCAmelCase_     ,     '''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
      _A:    Optional[int]						     =			self.parse_log(lowerCAmelCase_     ,     '''run'''     ,     lowerCAmelCase_	)
      self.assertEqual(logged_items['''tags''']     ,     '''my_tag'''	)

      # Actual logging
      _A:    Tuple						     =			self.parse_log(lowerCAmelCase_     ,     '''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						,						a						,						a ) ->  int:
   _A:    List[Any]						     =			run_name
   _A:    Any						     =			OfflineExperiment(project_name=a						,						offline_directory=a )
   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(SCREAMING_SNAKE_CASE__				,				'''__init__'''				,				SCREAMING_SNAKE_CASE__  )
class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   @staticmethod
   def 					__magic_name__							(   lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      for log in log_list:
         _A:    Optional[Any]						     =			json.loads(lowerCAmelCase_	)['''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 					__magic_name__							(   self	:				Optional[int]	):

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


   def 					__magic_name__							(   self	:				int	):

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






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       str												=       [
       '''total_loss''',
       '''iteration''',
       '''my_text''',
       '''learning_rate''',
       '''num_iterations''',
       '''some_boolean''',
       '''some_string''',
   ]

   __UpperCamelCase						:       Optional[int]												=       '''my_custom_tracker'''
   __UpperCamelCase						:       Optional[int]												=       False


   def __init__(   self	:				Dict     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Optional[int]						     =			open(F"""{dir}/log.csv"""     ,     '''w+'''	)
      _A:    str						     =			csv.DictWriter(self.f     ,     fieldnames=self._col_names	)
      self.writer.writeheader()


   @property
   def 					__magic_name__							(   self	:				int	):

      """simple docstring"""
      return self.writer


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				dict	):

      """simple docstring"""
      logger.info('''Call init'''	)
      self.writer.writerow(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				dict     ,     lowerCAmelCase_	:				Optional[int]	):

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


   def 					__magic_name__							(   self	:				Any	):

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






class UpperCAmelCase  (		unittest.TestCase  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple	):

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


   def 					__magic_name__							(   self	:				Any	):

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



