import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCAmelCase      		=					logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

UpperCAmelCase      		=					{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase      		=					{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase      		=					sorted(arg_to_scheduler.keys())
UpperCAmelCase      		=					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class  __lowerCAmelCase	(							pl.LightningModule    ):



 '''simple docstring'''

 def __init__(      self							:		Dict							,      lowerCamelCase							:		argparse.Namespace							,      lowerCamelCase							:		List[Any]=None							,      lowerCamelCase							:		List[str]="base"							,      lowerCamelCase							:		Union[str, Any]=None							,      lowerCamelCase							:		str=None							,      lowerCamelCase							:		Tuple=None							,      **lowerCamelCase							:		int							,      )			->				Any:
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	0
        _UpperCamelCase  :							Optional[Any]	    =	Path(self.hparams.output_dir	)
        _UpperCamelCase  :							List[str]	    =	self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
               _UpperCamelCase  :							Optional[Any]	    =	AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,      **({'num_labels': num_labels} if num_labels is not None else {})							,      cache_dir=lowerCamelCase							,      **lowerCamelCase							,      )
        else:
               _UpperCamelCase  :							PretrainedConfig	    =	config

        _UpperCamelCase  :							Optional[Any]	    =	('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
        for p in extra_model_params:
               if getattr(self.hparams							,      lowerCamelCase							,      lowerCamelCase	):
                      assert hasattr(self.config							,      lowerCamelCase	), f"model config doesn't have a `{p}` attribute"
                      setattr(self.config							,      lowerCamelCase							,      getattr(self.hparams							,      lowerCamelCase	)	)

        if tokenizer is None:
               _UpperCamelCase  :							str	    =	AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,      cache_dir=lowerCamelCase							,      )
        else:
               _UpperCamelCase  :							PreTrainedTokenizer	    =	tokenizer
        _UpperCamelCase  :							Optional[int]	    =	MODEL_MODES[mode]
        if model is None:
               _UpperCamelCase  :							Optional[int]	    =	self.model_type.from_pretrained(
                   self.hparams.model_name_or_path							,      from_tf=bool('.ckpt' in self.hparams.model_name_or_path	)							,      config=self.config							,      cache_dir=lowerCamelCase							,      )
        else:
               _UpperCamelCase  :							int	    =	model

 def        __snake_case       (      self							:		Dict							,      *lowerCamelCase							:		Dict							,      **lowerCamelCase							:		Union[str, Any]	)			->				Tuple:
        _UpperCamelCase  :							Dict	    =	self.model_type.from_pretrained(*lowerCamelCase							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							str	    =	arg_to_scheduler[self.hparams.lr_scheduler]
        _UpperCamelCase  :							Union[str, Any]	    =	get_schedule_func(
            self.opt							,      num_warmup_steps=self.hparams.warmup_steps							,      num_training_steps=self.total_steps()	)
        _UpperCamelCase  :							str	    =	{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
        return scheduler

 def        __snake_case       (      self							:		int	)			->				Optional[int]:
        _UpperCamelCase  :							Optional[int]	    =	self.model
        _UpperCamelCase  :							Union[str, Any]	    =	['bias', 'LayerNorm.weight']
        _UpperCamelCase  :							str	    =	[
            {
                'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
                'weight_decay': self.hparams.weight_decay,
            },
            {
                'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
                'weight_decay': 0.0,
            },
        ]
        if self.hparams.adafactor:
               _UpperCamelCase  :							Tuple	    =	Adafactor(
                   lowerCamelCase							,      lr=self.hparams.learning_rate							,      scale_parameter=lowerCamelCase							,      relative_step=lowerCamelCase	)

        else:
               _UpperCamelCase  :							int	    =	AdamW(
                   lowerCamelCase							,      lr=self.hparams.learning_rate							,      eps=self.hparams.adam_epsilon	)
        _UpperCamelCase  :							str	    =	optimizer

        _UpperCamelCase  :							List[str]	    =	self.get_lr_scheduler()

        return [optimizer], [scheduler]

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Any	)			->				List[str]:
        return self.validation_step(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		Optional[Any]	)			->				str:
        return self.validation_end(lowerCamelCase	)

 def        __snake_case       (      self							:		str	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	max(1							,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
        _UpperCamelCase  :							Dict	    =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Tuple	)			->				Union[str, Any]:
        if mode == "test":
               _UpperCamelCase  :							Any	    =	len(self.test_dataloader().dataset	)
        else:
               _UpperCamelCase  :							List[Any]	    =	self.get_dataloader('train'							,      self.hparams.train_batch_size							,      shuffle=lowerCamelCase	)
               _UpperCamelCase  :							Optional[Any]	    =	len(self.train_dataloader().dataset	)

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		bool = False	)			->				List[str]:
        raise NotImplementedError('You must implement this for your task'	)

 def        __snake_case       (      self							:		List[str]	)			->				Tuple:
        return self.train_loader

 def        __snake_case       (      self							:		Optional[int]	)			->				List[Any]:
        return self.get_dataloader('dev'							,      self.hparams.eval_batch_size							,      shuffle=lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        return self.get_dataloader('test'							,      self.hparams.eval_batch_size							,      shuffle=lowerCamelCase	)

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		int	)			->				Optional[Any]:
        return os.path.join(
            self.hparams.data_dir							,      'cached_{}_{}_{}'.format(
                lowerCamelCase							,      list(filter(lowerCamelCase							,      self.hparams.model_name_or_path.split('/'	)	)	).pop()							,      str(self.hparams.max_seq_length	)							,      )							,      )

 @pl.utilities.rank_zero_only
 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Dict[str, Any]	)			->				None:
        _UpperCamelCase  :							List[str]	    =	self.output_dir.joinpath('best_tfmr'	)
        _UpperCamelCase  :							Optional[int]	    =	self.step_count
        self.model.save_pretrained(lowerCamelCase	)
        self.tokenizer.save_pretrained(lowerCamelCase	)

 @staticmethod
 def        __snake_case       (      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Optional[Any]	)			->				int:
        parser.add_argument(
            '--model_name_or_path'							,      default=lowerCamelCase							,      type=lowerCamelCase							,      required=lowerCamelCase							,      help='Path to pretrained model or model identifier from huggingface.co/models'							,      )
        parser.add_argument(
            '--config_name'							,      default=''							,      type=lowerCamelCase							,      help='Pretrained config name or path if not the same as model_name'	)
        parser.add_argument(
            '--tokenizer_name'							,      default=lowerCamelCase							,      type=lowerCamelCase							,      help='Pretrained tokenizer name or path if not the same as model_name'							,      )
        parser.add_argument(
            '--cache_dir'							,      default=''							,      type=lowerCamelCase							,      help='Where do you want to store the pre-trained models downloaded from huggingface.co'							,      )
        parser.add_argument(
            '--encoder_layerdrop'							,      type=lowerCamelCase							,      help='Encoder layer dropout probability (Optional). Goes into model.config'							,      )
        parser.add_argument(
            '--decoder_layerdrop'							,      type=lowerCamelCase							,      help='Decoder layer dropout probability (Optional). Goes into model.config'							,      )
        parser.add_argument(
            '--dropout'							,      type=lowerCamelCase							,      help='Dropout probability (Optional). Goes into model.config'							,      )
        parser.add_argument(
            '--attention_dropout'							,      type=lowerCamelCase							,      help='Attention dropout probability (Optional). Goes into model.config'							,      )
        parser.add_argument('--learning_rate'							,      default=5e-5							,      type=lowerCamelCase							,      help='The initial learning rate for Adam.'	)
        parser.add_argument(
            '--lr_scheduler'							,      default='linear'							,      choices=lowerCamelCase							,      metavar=lowerCamelCase							,      type=lowerCamelCase							,      help='Learning rate scheduler'							,      )
        parser.add_argument('--weight_decay'							,      default=0.0							,      type=lowerCamelCase							,      help='Weight decay if we apply some.'	)
        parser.add_argument('--adam_epsilon'							,      default=1e-8							,      type=lowerCamelCase							,      help='Epsilon for Adam optimizer.'	)
        parser.add_argument('--warmup_steps'							,      default=0							,      type=lowerCamelCase							,      help='Linear warmup over warmup_steps.'	)
        parser.add_argument('--num_workers'							,      default=4							,      type=lowerCamelCase							,      help='kwarg passed to DataLoader'	)
        parser.add_argument('--num_train_epochs'							,      dest='max_epochs'							,      default=3							,      type=lowerCamelCase	)
        parser.add_argument('--train_batch_size'							,      default=3_2							,      type=lowerCamelCase	)
        parser.add_argument('--eval_batch_size'							,      default=3_2							,      type=lowerCamelCase	)
        parser.add_argument('--adafactor'							,      action='store_true'	)







class  __lowerCAmelCase	(							pl.Callback    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Tuple	)			->				List[Any]:
        _UpperCamelCase  :							int	    =	trainer.lr_schedulers[0]['scheduler']
        _UpperCamelCase  :							str	    =	{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
        pl_module.logger.log_metrics(lowerCamelCase	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		pl.Trainer							,      lowerCamelCase							:		pl.LightningModule	)			->				List[str]:
        rank_zero_info('***** Validation results *****'	)
        _UpperCamelCase  :							Tuple	    =	trainer.callback_metrics
        # Log results
        for key in sorted(lowerCamelCase	):
               if key not in ["log", "progress_bar"]:
                      rank_zero_info('{} = {}\n'.format(lowerCamelCase							,      str(metrics[key]	)	)	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		pl.Trainer							,      lowerCamelCase							:		pl.LightningModule	)			->				Tuple:
        rank_zero_info('***** Test results *****'	)
        _UpperCamelCase  :							Optional[int]	    =	trainer.callback_metrics
        # Log and save results to file
        _UpperCamelCase  :							int	    =	os.path.join(pl_module.hparams.output_dir							,      'test_results.txt'	)
        with open(lowerCamelCase							,      'w'	) as writer:
               for key in sorted(lowerCamelCase	):
                      if key not in ["log", "progress_bar"]:
                             rank_zero_info('{} = {}\n'.format(lowerCamelCase							,      str(metrics[key]	)	)	)
                             writer.write('{} = {}\n'.format(lowerCamelCase							,      str(metrics[key]	)	)	)







def SCREAMING_SNAKE_CASE					(  lowercase_:						int		,    lowercase_:						List[Any]					):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '--output_dir'		,    default=lowercase_		,    type=lowercase_		,    required=lowercase_		,    help='The output directory where the model predictions and checkpoints will be written.'		,    )
       parser.add_argument(
           '--fp16'		,    action='store_true'		,    help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'		,    )

       parser.add_argument(
           '--fp16_opt_level'		,    type=lowercase_		,    default='O2'		,    help=(
               'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
               'See details at https://nvidia.github.io/apex/amp.html'
           )		,    )
       parser.add_argument('--n_tpu_cores'		,    dest='tpu_cores'		,    type=lowercase_					)
       parser.add_argument('--max_grad_norm'		,    dest='gradient_clip_val'		,    default=1.0		,    type=lowercase_		,    help='Max gradient norm'					)
       parser.add_argument('--do_train'		,    action='store_true'		,    help='Whether to run training.'					)
       parser.add_argument('--do_predict'		,    action='store_true'		,    help='Whether to run predictions on the test set.'					)
       parser.add_argument(
           '--gradient_accumulation_steps'		,    dest='accumulate_grad_batches'		,    type=lowercase_		,    default=1		,    help='Number of updates steps to accumulate before performing a backward/update pass.'		,    )
       parser.add_argument('--seed'		,    type=lowercase_		,    default=42		,    help='random seed for initialization'					)
       parser.add_argument(
           '--data_dir'		,    default=lowercase_		,    type=lowercase_		,    required=lowercase_		,    help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		,    )





def SCREAMING_SNAKE_CASE					(  lowercase_:						BaseTransformer		,    lowercase_:						argparse.Namespace		,    lowercase_:						int=None		,    lowercase_:						Tuple=True		,    lowercase_:						int=[]		,    lowercase_:						Optional[Any]=None		,    lowercase_:						Dict=None		,    **lowercase_:						Any		,    ):
       pl.seed_everything(args.seed					)

       # init model
       _UpperCamelCase  :							Tuple	    =	Path(model.hparams.output_dir					)
       odir.mkdir(exist_ok=lowercase_					)

       # add custom checkpoints
       if checkpoint_callback is None:
              _UpperCamelCase  :							str	    =	pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir		,    prefix='checkpoint'		,    monitor='val_loss'		,    mode='min'		,    save_top_k=1					)
       if early_stopping_callback:
              extra_callbacks.append(lowercase_					)
       if logging_callback is None:
              _UpperCamelCase  :							Dict	    =	LoggingCallback()

       _UpperCamelCase  :							List[str]	    =	{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              _UpperCamelCase  :							List[Any]	    =	16
              _UpperCamelCase  :							Dict	    =	args.fpaa_opt_level

       if args.gpus > 1:
              _UpperCamelCase  :							Dict	    =	'ddp'

       _UpperCamelCase  :							Union[str, Any]	    =	args.accumulate_grad_batches
       _UpperCamelCase  :							Tuple	    =	extra_train_kwargs.get('accelerator'		,    lowercase_					)
       _UpperCamelCase  :							str	    =	extra_train_kwargs.get('profiler'		,    lowercase_					)

       _UpperCamelCase  :							Tuple	    =	pl.Trainer.from_argparse_args(
           lowercase_		,    weights_summary=lowercase_		,    callbacks=[logging_callback] + extra_callbacks		,    logger=lowercase_		,    checkpoint_callback=lowercase_		,    **lowercase_		,    )

       if args.do_train:
              trainer.fit(lowercase_					)

       return trainer




"""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       :    Any         =		logging.getLogger(__name__)
@require_tensorboard
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Tuple:
       _lowercase							     =       "test_project_with_config"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          for child in Path(f"""{dirpath}/{project_name}"""		).glob("*/**"		):
             _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						child.iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       str		)						->    str:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(_lowercase   ,						step=0		)
          accelerator.end_training()
          # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
          # Names are randomly generated each time
          _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						Path(f"""{dirpath}/{project_name}"""		).iterdir()		)		)[0]
          self.assertNotEqual(str(_lowercase		)   ,						""		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    int:
       _lowercase							     =       "test_project_with_log"
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"tensor": torch.tensor(1		)}
          accelerator.log(_lowercase   ,						step=0		)
          accelerator.end_training()
          # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
          # Names are randomly generated each time
          _lowercase							     =       list(filter(lambda _lowercase		: x.is_file()   ,						Path(f"""{dirpath}/{project_name}"""		).iterdir()		)		)[0]
          # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
          with open(_lowercase   ,						"rb"		) as f:
             _lowercase							     =       f.read()
          _lowercase							     =       False
          while data:
             _lowercase							     =       struct.unpack("Q"   ,						data[:8]		)

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

             event.ParseFromString(_lowercase		)
             if event.HasField("summary"		):
                for value in event.summary.value:
                   if value.simple_value == 1.0 and value.tag == "tensor":
                      _lowercase							     =       True
          self.assertTrue(_lowercase   ,						"Converted tensor was not found in the log file!"		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Dict:
       with self.assertRaisesRegex(_lowercase   ,						"Logging with `tensorboard` requires a `logging_dir`"		):
          _lowercase							     =       Accelerator(log_with="tensorboard"		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase		)




    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Dict:
       _lowercase							     =       ProjectConfiguration(total_limit=3_0		)
       with tempfile.TemporaryDirectory() as dirpath:
          _lowercase							     =       Accelerator(log_with="tensorboard"   ,						project_dir=_lowercase   ,						project_config=_lowercase		)
@require_wandb
@mock.patch.dict(os.environ,    {"""WANDB_MODE""": """offline"""}   )
class   UpperCAmelCase_	(				lowercase__,    lowercase__   ):
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       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 		_lowerCamelCase      (	_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       bool = True		)						->    Dict:
       _lowercase							     =       Rf"""{section} ([\S\s]*?)\n\n"""
       if record:
          _lowercase							     =       Rf"""Record: {pattern}"""
       _lowercase							     =       re.findall(_lowercase   ,						_lowercase		)[0]
       # A config
       if section == "config" or section == "history":
          _lowercase							     =       re.findall(R"\"([a-zA-Z0-9_.,]+)"   ,						_lowercase		)
          return {key: val for key, val in zip(cleaned_record[0::2]   ,						cleaned_record[1::2]		)}
       # Everything else
       else:
          return dict(re.findall(R"(\w+): \"([^\s]+)\""   ,						_lowercase		)		)




    @skip
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Union[str, Any]:
       _lowercase							     =       "test_project_with_config"
       _lowercase							     =       Accelerator(log_with="wandb"		)
       _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
       _lowercase							     =       {"wandb": {"tags": ["my_tag"]}}
       accelerator.init_trackers(_lowercase   ,						_lowercase   ,						_lowercase		)
       _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
       accelerator.log(_lowercase   ,						step=0		)
       accelerator.end_training()
       # The latest offline log is stored at wandb/latest-run/*.wandb
       for child in Path(f"""{self.tmpdir}/wandb/latest-run"""		).glob("*"		):
          if child.is_file() and child.suffix == ".wandb":
             _lowercase							     =       subprocess.check_output(
                 ["wandb", "sync", "--view", "--verbose", str(_lowercase		)]   ,						env=os.environ.copy()		).decode("utf8"   ,						"ignore"		)
             break

        # Check HPS through careful parsing and cleaning
       _lowercase							     =       self.parse_log(_lowercase   ,						"config"		)
       self.assertEqual(logged_items["num_iterations"]   ,						"12"		)
       self.assertEqual(logged_items["learning_rate"]   ,						"0.01"		)
       self.assertEqual(logged_items["some_boolean"]   ,						"false"		)
       self.assertEqual(logged_items["some_string"]   ,						"some_value"		)
       self.assertEqual(logged_items["some_string"]   ,						"some_value"		)

       # Run tags
       _lowercase							     =       self.parse_log(_lowercase   ,						"run"   ,						_lowercase		)
       self.assertEqual(logged_items["tags"]   ,						"my_tag"		)

       # Actual logging
       _lowercase							     =       self.parse_log(_lowercase   ,						"history"		)
       self.assertEqual(logged_items["total_loss"]   ,						"0.1"		)
       self.assertEqual(logged_items["iteration"]   ,						"1"		)
       self.assertEqual(logged_items["my_text"]   ,						"some_value"		)
       self.assertEqual(logged_items["_step"]   ,						"0"		)


def   __UpperCAmelCase     (	self		:       Optional[int],				_snake_case		:       str,				_snake_case		:       str       ):
   _lowercase							     =       run_name
   _lowercase							     =       OfflineExperiment(project_name=_snake_case,				offline_directory=_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(lowercase__,    """__init__""",    lowercase__   )
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @staticmethod
    def 		_lowerCamelCase      (	_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       bool = False		)						->    List[str]:
       for log in log_list:
          _lowercase							     =       json.loads(_lowercase		)["payload"]
          if is_param and "param" in j.keys():
             if j["param"]["paramName"] == key:
                return j["param"]["paramValue"]
          if "log_other" in j.keys():
             if j["log_other"]["key"] == key:
                return j["log_other"]["val"]
          if "metric" in j.keys():
             if j["metric"]["metricName"] == key:
                return j["metric"]["metricValue"]
    def 		_lowerCamelCase      (	self			:       int		)						->    List[str]:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       CometMLTracker("test_project_with_config"   ,						_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers(_lowercase   ,						_lowercase		)
          accelerator.end_training()
          _lowercase							     =       os.listdir(_lowercase		)[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          _lowercase							     =       os.path.join(_lowercase   ,						_lowercase		)
          _lowercase							     =       zipfile.ZipFile(_lowercase   ,						"r"		)
          _lowercase							     =       archive.open("messages.json"		).read().decode("utf-8"		)
       _lowercase							     =       log.split("\n"		)[:-1]
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"num_iterations"   ,						_lowercase		)   ,						1_2		)
       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 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[Any]:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       CometMLTracker("test_project_with_config"   ,						_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          accelerator.init_trackers(_lowercase		)
          _lowercase							     =       {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(_lowercase   ,						step=0		)
          accelerator.end_training()
          _lowercase							     =       os.listdir(_lowercase		)[0]  # Comet is nice, it's just a zip file here
          # We parse the raw logs
          _lowercase							     =       os.path.join(_lowercase   ,						_lowercase		)
          _lowercase							     =       zipfile.ZipFile(_lowercase   ,						"r"		)
          _lowercase							     =       archive.open("messages.json"		).read().decode("utf-8"		)
       _lowercase							     =       log.split("\n"		)[:-1]
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"curr_step"   ,						_lowercase		)   ,						0		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"total_loss"		)   ,						0.1		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"iteration"		)   ,						1		)
       self.assertEqual(self.get_value_from_key(_lowercase   ,						"my_text"		)   ,						"some_value"		)
class   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      [
        """total_loss""",
        """iteration""",
        """my_text""",
        """learning_rate""",
        """num_iterations""",
        """some_boolean""",
        """some_string""",
    ]

    snake_case_											=      """my_custom_tracker"""
    snake_case_											=      False
    def __init__(	self			:       List[Any]   ,						_lowercase			:       str		)						->    Dict:
       _lowercase							     =       open(f"""{dir}/log.csv"""   ,						"w+"		)
       _lowercase							     =       csv.DictWriter(self.f   ,						fieldnames=self._col_names		)
       self.writer.writeheader()
    @property
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Dict:
       return self.writer
    def 		_lowerCamelCase      (	self			:       Optional[Any]   ,						_lowercase			:       dict		)						->    Optional[int]:
       logger.info("Call init"		)
       self.writer.writerow(_lowercase		)
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       dict   ,						_lowercase			:       Optional[int]		)						->    List[str]:
       logger.info("Call log"		)
       self.writer.writerow(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       self.f.close()
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    str:
       with tempfile.TemporaryDirectory() as d:
          _lowercase							     =       MyCustomTracker(_lowercase		)
          _lowercase							     =       Accelerator(log_with=_lowercase		)
          _lowercase							     =       {"num_iterations": 1_2, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          accelerator.init_trackers("Some name"   ,						_lowercase		)
          accelerator.end_training()
          with open(f"""{d}/log.csv"""   ,						"r"		) as f:
             _lowercase							     =       csv.DictReader(_lowercase		)
             _lowercase							     =       next(_lowercase		)
             _lowercase							     =       {
                 "total_loss": "",
                 "iteration": "",
                 "my_text": "",
                 "learning_rate": "0.01",
                 "num_iterations": "12",
                 "some_boolean": "False",
                 "some_string": "some_value",
             }
             self.assertDictEqual(_lowercase   ,						_lowercase		)




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