



'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCamelCase__:       int							     =      "\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n"

UpperCamelCase__:       Tuple							     =      logging.getLogger(__name__)

@dataclass
class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__      			=	field(
	    default="""roberta-large-mnli"""							,		metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}      )
	lowerCamelCase__      			=	field(
	    default="""This example is {}."""							,		metadata={
	        """help""": (
	            """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
	            """where class name is inserted."""
	        )
	    }							,		)
	lowerCamelCase__      			=	field(
	    default=32							,		metadata={"""help""": """Batch size for generating teacher predictions."""}      )
	lowerCamelCase__      			=	field(
	    default=A__							,		metadata={
	        """help""": (
	            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
	            """multi-class multi-label classification)."""
	        )
	    }							,		)
	lowerCamelCase__      			=	field(
	    default=1.0							,		metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}      )

@dataclass
class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__      			=	field(
	    default="""distilbert-base-uncased"""							,		metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}      )

@dataclass
class     SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__      			=	field(metadata={"""help""": """Text file with one unlabeled instance per line."""}      )
	lowerCamelCase__      			=	field(metadata={"""help""": """Text file with one class name per line."""}      )
	lowerCamelCase__      			=	field(
	    default=A__							,		metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}							,		)

@dataclass
class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	field(
	    default=A__							,		metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}							,		)
	lowerCamelCase__      			=	field(
	    default=32							,		metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}      )
	lowerCamelCase__      			=	field(
	    default=128							,		metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}      )
	lowerCamelCase__      			=	field(default=1.0							,		metadata={"""help""": """Total number of training epochs to perform."""}      )
	lowerCamelCase__      			=	field(default=A__							,		metadata={"""help""": """Whether to run training of student model."""}      )
	lowerCamelCase__      			=	field(
	    default=A__							,		metadata={
	        """help""": (
	            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
	            """after training."""
	        )
	    }							,		)
	lowerCamelCase__      			=	field(
	    default=0							,		metadata={
	        """help""": (
	            """Limit the total amount of checkpoints."""
	            """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
	        )
	    }							,		)

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	def 					A     (						self      :					Optional[int]		,      __snake_case      :					Any		,      __snake_case      :					Optional[Any]		,      __snake_case      :					Any=False		)      ->		List[str]:
								UpperCAmelCase			:	List[Any]										=		inputs['''labels''']
								UpperCAmelCase			:	List[Any]										=		model(inputs['''input_ids''']		,      attention_mask=inputs['''attention_mask''']		)
								UpperCAmelCase			:	Optional[Any]										=		outputs[0]

								UpperCAmelCase			:	Union[str, Any]										=		-torch.sum(target_p * logits.log_softmax(dim=-1		)		,      axis=-1		).mean()

								if return_outputs:
															return loss, outputs

								return loss


def 			snake_case_					( _lowerCAmelCase  :						int  )				->       Tuple:
							UpperCAmelCase			:	int										=		[]
							with open(_lowerCAmelCase       ,				'''r'''  ) as f:
														for line in f:
																					UpperCAmelCase			:	Optional[int]										=		line.strip()
																					if len(_lowerCAmelCase  ) > 0:
																												lines.append(_lowerCAmelCase  )
							return lines


def 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						List[Any]  )				->       List[Any]:
							UpperCAmelCase			:	List[Any]										=		[]
							UpperCAmelCase			:	str										=		[]
							for example in examples:
														for name in class_names:
																					premises.append(_lowerCAmelCase  )
																					hypotheses.append(hypothesis_template.format(_lowerCAmelCase  )  )
							return premises, hypotheses


def 			snake_case_					( _lowerCAmelCase  :						Dict  )				->       Union[str, Any]:
							for label, ind in config.labelaid.items():
														if label.lower().startswith('''entail'''  ):
																					return ind
							logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''  )
							return -1


def 			snake_case_					( _lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						int       ,				_lowerCAmelCase  :						float       ,				_lowerCAmelCase  :						bool       ,				_lowerCAmelCase  :						bool       ,				_lowerCAmelCase  :						bool       ,				_lowerCAmelCase  :						bool       ,				)				->       int:
							UpperCAmelCase			:	Any										=		AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase  )
							UpperCAmelCase			:	Dict										=		model.config
							if not no_cuda and torch.cuda.is_available():
														UpperCAmelCase			:	Optional[Any]										=		nn.DataParallel(model.cuda()  )
														batch_size *= len(model.device_ids  )
							UpperCAmelCase			:	List[Any]										=		AutoTokenizer.from_pretrained(_lowerCAmelCase       ,				use_fast=_lowerCAmelCase  )

							UpperCAmelCase							,		UpperCAmelCase			:	Optional[Any]										=		get_premise_hypothesis_pairs(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase  )
							UpperCAmelCase			:	Any										=		[]

							for i in tqdm(range(0       ,				len(_lowerCAmelCase  )       ,				_lowerCAmelCase  )  ):
														UpperCAmelCase			:	str										=		premises[i : i + batch_size]
														UpperCAmelCase			:	List[str]										=		hypotheses[i : i + batch_size]

														UpperCAmelCase			:	Tuple										=		tokenizer(
														    _lowerCAmelCase       ,				_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				truncation='''only_first'''       ,				return_tensors='''pt'''       ,				)

														with torch.cuda.amp.autocast(enabled=_lowerCAmelCase  ):
																					with torch.no_grad():
																												UpperCAmelCase			:	int										=		model(**_lowerCAmelCase  )
														logits.append(outputs.logits.detach().cpu().float()  )

							UpperCAmelCase			:	Dict										=		get_entailment_id(_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[Any]										=		-1 if entail_id == 0 else 0
							UpperCAmelCase			:	Optional[Any]										=		torch.cat(_lowerCAmelCase       ,				dim=0  )  # N*K x 3
							UpperCAmelCase			:	Optional[int]										=		logits.reshape(len(_lowerCAmelCase  )       ,				len(_lowerCAmelCase  )       ,				-1  )[..., [contr_id, entail_id]]  # N x K x 2

							if multi_label:
														# softmax over (contr, entail) logits for each class independently
														UpperCAmelCase			:	int										=		(nli_logits / temperature).softmax(-1  )
							else:
														# softmax over entail logits across classes s.t. class probabilities sum to 1.
														UpperCAmelCase			:	str										=		(nli_logits / temperature).softmax(1  )

							return nli_prob[..., 1]  # N x K


def 			snake_case_					( )				->       int:
							UpperCAmelCase			:	str										=		HfArgumentParser(
							    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)       ,				description=_lowerCAmelCase       ,				)

							if len(sys.argv  ) == 2 and sys.argv[1].endswith('''.json'''  ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Tuple										=		parser.parse_json_file(
														    json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase			:	Union[str, Any]										=		parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							UpperCAmelCase			:	Union[str, Any]										=		None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCAmelCase			:	Union[str, Any]										=		get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    '''Use --overwrite_output_dir to overcome.'''  )
														elif last_checkpoint is not None:
																					logger.info(
																					    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''  )

    # Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,				datefmt='''%m/%d/%Y %H:%M:%S'''       ,				handlers=[logging.StreamHandler(sys.stdout  )]       ,				)
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

							# Log on each process the small summary:
							logger.warning(
							    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank  ):
														utils.logging.set_verbosity_info()
														utils.logging.enable_default_handler()
														utils.logging.enable_explicit_format()

							if training_args.local_rank != -1:
														raise ValueError('''Distributed training is not currently supported.'''  )
							if training_args.tpu_num_cores is not None:
														raise ValueError('''TPU acceleration is not currently supported.'''  )

							logger.info(f"""Training/evaluation parameters {training_args}"""  )

							# Set seed before initializing model.
							set_seed(training_args.seed  )

							# 1. read in data
							UpperCAmelCase			:	Dict										=		read_lines(data_args.data_file  )
							UpperCAmelCase			:	List[Any]										=		read_lines(data_args.class_names_file  )

							# 2. get teacher predictions and load into dataset
							logger.info('''Generating predictions from zero-shot teacher model'''  )
							UpperCAmelCase			:	Optional[Any]										=		get_teacher_predictions(
							    teacher_args.teacher_name_or_path       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				teacher_args.hypothesis_template       ,				teacher_args.teacher_batch_size       ,				teacher_args.temperature       ,				teacher_args.multi_label       ,				data_args.use_fast_tokenizer       ,				training_args.no_cuda       ,				training_args.fpaa       ,				)
							UpperCAmelCase			:	List[Any]										=		Dataset.from_dict(
							    {
							        '''text''': examples,
							        '''labels''': teacher_soft_preds,
							    }  )

							# 3. create student
							logger.info('''Initializing student model'''  )
							UpperCAmelCase			:	Optional[int]										=		AutoModelForSequenceClassification.from_pretrained(
							    student_args.student_name_or_path       ,				num_labels=len(_lowerCAmelCase  )  )
							UpperCAmelCase			:	Union[str, Any]										=		AutoTokenizer.from_pretrained(student_args.student_name_or_path       ,				use_fast=data_args.use_fast_tokenizer  )
							UpperCAmelCase			:	Dict										=		dict(enumerate(_lowerCAmelCase  )  )
							UpperCAmelCase			:	Tuple										=		{label: i for i, label in enumerate(_lowerCAmelCase  )}

							# 4. train student on teacher predictions
							UpperCAmelCase			:	List[str]										=		dataset.map(_lowerCAmelCase       ,				input_columns='''text'''  )
							dataset.set_format('''torch'''  )

							def compute_metrics(_lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						List[Any]=False  ):
														UpperCAmelCase			:	List[str]										=		p.predictions.argmax(-1  )
														UpperCAmelCase			:	Union[str, Any]										=		p.label_ids.argmax(-1  )  # "label_ids" are actually distributions
														return {"agreement": (preds == proxy_labels).mean().item()}

							UpperCAmelCase			:	Tuple										=		DistillationTrainer(
							    model=_lowerCAmelCase       ,				tokenizer=_lowerCAmelCase       ,				args=_lowerCAmelCase       ,				train_dataset=_lowerCAmelCase       ,				compute_metrics=_lowerCAmelCase       ,				)

							if training_args.do_train:
														logger.info('''Training student model on teacher predictions'''  )
														trainer.train()

							if training_args.do_eval:
														UpperCAmelCase			:	Any										=		trainer.evaluate(eval_dataset=_lowerCAmelCase  )['''eval_agreement''']
														logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""  )

							trainer.save_model()


if __name__ == "__main__":
						main()










'''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__:       Optional[Any]							     =      logging.getLogger(__name__)

@require_tensorboard
class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	def 					A     (						self      :					Union[str, Any]		)      ->		List[str]:
								UpperCAmelCase			:	Dict										=		'''test_project_with_config'''
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		)
															UpperCAmelCase			:	Tuple										=		{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
															accelerator.init_trackers(__snake_case		,      __snake_case		)
															accelerator.end_training()
															for child in Path(F"""{dirpath}/{project_name}"""		).glob('''*/**'''		):
																						UpperCAmelCase			:	Dict										=		list(filter(lambda __snake_case		: x.is_file()		,      child.iterdir()		)		)[0]
															self.assertNotEqual(str(__snake_case		)		,      ''''''		)







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







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

																						UpperCAmelCase			:	str										=		data[12 : 12 + int(header[0]		)]  # 8+4
																						UpperCAmelCase			:	Union[str, Any]										=		data[12 + int(header[0]		) + 4 :]
																						UpperCAmelCase			:	Union[str, Any]										=		event_pba.Event()

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







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







	def 					A     (						self      :					Dict		)      ->		Union[str, Any]:
								UpperCAmelCase			:	int										=		ProjectConfiguration(total_limit=30		)
								with tempfile.TemporaryDirectory() as dirpath:
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''tensorboard'''		,      project_dir=__snake_case		,      project_config=__snake_case		)

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







	"""simple docstring"""






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







	@skip
	def 					A     (						self      :					int		)      ->		str:
								UpperCAmelCase			:	Any										=		'''test_project_with_config'''
								UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with='''wandb'''		)
								UpperCAmelCase			:	List[str]										=		{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
								UpperCAmelCase			:	Dict										=		{'''wandb''': {'''tags''': ['''my_tag''']}}
								accelerator.init_trackers(__snake_case		,      __snake_case		,      __snake_case		)
								UpperCAmelCase			:	int										=		{'''total_loss''': 0.1, '''iteration''': 1, '''my_text''': '''some_value'''}
								accelerator.log(__snake_case		,      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			:	str										=		subprocess.check_output(
																						    ['''wandb''', '''sync''', '''--view''', '''--verbose''', str(__snake_case		)]		,      env=os.environ.copy()		).decode('''utf8'''		,      '''ignore'''		)
																						break

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

								# Actual logging
								UpperCAmelCase			:	List[str]										=		self.parse_log(__snake_case		,      '''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 			snake_case_					( self  :						Union[str, Any]       ,				_lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						str  )				->       Optional[Any]:
							UpperCAmelCase			:	Dict										=		run_name
							UpperCAmelCase			:	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     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






	@staticmethod
	def 					A     (						__snake_case      :					Union[str, Any]		,      __snake_case      :					str		,      __snake_case      :					bool = False		)      ->		List[Any]:
								for log in log_list:
															UpperCAmelCase			:	List[str]										=		json.loads(__snake_case		)['''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 					A     (						self      :					Dict		)      ->		Union[str, Any]:
								with tempfile.TemporaryDirectory() as d:
															UpperCAmelCase			:	Optional[Any]										=		CometMLTracker('''test_project_with_config'''		,      __snake_case		)
															UpperCAmelCase			:	Optional[int]										=		Accelerator(log_with=__snake_case		)
															UpperCAmelCase			:	Union[str, Any]										=		{'''num_iterations''': 12, '''learning_rate''': 1E-2, '''some_boolean''': False, '''some_string''': '''some_value'''}
															accelerator.init_trackers(__snake_case		,      __snake_case		)
															accelerator.end_training()
															UpperCAmelCase			:	Dict										=		os.listdir(__snake_case		)[0]  # Comet is nice, it's just a zip file here
															# We parse the raw logs
															UpperCAmelCase			:	List[str]										=		os.path.join(__snake_case		,      __snake_case		)
															UpperCAmelCase			:	Union[str, Any]										=		zipfile.ZipFile(__snake_case		,      '''r'''		)
															UpperCAmelCase			:	Tuple										=		archive.open('''messages.json'''		).read().decode('''utf-8'''		)
								UpperCAmelCase			:	str										=		log.split('''\n'''		)[:-1]
								self.assertEqual(self.get_value_from_key(__snake_case		,      '''num_iterations'''		,      __snake_case		)		,      12		)
								self.assertEqual(self.get_value_from_key(__snake_case		,      '''learning_rate'''		,      __snake_case		)		,      0.01		)
								self.assertEqual(self.get_value_from_key(__snake_case		,      '''some_boolean'''		,      __snake_case		)		,      __snake_case		)
								self.assertEqual(self.get_value_from_key(__snake_case		,      '''some_string'''		,      __snake_case		)		,      '''some_value'''		)







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

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	[
	    """total_loss""",
	    """iteration""",
	    """my_text""",
	    """learning_rate""",
	    """num_iterations""",
	    """some_boolean""",
	    """some_string""",
	]

	lowerCamelCase__      			=	"""my_custom_tracker"""
	lowerCamelCase__      			=	False







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







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







	def 					A     (						self      :					int		,      __snake_case      :					dict		)      ->		List[Any]:
								logger.info('''Call init'''		)
								self.writer.writerow(__snake_case		)







	def 					A     (						self      :					Dict		,      __snake_case      :					dict		,      __snake_case      :					Optional[int]		)      ->		List[Any]:
								logger.info('''Call log'''		)
								self.writer.writerow(__snake_case		)







	def 					A     (						self      :					Optional[Any]		)      ->		int:
								self.f.close()

class     SCREAMING_SNAKE_CASE(							unittest.TestCase      ):







	"""simple docstring"""






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







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





