


'''simple docstring'''

import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowercase__					  =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def __UpperCamelCase (      __lowerCamelCase		:			Tuple					)   ->  int:







     '''simple docstring'''






     if "token" in model_name_or_path:
          return "rag_token"
     if "sequence" in model_name_or_path:
          return "rag_sequence"
     if "bart" in model_name_or_path:
          return "bart"
     return None






def __UpperCamelCase (      __lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			Tuple					)   ->  Any:







     '''simple docstring'''






     return max(metric_fn(__lowerCamelCase   ,	__lowerCamelCase					) for gt in ground_truths					)






def __UpperCamelCase (      __lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Any					)   ->  Optional[Any]:







     '''simple docstring'''






     _a            =		[line.strip() for line in open(__lowerCamelCase   ,	"r"					).readlines()]
     _a            =		[]

     if args.gold_data_mode == "qa":
          _a            =		pd.read_csv(__lowerCamelCase   ,	sep="\t"   ,	header=__lowerCamelCase					)
          for answer_list in data[1]:
               _a            =		ast.literal_eval(__lowerCamelCase					)
               answers.append(__lowerCamelCase					)
     else:
          _a            =		[line.strip() for line in open(__lowerCamelCase   ,	"r"					).readlines()]
          _a            =		[[reference] for reference in references]

     _a            =		_a            =		_a            =		0
     for prediction, ground_truths in zip(__lowerCamelCase   ,	__lowerCamelCase					):
          total += 1
          em += metric_max_over_ground_truths(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase					)
          fa += metric_max_over_ground_truths(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase					)

     _a            =		1_00.0 * em / total
     _a            =		1_00.0 * fa / total

     logger.info(F"F1: {fa:.2f}"					)
     logger.info(F"EM: {em:.2f}"					)






def __UpperCamelCase (      __lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			Tuple   ,	__lowerCamelCase		:			List[Any]					)   ->  Optional[Any]:







     '''simple docstring'''






     _a            =		args.k
     _a            =		[line.strip() for line in open(__lowerCamelCase   ,	"r"					).readlines()]
     _a            =		[line.strip() for line in open(__lowerCamelCase   ,	"r"					).readlines()]

     _a            =		_a            =		0
     for hypo, reference in zip(__lowerCamelCase   ,	__lowerCamelCase					):
          _a            =		set(hypo.split("\t"					)[:k]					)
          _a            =		set(reference.split("\t"					)					)
          total += 1
          em += len(hypo_provenance & ref_provenance					) / k

     _a            =		1_00.0 * em / total
     logger.info(F"Precision@{k}: {em: .2f}"					)






def __UpperCamelCase (      __lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			List[str]					)   ->  List[str]:







     '''simple docstring'''






     def strip_title(__lowerCamelCase		:			Union[str, Any]					):
          if title.startswith("\""					):
               _a            =		title[1:]
          if title.endswith("\""					):
               _a            =		title[:-1]
          return title

     _a            =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         __lowerCamelCase   ,	return_tensors="pt"   ,	padding=__lowerCamelCase   ,	truncation=__lowerCamelCase   ,	)["input_ids"].to(args.device					)

     _a            =		rag_model.rag.question_encoder(__lowerCamelCase					)
     _a            =		question_enc_outputs[0]

     _a            =		rag_model.retriever(
         __lowerCamelCase   ,	question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()   ,	prefix=rag_model.rag.generator.config.prefix   ,	n_docs=rag_model.config.n_docs   ,	return_tensors="pt"   ,	)
     _a            =		rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
     _a            =		[]
     for docs in all_docs:
          _a            =		[strip_title(__lowerCamelCase					) for title in docs["title"]]
          provenance_strings.append("\t".join(__lowerCamelCase					)					)
     return provenance_strings






def __UpperCamelCase (      __lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			Optional[Any]					)   ->  Union[str, Any]:







     '''simple docstring'''






     with torch.no_grad():
          _a            =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              __lowerCamelCase   ,	return_tensors="pt"   ,	padding=__lowerCamelCase   ,	truncation=__lowerCamelCase					)

          _a            =		inputs_dict.input_ids.to(args.device					)
          _a            =		inputs_dict.attention_mask.to(args.device					)
          _a            =		rag_model.generate(  # rag_model overwrites generate
              __lowerCamelCase   ,	attention_mask=__lowerCamelCase   ,	num_beams=args.num_beams   ,	min_length=args.min_length   ,	max_length=args.max_length   ,	early_stopping=__lowerCamelCase   ,	num_return_sequences=1   ,	bad_words_ids=[[0, 0]]   ,	)
          _a            =		rag_model.retriever.generator_tokenizer.batch_decode(__lowerCamelCase   ,	skip_special_tokens=__lowerCamelCase					)

          if args.print_predictions:
               for q, a in zip(__lowerCamelCase   ,	__lowerCamelCase					):
                    logger.info("Q: {} - A: {}".format(__lowerCamelCase   ,	__lowerCamelCase					)					)

          return answers






def __UpperCamelCase (      )   ->  Any:







     '''simple docstring'''






     _a            =		argparse.ArgumentParser()
     parser.add_argument(
         "--model_type"   ,	choices=["rag_sequence", "rag_token", "bart"]   ,	type=__lowerCamelCase   ,	help=(
             "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
             " model_name_or_path"
         )   ,	)
     parser.add_argument(
         "--index_name"   ,	default=__lowerCamelCase   ,	choices=["exact", "compressed", "legacy"]   ,	type=__lowerCamelCase   ,	help="RAG model retriever type"   ,	)
     parser.add_argument(
         "--index_path"   ,	default=__lowerCamelCase   ,	type=__lowerCamelCase   ,	help="Path to the retrieval index"   ,	)
     parser.add_argument("--n_docs"   ,	default=5   ,	type=__lowerCamelCase   ,	help="Number of retrieved docs"					)
     parser.add_argument(
         "--model_name_or_path"   ,	default=__lowerCamelCase   ,	type=__lowerCamelCase   ,	required=__lowerCamelCase   ,	help="Path to pretrained checkpoints or model identifier from huggingface.co/models"   ,	)
     parser.add_argument(
         "--eval_mode"   ,	choices=["e2e", "retrieval"]   ,	default="e2e"   ,	type=__lowerCamelCase   ,	help=(
             "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
             " precision@k."
         )   ,	)
     parser.add_argument("--k"   ,	default=1   ,	type=__lowerCamelCase   ,	help="k for the precision@k calculation"					)
     parser.add_argument(
         "--evaluation_set"   ,	default=__lowerCamelCase   ,	type=__lowerCamelCase   ,	required=__lowerCamelCase   ,	help="Path to a file containing evaluation samples"   ,	)
     parser.add_argument(
         "--gold_data_path"   ,	default=__lowerCamelCase   ,	type=__lowerCamelCase   ,	required=__lowerCamelCase   ,	help="Path to a tab-separated file with gold samples"   ,	)
     parser.add_argument(
         "--gold_data_mode"   ,	default="qa"   ,	type=__lowerCamelCase   ,	choices=["qa", "ans"]   ,	help=(
             "Format of the gold data file"
             "qa - a single line in the following format: question [tab] answer_list"
             "ans - a single line of the gold file contains the expected answer string"
         )   ,	)
     parser.add_argument(
         "--predictions_path"   ,	type=__lowerCamelCase   ,	default="predictions.txt"   ,	help="Name of the predictions file, to be stored in the checkpoints directory"   ,	)
     parser.add_argument(
         "--eval_all_checkpoints"   ,	action="store_true"   ,	help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"   ,	)
     parser.add_argument(
         "--eval_batch_size"   ,	default=8   ,	type=__lowerCamelCase   ,	help="Batch size per GPU/CPU for evaluation."   ,	)
     parser.add_argument(
         "--recalculate"   ,	help="Recalculate predictions even if the prediction file exists"   ,	action="store_true"   ,	)
     parser.add_argument(
         "--num_beams"   ,	default=4   ,	type=__lowerCamelCase   ,	help="Number of beams to be used when generating answers"   ,	)
     parser.add_argument("--min_length"   ,	default=1   ,	type=__lowerCamelCase   ,	help="Min length of the generated answers"					)
     parser.add_argument("--max_length"   ,	default=50   ,	type=__lowerCamelCase   ,	help="Max length of the generated answers"					)

     parser.add_argument(
         "--print_predictions"   ,	action="store_true"   ,	help="If True, prints predictions while evaluating."   ,	)
     parser.add_argument(
         "--print_docs"   ,	action="store_true"   ,	help="If True, prints docs retried while generating."   ,	)
     _a            =		parser.parse_args()
     _a            =		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
     return args






def __UpperCamelCase (      __lowerCamelCase		:			Union[str, Any]					)   ->  List[str]:







     '''simple docstring'''






     _a            =		{}
     if args.model_type is None:
          _a            =		infer_model_type(args.model_name_or_path					)
          assert args.model_type is not None
     if args.model_type.startswith("rag"					):
          _a            =		RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
          _a            =		args.n_docs
          if args.index_name is not None:
               _a            =		args.index_name
          if args.index_path is not None:
               _a            =		args.index_path
     else:
          _a            =		BartForConditionalGeneration

     _a            =		(
         [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
         if args.eval_all_checkpoints
         else [args.model_name_or_path]
     )

     logger.info("Evaluate the following checkpoints: %s"   ,	__lowerCamelCase					)

     _a            =		get_scores if args.eval_mode == "e2e" else get_precision_at_k
     _a            =		evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

     for checkpoint in checkpoints:
          if os.path.exists(args.predictions_path					) and (not args.recalculate):
               logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path					)					)
               score_fn(__lowerCamelCase   ,	args.predictions_path   ,	args.gold_data_path					)
               continue

          logger.info("***** Running evaluation for {} *****".format(__lowerCamelCase					)					)
          logger.info("  Batch size = %d"   ,	args.eval_batch_size					)
          logger.info("  Predictions will be stored under {}".format(args.predictions_path					)					)

          if args.model_type.startswith("rag"					):
               _a            =		RagRetriever.from_pretrained(__lowerCamelCase   ,	**__lowerCamelCase					)
               _a            =		model_class.from_pretrained(__lowerCamelCase   ,	retriever=__lowerCamelCase   ,	**__lowerCamelCase					)
               model.retriever.init_retrieval()
          else:
               _a            =		model_class.from_pretrained(__lowerCamelCase   ,	**__lowerCamelCase					)
          model.to(args.device					)

          with open(args.evaluation_set   ,	"r"					) as eval_file, open(args.predictions_path   ,	"w"					) as preds_file:
               _a            =		[]
               for line in tqdm(__lowerCamelCase					):
                    questions.append(line.strip()					)
                    if len(__lowerCamelCase					) == args.eval_batch_size:
                         _a            =		evaluate_batch_fn(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase					)
                         preds_file.write("\n".join(__lowerCamelCase					) + "\n"					)
                         preds_file.flush()
                         _a            =		[]
               if len(__lowerCamelCase					) > 0:
                    _a            =		evaluate_batch_fn(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase					)
                    preds_file.write("\n".join(__lowerCamelCase					)					)
                    preds_file.flush()

               score_fn(__lowerCamelCase   ,	args.predictions_path   ,	args.gold_data_path					)


if __name__ == "__main__":
   lowercase__					  =		get_args()
   main(args)





'''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

lowercase__					  =		logging.getLogger(__name__)





@require_tensorboard
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Tuple:
          _a            =		"test_project_with_config"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers(__UpperCamelCase  ,				__UpperCamelCase		)
               accelerator.end_training()
               for child in Path(f"{dirpath}/{project_name}"		).glob("*/**"		):
                    _a            =		list(filter(lambda __UpperCamelCase		: x.is_file()  ,				child.iterdir()		)		)[0]
               self.assertNotEqual(str(__UpperCamelCase		)  ,				""		)






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






     def 			a_       (      self		)							->     str:
          _a            =		"test_project_with_log"
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)
               accelerator.init_trackers(__UpperCamelCase		)
               _a            =		{"tensor": torch.tensor(1		)}
               accelerator.log(__UpperCamelCase  ,				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            =		list(filter(lambda __UpperCamelCase		: 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(__UpperCamelCase  ,				"rb"		) as f:
                    _a            =		f.read()
               _a            =		False
               while data:
                    _a            =		struct.unpack("Q"  ,				data[:8]		)

                    _a            =		data[12 : 12 + int(header[0]		)]  # 8+4
                    _a            =		data[12 + int(header[0]		) + 4 :]
                    _a            =		event_pba.Event()

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






     def 			a_       (      self		)							->     Union[str, Any]:
          with self.assertRaisesRegex(__UpperCamelCase  ,				"Logging with `tensorboard` requires a `logging_dir`"		):
               _a            =		Accelerator(log_with="tensorboard"		)
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase		)






     def 			a_       (      self		)							->     Any:
          _a            =		ProjectConfiguration(total_limit=30		)
          with tempfile.TemporaryDirectory() as dirpath:
               _a            =		Accelerator(log_with="tensorboard"  ,				project_dir=__UpperCamelCase  ,				project_config=__UpperCamelCase		)







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






     def 			a_       (      self		)							->     List[Any]:
          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_       (      __UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase = True		)							->     Optional[int]:
          _a            =		Rf"{section} ([\S\s]*?)\n\n"
          if record:
               _a            =		Rf"Record: {pattern}"
          _a            =		re.findall(__UpperCamelCase  ,				__UpperCamelCase		)[0]
          # A config
          if section == "config" or section == "history":
               _a            =		re.findall(R"\"([a-zA-Z0-9_.,]+)"  ,				__UpperCamelCase		)
               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]+)\""  ,				__UpperCamelCase		)		)






     @skip
     def 			a_       (      self		)							->     Dict:
          _a            =		"test_project_with_config"
          _a            =		Accelerator(log_with="wandb"		)
          _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
          _a            =		{"wandb": {"tags": ["my_tag"]}}
          accelerator.init_trackers(__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)
          _a            =		{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
          accelerator.log(__UpperCamelCase  ,				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            =		subprocess.check_output(
                        ["wandb", "sync", "--view", "--verbose", str(__UpperCamelCase		)]  ,				env=os.environ.copy()		).decode("utf8"  ,				"ignore"		)
                    break

        # Check HPS through careful parsing and cleaning
          _a            =		self.parse_log(__UpperCamelCase  ,				"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            =		self.parse_log(__UpperCamelCase  ,				"run"  ,				__UpperCamelCase		)
          self.assertEqual(logged_items["tags"]  ,				"my_tag"		)

          # Actual logging
          _a            =		self.parse_log(__UpperCamelCase  ,				"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		:			List[Any]   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			str					)   ->  List[Any]:







     '''simple docstring'''






     _a            =		run_name
     _a            =		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(lowerCamelCase__			,      '''__init__'''			,      lowerCamelCase__			)
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






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






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







class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					[
         '''total_loss''',
         '''iteration''',
         '''my_text''',
         '''learning_rate''',
         '''num_iterations''',
         '''some_boolean''',
         '''some_string''',
     ]

     UpperCAmelCase			       =					'''my_custom_tracker'''
     UpperCAmelCase			       =					False






     def __init__(      self  ,				__UpperCamelCase		)							->     int:
          _a            =		open(f"{dir}/log.csv"  ,				"w+"		)
          _a            =		csv.DictWriter(self.f  ,				fieldnames=self._col_names		)
          self.writer.writeheader()






     @property
     def 			a_       (      self		)							->     Optional[int]:
          return self.writer






     def 			a_       (      self  ,				__UpperCamelCase		)							->     List[str]:
          logger.info("Call init"		)
          self.writer.writerow(__UpperCamelCase		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Tuple:
          logger.info("Call log"		)
          self.writer.writerow(__UpperCamelCase		)






     def 			a_       (      self		)							->     Union[str, Any]:
          self.f.close()







class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Optional[int]:
          with tempfile.TemporaryDirectory() as d:
               _a            =		MyCustomTracker(__UpperCamelCase		)
               _a            =		Accelerator(log_with=__UpperCamelCase		)
               _a            =		{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
               accelerator.init_trackers("Some name"  ,				__UpperCamelCase		)
               accelerator.end_training()
               with open(f"{d}/log.csv"  ,				"r"		) as f:
                    _a            =		csv.DictReader(__UpperCamelCase		)
                    _a            =		next(__UpperCamelCase		)
                    _a            =		{
                        "total_loss": "",
                        "iteration": "",
                        "my_text": "",
                        "learning_rate": "0.01",
                        "num_iterations": "12",
                        "some_boolean": "False",
                        "some_string": "some_value",
                    }
                    self.assertDictEqual(__UpperCamelCase  ,				__UpperCamelCase		)






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

