




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def    snake_case   (				A=None		,  A=None     )				->				str:




	'''simple docstring'''


	return field(default_factory=lambda: default		,  metadata=A     )


@dataclass
class 							__SCREAMING_SNAKE_CASE				:
					_lowerCamelCase						    =					field(
					    metadata={"""help""": """The csv file to plot."""}  ,	)
					_lowerCamelCase						    =					field(
					    default=lowerCamelCase  ,	metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}  ,	)
					_lowerCamelCase						    =					field(
					    default=lowerCamelCase  ,	metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}  ,	)
					_lowerCamelCase						    =					field(
					    default=lowerCamelCase  ,	metadata={"""help""": """Disable logarithmic scale when plotting"""}  ,	)
					_lowerCamelCase						    =					field(
					    default=lowerCamelCase  ,	metadata={
					        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
					    }  ,	)
					_lowerCamelCase						    =					field(
					    default=lowerCamelCase  ,	metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}  ,	)
					_lowerCamelCase						    =					list_field(
					    default=lowerCamelCase  ,	metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}    )






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	try:
		int(A     )
		return True
	except ValueError:
		return False






def    snake_case   (				A     )				->				Any:




	'''simple docstring'''


	try:
		float(A     )
		return True
	except ValueError:
		return False


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				args
						lowerCamelCase_				:						int				  =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

						with open(self.args.csv_file					,			newline=''   ) as csv_file:
							lowerCamelCase_				:						Tuple				  =				csv.DictReader(__lowercase   )
							for row in reader:
								lowerCamelCase_				:						List[Any]				  =				row['model']
								self.result_dict[model_name]["bsz"].append(int(row['batch_size']   )   )
								self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']   )   )
								if can_convert_to_int(row['result']   ):
									# value is not None
									lowerCamelCase_				:						List[Any]				  =				int(row['result']   )
								elif can_convert_to_float(row['result']   ):
									# value is not None
									lowerCamelCase_				:						Optional[Any]				  =				float(row['result']   )





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_  ,     lowerCamelCase_				:						Tuple				  =				plt.subplots()
						lowerCamelCase_				:						int				  =				'Time usage' if self.args.is_time else 'Memory usage'
						lowerCamelCase_				:						str				  =				title_str + ' for training' if self.args.is_train else title_str + ' for inference'

						if not self.args.no_log_scale:
							# set logarithm scales
							ax.set_xscale('log'   )
							ax.set_yscale('log'   )

						for axis in [ax.xaxis, ax.yaxis]:
							axis.set_major_formatter(ScalarFormatter()   )

						for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
							lowerCamelCase_				:						Union[str, Any]				  =				sorted(set(self.result_dict[model_name]['bsz']   )   )
							lowerCamelCase_				:						Tuple				  =				sorted(set(self.result_dict[model_name]['seq_len']   )   )
							lowerCamelCase_				:						Optional[Any]				  =				self.result_dict[model_name]['result']

							((lowerCamelCase_)  ,     (lowerCamelCase_))				:						Optional[Any]				  =				(
							    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
							)

							lowerCamelCase_				:						List[str]				  =				(
							    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
							)

							for inner_loop_value in inner_loop_array:
								if self.args.plot_along_batch:
									lowerCamelCase_				:						List[Any]				  =				np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,			dtype=__lowercase					,			)
								else:
									lowerCamelCase_				:						int				  =				np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,			dtype=np.floataa					,			)

								((lowerCamelCase_)  ,     (lowerCamelCase_))				:						Dict				  =				(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								lowerCamelCase_				:						Union[str, Any]				  =				np.asarray(__lowercase					,			__lowercase   )[: len(__lowercase   )]
								plt.scatter(
								    __lowercase					,			__lowercase					,			label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"   )
								plt.plot(__lowercase					,			__lowercase					,			'--'   )

							title_str += f" {label_model_name} vs."

						lowerCamelCase_				:						Tuple				  =				title_str[:-4]
						lowerCamelCase_				:						Optional[Any]				  =				'Time in s' if self.args.is_time else 'Memory in MB'

						# plot
						plt.title(__lowercase   )
						plt.xlabel(__lowercase   )
						plt.ylabel(__lowercase   )
						plt.legend()

						if self.args.figure_png_file is not None:
							plt.savefig(self.args.figure_png_file   )
						else:
							plt.show()






def    snake_case   (				)				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Tuple				  =				HfArgumentParser(A     )
	lowerCamelCase_				:						Optional[Any]				  =				parser.parse_args_into_dataclasses()[0]
	lowerCamelCase_				:						Optional[int]				  =				Plot(args=A     )
	plot.plot()


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

lowerCAmelCase__      :   Tuple      	=			logging.getLogger(__name__)


@require_tensorboard
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     "test_project_with_config"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							str     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									__UpperCAmelCase	:							Union[str, Any]     =     {"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("*/**"		):
											__UpperCAmelCase	:							Optional[Any]     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	child.iterdir()		)		)[0]
									self.assertNotEqual(str(UpperCAmelCase_		)		,	""		)
					def 		lowerCamelCase_   (   self       :      List[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Any     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     {"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
									__UpperCAmelCase	:							Union[str, Any]     =     list(filter(lambda UpperCAmelCase_		: x.is_file()		,	Path(f"{dirpath}/{project_name}"		).iterdir()		)		)[0]
									self.assertNotEqual(str(UpperCAmelCase_		)		,	""		)
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							List[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)
									accelerator.init_trackers(UpperCAmelCase_		)
									__UpperCAmelCase	:							int     =     {"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
									__UpperCAmelCase	:							Dict     =     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:
											__UpperCAmelCase	:							Dict     =     f.read()
									__UpperCAmelCase	:							Dict     =     False
									while data:
											__UpperCAmelCase	:							str     =     struct.unpack("Q"		,	data[:8]		)

											__UpperCAmelCase	:							List[str]     =     data[12 : 12 + int(header[0]		)]  # 8+4
											__UpperCAmelCase	:							Tuple     =     data[12 + int(header[0]		) + 4 :]
											__UpperCAmelCase	:							Optional[int]     =     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":
																	__UpperCAmelCase	:							List[str]     =     True
									self.assertTrue(UpperCAmelCase_		,	"Converted tensor was not found in the log file!"		)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							with self.assertRaisesRegex(UpperCAmelCase_		,	"Logging with `tensorboard` requires a `logging_dir`"		):
									__UpperCAmelCase	:							Union[str, Any]     =     Accelerator(log_with="tensorboard"		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							str     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     ProjectConfiguration(total_limit=30		)
							with tempfile.TemporaryDirectory() as dirpath:
									__UpperCAmelCase	:							Optional[Any]     =     Accelerator(log_with="tensorboard"		,	project_dir=UpperCAmelCase_		,	project_config=UpperCAmelCase_		)






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







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							super().setUp()
							# wandb let's us override where logs are stored to via the WANDB_DIR env var
							self.add_mocks(mock.patch.dict(os.environ		,	{"WANDB_DIR": self.tmpdir}		)		)
					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = True		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     Rf"{section} ([\S\s]*?)\n\n"
							if record:
									__UpperCAmelCase	:							Optional[Any]     =     Rf"Record: {pattern}"
							__UpperCAmelCase	:							Optional[Any]     =     re.findall(UpperCAmelCase_		,	UpperCAmelCase_		)[0]
							# A config
							if section == "config" or section == "history":
									__UpperCAmelCase	:							Optional[Any]     =     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 		lowerCamelCase_   (   self       :      str		):







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

        # Check HPS through careful parsing and cleaning
							__UpperCAmelCase	:							Dict     =     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
							__UpperCAmelCase	:							Union[str, Any]     =     self.parse_log(UpperCAmelCase_		,	"run"		,	UpperCAmelCase_		)
							self.assertEqual(logged_items["tags"]		,	"my_tag"		)

							# Actual logging
							__UpperCAmelCase	:							Optional[Any]     =     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,       _UpperCAmelCase,       _UpperCAmelCase  ):
		__UpperCAmelCase	:							str     =     run_name
		__UpperCAmelCase	:							Optional[int]     =     OfflineExperiment(project_name=_UpperCAmelCase,       offline_directory=_UpperCAmelCase  )
		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(snake_case__							,'''__init__'''							,snake_case__		)
class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					@staticmethod
					def 		lowerCamelCase_   (   UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      bool = False		):







							"""simple docstring"""
							for log in log_list:
									__UpperCAmelCase	:							List[Any]     =     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 		lowerCamelCase_   (   self       :      int		):







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







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







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	[
					    '''total_loss''',
					    '''iteration''',
					    '''my_text''',
					    '''learning_rate''',
					    '''num_iterations''',
					    '''some_boolean''',
					    '''some_string''',
					]

					SCREAMING_SNAKE_CASE							=	'''my_custom_tracker'''
					SCREAMING_SNAKE_CASE							=	False
					def __init__(   self       :      List[Any]		,	UpperCAmelCase_       :      str		):







							"""simple docstring"""
							__UpperCAmelCase	:							int     =     open(f"{dir}/log.csv"		,	"w+"		)
							__UpperCAmelCase	:							Optional[int]     =     csv.DictWriter(self.f		,	fieldnames=self._col_names		)
							self.writer.writeheader()
					@property
					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							return self.writer
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      dict		):







							"""simple docstring"""
							logger.info("Call init"		)
							self.writer.writerow(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      dict		,	UpperCAmelCase_       :      Optional[int]		):







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






					def 		lowerCamelCase_   (   self       :      str		):







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






class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Tuple		):







							"""simple docstring"""
							with tempfile.TemporaryDirectory() as d:
									__UpperCAmelCase	:							Optional[Any]     =     MyCustomTracker(UpperCAmelCase_		)
									__UpperCAmelCase	:							int     =     Accelerator(log_with=UpperCAmelCase_		)
									__UpperCAmelCase	:							List[Any]     =     {"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:
											__UpperCAmelCase	:							Optional[int]     =     csv.DictReader(UpperCAmelCase_		)
											__UpperCAmelCase	:							Optional[int]     =     next(UpperCAmelCase_		)
											__UpperCAmelCase	:							Union[str, Any]     =     {
											    "total_loss": "",
											    "iteration": "",
											    "my_text": "",
											    "learning_rate": "0.01",
											    "num_iterations": "12",
											    "some_boolean": "False",
											    "some_string": "some_value",
											}
											self.assertDictEqual(UpperCAmelCase_		,	UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							with tempfile.TemporaryDirectory() as d:
									__UpperCAmelCase	:							List[Any]     =     MyCustomTracker(UpperCAmelCase_		)
									__UpperCAmelCase	:							List[str]     =     Accelerator(log_with=UpperCAmelCase_		)
									accelerator.init_trackers("Some name"		)
									__UpperCAmelCase	:							List[Any]     =     {"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:
											__UpperCAmelCase	:							Any     =     csv.DictReader(UpperCAmelCase_		)
											__UpperCAmelCase	:							Any     =     next(UpperCAmelCase_		)
											__UpperCAmelCase	:							Union[str, Any]     =     {
											    "total_loss": "0.1",
											    "iteration": "1",
											    "my_text": "some_value",
											    "learning_rate": "",
											    "num_iterations": "",
											    "some_boolean": "",
											    "some_string": "",
											}
											self.assertDictEqual(UpperCAmelCase_		,	UpperCAmelCase_		)




