






import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_a       =    "1.5.0"

_a       =    ["vision-encoder-decoder"]


def __A       ( __lowerCAmelCase      )->    Optional[int]:





						"""simple docstring"""



						if is_optimum_available():
												from optimum.version import __version__ as optimum_version

												_UpperCAmelCase							   =     version.parse(_UpperCAmelCase      )
												if parsed_optimum_version < version.parse(_UpperCAmelCase      ):
																		raise RuntimeError(
																		    F"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
																		    'can upgrade optimum by running: pip install -U optimum[exporters]'      )
						else:
												raise RuntimeError(
												    'transformers.onnx requires optimum to run, you can install the library by running: pip install '
												    'optimum[exporters]'      )
						_UpperCAmelCase							   =     [
						    sys.executable,
						    "-m",
						    "optimum.exporters.onnx",
						    F"""--model {args.model}""",
						    F"""--task {args.feature}""",
						    F"""--framework {args.framework}""" if args.framework is not None else "",
						    F"""{args.output}""",
						]
						_UpperCAmelCase							   =     subprocess.Popen(' '.join(_UpperCAmelCase      )					,						stdout=subprocess.PIPE					,						shell=_UpperCAmelCase      )
						proc.wait()

						logger.info(
						    'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
						    'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
						    'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'      )


def __A       ( __lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     args.output if args.output.is_file() else args.output.joinpath('model.onnx'      )
						if not args.output.parent.exists():
												args.output.parent.mkdir(parents=_UpperCAmelCase      )

						# Allocate the model
						_UpperCAmelCase							   =     FeaturesManager.get_model_from_feature(
						    args.feature					,						args.model					,						framework=args.framework					,						cache_dir=args.cache_dir      )

						_UpperCAmelCase							   =     FeaturesManager.check_supported_model_or_raise(_UpperCAmelCase					,						feature=args.feature      )
						_UpperCAmelCase							   =     model_onnx_config(model.config      )

						if model_kind in ENCODER_DECODER_MODELS:
												_UpperCAmelCase							   =     model.get_encoder()
												_UpperCAmelCase							   =     model.get_decoder()

												_UpperCAmelCase							   =     onnx_config.get_encoder_config(encoder_model.config      )
												_UpperCAmelCase							   =     onnx_config.get_decoder_config(
												    encoder_model.config					,						decoder_model.config					,						feature=args.feature      )

												if args.opset is None:
																		_UpperCAmelCase							   =     max(encoder_onnx_config.default_onnx_opset					,						decoder_onnx_config.default_onnx_opset      )

												if args.opset < min(encoder_onnx_config.default_onnx_opset					,						decoder_onnx_config.default_onnx_opset      ):
																		raise ValueError(
																		    F"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
																		    F""" {min(encoder_onnx_config.default_onnx_opset					,						decoder_onnx_config.default_onnx_opset      )} is required."""      )

												_UpperCAmelCase							   =     AutoFeatureExtractor.from_pretrained(args.model      )

												_UpperCAmelCase							   =     export(
												    _UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.opset					,						args.output.parent.joinpath('encoder_model.onnx'      )					,						)

												validate_model_outputs(
												    _UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.output.parent.joinpath('encoder_model.onnx'      )					,						_UpperCAmelCase					,						args.atol if args.atol else encoder_onnx_config.atol_for_validation					,						)

												_UpperCAmelCase							   =     AutoTokenizer.from_pretrained(args.model      )

												_UpperCAmelCase							   =     export(
												    _UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.opset					,						args.output.parent.joinpath('decoder_model.onnx'      )					,						)

												validate_model_outputs(
												    _UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.output.parent.joinpath('decoder_model.onnx'      )					,						_UpperCAmelCase					,						args.atol if args.atol else decoder_onnx_config.atol_for_validation					,						)
												logger.info(
												    F"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"      ).as_posix()},"""
												    F""" {args.output.parent.joinpath("decoder_model.onnx"      ).as_posix()}"""      )

						else:
												# Instantiate the appropriate preprocessor
												if args.preprocessor == "auto":
																		_UpperCAmelCase							   =     get_preprocessor(args.model      )
												elif args.preprocessor == "tokenizer":
																		_UpperCAmelCase							   =     AutoTokenizer.from_pretrained(args.model      )
												elif args.preprocessor == "image_processor":
																		_UpperCAmelCase							   =     AutoImageProcessor.from_pretrained(args.model      )
												elif args.preprocessor == "feature_extractor":
																		_UpperCAmelCase							   =     AutoFeatureExtractor.from_pretrained(args.model      )
												elif args.preprocessor == "processor":
																		_UpperCAmelCase							   =     AutoProcessor.from_pretrained(args.model      )
												else:
																		raise ValueError(F"""Unknown preprocessor type '{args.preprocessor}'"""      )

												# Ensure the requested opset is sufficient
												if args.opset is None:
																		_UpperCAmelCase							   =     onnx_config.default_onnx_opset

												if args.opset < onnx_config.default_onnx_opset:
																		raise ValueError(
																		    F"""Opset {args.opset} is not sufficient to export {model_kind}. """
																		    F"""At least  {onnx_config.default_onnx_opset} is required."""      )

												_UpperCAmelCase							   =     export(
												    _UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.opset					,						args.output					,						)

												if args.atol is None:
																		_UpperCAmelCase							   =     onnx_config.atol_for_validation

												validate_model_outputs(_UpperCAmelCase					,						_UpperCAmelCase					,						_UpperCAmelCase					,						args.output					,						_UpperCAmelCase					,						args.atol      )
												logger.info(F"""All good, model saved at: {args.output.as_posix()}"""      )
												warnings.warn(
												    'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
												    ' using optimum.exporters.onnx in future. You can find more information here:'
												    ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'					,						_UpperCAmelCase					,						)


def __A       ( )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     ArgumentParser('Hugging Face Transformers ONNX exporter'      )
						parser.add_argument(
						    '-m'					,						'--model'					,						type=_UpperCAmelCase					,						required=_UpperCAmelCase					,						help='Model ID on huggingface.co or path on disk to load model from.'      )
						parser.add_argument(
						    '--feature'					,						default='default'					,						help='The type of features to export the model with.'					,						)
						parser.add_argument('--opset'					,						type=_UpperCAmelCase					,						default=_UpperCAmelCase					,						help='ONNX opset version to export the model with.'      )
						parser.add_argument(
						    '--atol'					,						type=_UpperCAmelCase					,						default=_UpperCAmelCase					,						help='Absolute difference tolerance when validating the model.'      )
						parser.add_argument(
						    '--framework'					,						type=_UpperCAmelCase					,						choices=['pt', 'tf']					,						default=_UpperCAmelCase					,						help=(
						        'The framework to use for the ONNX export.'
						        ' If not provided, will attempt to use the local checkpoint\'s original framework'
						        ' or what is available in the environment.'
						    )					,						)
						parser.add_argument('output'					,						type=_UpperCAmelCase					,						help='Path indicating where to store generated ONNX model.'      )
						parser.add_argument('--cache_dir'					,						type=_UpperCAmelCase					,						default=_UpperCAmelCase					,						help='Path indicating where to store cache.'      )
						parser.add_argument(
						    '--preprocessor'					,						type=_UpperCAmelCase					,						choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor']					,						default='auto'					,						help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.'					,						)
						parser.add_argument(
						    '--export_with_transformers'					,						action='store_true'					,						help=(
						        'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
						        'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
						        'recommended.'
						    )					,						)

						_UpperCAmelCase							   =     parser.parse_args()
						if args.export_with_transformers or not is_optimum_available():
												export_with_transformers(_UpperCAmelCase      )
						else:
												export_with_optimum(_UpperCAmelCase      )


if __name__ == "__main__":
		_a       =    logging.get_logger('''transformers.onnx''')  # pylint: disable=invalid-name
		logger.setLevel(logging.INFO)
		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_		)




