
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__        =						logging.getLogger(__name__)







@require_tensorboard
class 	A__				(	unittest.TestCase      ):






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						"test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase__ : List[str]   =						Accelerator(log_with='tensorboard'       ,	project_dir=lowerCamelCase__  )
										accelerator.init_trackers(lowerCamelCase__  )
										lowerCAmelCase__ : Optional[Any]   =						{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
										accelerator.log(lowerCamelCase__       ,	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
										lowerCAmelCase__ : int   =						list(filter(lambda a  : x.is_file()       ,	Path(f'''{dirpath}/{project_name}'''  ).iterdir()  )  )[0]
										self.assertNotEqual(str(lowerCamelCase__  )       ,	''  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						"test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase__ : Tuple   =						Accelerator(log_with='tensorboard'       ,	project_dir=lowerCamelCase__  )
										accelerator.init_trackers(lowerCamelCase__  )
										lowerCAmelCase__ : List[Any]   =						{"tensor": torch.tensor(1  )}
										accelerator.log(lowerCamelCase__       ,	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
										lowerCAmelCase__ : List[str]   =						list(filter(lambda a  : 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(lowerCamelCase__       ,	'rb'  ) as f:
													lowerCAmelCase__ : Union[str, Any]   =						f.read()
										lowerCAmelCase__ : List[str]   =						False
										while data:
													lowerCAmelCase__ : str   =						struct.unpack('Q'       ,	data[:8]  )

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

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






				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







							with self.assertRaisesRegex(lowerCamelCase__       ,	'Logging with `tensorboard` requires a `logging_dir`'  ):
										lowerCAmelCase__ : Dict   =						Accelerator(log_with='tensorboard'  )
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase__ : Any   =						Accelerator(log_with='tensorboard'       ,	project_dir=lowerCamelCase__  )






				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						ProjectConfiguration(total_limit=30  )
							with tempfile.TemporaryDirectory() as dirpath:
										lowerCAmelCase__ : Any   =						Accelerator(log_with='tensorboard'       ,	project_dir=lowerCamelCase__       ,	project_config=lowerCamelCase__  )







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






				def 				_lowerCamelCase						(		self   : int  ):






							'''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						(		a   : str       ,	a   : str       ,	a   : bool = True  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						Rf'''{section} ([\S\s]*?)\n\n'''
							if record:
										lowerCAmelCase__ : Union[str, Any]   =						Rf'''Record: {pattern}'''
							lowerCAmelCase__ : Optional[int]   =						re.findall(lowerCamelCase__       ,	lowerCamelCase__  )[0]
							# A config
							if section == "config" or section == "history":
										lowerCAmelCase__ : Optional[Any]   =						re.findall(R'\"([a-zA-Z0-9_.,]+)'       ,	lowerCamelCase__  )
										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]+)\"'       ,	lowerCamelCase__  )  )






				@skip
				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







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

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

							# Actual logging
							lowerCAmelCase__ : Optional[Any]   =						self.parse_log(lowerCamelCase__       ,	'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 					lowerCAmelCase__     (    self			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						Tuple:
			lowerCAmelCase__ : int   =						run_name
			lowerCAmelCase__ : Any   =						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(UpperCAmelCase__		,  '__init__'		,  UpperCAmelCase__      )
class 	A__				(	unittest.TestCase      ):






				@staticmethod
				def 				_lowerCamelCase						(		a   : Optional[Any]       ,	a   : str       ,	a   : bool = False  ):






							'''simple docstring'''







							for log in log_list:
										lowerCAmelCase__ : List[Any]   =						json.loads(lowerCamelCase__  )["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   : Any  ):






							'''simple docstring'''







							with tempfile.TemporaryDirectory() as d:
										lowerCAmelCase__ : Optional[int]   =						CometMLTracker('test_project_with_config'       ,	lowerCamelCase__  )
										lowerCAmelCase__ : Any   =						Accelerator(log_with=lowerCamelCase__  )
										lowerCAmelCase__ : Union[str, Any]   =						{"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
										accelerator.init_trackers(lowerCamelCase__       ,	lowerCamelCase__  )
										accelerator.end_training()
										lowerCAmelCase__ : str   =						os.listdir(lowerCamelCase__  )[0]  # Comet is nice, it's just a zip file here
										# We parse the raw logs
										lowerCAmelCase__ : List[Any]   =						os.path.join(lowerCamelCase__       ,	lowerCamelCase__  )
										lowerCAmelCase__ : Optional[int]   =						zipfile.ZipFile(lowerCamelCase__       ,	'r'  )
										lowerCAmelCase__ : Optional[int]   =						archive.open('messages.json'  ).read().decode('utf-8'  )
							lowerCAmelCase__ : Tuple   =						log.split('\n'  )[:-1]
							self.assertEqual(self.get_value_from_key(lowerCamelCase__       ,	'num_iterations'       ,	lowerCamelCase__  )       ,	12  )
							self.assertEqual(self.get_value_from_key(lowerCamelCase__       ,	'learning_rate'       ,	lowerCamelCase__  )       ,	0.0_1  )
							self.assertEqual(self.get_value_from_key(lowerCamelCase__       ,	'some_boolean'       ,	lowerCamelCase__  )       ,	lowerCamelCase__  )
							self.assertEqual(self.get_value_from_key(lowerCamelCase__       ,	'some_string'       ,	lowerCamelCase__  )       ,	'some_value'  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







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







class 	A__				(	UpperCAmelCase__      ):
				lowercase            =    [
				    "total_loss",
				    "iteration",
				    "my_text",
				    "learning_rate",
				    "num_iterations",
				    "some_boolean",
				    "some_string",
				]

				lowercase            =    "my_custom_tracker"
				lowercase            =    False






				def __init__(		self   : int       ,	a   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						open(f'''{dir}/log.csv'''       ,	'w+'  )
							lowerCAmelCase__ : Union[str, Any]   =						csv.DictWriter(self.f       ,	fieldnames=self._col_names  )
							self.writer.writeheader()






				@property
				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							return self.writer






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : dict  ):






							'''simple docstring'''







							logger.info('Call init'  )
							self.writer.writerow(lowerCamelCase__  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : dict       ,	a   : Optional[int]  ):






							'''simple docstring'''







							logger.info('Call log'  )
							self.writer.writerow(lowerCamelCase__  )






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.f.close()







class 	A__				(	unittest.TestCase      ):






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







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



'''simple docstring'''


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			**__lowerCAmelCase ):
		return RunBeamCommand(
		    args.dataset   ,			args.name   ,			args.cache_dir   ,			args.beam_pipeline_options   ,			args.data_dir   ,			args.all_configs   ,			args.save_info or args.save_infos   ,			args.ignore_verifications   ,			args.force_redownload   ,			**__lowerCAmelCase   ,			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  ArgumentParser			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   parser.add_parser("run_beam"						,	help="Run a Beam dataset processing pipeline"			)
					run_beam_parser.add_argument("dataset"						,	type=lowerCamelCase__						,	help="Name of the dataset to download"			)
					run_beam_parser.add_argument("--name"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Dataset config name"			)
					run_beam_parser.add_argument(
					    "--cache_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Cache directory where the datasets are stored"						,	)
					run_beam_parser.add_argument(
					    "--beam_pipeline_options"						,	type=lowerCamelCase__						,	default=""						,	help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"						,	)
					run_beam_parser.add_argument(
					    "--data_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Can be used to specify a manual directory to get the files from"						,	)
					run_beam_parser.add_argument("--all_configs"						,	action="store_true"						,	help="Test all dataset configurations"			)
					run_beam_parser.add_argument("--save_info"						,	action="store_true"						,	help="Save the dataset infos file"			)
					run_beam_parser.add_argument(
					    "--ignore_verifications"						,	action="store_true"						,	help="Run the test without checksums and splits checks"			)
					run_beam_parser.add_argument("--force_redownload"						,	action="store_true"						,	help="Force dataset redownload"			)
					# aliases
					run_beam_parser.add_argument("--save_infos"						,	action="store_true"						,	help="alias for save_info"			)
					run_beam_parser.set_defaults(func=lowerCamelCase__			)




			def __init__(		self	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	**lowerCamelCase__	:  Optional[Any]						,	) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   dataset
					_UpperCAmelCase		:							str             =   name
					_UpperCAmelCase		:							int             =   cache_dir
					_UpperCAmelCase		:							Optional[int]             =   beam_pipeline_options
					_UpperCAmelCase		:							int             =   data_dir
					_UpperCAmelCase		:							Optional[int]             =   all_configs
					_UpperCAmelCase		:							Any             =   save_infos
					_UpperCAmelCase		:							Optional[Any]             =   ignore_verifications
					_UpperCAmelCase		:							Any             =   force_redownload
					_UpperCAmelCase		:							Tuple             =   config_kwargs






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					import apache_beam as beam

					if self._name is not None and self._all_configs:
							print("Both parameters `name` and `all_configs` can't be used at once."			)
							exit(1			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self._dataset, self._name
					_UpperCAmelCase		:							Union[str, Any]             =   dataset_module_factory(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   import_main_class(dataset_module.module_path			)
					_UpperCAmelCase		:							List[DatasetBuilder]             =   []
					if self._beam_pipeline_options:
							_UpperCAmelCase		:							Optional[int]             =   beam.options.pipeline_options.PipelineOptions(
							    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","			) if opt]			)
					else:
							_UpperCAmelCase		:							str             =   None
					if self._all_configs and len(builder_cls.BUILDER_CONFIGS			) > 0:
							for builder_config in builder_cls.BUILDER_CONFIGS:
									builders.append(
									    builder_cls(
									        config_name=builder_config.name						,	data_dir=self._data_dir						,	hash=dataset_module.hash						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	)			)
					else:
							builders.append(
							    builder_cls(
							        config_name=lowerCamelCase__						,	data_dir=self._data_dir						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	**self._config_kwargs						,	)			)

					for builder in builders:
							builder.download_and_prepare(
							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
							    if not self._force_redownload
							    else DownloadMode.FORCE_REDOWNLOAD						,	download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH			)						,	verification_mode=VerificationMode.NO_CHECKS
							    if self._ignore_verifications
							    else VerificationMode.ALL_CHECKS						,	try_from_hf_gcs=lowerCamelCase__						,	)
							if self._save_infos:
									builder._save_infos()

					print("Apache beam run successful."			)

					# If save_infos=True, the dataset infos file is created next to the loaded module file.
					# Let's move it to the original directory of the dataset script, to allow the user to
					# upload them on S3 at the same time afterwards.
					if self._save_infos:
							_UpperCAmelCase		:							Optional[Any]             =   os.path.join(builder_cls.get_imported_module_dir()						,	config.DATASETDICT_INFOS_FILENAME			)

							_UpperCAmelCase		:							List[str]             =   Path(lowerCamelCase__			).name + ".py"

							_UpperCAmelCase		:							int             =   os.path.join(lowerCamelCase__						,	lowerCamelCase__			)
							if os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Any             =   os.path.dirname(lowerCamelCase__			)
							elif os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Tuple             =   path
							else:  # in case of a remote dataset
									print(F"""Dataset Infos file saved at {dataset_infos_path}"""			)
									exit(1			)

							# Move datasetinfo back to the user
							_UpperCAmelCase		:							Union[str, Any]             =   os.path.join(lowerCamelCase__						,	config.DATASETDICT_INFOS_FILENAME			)
							copyfile(lowerCamelCase__						,	lowerCamelCase__			)
							print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""			)



