"""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_      (_SCREAMING_SNAKE_CASE	:Optional[Any]		,			**_SCREAMING_SNAKE_CASE	:Dict  )				-> List[Any]:
						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		,			**_SCREAMING_SNAKE_CASE		,			)



class   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""







	@staticmethod
	def       A    (			_SCREAMING_SNAKE_CASE )    ->	Any:
							a_							:			List[Any]     		=					parser.add_parser("run_beam"       ,     help="Run a Beam dataset processing pipeline" )
							run_beam_parser.add_argument("dataset"       ,     type=_SCREAMING_SNAKE_CASE       ,     help="Name of the dataset to download" )
							run_beam_parser.add_argument("--name"       ,     type=_SCREAMING_SNAKE_CASE       ,     default=_SCREAMING_SNAKE_CASE       ,     help="Dataset config name" )
							run_beam_parser.add_argument(
							    "--cache_dir"       ,     type=_SCREAMING_SNAKE_CASE       ,     default=_SCREAMING_SNAKE_CASE       ,     help="Cache directory where the datasets are stored"       ,     )
							run_beam_parser.add_argument(
							    "--beam_pipeline_options"       ,     type=_SCREAMING_SNAKE_CASE       ,     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=_SCREAMING_SNAKE_CASE       ,     default=_SCREAMING_SNAKE_CASE       ,     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=_SCREAMING_SNAKE_CASE )







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	Any:
							a_							:			Tuple     		=					dataset
							a_							:			Dict     		=					name
							a_							:			Optional[Any]     		=					cache_dir
							a_							:			Optional[int]     		=					beam_pipeline_options
							a_							:			Dict     		=					data_dir
							a_							:			Tuple     		=					all_configs
							a_							:			Optional[Any]     		=					save_infos
							a_							:			Optional[int]     		=					ignore_verifications
							a_							:			Any     		=					force_redownload
							a_							:			Optional[Any]     		=					config_kwargs







	def       A    (			self )    ->	int:
							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 )
							a_		,					a_							:			Tuple     		=					self._dataset, self._name
							a_							:			Dict     		=					dataset_module_factory(_SCREAMING_SNAKE_CASE )
							a_							:			Optional[Any]     		=					import_main_class(dataset_module.module_path )
							a_							:			List[DatasetBuilder]     		=					[]
							if self._beam_pipeline_options:
													a_							:			int     		=					beam.options.pipeline_options.PipelineOptions(
													    flags=[f'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split("," ) if opt] )
							else:
													a_							:			List[Any]     		=					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=_SCREAMING_SNAKE_CASE       ,     cache_dir=self._cache_dir       ,     base_path=dataset_module.builder_kwargs.get("base_path" )       ,     ) )
							else:
													builders.append(
													    builder_cls(
													        config_name=_SCREAMING_SNAKE_CASE       ,     data_dir=self._data_dir       ,     beam_options=_SCREAMING_SNAKE_CASE       ,     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=_SCREAMING_SNAKE_CASE       ,     )
													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:
													a_							:			Any     		=					os.path.join(builder_cls.get_imported_module_dir()       ,     config.DATASETDICT_INFOS_FILENAME )

													a_							:			int     		=					Path(_SCREAMING_SNAKE_CASE ).name + ".py"

													a_							:			Optional[Any]     		=					os.path.join(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													if os.path.isfile(_SCREAMING_SNAKE_CASE ):
																			a_							:			Optional[int]     		=					os.path.dirname(_SCREAMING_SNAKE_CASE )
													elif os.path.isfile(_SCREAMING_SNAKE_CASE ):
																			a_							:			int     		=					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
													a_							:			Optional[int]     		=					os.path.join(_SCREAMING_SNAKE_CASE       ,     config.DATASETDICT_INFOS_FILENAME )
													copyfile(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )
													print(f'''Dataset Infos file saved at {user_dataset_infos_path}''' )





"""simple docstring"""






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


UpperCamelCase     					=     (30, 4_87)
UpperCamelCase     					=     (36, 10_24)
UpperCamelCase     					=     (1_00, 1_00)
UpperCamelCase     					=     1_00

UpperCamelCase     					=     datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

UpperCamelCase					,      UpperCamelCase     					=     os.path.split(__file__)
UpperCamelCase     					=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> Any:
						with ArrowWriter(features=_SCREAMING_SNAKE_CASE		,			path=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )  ) as writer:
												for key, record in dummy_data:
																		a_							:			int     		=					my_features.encode_example(_SCREAMING_SNAKE_CASE  )
																		writer.write(_SCREAMING_SNAKE_CASE  )
												a_		,					a_							:			str     		=					writer.finalize()

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> Tuple:
						a_							:			Dict     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Dict  )				-> Optional[Any]:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> List[str]:
						a_							:			Optional[Any]     		=					10
						a_							:			Optional[int]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			List[str]     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> Optional[int]:
						a_							:			List[str]     		=					10
						a_							:			int     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			str     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for col in feats:
												a_							:			List[Any]     		=					dataset[col]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			Any     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for col in feats:
												a_							:			Any     		=					dataset[col]

def  lowerCAmelCase_      ()				-> Optional[Any]:
						a_							:			List[Any]     		=					{}
						a_							:			str     		=					(
						    read_unformated,
						    read_formatted_as_numpy,
						    read_batch_unformated,
						    read_batch_formatted_as_numpy,
						    read_col_unformated,
						    read_col_formatted_as_numpy,
						)
						with tempfile.TemporaryDirectory() as tmp_dir:
												a_							:			Optional[int]     		=					datasets.Features({"image": ArrayaD(_SCREAMING_SNAKE_CASE		,			dtype="float32"  )}  )
												a_							:			Optional[Any]     		=					generate_examples(features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE  )
												a_							:			List[str]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			Optional[int]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
												# )
												a_							:			Union[str, Any]     		=					datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"  )  )  )}  )
												a_							:			List[str]     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": SPEED_TEST_SHAPE}  )
												a_							:			str     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			List[str]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
												# )
												a_							:			int     		=					datasets.Features({"image": datasets.Sequence(datasets.Value("float32"  )  )}  )
												a_							:			str     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,			)
												a_							:			List[Any]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			str     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						with open(_SCREAMING_SNAKE_CASE		,			"wb"  ) as f:
												f.write(json.dumps(_SCREAMING_SNAKE_CASE  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_array_xd()




