





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 	A(__a:       str		,					**__a:       str							):
		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		,					**lowercase__		,					)


class      __magic_name__       (_A      ):




					@staticmethod
					def  __a			(    _a		)       -> Optional[Any]:
							lowerCAmelCase_				=							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     ,					_a     ,					_a     ,					_a     ,					_a     ,					_a     ,					_a     ,					_a     ,					_a     ,					_a     ,					**_a     ,					)       -> Tuple:
							lowerCAmelCase_				=							dataset
							lowerCAmelCase_				=							name
							lowerCAmelCase_				=							cache_dir
							lowerCAmelCase_				=							beam_pipeline_options
							lowerCAmelCase_				=							data_dir
							lowerCAmelCase_				=							all_configs
							lowerCAmelCase_				=							save_infos
							lowerCAmelCase_				=							ignore_verifications
							lowerCAmelCase_				=							force_redownload
							lowerCAmelCase_				=							config_kwargs




					def  __a			(    self		)       -> str:
							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		)
							lowerCAmelCase_		,   lowerCAmelCase_				=							self._dataset, self._name
							lowerCAmelCase_				=							dataset_module_factory(__SCREAMING_SNAKE_CASE		)
							lowerCAmelCase_				=							import_main_class(dataset_module.module_path		)
							lowerCAmelCase_				=							[]
							if self._beam_pipeline_options:
									lowerCAmelCase_				=							beam.options.pipeline_options.PipelineOptions(
									    flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","		) if opt]		)
							else:
									lowerCAmelCase_				=							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:
									lowerCAmelCase_				=							os.path.join(builder_cls.get_imported_module_dir()     ,					config.DATASETDICT_INFOS_FILENAME		)

									lowerCAmelCase_				=							Path(__SCREAMING_SNAKE_CASE		).name + ".py"

									lowerCAmelCase_				=							os.path.join(__SCREAMING_SNAKE_CASE     ,					__SCREAMING_SNAKE_CASE		)
									if os.path.isfile(__SCREAMING_SNAKE_CASE		):
											lowerCAmelCase_				=							os.path.dirname(__SCREAMING_SNAKE_CASE		)
									elif os.path.isfile(__SCREAMING_SNAKE_CASE		):
											lowerCAmelCase_				=							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
									lowerCAmelCase_				=							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 textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text.txt"
					A			=					textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "      )
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.fixture
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					tmp_path / "text_with_image.txt"
					with open(lowercase__			,							"w"			,							encoding="utf-8"      ) as f:
										f.write(lowercase__      )
					return str(lowercase__      )
@pytest.mark.parametrize("keep_linebreaks"			,							[True, False]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines(keepends=lowercase__      )
					A			=					Text(keep_linebreaks=lowercase__			,							encoding="utf-8"      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content
@require_pil
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read().splitlines()[0]
					A			=					Text(encoding="utf-8"			,							features=Features({"image": Image()}      )      )
					A			=					text._generate_tables([[text_file_with_image]]      )
					A			=					pa.concat_tables([table for _, table in generator]      )
					assert pa_table.schema.field("image"      ).type == Image()()
					A			=					pa_table.to_pydict()["image"]
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"			,							["line", "paragraph", "document"]      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					with open(lowercase__			,							encoding="utf-8"      ) as f:
										A			=					f.read()
					if sample_by == "line":
										A			=					expected_content.splitlines()
					elif sample_by == "paragraph":
										A			=					expected_content.split("\n\n"      )
					elif sample_by == "document":
										A			=					[expected_content]
					A			=					Text(sample_by=lowercase__			,							encoding="utf-8"			,							chunksize=100      )
					A			=					text._generate_tables([[text_file]]      )
					A			=					pa.concat_tables([table for _, table in generator]      ).to_pydict()["text"]
					assert generated_content == expected_content





