
'''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       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      **_SCREAMING_SNAKE_CASE		: 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						,      **lowercase__						,      )




class       lowerCAmelCase			(					A__      ):




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




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase       ,    ):
								_SCREAMING_SNAKE_CASE					=			dataset
								_SCREAMING_SNAKE_CASE					=			name
								_SCREAMING_SNAKE_CASE					=			cache_dir
								_SCREAMING_SNAKE_CASE					=			beam_pipeline_options
								_SCREAMING_SNAKE_CASE					=			data_dir
								_SCREAMING_SNAKE_CASE					=			all_configs
								_SCREAMING_SNAKE_CASE					=			save_infos
								_SCREAMING_SNAKE_CASE					=			ignore_verifications
								_SCREAMING_SNAKE_CASE					=			force_redownload
								_SCREAMING_SNAKE_CASE					=			config_kwargs




		def      lowercase							(					self   ):
								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   )
								_SCREAMING_SNAKE_CASE					=			self._dataset, self._name
								_SCREAMING_SNAKE_CASE					=			dataset_module_factory(__A   )
								_SCREAMING_SNAKE_CASE					=			import_main_class(dataset_module.module_path   )
								_SCREAMING_SNAKE_CASE					=			[]
								if self._beam_pipeline_options:
														_SCREAMING_SNAKE_CASE					=			beam.options.pipeline_options.PipelineOptions(
														    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(","   ) if opt]   )
								else:
														_SCREAMING_SNAKE_CASE					=			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=__A       ,    cache_dir=self._cache_dir       ,    base_path=dataset_module.builder_kwargs.get("base_path"   )       ,    )   )
								else:
														builders.append(
														    builder_cls(
														        config_name=__A       ,    data_dir=self._data_dir       ,    beam_options=__A       ,    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=__A       ,    )
														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:
														_SCREAMING_SNAKE_CASE					=			os.path.join(builder_cls.get_imported_module_dir()       ,    config.DATASETDICT_INFOS_FILENAME   )

														_SCREAMING_SNAKE_CASE					=			Path(__A   ).name + """.py"""

														_SCREAMING_SNAKE_CASE					=			os.path.join(__A       ,    __A   )
														if os.path.isfile(__A   ):
																				_SCREAMING_SNAKE_CASE					=			os.path.dirname(__A   )
														elif os.path.isfile(__A   ):
																				_SCREAMING_SNAKE_CASE					=			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
														_SCREAMING_SNAKE_CASE					=			os.path.join(__A       ,    config.DATASETDICT_INFOS_FILENAME   )
														copyfile(__A       ,    __A   )
														print(F'Dataset Infos file saved at {user_dataset_infos_path}'   )

"""simple docstring"""






from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Union[str, Any]												=		"MarkupLMFeatureExtractor"
			UpperCAmelCase_    :Optional[Any]												=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
			UpperCAmelCase_    :List[Any]												=		True






			def __call__(   self			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A = True			,  __A = False			,  __A = None			,  __A = None			,  __A = 0			,  __A = None			,  __A = None			,  __A = None			,  __A = False			,  __A = False			,  __A = False			,  __A = False			,  __A = True			,  __A = None			,  **__A			,  )     ->	BatchEncoding:
						# first, create nodes and xpaths
						if self.parse_html:
									if html_strings is None:
												raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""			)

									if nodes is not None or xpaths is not None or node_labels is not None:
												raise ValueError(
												    """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""			)

									lowerCAmelCase_       :Union[str, Any]     	=    self.feature_extractor(__A			)
									lowerCAmelCase_       :Any     	=    features["""nodes"""]
									lowerCAmelCase_       :Union[str, Any]     	=    features["""xpaths"""]
						else:
									if html_strings is not None:
												raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""			)
									if nodes is None or xpaths is None:
												raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""			)

        # # second, apply the tokenizer
						if questions is not None and self.parse_html:
									if isinstance(__A			,  __A			):
												lowerCAmelCase_       :Dict     	=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

						lowerCAmelCase_       :Optional[Any]     	=    self.tokenizer(
						    text=questions if questions is not None else nodes			,  text_pair=nodes if questions is not None else None			,  xpaths=__A			,  node_labels=__A			,  add_special_tokens=__A			,  padding=__A			,  truncation=__A			,  max_length=__A			,  stride=__A			,  pad_to_multiple_of=__A			,  return_token_type_ids=__A			,  return_attention_mask=__A			,  return_overflowing_tokens=__A			,  return_special_tokens_mask=__A			,  return_offsets_mapping=__A			,  return_length=__A			,  verbose=__A			,  return_tensors=__A			,  **__A			,  )

						return encoded_inputs






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	Tuple:
						return self.tokenizer.batch_decode(*__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	str:
						return self.tokenizer.decode(*__A			,  **__A			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Any     	=    self.tokenizer.model_input_names
						return tokenizer_input_names






