




'''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 						_lowercase						(      lowercase_		,				**lowercase_    ):



		'''simple docstring'''







		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_				(			UpperCAmelCase_				):




					'''simple docstring'''






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






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






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

									_snake_case      			=			Path(__UpperCamelCase					).name + ".py"

									_snake_case      			=			os.path.join(__UpperCamelCase,    __UpperCamelCase					)
									if os.path.isfile(__UpperCamelCase					):
											_snake_case      			=			os.path.dirname(__UpperCamelCase					)
									elif os.path.isfile(__UpperCamelCase					):
											_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
									_snake_case      			=			os.path.join(__UpperCamelCase,    config.DATASETDICT_INFOS_FILENAME					)
									copyfile(__UpperCamelCase,    __UpperCamelCase					)
									print(f'''Dataset Infos file saved at {user_dataset_infos_path}'''					)











'''simple docstring'''







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_lowerCamelCase				:		str				      =    '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

_lowerCamelCase				:		Union[str, Any]				      =    '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

_lowerCamelCase				:		List[Any]				      =    '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,			_KWARGS_DESCRIPTION				)
class 	lowerCamelCase_				(			datasets.Metric				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return datasets.MetricInfo(
							    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"					),
							                "prediction_text": datasets.Value("string"					),
							                "no_answer_probability": datasets.Value("float32"					),
							            },
							            "references": {
							                "id": datasets.Value("string"					),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"					), "answer_start": datasets.Value("int32"					)}					),
							            },
							        }					),    codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    )






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1.0					):
							_snake_case      			=			{p["id"]: p["no_answer_probability"] for p in predictions}
							_snake_case      			=			[{"paragraphs": [{"qas": references}]}]
							_snake_case      			=			{p["id"]: p["prediction_text"] for p in predictions}

							_snake_case      			=			make_qid_to_has_ans(__UpperCamelCase					)  # maps qid to True/False
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if v]
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if not v]

							_snake_case			,       _snake_case      			=			get_raw_scores(__UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase					)

							if has_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "HasAns"					)
							if no_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "NoAns"					)
							find_all_best_thresh(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							return dict(__UpperCamelCase					)





