
"""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__ (		__magic_name__ ,      **__magic_name__			)	->List[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 ,      **_A ,      )







class 				__a    (   _UpperCAmelCase					):
							@staticmethod
							def      SCREAMING_SNAKE_CASE	(    _lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					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     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase     ,						) ->       Any:




										'''simple docstring'''
										__lowercase						      =					dataset
										__lowercase						      =					name
										__lowercase						      =					cache_dir
										__lowercase						      =					beam_pipeline_options
										__lowercase						      =					data_dir
										__lowercase						      =					all_configs
										__lowercase						      =					save_infos
										__lowercase						      =					ignore_verifications
										__lowercase						      =					force_redownload
										__lowercase						      =					config_kwargs





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

													__lowercase						      =					Path(_lowerCamelCase					).name + ".py"

													__lowercase						      =					os.path.join(_lowerCamelCase     ,						_lowerCamelCase					)
													if os.path.isfile(_lowerCamelCase					):
																__lowercase						      =					os.path.dirname(_lowerCamelCase					)
													elif os.path.isfile(_lowerCamelCase					):
																__lowercase						      =					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
													__lowercase						      =					os.path.join(_lowerCamelCase     ,						config.DATASETDICT_INFOS_FILENAME					)
													copyfile(_lowerCamelCase     ,						_lowerCamelCase					)
													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,
)


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

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'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}
'''

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



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										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     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

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

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

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





