




'''simple docstring'''







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_lowerCamelCase				:		Optional[Any]				      =    get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 						_lowercase						(      lowercase_		,				lowercase_ = None    ):



		'''simple docstring'''







		_snake_case      			=			importlib.import_module(lowercase_    )

		# TODO(QL): always update the module to add subsequent new authentication
		if hasattr(lowercase_		,				"_patched_for_streaming"    ) and module._patched_for_streaming:
				return

		def wrap_auth(lowercase_    ):
				@wraps(lowercase_    )
				def wrapper(*lowercase_		,				**lowercase_    ):
						return function(*lowercase_		,				use_auth_token=lowercase_		,				**lowercase_    )

				_snake_case      			=			"wrap_auth"
				return wrapper

		# open files in a streaming fashion
		patch_submodule(lowercase_		,				"open"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"os.listdir"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"os.walk"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"glob.glob"		,				wrap_auth(lowercase_    )    ).start()
		# allow to navigate in remote zip files
		patch_submodule(lowercase_		,				"os.path.join"		,				lowercase_    ).start()
		patch_submodule(lowercase_		,				"os.path.dirname"		,				lowercase_    ).start()
		patch_submodule(lowercase_		,				"os.path.basename"		,				lowercase_    ).start()
		patch_submodule(lowercase_		,				"os.path.relpath"		,				lowercase_    ).start()
		patch_submodule(lowercase_		,				"os.path.split"		,				lowercase_    ).start()
		patch_submodule(lowercase_		,				"os.path.splitext"		,				lowercase_    ).start()
		# allow checks on paths
		patch_submodule(lowercase_		,				"os.path.exists"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"os.path.isdir"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"os.path.isfile"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"os.path.getsize"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"pathlib.Path"		,				lowercase_    ).start()
		# file readers
		patch_submodule(lowercase_		,				"gzip.open"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"numpy.load"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"pandas.read_csv"		,				wrap_auth(lowercase_    )		,				attrs=["__version__"]    ).start()
		patch_submodule(lowercase_		,				"pandas.read_excel"		,				wrap_auth(lowercase_    )		,				attrs=["__version__"]    ).start()
		patch_submodule(lowercase_		,				"scipy.io.loadmat"		,				wrap_auth(lowercase_    )		,				attrs=["__version__"]    ).start()
		patch_submodule(lowercase_		,				"xml.etree.ElementTree.parse"		,				wrap_auth(lowercase_    )    ).start()
		patch_submodule(lowercase_		,				"xml.dom.minidom.parse"		,				wrap_auth(lowercase_    )    ).start()
		_snake_case      			=			True






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		extend_module_for_streaming(builder.__module__		,				use_auth_token=builder.use_auth_token    )
		# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
		if not builder.__module__.startswith("datasets."    ):  # check that it's not a packaged builder like csv
				for imports in get_imports(inspect.getfile(builder.__class__    )    ):
						if imports[0] == "internal":
								_snake_case      			=			imports[1]
								_snake_case      			=			".".join(builder.__module__.split("."    )[:-1] + [internal_import_name]    )
								extend_module_for_streaming(lowercase_		,				use_auth_token=builder.use_auth_token    )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
		from .builder import DatasetBuilder

		_snake_case      			=			[
		    cls.__module__
		    for cls in type(lowercase_    ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls		,				lowercase_    ) and cls.__module__ != DatasetBuilder.__module__
		]  # check it's not a standard builder from datasets.builder
		for module in parent_builder_modules:
				extend_module_for_streaming(lowercase_		,				use_auth_token=builder.use_auth_token    )











'''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					)





