




'''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					)











'''simple docstring'''







import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase				:		str				      =    'src/transformers'

# Matches is_xxx_available()
_lowerCamelCase				:		str				      =    re.compile(R'is\_([a-z_]*)_available()')
# Matches from xxx import bla
_lowerCamelCase				:		Union[str, Any]				      =    re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
_lowerCamelCase				:		Dict				      =    re.compile(R'^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)')


_lowerCamelCase				:		int				      =    '\n{0} = None\n'

_lowerCamelCase				:		List[Any]				      =    '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_lowerCamelCase				:		Any				      =    '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		if _re_test_backend.search(lowercase_    ) is None:
				return None
		_snake_case      			=			[b[0] for b in _re_backend.findall(lowercase_    )]
		backends.sort()
		return "_and_".join(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		with open(os.path.join(lowercase_		,				"__init__.py"    )		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
				_snake_case      			=			f.readlines()

		# Get to the point we do the actual imports for type checking
		_snake_case      			=			0
		while not lines[line_index].startswith("if TYPE_CHECKING"    ):
				line_index += 1

		_snake_case      			=			{}
		# Go through the end of the file
		while line_index < len(lowercase_    ):
				# If the line is an if is_backend_available, we grab all objects associated.
				_snake_case      			=			find_backend(lines[line_index]    )
				if backend is not None:
						while not lines[line_index].startswith("    else:"    ):
								line_index += 1
						line_index += 1

						_snake_case      			=			[]
						# Until we unindent, add backend objects to the list
						while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(" " * 8    ):
								_snake_case      			=			lines[line_index]
								_snake_case      			=			_re_single_line_import.search(lowercase_    )
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(", "    )    )
								elif line.startswith(" " * 12    ):
										objects.append(line[12:-2]    )
								line_index += 1

						_snake_case      			=			objects
				else:
						line_index += 1

		return backend_specific_objects






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if name.isupper():
				return DUMMY_CONSTANT.format(lowercase_    )
		elif name.islower():
				return DUMMY_FUNCTION.format(lowercase_		,				lowercase_    )
		else:
				return DUMMY_CLASS.format(lowercase_		,				lowercase_    )






def 						_lowercase						(      lowercase_=None    ):



		'''simple docstring'''







		if backend_specific_objects is None:
				_snake_case      			=			read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_snake_case      			=			{}

		for backend, objects in backend_specific_objects.items():
				_snake_case      			=			"[" + ", ".join(f'''"{b}"''' for b in backend.split("_and_"    )    ) + "]"
				_snake_case      			=			"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(lowercase_		,				lowercase_    ) for o in objects]    )
				_snake_case      			=			dummy_file

		return dummy_files






def 						_lowercase						(      lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_snake_case      			=			{"torch": "pt"}

		# Locate actual dummy modules and read their content.
		_snake_case      			=			os.path.join(lowercase_		,				"utils"    )
		_snake_case      			=			{
		    backend: os.path.join(lowercase_		,				f'''dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py'''    )
		    for backend in dummy_files.keys()
		}

		_snake_case      			=			{}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(lowercase_    ):
						with open(lowercase_		,				"r"		,				encoding="utf-8"		,				newline="\n"    ) as f:
								_snake_case      			=			f.read()
				else:
						_snake_case      			=			""

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py as the main '''
								    "__init__ has new objects."    )
								with open(dummy_file_paths[backend]		,				"w"		,				encoding="utf-8"		,				newline="\n"    ) as f:
										f.write(dummy_files[backend]    )
						else:
								raise ValueError(
								    "The main __init__ has objects that are not present in "
								    f'''transformers.utils.dummy_{short_names.get(lowercase_		,				lowercase_    )}_objects.py. Run `make fix-copies` '''
								    "to fix this."    )


if __name__ == "__main__":
						_lowerCamelCase				:		int				      =    argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()

						check_dummies(args.fix_and_overwrite)





