import datasets

from .evaluate import evaluate


A_      =      "\\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"

A_      =      "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering 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"

A_      =      "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the 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 Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"






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

						def        __a		(	self				:      int   )							->		Union[str, Any]:


									'''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'''   )},
									            '''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        __a		(	self				:      Tuple							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									lowercase							=		[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									lowercase							=		evaluate(dataset=__lowerCamelCase							,							predictions=__lowerCamelCase   )
									return score






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

A_      =      TypeVar("KEY")
A_      =      TypeVar("VAL")






@dataclass(frozen=_A       ,     slots=_A    )
class      __lowercase					(    Generic[KEY, VAL]    ):
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _Item    ):

						def __init__(	self				:      Optional[int]   )							->		None:


									'''simple docstring'''

									super().__init__(__lowerCamelCase							,							__lowerCamelCase   )



						def __bool__(	self				:      str   )							->		bool:


									'''simple docstring'''

									return False





A_      =      _DeletedItem()






class      __lowercase					(    MutableMapping[KEY, VAL]    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      int = 8							,							__lowerCamelCase				:      float = 0.75   )							->		None:


									'''simple docstring'''

									lowercase							=		initial_block_size
									lowercase							=		[None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowercase							=		capacity_factor
									lowercase							=		0

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KEY   )							->		int:


									'''simple docstring'''

									return hash(__lowerCamelCase   ) % len(self._buckets   )

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		int:


									'''simple docstring'''

									return (ind + 1) % len(self._buckets   )

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		bool:


									'''simple docstring'''

									lowercase							=		self._buckets[ind]
									if not stored:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												self._len += 1
												return True
									elif stored.key == key:
												lowercase							=		_Item(__lowerCamelCase							,							__lowerCamelCase   )
												return True
									else:
												return False

						def        __a		(	self				:      Optional[Any]   )							->		bool:


									'''simple docstring'''

									lowercase							=		len(self._buckets   ) * self._capacity_factor
									return len(self   ) >= int(__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		bool:


									'''simple docstring'''

									if len(self._buckets   ) <= self._initial_block_size:
												return False
									lowercase							=		len(self._buckets   ) * self._capacity_factor / 2
									return len(self   ) < limit

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		self._buckets
									lowercase							=		[None] * new_size
									lowercase							=		0
									for item in old_buckets:
												if item:
															self._add_item(item.key							,							item.val   )

						def        __a		(	self				:      List[str]   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) * 2   )

						def        __a		(	self				:      Any   )							->		None:


									'''simple docstring'''

									self._resize(len(self._buckets   ) // 2   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY   )							->		Iterator[int]:


									'''simple docstring'''

									lowercase							=		self._get_bucket_index(__lowerCamelCase   )
									for _ in range(len(self._buckets   )   ):
												yield ind
												lowercase							=		self._get_next_ind(__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												if self._try_set(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ):
															break

						def __setitem__(	self				:      Optional[int]							,							__lowerCamelCase				:      KEY							,							__lowerCamelCase				:      VAL   )							->		None:


									'''simple docstring'''

									if self._is_full():
												self._size_up()

									self._add_item(__lowerCamelCase							,							__lowerCamelCase   )

						def __delitem__(	self				:      List[str]							,							__lowerCamelCase				:      KEY   )							->		None:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															raise KeyError(__lowerCamelCase   )
												if item is _deleted:
															continue
												if item.key == key:
															lowercase							=		_deleted
															self._len -= 1
															break
									if self._is_sparse():
												self._size_down()

						def __getitem__(	self				:      Any							,							__lowerCamelCase				:      KEY   )							->		VAL:


									'''simple docstring'''

									for ind in self._iterate_buckets(__lowerCamelCase   ):
												lowercase							=		self._buckets[ind]
												if item is None:
															break
												if item is _deleted:
															continue
												if item.key == key:
															return item.val
									raise KeyError(__lowerCamelCase   )

						def __len__(	self				:      str   )							->		int:


									'''simple docstring'''

									return self._len

						def __iter__(	self				:      Union[str, Any]   )							->		Iterator[KEY]:


									'''simple docstring'''

									yield from (item.key for item in self._buckets if item)



						def __repr__(	self				:      Any   )							->		str:


									'''simple docstring'''

									lowercase							=		''' ,'''.join(
									    f'{item.key}: {item.val}' for item in self._buckets if item   )
									return f'HashMap({val_string})'





