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,
)


UpperCAmelCase      		=					"\\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"

UpperCAmelCase      		=					"\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"

UpperCAmelCase      		=					"\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        __snake_case       (      self							:		Optional[int]	)			->				List[str]:
        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        __snake_case       (      self							:		int							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		int=1.0	)			->				Tuple:
        _UpperCamelCase  :							Any	    =	{p['id']: p['no_answer_probability'] for p in predictions}
        _UpperCamelCase  :							str	    =	[{'paragraphs': [{'qas': references}]}]
        _UpperCamelCase  :							Dict	    =	{p['id']: p['prediction_text'] for p in predictions}

        _UpperCamelCase  :							List[str]	    =	make_qid_to_has_ans(lowerCamelCase_	)  # maps qid to True/False
        _UpperCamelCase  :							List[Any]	    =	[k for k, v in qid_to_has_ans.items() if v]
        _UpperCamelCase  :							List[Any]	    =	[k for k, v in qid_to_has_ans.items() if not v]

        _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	get_raw_scores(lowerCamelCase_							,      lowerCamelCase_	)
        _UpperCamelCase  :							str	    =	apply_no_ans_threshold(lowerCamelCase_							,      lowerCamelCase_							,      lowerCamelCase_							,      lowerCamelCase_	)
        _UpperCamelCase  :							List[str]	    =	apply_no_ans_threshold(lowerCamelCase_							,      lowerCamelCase_							,      lowerCamelCase_							,      lowerCamelCase_	)
        _UpperCamelCase  :							List[Any]	    =	make_eval_dict(lowerCamelCase_							,      lowerCamelCase_	)

        if has_ans_qids:
               _UpperCamelCase  :							str	    =	make_eval_dict(lowerCamelCase_							,      lowerCamelCase_							,      qid_list=lowerCamelCase_	)
               merge_eval(lowerCamelCase_							,      lowerCamelCase_							,      'HasAns'	)
        if no_ans_qids:
               _UpperCamelCase  :							int	    =	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_	)








from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class       _lowercase				:





	"""simple docstring"""
	__A		      =				42
	__A		      =				None
	__A		      =				None






def        a( )						->			TreeNode:
					"""simple docstring"""





					return TreeNode(3					,	TreeNode(9			)					,	TreeNode(20					,	TreeNode(15			)					,	TreeNode(7			)			)			)






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															right_view.append(root.val			)

										depth_first_search(root.right					,	depth + 1					,	A			)
										depth_first_search(root.left					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return right_view

					depth_first_search(A					,	0					,	A			)
					return right_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															left_view.append(root.val			)

										depth_first_search(root.left					,	depth + 1					,	A			)
										depth_first_search(root.right					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return left_view

					depth_first_search(A					,	0					,	A			)
					return left_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first

															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															top_view.append(pair[1][0]			)

					a   	=  []
					if not root:
										return top_view

					breadth_first_search(A					,	A			)
					return top_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					from collections import defaultdict

					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first
															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															bottom_view.append(pair[1][-1]			)

					a   	=  []
					if not root:
										return bottom_view

					breadth_first_search(A					,	A			)
					return bottom_view


if __name__ == "__main__":
				import doctest

				doctest.testmod()
