



"""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,
)


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 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'

A    =					'\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 			_a     (   datasets.Metric):



			def 							__lowercase				( self       :							Any		)       ->				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 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							str      ,  _lowercase       :							Tuple=1.0		)       ->				Optional[Any]:
									snake_case      : Any							=   {p["id"]: p["no_answer_probability"] for p in predictions}
									snake_case      : Any							=   [{"paragraphs": [{"qas": references}]}]
									snake_case      : Tuple							=   {p["id"]: p["prediction_text"] for p in predictions}

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

									snake_case      : Optional[Any]							=   get_raw_scores(_lowercase      ,  _lowercase		)
									snake_case      : Any							=   apply_no_ans_threshold(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									snake_case      : List[Any]							=   apply_no_ans_threshold(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									snake_case      : Any							=   make_eval_dict(_lowercase      ,  _lowercase		)

									if has_ans_qids:
															snake_case      : Union[str, Any]							=   make_eval_dict(_lowercase      ,  _lowercase      ,  qid_list=_lowercase		)
															merge_eval(_lowercase      ,  _lowercase      ,  "HasAns"		)
									if no_ans_qids:
															snake_case      : List[str]							=   make_eval_dict(_lowercase      ,  _lowercase      ,  qid_list=_lowercase		)
															merge_eval(_lowercase      ,  _lowercase      ,  "NoAns"		)
									find_all_best_thresh(_lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase      ,  _lowercase		)
									return dict(_lowercase		)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =	logging.get_logger(__name__)

A    =	{
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}

class 			_a     (   SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    """yoso"""



			def __init__( self       :							List[str]      ,  _lowercase       :							List[str]=50265      ,  _lowercase       :							List[str]=768      ,  _lowercase       :							Optional[int]=12      ,  _lowercase       :							str=12      ,  _lowercase       :							List[Any]=3072      ,  _lowercase       :							Dict="gelu"      ,  _lowercase       :							int=0.1      ,  _lowercase       :							Optional[Any]=0.1      ,  _lowercase       :							Dict=4096      ,  _lowercase       :							Optional[Any]=1      ,  _lowercase       :							Any=0.02      ,  _lowercase       :							Union[str, Any]=1E-12      ,  _lowercase       :							int="absolute"      ,  _lowercase       :							Any=True      ,  _lowercase       :							Dict=9      ,  _lowercase       :							int=64      ,  _lowercase       :							Any=None      ,  _lowercase       :							List[str]=True      ,  _lowercase       :							Optional[Any]=True      ,  _lowercase       :							Any=1      ,  _lowercase       :							int=0      ,  _lowercase       :							List[str]=2      ,  **_lowercase       :							List[Any]      ,  )       ->				Union[str, Any]:
									super().__init__(pad_token_id=_lowercase      ,  bos_token_id=_lowercase      ,  eos_token_id=_lowercase      ,  **_lowercase		)

									snake_case      : List[str]							=   vocab_size
									snake_case      : Tuple							=   max_position_embeddings
									snake_case      : Optional[Any]							=   hidden_size
									snake_case      : Tuple							=   num_hidden_layers
									snake_case      : Tuple							=   num_attention_heads
									snake_case      : Any							=   intermediate_size
									snake_case      : Union[str, Any]							=   hidden_act
									snake_case      : int							=   hidden_dropout_prob
									snake_case      : Dict							=   attention_probs_dropout_prob
									snake_case      : Any							=   initializer_range
									snake_case      : Optional[Any]							=   type_vocab_size
									snake_case      : List[str]							=   layer_norm_eps
									snake_case      : Dict							=   position_embedding_type
									snake_case      : Tuple							=   use_expectation
									snake_case      : Dict							=   hash_code_len
									snake_case      : Optional[Any]							=   num_hash
									snake_case      : List[str]							=   conv_window
									snake_case      : Optional[Any]							=   use_fast_hash
									snake_case      : Tuple							=   lsh_backward



