



"""simple docstring"""






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


_lowercase					:					Dict 		=		logging.get_logger(__name__)

_lowercase					:					Optional[Any] 		=		{
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				'reformer'
					_a       =				['past_buckets_states']
					_a       =				{}



					def __init__(			self				:    Tuple,    lowerCamelCase				:    int=64,    lowerCamelCase				:    Optional[Any]=["local", "lsh", "local", "lsh", "local", "lsh"],    lowerCamelCase				:    Any=1.0,    lowerCamelCase				:    Union[str, Any]=True,    lowerCamelCase				:    str=[64, 64],    lowerCamelCase				:    Tuple=[64, 192],    lowerCamelCase				:    Tuple=0,    lowerCamelCase				:    List[str]=2,    lowerCamelCase				:    Union[str, Any]=512,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    Tuple="relu",    lowerCamelCase				:    Optional[int]=0.05,    lowerCamelCase				:    Optional[int]=256,    lowerCamelCase				:    Dict=0.02,    lowerCamelCase				:    Optional[int]=False,    lowerCamelCase				:    int=1E-12,    lowerCamelCase				:    int=1,    lowerCamelCase				:    List[Any]=0,    lowerCamelCase				:    str=0.05,    lowerCamelCase				:    Optional[int]=64,    lowerCamelCase				:    Tuple=64,    lowerCamelCase				:    List[Any]=0.0,    lowerCamelCase				:    int=1,    lowerCamelCase				:    Any=0,    lowerCamelCase				:    Union[str, Any]=4096,    lowerCamelCase				:    Any=12,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Optional[int]=1,    lowerCamelCase				:    List[str]=0,    lowerCamelCase				:    Tuple=320,    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=None,    **lowerCamelCase				:    str,    )->   Union[str, Any]:
								lowerCamelCase__						:						List[Any]						      =hash_seed
								lowerCamelCase__						:						int						      =vocab_size
								lowerCamelCase__						:						List[Any]						      =attention_head_size
								lowerCamelCase__						:						Any						      =hidden_size
								lowerCamelCase__						:						Tuple						      =num_attention_heads
								lowerCamelCase__						:						List[str]						      =num_hashes
								lowerCamelCase__						:						Union[str, Any]						      =len(lowerCamelCase	)
								lowerCamelCase__						:						List[str]						      =tuple(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else num_buckets
								lowerCamelCase__						:						Union[str, Any]						      =lsh_attn_chunk_length
								lowerCamelCase__						:						Optional[int]						      =local_attn_chunk_length
								lowerCamelCase__						:						List[str]						      =lsh_num_chunks_after
								lowerCamelCase__						:						List[Any]						      =lsh_num_chunks_before
								lowerCamelCase__						:						int						      =local_num_chunks_after
								lowerCamelCase__						:						Optional[Any]						      =local_num_chunks_before
								lowerCamelCase__						:						List[str]						      =hidden_act
								lowerCamelCase__						:						List[Any]						      =feed_forward_size
								lowerCamelCase__						:						Any						      =hidden_dropout_prob
								lowerCamelCase__						:						Tuple						      =lsh_attention_probs_dropout_prob
								lowerCamelCase__						:						List[str]						      =local_attention_probs_dropout_prob
								lowerCamelCase__						:						List[Any]						      =max_position_embeddings
								lowerCamelCase__						:						Union[str, Any]						      =initializer_range
								lowerCamelCase__						:						Optional[Any]						      =layer_norm_eps
								lowerCamelCase__						:						str						      =axial_pos_embds
								lowerCamelCase__						:						List[str]						      =tuple(lowerCamelCase	)
								lowerCamelCase__						:						int						      =tuple(lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =axial_norm_std
								lowerCamelCase__						:						Optional[Any]						      =chunk_size_lm_head
								lowerCamelCase__						:						int						      =attn_layers
								lowerCamelCase__						:						int						      =use_cache
								lowerCamelCase__						:						Union[str, Any]						      =classifier_dropout
								super().__init__(
								    pad_token_id=lowerCamelCase,    eos_token_id=lowerCamelCase,    is_decoder=lowerCamelCase,    tie_word_embeddings=lowerCamelCase,    **lowerCamelCase,    )









'''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__	  =		'\\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__	  =		'\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__	  =		'\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 						__lowercase      (						self							:       Optional[Any]		)      ->			Dict:







									'''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'''		),
									                '''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]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Dict=1.0		)      ->			Dict:







									'''simple docstring'''





									_lowercase							:		Union[str, Any]				=   {p['''id''']: p['''no_answer_probability'''] for p in predictions}
									_lowercase							:		Optional[int]				=   [{'''paragraphs''': [{'''qas''': references}]}]
									_lowercase							:		List[str]				=   {p['''id''']: p['''prediction_text'''] for p in predictions}

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

									_lowercase			, _lowercase							:		Tuple				=   get_raw_scores(UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		int				=   apply_no_ans_threshold(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		int				=   apply_no_ans_threshold(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)
									_lowercase							:		List[str]				=   make_eval_dict(UpperCamelCase_      ,		UpperCamelCase_		)

									if has_ans_qids:
																_lowercase							:		Optional[int]				=   make_eval_dict(UpperCamelCase_      ,		UpperCamelCase_      ,		qid_list=UpperCamelCase_		)
																merge_eval(UpperCamelCase_      ,		UpperCamelCase_      ,		'''HasAns'''		)
									if no_ans_qids:
																_lowercase							:		int				=   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_		)



