
"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}

class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: Any					      =      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							_lowerCamelCase				: Optional[int]					      =      """mobilebert"""
							def __init__(    self     ,						_lowerCamelCase=30_522     ,						_lowerCamelCase=512     ,						_lowerCamelCase=24     ,						_lowerCamelCase=4     ,						_lowerCamelCase=512     ,						_lowerCamelCase="relu"     ,						_lowerCamelCase=0.0     ,						_lowerCamelCase=0.1     ,						_lowerCamelCase=512     ,						_lowerCamelCase=2     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=1e-12     ,						_lowerCamelCase=0     ,						_lowerCamelCase=128     ,						_lowerCamelCase=True     ,						_lowerCamelCase=True     ,						_lowerCamelCase=128     ,						_lowerCamelCase=False     ,						_lowerCamelCase=True     ,						_lowerCamelCase=4     ,						_lowerCamelCase="no_norm"     ,						_lowerCamelCase=True     ,						_lowerCamelCase=None     ,						**_lowerCamelCase     ,						) ->       Tuple:




										'''simple docstring'''
										super().__init__(pad_token_id=_lowerCamelCase     ,						**_lowerCamelCase					)

										__lowercase						      =					vocab_size
										__lowercase						      =					hidden_size
										__lowercase						      =					num_hidden_layers
										__lowercase						      =					num_attention_heads
										__lowercase						      =					hidden_act
										__lowercase						      =					intermediate_size
										__lowercase						      =					hidden_dropout_prob
										__lowercase						      =					attention_probs_dropout_prob
										__lowercase						      =					max_position_embeddings
										__lowercase						      =					type_vocab_size
										__lowercase						      =					initializer_range
										__lowercase						      =					layer_norm_eps
										__lowercase						      =					embedding_size
										__lowercase						      =					trigram_input
										__lowercase						      =					use_bottleneck
										__lowercase						      =					intra_bottleneck_size
										__lowercase						      =					use_bottleneck_attention
										__lowercase						      =					key_query_shared_bottleneck
										__lowercase						      =					num_feedforward_networks
										__lowercase						      =					normalization_type
										__lowercase						      =					classifier_activation

										if self.use_bottleneck:
													__lowercase						      =					intra_bottleneck_size
										else:
													__lowercase						      =					hidden_size

										__lowercase						      =					classifier_dropout





class 				__a    (   __a					):



							'''simple docstring'''







							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										if self.task == "multiple-choice":
													__lowercase						      =					{0: "batch", 1: "choice", 2: "sequence"}
										else:
													__lowercase						      =					{0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]					)







"""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,
)


_lowercase							 =							'''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'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}
'''

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



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

										__lowercase						      =					make_qid_to_has_ans(_lowerCamelCase					)  # maps qid to True/False
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if v]
										__lowercase						      =					[k for k, v in qid_to_has_ans.items() if not v]

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

										if has_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"HasAns"					)
										if no_ans_qids:
													__lowercase						      =					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					)





