


'''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_  :    List[str]      =			"""\
@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}
}
"""

a_  :    Tuple      =			"""
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.
"""

a_  :    Any      =			"""
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 					snake_case   (   datasets.Metric      ):





  """simple docstring"""




  def   snake_case		(   self					):







   """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   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase=1.0					):







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

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

   lowerCamelCase_			,lowerCamelCase_								=  get_raw_scores(UpperCamelCase      ,  UpperCamelCase					)
   lowerCamelCase_								=  apply_no_ans_threshold(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					)
   lowerCamelCase_								=  apply_no_ans_threshold(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					)
   lowerCamelCase_								=  make_eval_dict(UpperCamelCase      ,  UpperCamelCase					)

   if has_ans_qids:
    lowerCamelCase_								=  make_eval_dict(UpperCamelCase      ,  UpperCamelCase      ,  qid_list=UpperCamelCase					)
    merge_eval(UpperCamelCase      ,  UpperCamelCase      ,  "HasAns"					)
   if no_ans_qids:
    lowerCamelCase_								=  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					)







'''simple docstring'''

import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers.activations_tf import get_tf_activation





@require_tf
class 					snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]					)
   lowerCamelCase_								=  get_tf_activation("gelu"					)
   lowerCamelCase_								=  get_tf_activation("gelu_10"					)

   lowerCamelCase_								=  gelu(UpperCamelCase					)
   lowerCamelCase_								=  geluaa(UpperCamelCase					)

   lowerCamelCase_								=  tf.where(y_gelu_aa < 10.0      ,  1.0      ,  0.0					)

   self.assertEqual(tf.math.reduce_max(UpperCamelCase					).numpy().item()      ,  10.0					)
   self.assertTrue(np.allclose(y_gelu * clipped_mask      ,  y_gelu_aa * clipped_mask					)					)







  def   snake_case		(   self					):







   """simple docstring"""
   get_tf_activation("gelu"					)
   get_tf_activation("gelu_10"					)
   get_tf_activation("gelu_fast"					)
   get_tf_activation("gelu_new"					)
   get_tf_activation("glu"					)
   get_tf_activation("mish"					)
   get_tf_activation("quick_gelu"					)
   get_tf_activation("relu"					)
   get_tf_activation("sigmoid"					)
   get_tf_activation("silu"					)
   get_tf_activation("swish"					)
   get_tf_activation("tanh"					)
   with self.assertRaises(UpperCamelCase					):
    get_tf_activation("bogus"					)
   with self.assertRaises(UpperCamelCase					):
    get_tf_activation(UpperCamelCase					)



