
"""simple docstring"""

import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCamelCase      :							List[Any]          =					"\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

UpperCamelCase      :							List[str]          =					"\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

UpperCamelCase      :							Dict          =					"\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"

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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return datasets.MetricInfo(
          description=_DESCRIPTION      ,	citation=_CITATION      ,	homepage='https://github.com/chakki-works/seqeval'      ,	inputs_description=_KWARGS_DESCRIPTION      ,	features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('string'      ,	id='label' )      ,	id='sequence' ),
                  'references': datasets.Sequence(datasets.Value('string'      ,	id='label' )      ,	id='sequence' ),
              } )      ,	codebase_urls=['https://github.com/chakki-works/seqeval']      ,	reference_urls=['https://github.com/chakki-works/seqeval']      ,	)







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = "warn"      ,	):

      '''simple docstring'''




      if scheme is not None:
       try:
        __UpperCamelCase							    =			importlib.import_module('seqeval.scheme' )
        __UpperCamelCase							    =			getattr(__UpperCAmelCase      ,	__UpperCAmelCase )
       except AttributeError:
        raise ValueError(F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}' )
      __UpperCamelCase							    =			classification_report(
          y_true=__UpperCAmelCase      ,	y_pred=__UpperCAmelCase      ,	suffix=__UpperCAmelCase      ,	output_dict=__UpperCAmelCase      ,	scheme=__UpperCAmelCase      ,	mode=__UpperCAmelCase      ,	sample_weight=__UpperCAmelCase      ,	zero_division=__UpperCAmelCase      ,	)
      report.pop('macro avg' )
      report.pop('weighted avg' )
      __UpperCamelCase							    =			report.pop('micro avg' )

      __UpperCamelCase							    =			{
          type_name: {
              'precision': score['precision'],
              'recall': score['recall'],
              'f1': score['f1-score'],
              'number': score['support'],
          }
          for type_name, score in report.items()
      }
      __UpperCamelCase							    =			overall_score['precision']
      __UpperCamelCase							    =			overall_score['recall']
      __UpperCamelCase							    =			overall_score['f1-score']
      __UpperCamelCase							    =			accuracy_score(y_true=__UpperCAmelCase      ,	y_pred=__UpperCAmelCase )

      return scores





"""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,
)


UpperCamelCase      :							List[str]          =					"\\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      :							int          =					"\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      :							Optional[Any]          =					"\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     ):





     def 		UpperCAmelCase   (      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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=1.0 ):

      '''simple docstring'''




      __UpperCamelCase							    =			{p['id']: p['no_answer_probability'] for p in predictions}
      __UpperCamelCase							    =			[{'paragraphs': [{'qas': references}]}]
      __UpperCamelCase							    =			{p['id']: p['prediction_text'] for p in predictions}

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

      __UpperCamelCase    ,      __UpperCamelCase							    =			get_raw_scores(__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase )

      if has_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'HasAns' )
      if no_ans_qids:
       __UpperCamelCase							    =			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 )



