code: 
  
  
  
  
  
  
  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,
  )
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							"""\
  @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}
  }
  """
  
  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							"""
  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.
  """
  
  SCREAMING_SNAKE_CASE__		:       List[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 							UpperCAmelCase_     (		datasets.Metric		):
  
  
  
  
  					def      __UpperCAmelCase     (							self		):
  										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  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=1.0		):
  										UpperCAmelCase__			:     int										= {p["id"]: p["no_answer_probability"] for p in predictions}
  										UpperCAmelCase__			:     Optional[Any]										= [{"paragraphs": [{"qas": references}]}]
  										UpperCAmelCase__			:     Union[str, Any]										= {p["id"]: p["prediction_text"] for p in predictions}
  
  										UpperCAmelCase__			:     List[str]										= make_qid_to_has_ans(_lowerCAmelCase		)  # maps qid to True/False
  										UpperCAmelCase__			:     Dict										= [k for k, v in qid_to_has_ans.items() if v]
  										UpperCAmelCase__			:     Tuple										= [k for k, v in qid_to_has_ans.items() if not v]
  
  										UpperCAmelCase__			:     Any										= get_raw_scores(_lowerCAmelCase  ,	_lowerCAmelCase		)
  										UpperCAmelCase__			:     Optional[Any]										= apply_no_ans_threshold(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  										UpperCAmelCase__			:     Optional[Any]										= apply_no_ans_threshold(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  										UpperCAmelCase__			:     Optional[Any]										= make_eval_dict(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
  										if has_ans_qids:
  															UpperCAmelCase__			:     List[str]										= make_eval_dict(_lowerCAmelCase  ,	_lowerCAmelCase  ,	qid_list=_lowerCAmelCase		)
  															merge_eval(_lowerCAmelCase  ,	_lowerCAmelCase  ,	"""HasAns"""		)
  										if no_ans_qids:
  															UpperCAmelCase__			:     Any										= 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		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  