
'''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__					:						Any											=     "\\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__					:						List[Any]											=     "\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[int]											=     "\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       _a (datasets.Metric):





       """simple docstring"""




       def 				UpperCamelCase (							self					)			->	Tuple:
          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				,							A__				,							A__				,							A__=1.0					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = {p["""id"""]: p["""no_answer_probability"""] for p in predictions}
          _SCREAMING_SNAKE_CASE          = [{"""paragraphs""": [{"""qas""": references}]}]
          _SCREAMING_SNAKE_CASE          = {p["""id"""]: p["""prediction_text"""] for p in predictions}

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

          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = get_raw_scores(A__				,							A__					)
          _SCREAMING_SNAKE_CASE          = apply_no_ans_threshold(A__				,							A__				,							A__				,							A__					)
          _SCREAMING_SNAKE_CASE          = apply_no_ans_threshold(A__				,							A__				,							A__				,							A__					)
          _SCREAMING_SNAKE_CASE          = make_eval_dict(A__				,							A__					)

          if has_ans_qids:
             _SCREAMING_SNAKE_CASE          = make_eval_dict(A__				,							A__				,							qid_list=A__					)
             merge_eval(A__				,							A__				,							"""HasAns"""					)
          if no_ans_qids:
             _SCREAMING_SNAKE_CASE          = make_eval_dict(A__				,							A__				,							qid_list=A__					)
             merge_eval(A__				,							A__				,							"""NoAns"""					)
          find_all_best_thresh(A__				,							A__				,							A__				,							A__				,							A__				,							A__					)
          return dict(A__					)








'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






