




'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a		:	Optional[Any]       =      logging.get_logger(__name__)




class      UpperCamelCase       (	snake_case__       ):
  a     :List[str]          =					['input_values', 'attention_mask']



  def __init__(			self		,     __lowerCAmelCase=1		,     __lowerCAmelCase=1_6000		,     __lowerCAmelCase=128		,     __lowerCAmelCase=1024		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=-4.2677393		,     __lowerCAmelCase=4.5689974		,     __lowerCAmelCase=False		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(feature_size=__lowerCAmelCase		,     sampling_rate=__lowerCAmelCase		,     padding_value=__lowerCAmelCase		,     **__lowerCAmelCase )
      _a				:							Union[str, Any]							       =      num_mel_bins
      _a				:							Dict							       =      max_length
      _a				:							Union[str, Any]							       =      do_normalize
      _a				:							Optional[int]							       =      mean
      _a				:							Dict							       =      std
      _a				:							int							       =      return_attention_mask



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      torch.from_numpy(__lowerCAmelCase ).unsqueeze(0 )
      _a				:							Union[str, Any]							       =      ta_kaldi.fbank(
          __lowerCAmelCase		,     htk_compat=__lowerCAmelCase		,     sample_frequency=self.sampling_rate		,     use_energy=__lowerCAmelCase		,     window_type="""hanning"""		,     num_mel_bins=self.num_mel_bins		,     dither=0.0		,     frame_shift=10		,     )

      _a				:							Tuple							       =      fbank.shape[0]
      _a				:							Optional[int]							       =      max_length - n_frames

      # pad or truncate, depending on difference
      if difference > 0:
          _a				:							Union[str, Any]							       =      torch.nn.ZeroPadad((0, 0, 0, difference) )
          _a				:							List[str]							       =      pad_module(__lowerCAmelCase )
      elif difference < 0:
          _a				:							List[Any]							       =      fbank[0:max_length, :]

      _a				:							List[Any]							       =      fbank.numpy()

      return fbank



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return (input_values - (self.mean)) / (self.std * 2)



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = None		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
              raise ValueError(
                  f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                  f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                  f''' {self.sampling_rate} and not {sampling_rate}.''' )
      else:
          logger.warning(
              """It is strongly recommended to pass the `sampling_rate` argument to this function. """
              """Failing to do so can result in silent errors that might be hard to debug.""" )

      _a				:							Dict							       =      isinstance(__lowerCAmelCase		,     np.ndarray ) and len(raw_speech.shape ) > 1
      if is_batched_numpy and len(raw_speech.shape ) > 2:
          raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
      _a				:							str							       =      is_batched_numpy or (
          isinstance(__lowerCAmelCase		,     (list, tuple) ) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list) ))
      )

      if is_batched:
          _a				:							int							       =      [np.asarray(__lowerCAmelCase		,     dtype=np.floataa ) for speech in raw_speech]
      elif not is_batched and not isinstance(__lowerCAmelCase		,     np.ndarray ):
          _a				:							Any							       =      np.asarray(__lowerCAmelCase		,     dtype=np.floataa )
      elif isinstance(__lowerCAmelCase		,     np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
          _a				:							Any							       =      raw_speech.astype(np.floataa )

      # always return batch
      if not is_batched:
          _a				:							Any							       =      [raw_speech]

      # extract fbank features and pad/truncate to max_length
      _a				:							List[str]							       =      [self._extract_fbank_features(__lowerCAmelCase		,     max_length=self.max_length ) for waveform in raw_speech]

      # convert into BatchFeature
      _a				:							List[str]							       =      BatchFeature({"""input_values""": features} )

      # make sure list is in array format
      _a				:							Any							       =      padded_inputs.get("""input_values""" )
      if isinstance(input_values[0]		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      [np.asarray(__lowerCAmelCase		,     dtype=np.floataa ) for feature in input_values]

      # normalization
      if self.do_normalize:
          _a				:							Union[str, Any]							       =      [self.normalize(__lowerCAmelCase ) for feature in input_values]

      if return_tensors is not None:
          _a				:							Optional[int]							       =      padded_inputs.convert_to_tensors(__lowerCAmelCase )

      return padded_inputs






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,
)


__lowerCAmelCase									=				'''\
@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}
}
'''

__lowerCAmelCase									=				'''
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.
'''

__lowerCAmelCase									=				'''
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 ):
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''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					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  {p['id']: p['no_answer_probability'] for p in predictions}
        lowercase__:  int         =  [{'paragraphs': [{'qas': references}]}]
        lowercase__:  Optional[int]         =  {p['id']: p['prediction_text'] for p in predictions}

        lowercase__:  Any         =  make_qid_to_has_ans(lowerCAmelCase__					)  # maps qid to True/False
        lowercase__:  Optional[Any]         =  [k for k, v in qid_to_has_ans.items() if v]
        lowercase__:  Dict         =  [k for k, v in qid_to_has_ans.items() if not v]

        lowercase__     ,		lowercase__:  Dict         =  get_raw_scores(lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Optional[int]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  List[Any]         =  apply_no_ans_threshold(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
        lowercase__:  Dict         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__					)

        if has_ans_qids:
               lowercase__:  List[Any]         =  make_eval_dict(lowerCAmelCase__							,					lowerCAmelCase__							,					qid_list=lowerCAmelCase__					)
               merge_eval(lowerCAmelCase__							,					lowerCAmelCase__							,					'HasAns'					)
        if no_ans_qids:
               lowercase__:  int         =  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__					)


