


'''simple docstring'''






import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


__A :  Optional[int]    							=				"\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n"

__A :  int    							=				"\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"

__A :  List[Any]    							=				"\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"precision\": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'precision@10': 1.0}\n\n"







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       int ):
  '''simple docstring'''





  return float((preds == labels).mean() )







def     UpperCamelCase_      (   A__ :       str							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       simple_accuracy(A__							,   A__ )
  lowerCAmelCase_      :				List[str]      =       float(fa_score(y_true=A__							,   y_pred=A__ ) )
  return {
      "accuracy": acc,
      "f1": fa,
  }







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Union[str, Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       np.array(A__ )
  lowerCAmelCase_      :				List[Any]      =       np.array(A__ )
  lowerCAmelCase_      :				Optional[Any]      =       en_sentvecs.shape[0]

  # mean centering
  lowerCAmelCase_      :				int      =       en_sentvecs - np.mean(A__							,   axis=0 )
  lowerCAmelCase_      :				Any      =       in_sentvecs - np.mean(A__							,   axis=0 )

  lowerCAmelCase_      :				int      =       cdist(A__							,   A__							,   """cosine""" )
  lowerCAmelCase_      :				List[str]      =       np.array(range(A__ ) )
  lowerCAmelCase_      :				Union[str, Any]      =       sim.argsort(axis=1 )[:, :10]
  lowerCAmelCase_      :				Tuple      =       np.any(preds == actual[:, None]							,   axis=1 )
  return float(matches.mean() )






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







    """simple docstring"""



    def __lowercase       (       self   :  int		)       ->  List[Any]:
      if self.config_name not in [
          "wnli",
          "copa",
          "sna",
          "csqa",
          "wstp",
          "inltkh",
          "bbca",
          "cvit-mkb-clsr",
          "iitp-mr",
          "iitp-pr",
          "actsa-sc",
          "md",
          "wiki-ner",
      ]:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """
            """\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """
            """\"wiki-ner\"]"""		)
      return datasets.MetricInfo(
          description=_DESCRIPTION   ,						citation=_CITATION   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
              {
                  """predictions""": datasets.Value("""int64"""		)
                  if self.config_name != """cvit-mkb-clsr"""
                  else datasets.Sequence(datasets.Value("""float32"""		)		),
                  """references""": datasets.Value("""int64"""		)
                  if self.config_name != """cvit-mkb-clsr"""
                  else datasets.Sequence(datasets.Value("""float32"""		)		),
              }		)   ,						codebase_urls=[]   ,						reference_urls=[]   ,						format="""numpy""" if self.config_name != """cvit-mkb-clsr""" else None   ,						)





    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Any:
      if self.config_name == "cvit-mkb-clsr":
        return {"precision@10": precision_at_aa(lowerCamelCase   ,						lowerCamelCase		)}
      elif self.config_name in ["wiki-ner"]:
        return acc_and_fa(lowerCamelCase   ,						lowerCamelCase		)
      elif self.config_name in [
          "wnli",
          "copa",
          "sna",
          "csqa",
          "wstp",
          "inltkh",
          "bbca",
          "iitp-mr",
          "iitp-pr",
          "actsa-sc",
          "md",
      ]:
        return {"accuracy": simple_accuracy(lowerCamelCase   ,						lowerCamelCase		)}
      else:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """
            """\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """
            """\"wiki-ner\"]"""		)







'''simple docstring'''






import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def     UpperCamelCase_      (   A__ :       bytes							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				str      =       """1"""
  lowerCAmelCase_      :				Optional[int]      =       """f32le"""
  lowerCAmelCase_      :				Any      =       [
      """ffmpeg""",
      """-i""",
      """pipe:0""",
      """-ac""",
      ac,
      """-ar""",
      ar,
      """-f""",
      format_for_conversion,
      """-hide_banner""",
      """-loglevel""",
      """quiet""",
      """pipe:1""",
  ]

  try:
    with subprocess.Popen(A__							,   stdin=subprocess.PIPE							,   stdout=subprocess.PIPE ) as ffmpeg_process:
      lowerCAmelCase_      :				Optional[int]      =       ffmpeg_process.communicate(A__ )
  except FileNotFoundError as error:
    raise ValueError("""ffmpeg was not found but is required to load audio files from filename""" ) from error
  lowerCAmelCase_      :				Optional[Any]      =       output_stream[0]
  lowerCAmelCase_      :				Optional[int]      =       np.frombuffer(A__							,   np.floataa )
  if audio.shape[0] == 0:
    raise ValueError("""Malformed soundfile""" )
  return audio







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       f'{sampling_rate}'
  lowerCAmelCase_      :				Any      =       """1"""
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Optional[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Union[str, Any]      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

  lowerCAmelCase_      :				int      =       platform.system()
  if system == "Linux":
    lowerCAmelCase_      :				int      =       """alsa"""
    lowerCAmelCase_      :				int      =       """default"""
  elif system == "Darwin":
    lowerCAmelCase_      :				List[str]      =       """avfoundation"""
    lowerCAmelCase_      :				Union[str, Any]      =       """:0"""
  elif system == "Windows":
    lowerCAmelCase_      :				List[Any]      =       """dshow"""
    lowerCAmelCase_      :				Union[str, Any]      =       """default"""

  lowerCAmelCase_      :				Tuple      =       [
      """ffmpeg""",
      """-f""",
      format_,
      """-i""",
      input_,
      """-ac""",
      ac,
      """-ar""",
      ar,
      """-f""",
      format_for_conversion,
      """-fflags""",
      """nobuffer""",
      """-hide_banner""",
      """-loglevel""",
      """quiet""",
      """pipe:1""",
  ]
  lowerCAmelCase_      :				Tuple      =       int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
  lowerCAmelCase_      :				List[str]      =       _ffmpeg_stream(A__							,   A__ )
  for item in iterator:
    yield item







def     UpperCamelCase_      (   A__ :       int							,   A__ :       float							,   A__ :       Optional[int] = None							,   A__ :       Optional[Union[Tuple[float, float], float]] = None							,   A__ :       str = "f32le"							,   ):
  '''simple docstring'''





  if stream_chunk_s is not None:
    lowerCAmelCase_      :				Union[str, Any]      =       stream_chunk_s
  else:
    lowerCAmelCase_      :				Tuple      =       chunk_length_s

  lowerCAmelCase_      :				List[Any]      =       ffmpeg_microphone(A__							,   A__							,   format_for_conversion=A__ )
  if format_for_conversion == "s16le":
    lowerCAmelCase_      :				Tuple      =       np.intaa
    lowerCAmelCase_      :				List[Any]      =       2
  elif format_for_conversion == "f32le":
    lowerCAmelCase_      :				Dict      =       np.floataa
    lowerCAmelCase_      :				int      =       4
  else:
    raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )

  if stride_length_s is None:
    lowerCAmelCase_      :				Optional[Any]      =       chunk_length_s / 6
  lowerCAmelCase_      :				List[str]      =       int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
  if isinstance(A__							,   (int, float) ):
    lowerCAmelCase_      :				int      =       [stride_length_s, stride_length_s]

  lowerCAmelCase_      :				str      =       int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
  lowerCAmelCase_      :				List[str]      =       int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
  lowerCAmelCase_      :				Dict      =       datetime.datetime.now()
  lowerCAmelCase_      :				Any      =       datetime.timedelta(seconds=A__ )
  for item in chunk_bytes_iter(A__							,   A__							,   stride=(stride_left, stride_right)							,   stream=A__ ):
    # Put everything back in numpy scale
    lowerCAmelCase_      :				Optional[int]      =       np.frombuffer(item["""raw"""]							,   dtype=A__ )
    lowerCAmelCase_      :				Dict      =       (
        item["""stride"""][0] // size_of_sample,
        item["""stride"""][1] // size_of_sample,
    )
    lowerCAmelCase_      :				Dict      =       sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 10 * delta:
      # We're late !! SKIP
      continue
    yield item







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       int							,   A__ :       Tuple[int, int]							,   A__ :       bool = False ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       B""""""
  lowerCAmelCase_,							lowerCAmelCase_      :				Any      =       stride
  if stride_left + stride_right >= chunk_len:
    raise ValueError(
        f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
  lowerCAmelCase_      :				Union[str, Any]      =       0
  for raw in iterator:
    acc += raw
    if stream and len(A__ ) < chunk_len:
      lowerCAmelCase_      :				Dict      =       (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(A__ ) >= chunk_len:
        # We are flushing the accumulator
        lowerCAmelCase_      :				Optional[Any]      =       (_stride_left, stride_right)
        lowerCAmelCase_      :				List[str]      =       {"""raw""": acc[:chunk_len], """stride""": stride}
        if stream:
          lowerCAmelCase_      :				List[Any]      =       False
        yield item
        lowerCAmelCase_      :				str      =       stride_left
        lowerCAmelCase_      :				str      =       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(A__ ) > stride_left:
    lowerCAmelCase_      :				Tuple      =       {"""raw""": acc, """stride""": (_stride_left, 0)}
    if stream:
      lowerCAmelCase_      :				Optional[Any]      =       False
    yield item







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       2**24  # 16Mo
  try:
    with subprocess.Popen(A__							,   stdout=subprocess.PIPE							,   bufsize=A__ ) as ffmpeg_process:
      while True:
        lowerCAmelCase_      :				Union[str, Any]      =       ffmpeg_process.stdout.read(A__ )
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError("""ffmpeg was not found but is required to stream audio files from filename""" ) from error



