


"""simple docstring"""




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

import numpy as np
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[str]		 =						f'{sampling_rate}'
 __lowerCamelCase		:							Dict		 =						'1'
 __lowerCamelCase		:							Any		 =						'f32le'
 __lowerCamelCase		:							str		 =						[
     '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[Any]		 =						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		:							str		 =						np.frombuffer(_A      ,	np.floataa						)
 if audio.shape[0] == 0:
  raise ValueError('Malformed soundfile'						)
 return audio
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__ = "f32le"      ,	):
 __lowerCamelCase		:							Optional[int]		 =						f'{sampling_rate}'
 __lowerCamelCase		:							Tuple		 =						'1'
 if format_for_conversion == "s16le":
  __lowerCamelCase		:							int		 =						2
 elif format_for_conversion == "f32le":
  __lowerCamelCase		:							List[Any]		 =						4
 else:
  raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'						)

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

 __lowerCamelCase		:							Optional[int]		 =						[
     'ffmpeg',
     '-f',
     format_,
     '-i',
     input_,
     '-ac',
     ac,
     '-ar',
     ar,
     '-f',
     format_for_conversion,
     '-fflags',
     'nobuffer',
     '-hide_banner',
     '-loglevel',
     'quiet',
     'pipe:1',
 ]
 __lowerCamelCase		:							Any		 =						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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__ = None      ,	SCREAMING_SNAKE_CASE__ = None      ,	SCREAMING_SNAKE_CASE__ = "f32le"      ,	):
 if stream_chunk_s is not None:
  __lowerCamelCase		:							int		 =						stream_chunk_s
 else:
  __lowerCamelCase		:							Optional[Any]		 =						chunk_length_s

 __lowerCamelCase		:							List[Any]		 =						ffmpeg_microphone(_A      ,	_A      ,	format_for_conversion=_A						)
 if format_for_conversion == "s16le":
  __lowerCamelCase		:							str		 =						np.intaa
  __lowerCamelCase		:							Tuple		 =						2
 elif format_for_conversion == "f32le":
  __lowerCamelCase		:							int		 =						np.floataa
  __lowerCamelCase		:							str		 =						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[Any]		 =						int(round(sampling_rate * chunk_length_s						)						) * size_of_sample
 if isinstance(_A      ,	(int, float)						):
  __lowerCamelCase		:							Optional[int]		 =						[stride_length_s, stride_length_s]

 __lowerCamelCase		:							int		 =						int(round(sampling_rate * stride_length_s[0]						)						) * size_of_sample
 __lowerCamelCase		:							Optional[int]		 =						int(round(sampling_rate * stride_length_s[1]						)						) * size_of_sample
 __lowerCamelCase		:							str		 =						datetime.datetime.now()
 __lowerCamelCase		:							Optional[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		:							Optional[int]		 =						sampling_rate
  audio_time += delta
  if datetime.datetime.now() > audio_time + 10 * delta:
   # We're late !! SKIP
   continue
  yield item
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__ = False						):
 __lowerCamelCase		:							Any		 =						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		:							Dict		 =						0
 for raw in iterator:
  acc += raw
  if stream and len(_A						) < chunk_len:
   __lowerCamelCase		:							Optional[int]		 =						(_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[int]		 =						(_stride_left, stride_right)
    __lowerCamelCase		:							List[Any]		 =						{'raw': acc[:chunk_len], 'stride': stride}
    if stream:
     __lowerCamelCase		:							Tuple		 =						False
    yield item
    __lowerCamelCase		:							Optional[int]		 =						stride_left
    __lowerCamelCase		:							Union[str, Any]		 =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
 if len(_A						) > stride_left:
  __lowerCamelCase		:							List[Any]		 =						{'raw': acc, 'stride': (_stride_left, 0)}
  if stream:
   __lowerCamelCase		:							Union[str, Any]		 =						False
  yield item



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Any		 =						2**24  # 16Mo
 try:
  with subprocess.Popen(_A      ,	stdout=subprocess.PIPE      ,	bufsize=_A						) as ffmpeg_process:
   while True:
    __lowerCamelCase		:							Dict		 =						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





import datasets


lowercase_	   =     '\\n@InProceedings{conneau2018xnli,\n  author = "Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin",\n  title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n  booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing",\n  year = "2018",\n  publisher = "Association for Computational Linguistics",\n  location = "Brussels, Belgium",\n}\n'

lowercase_	   =     '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

lowercase_	   =     '\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric("xnli")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return (preds == labels).mean()



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




      '''simple docstring'''


      def 					_snake_case     (    self:	Union[str, Any]    ):
       return datasets.MetricInfo(
           description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
                   'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
               }    )						,				codebase_urls=[]						,				reference_urls=[]						,				format='numpy'						,				)




      def 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Optional[Any]    ):
       return {"accuracy": simple_accuracy(a						,				a    )}

