



"""simple docstring"""





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

import numpy as np
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				f"""{sampling_rate}"""
      UpperCAmelCase_     					=				"1"
      UpperCAmelCase_     					=				"f32le"
      UpperCAmelCase_     					=				[
          "ffmpeg",
          "-i",
          "pipe:0",
          "-ac",
          ac,
          "-ar",
          ar,
          "-f",
          format_for_conversion,
          "-hide_banner",
          "-loglevel",
          "quiet",
          "pipe:1",
      ]

      try:
            with subprocess.Popen(lowerCAmelCase__      ,     stdin=subprocess.PIPE      ,     stdout=subprocess.PIPE ) as ffmpeg_process:
                  UpperCAmelCase_     					=				ffmpeg_process.communicate(lowerCAmelCase__ )
      except FileNotFoundError as error:
            raise ValueError("ffmpeg was not found but is required to load audio files from filename" ) from error
      UpperCAmelCase_     					=				output_stream[0]
      UpperCAmelCase_     					=				np.frombuffer(lowerCAmelCase__      ,     np.floataa )
      if audio.shape[0] == 0:
            raise ValueError("Malformed soundfile" )
      return audio
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ = "f32le"      ,     ):
      UpperCAmelCase_     					=				f"""{sampling_rate}"""
      UpperCAmelCase_     					=				"1"
      if format_for_conversion == "s16le":
            UpperCAmelCase_     					=				2
      elif format_for_conversion == "f32le":
            UpperCAmelCase_     					=				4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )

      UpperCAmelCase_     					=				platform.system()
      if system == "Linux":
            UpperCAmelCase_     					=				"alsa"
            UpperCAmelCase_     					=				"default"
      elif system == "Darwin":
            UpperCAmelCase_     					=				"avfoundation"
            UpperCAmelCase_     					=				":0"
      elif system == "Windows":
            UpperCAmelCase_     					=				"dshow"
            UpperCAmelCase_     					=				"default"

      UpperCAmelCase_     					=				[
          "ffmpeg",
          "-f",
          format_,
          "-i",
          input_,
          "-ac",
          ac,
          "-ar",
          ar,
          "-f",
          format_for_conversion,
          "-fflags",
          "nobuffer",
          "-hide_banner",
          "-loglevel",
          "quiet",
          "pipe:1",
      ]
      UpperCAmelCase_     					=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
      UpperCAmelCase_     					=				_ffmpeg_stream(lowerCAmelCase__      ,     lowerCAmelCase__ )
      for item in iterator:
            yield item
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ = None      ,     lowerCAmelCase__ = None      ,     lowerCAmelCase__ = "f32le"      ,     ):
      if stream_chunk_s is not None:
            UpperCAmelCase_     					=				stream_chunk_s
      else:
            UpperCAmelCase_     					=				chunk_length_s

      UpperCAmelCase_     					=				ffmpeg_microphone(lowerCAmelCase__      ,     lowerCAmelCase__      ,     format_for_conversion=lowerCAmelCase__ )
      if format_for_conversion == "s16le":
            UpperCAmelCase_     					=				np.intaa
            UpperCAmelCase_     					=				2
      elif format_for_conversion == "f32le":
            UpperCAmelCase_     					=				np.floataa
            UpperCAmelCase_     					=				4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`""" )

      if stride_length_s is None:
            UpperCAmelCase_     					=				chunk_length_s / 6
      UpperCAmelCase_     					=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
      if isinstance(lowerCAmelCase__      ,     (int, float) ):
            UpperCAmelCase_     					=				[stride_length_s, stride_length_s]

      UpperCAmelCase_     					=				int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
      UpperCAmelCase_     					=				int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
      UpperCAmelCase_     					=				datetime.datetime.now()
      UpperCAmelCase_     					=				datetime.timedelta(seconds=lowerCAmelCase__ )
      for item in chunk_bytes_iter(lowerCAmelCase__      ,     lowerCAmelCase__      ,     stride=(stride_left, stride_right)      ,     stream=lowerCAmelCase__ ):
            # Put everything back in numpy scale
            UpperCAmelCase_     					=				np.frombuffer(item["raw"]      ,     dtype=lowerCAmelCase__ )
            UpperCAmelCase_     					=				(
                item["stride"][0] // size_of_sample,
                item["stride"][1] // size_of_sample,
            )
            UpperCAmelCase_     					=				sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ = False ):
      UpperCAmelCase_     					=				b""
      UpperCAmelCase_					,       UpperCAmelCase_     					=				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}""" )
      UpperCAmelCase_     					=				0
      for raw in iterator:
            acc += raw
            if stream and len(lowerCAmelCase__ ) < chunk_len:
                  UpperCAmelCase_     					=				(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(lowerCAmelCase__ ) >= chunk_len:
                        # We are flushing the accumulator
                        UpperCAmelCase_     					=				(_stride_left, stride_right)
                        UpperCAmelCase_     					=				{"raw": acc[:chunk_len], "stride": stride}
                        if stream:
                              UpperCAmelCase_     					=				False
                        yield item
                        UpperCAmelCase_     					=				stride_left
                        UpperCAmelCase_     					=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(lowerCAmelCase__ ) > stride_left:
            UpperCAmelCase_     					=				{"raw": acc, "stride": (_stride_left, 0)}
            if stream:
                  UpperCAmelCase_     					=				False
            yield item


def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				2**24  # 16Mo
      try:
            with subprocess.Popen(lowerCAmelCase__      ,     stdout=subprocess.PIPE      ,     bufsize=lowerCAmelCase__ ) as ffmpeg_process:
                  while True:
                        UpperCAmelCase_     					=				ffmpeg_process.stdout.read(lowerCAmelCase__ )
                        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 faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowercase    	=		"\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase    	=		"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase    	=		"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"



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




     def 		snake_case_		(					self):
         return datasets.MetricInfo(
             description=_DESCRIPTION			,    citation=_CITATION			,    homepage='''https://github.com/krishnap25/mauve'''			,    inputs_description=_KWARGS_DESCRIPTION			,    features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''string'''			,    id='''sequence'''),
                     '''references''': datasets.Value('''string'''			,    id='''sequence'''),
                 })			,    codebase_urls=['''https://github.com/krishnap25/mauve''']			,    reference_urls=[
                 '''https://arxiv.org/abs/2102.01454''',
                 '''https://github.com/krishnap25/mauve''',
             ]			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__="auto"			,    a__=-1			,    a__=0.9			,    a__=5			,    a__=5_0_0			,    a__="gpt2-large"			,    a__=-1			,    a__=1_0_2_4			,    a__=2_5			,    a__=5			,    a__=True			,    a__=2_5			,    ):
         A__					=       compute_mauve(
             p_text=a__			,    q_text=a__			,    p_features=a__			,    q_features=a__			,    p_tokens=a__			,    q_tokens=a__			,    num_buckets=a__			,    pca_max_data=a__			,    kmeans_explained_var=a__			,    kmeans_num_redo=a__			,    kmeans_max_iter=a__			,    featurize_model_name=a__			,    device_id=a__			,    max_text_length=a__			,    divergence_curve_discretization_size=a__			,    mauve_scaling_factor=a__			,    verbose=a__			,    seed=a__			,    )
         return out
