





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowercase_					= {
    """configuration_audio_spectrogram_transformer""": [
        """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ASTConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= [
          """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ASTForAudioClassification""",
          """ASTModel""",
          """ASTPreTrainedModel""",
      ]

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					= ["""ASTFeatureExtractor"""]

if TYPE_CHECKING:
      from .configuration_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          ASTConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_audio_spectrogram_transformer import (
                AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ASTForAudioClassification,
                ASTModel,
                ASTPreTrainedModel,
            )

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
      import sys

      lowercase_					= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""simple docstring"""


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 					snake_case		(							datasets.Metric						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     str="auto",       _lowerCamelCase :     Union[str, Any]=-1,       _lowerCamelCase :     List[str]=0.9,       _lowerCamelCase :     int=5,       _lowerCamelCase :     Tuple=5_00,       _lowerCamelCase :     Union[str, Any]="gpt2-large",       _lowerCamelCase :     int=-1,       _lowerCamelCase :     Union[str, Any]=10_24,       _lowerCamelCase :     Union[str, Any]=25,       _lowerCamelCase :     str=5,       _lowerCamelCase :     Any=True,       _lowerCamelCase :     Union[str, Any]=25,       ):

     '''simple docstring'''
     __A									= compute_mauve(
         p_text=_lowerCamelCase,       q_text=_lowerCamelCase,       p_features=_lowerCamelCase,       q_features=_lowerCamelCase,       p_tokens=_lowerCamelCase,       q_tokens=_lowerCamelCase,       num_buckets=_lowerCamelCase,       pca_max_data=_lowerCamelCase,       kmeans_explained_var=_lowerCamelCase,       kmeans_num_redo=_lowerCamelCase,       kmeans_max_iter=_lowerCamelCase,       featurize_model_name=_lowerCamelCase,       device_id=_lowerCamelCase,       max_text_length=_lowerCamelCase,       divergence_curve_discretization_size=_lowerCamelCase,       mauve_scaling_factor=_lowerCamelCase,       verbose=_lowerCamelCase,       seed=_lowerCamelCase,       )
     return out




