


"""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


A	:  str            =     "\\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"

A	:  str            =     "\\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"

A	:  List[str]            =     "\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						):
							'''simple docstring'''







							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_00       ,  __a="gpt2-large"       ,  __a=-1       ,  __a=10_24       ,  __a=25       ,  __a=5       ,  __a=True       ,  __a=25       ,  ):
										__lowerCAmelCase							       =     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








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

