






'''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


UpperCamelCase							="\\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"

UpperCamelCase							="\\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"

UpperCamelCase							="\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     A    (      datasets.Metric						):






 """simple docstring"""





 def   _UpperCAmelCase       (							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase="auto"			,     __lowerCAmelCase=-1			,     __lowerCAmelCase=0.9			,     __lowerCAmelCase=5			,     __lowerCAmelCase=5_00			,     __lowerCAmelCase="gpt2-large"			,     __lowerCAmelCase=-1			,     __lowerCAmelCase=10_24			,     __lowerCAmelCase=25			,     __lowerCAmelCase=5			,     __lowerCAmelCase=True			,     __lowerCAmelCase=25			,     ):
       UpperCamelCase_				:   int					    =							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














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






