





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

"""

__lowercase	: Any       ="""\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

__lowercase	: int       ="""
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    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\'].
    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
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric(\'mauve\')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""

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



      def 						lowerCAmelCase__     (						self:    str							)    ->     str:
         '''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 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    List[Any]=None		,	_lowerCAmelCase:    Optional[int]=None		,	_lowerCAmelCase:    Tuple=None		,	_lowerCAmelCase:    Dict=None		,	_lowerCAmelCase:    Optional[Any]="auto"		,	_lowerCAmelCase:    Union[str, Any]=-1		,	_lowerCAmelCase:    Any=0.9		,	_lowerCAmelCase:    List[Any]=5		,	_lowerCAmelCase:    Any=500		,	_lowerCAmelCase:    List[Any]="gpt2-large"		,	_lowerCAmelCase:    Union[str, Any]=-1		,	_lowerCAmelCase:    Tuple=1024		,	_lowerCAmelCase:    int=25		,	_lowerCAmelCase:    Optional[Any]=5		,	_lowerCAmelCase:    Tuple=True		,	_lowerCAmelCase:    Tuple=25		,	)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =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"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





