


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


snake_case							: Optional[int]      =   '''\\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'''

snake_case							: 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'''

snake_case							: Tuple      =   '''\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   ):
					def 				lowerCamelCase__(   self     :List[str]   )      ->					str:
												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     :Optional[Any]    ,__snake_case     :Any    ,__snake_case     :str    ,__snake_case     :Optional[Any]=None    ,__snake_case     :Tuple=None    ,__snake_case     :Any=None    ,__snake_case     :int=None    ,__snake_case     :Dict="auto"    ,__snake_case     :List[Any]=-1    ,__snake_case     :Tuple=0.9    ,__snake_case     :Tuple=5    ,__snake_case     :str=5_00    ,__snake_case     :Tuple="gpt2-large"    ,__snake_case     :List[Any]=-1    ,__snake_case     :Optional[int]=10_24    ,__snake_case     :List[Any]=25    ,__snake_case     :Dict=5    ,__snake_case     :Tuple=True    ,__snake_case     :Optional[Any]=25    ,)      ->					Union[str, Any]:
												a__    =  compute_mauve(
												    p_text=__UpperCamelCase    ,q_text=__UpperCamelCase    ,p_features=__UpperCamelCase    ,q_features=__UpperCamelCase    ,p_tokens=__UpperCamelCase    ,q_tokens=__UpperCamelCase    ,num_buckets=__UpperCamelCase    ,pca_max_data=__UpperCamelCase    ,kmeans_explained_var=__UpperCamelCase    ,kmeans_num_redo=__UpperCamelCase    ,kmeans_max_iter=__UpperCamelCase    ,featurize_model_name=__UpperCamelCase    ,device_id=__UpperCamelCase    ,max_text_length=__UpperCamelCase    ,divergence_curve_discretization_size=__UpperCamelCase    ,mauve_scaling_factor=__UpperCamelCase    ,verbose=__UpperCamelCase    ,seed=__UpperCamelCase    ,)
												return out




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						snake_case							: Union[str, Any]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




