
"""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__   :					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'

UpperCAmelCase__   :					Any												=				'\\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__   :					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 			lowerCAmelCase_		(datasets.Metric						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	List[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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__="auto"     , SCREAMING_SNAKE_CASE__=-1     , SCREAMING_SNAKE_CASE__=0.9     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=5_00     , SCREAMING_SNAKE_CASE__="gpt2-large"     , SCREAMING_SNAKE_CASE__=-1     , SCREAMING_SNAKE_CASE__=10_24     , SCREAMING_SNAKE_CASE__=25     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=25     , )      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     compute_mauve(
											    p_text=SCREAMING_SNAKE_CASE__     , q_text=SCREAMING_SNAKE_CASE__     , p_features=SCREAMING_SNAKE_CASE__     , q_features=SCREAMING_SNAKE_CASE__     , p_tokens=SCREAMING_SNAKE_CASE__     , q_tokens=SCREAMING_SNAKE_CASE__     , num_buckets=SCREAMING_SNAKE_CASE__     , pca_max_data=SCREAMING_SNAKE_CASE__     , kmeans_explained_var=SCREAMING_SNAKE_CASE__     , kmeans_num_redo=SCREAMING_SNAKE_CASE__     , kmeans_max_iter=SCREAMING_SNAKE_CASE__     , featurize_model_name=SCREAMING_SNAKE_CASE__     , device_id=SCREAMING_SNAKE_CASE__     , max_text_length=SCREAMING_SNAKE_CASE__     , divergence_curve_discretization_size=SCREAMING_SNAKE_CASE__     , mauve_scaling_factor=SCREAMING_SNAKE_CASE__     , verbose=SCREAMING_SNAKE_CASE__     , seed=SCREAMING_SNAKE_CASE__     , )
											return out




"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

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




