



"""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


_lowercase		:				Dict      		="\\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"

_lowercase		:				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"

_lowercase		:				Optional[int]      		="\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		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       __lowercase="auto" ,       __lowercase=-1 ,       __lowercase=0.9 ,       __lowercase=5 ,       __lowercase=5_0_0 ,       __lowercase="gpt2-large" ,       __lowercase=-1 ,       __lowercase=1_0_2_4 ,       __lowercase=2_5 ,       __lowercase=5 ,       __lowercase=True ,       __lowercase=2_5 ,       ) ->				Any:
									"""simple docstring"""




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









from typing import TYPE_CHECKING

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


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

		_lowercase		:				Any      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




