
'''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 :  Optional[Any]    							=       "\\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 :  Optional[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 :  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  lowerCAmelCase__   (  datasets.Metric     ):






					def 	_snake_case    (   self		):


											"""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 	_snake_case    (   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				,							):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       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'''







import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format






class  lowerCAmelCase__   :
					lowerCAmelCase_        =     None






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.feature_extraction_class(**self.feat_extract_dict		)
											lowercase_	:     Any													=       json.loads(feat_extract.to_json_string()		)
											for key, value in self.feat_extract_dict.items():
																	self.assertEqual(obj[key]				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       self.feature_extraction_class(**self.feat_extract_dict		)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_	:     str													=       os.path.join(__SCREAMING_SNAKE_CASE				,							'''feat_extract.json'''		)
																	feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     str													=       self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE		)

											self.assertEqual(feat_extract_second.to_dict()				,							feat_extract_first.to_dict()		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.feature_extraction_class(**self.feat_extract_dict		)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_	:     Union[str, Any]													=       feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE		)[0]
																	check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     str													=       self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE		)

											self.assertEqual(feat_extract_second.to_dict()				,							feat_extract_first.to_dict()		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.feature_extraction_class()
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)



