
"""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


lowerCamelCase__   					=		"""\
@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}
}

"""

lowerCamelCase__   					=		"""\
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
"""

lowerCamelCase__   					=		"""
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 ):
			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       ,		_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       ,		):
			__lowerCAmelCase			:						str									=      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


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)