





'''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


_SCREAMING_SNAKE_CASE									=				"\\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"

_SCREAMING_SNAKE_CASE									=				"\\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"

_SCREAMING_SNAKE_CASE									=				"\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		)     ->				Union[str, Any]:
     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      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="auto"      ,  _lowerCAmelCase=-1      ,  _lowerCAmelCase=0.9      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=500      ,  _lowerCAmelCase="gpt2-large"      ,  _lowerCAmelCase=-1      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=25      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=25      ,  )     ->				Optional[int]:
     _lowerCAmelCase						      =   compute_mauve(
         p_text=_lowerCAmelCase      ,  q_text=_lowerCAmelCase      ,  p_features=_lowerCAmelCase      ,  q_features=_lowerCAmelCase      ,  p_tokens=_lowerCAmelCase      ,  q_tokens=_lowerCAmelCase      ,  num_buckets=_lowerCAmelCase      ,  pca_max_data=_lowerCAmelCase      ,  kmeans_explained_var=_lowerCAmelCase      ,  kmeans_num_redo=_lowerCAmelCase      ,  kmeans_max_iter=_lowerCAmelCase      ,  featurize_model_name=_lowerCAmelCase      ,  device_id=_lowerCAmelCase      ,  max_text_length=_lowerCAmelCase      ,  divergence_curve_discretization_size=_lowerCAmelCase      ,  mauve_scaling_factor=_lowerCAmelCase      ,  verbose=_lowerCAmelCase      ,  seed=_lowerCAmelCase      ,  )
     return out




from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




