





'''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	 =							"""\
@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}
}

"""

_SCREAMING_SNAKE_CASE	 =							"""\
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
"""

_SCREAMING_SNAKE_CASE	 =							"""
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    lowerCAmelCase_		(		datasets.Metric		):




 def 							_snake_case   (	self		)    ->							Optional[int]:
     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			,     )    ->							str:
     _lowerCAmelCase				=      compute_mauve(
         p_text=UpperCamelCase__			,     q_text=UpperCamelCase__			,     p_features=UpperCamelCase__			,     q_features=UpperCamelCase__			,     p_tokens=UpperCamelCase__			,     q_tokens=UpperCamelCase__			,     num_buckets=UpperCamelCase__			,     pca_max_data=UpperCamelCase__			,     kmeans_explained_var=UpperCamelCase__			,     kmeans_num_redo=UpperCamelCase__			,     kmeans_max_iter=UpperCamelCase__			,     featurize_model_name=UpperCamelCase__			,     device_id=UpperCamelCase__			,     max_text_length=UpperCamelCase__			,     divergence_curve_discretization_size=UpperCamelCase__			,     mauve_scaling_factor=UpperCamelCase__			,     verbose=UpperCamelCase__			,     seed=UpperCamelCase__			,     )
     return out









'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


