





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        _lowerCAmelCase    (			datasets.Metric							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''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 UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					Tuple="auto"      ,				UpperCamelCase      :					str=-1      ,				UpperCamelCase      :					Tuple=0.9      ,				UpperCamelCase      :					Optional[int]=5      ,				UpperCamelCase      :					Any=5_00      ,				UpperCamelCase      :					Optional[Any]="gpt2-large"      ,				UpperCamelCase      :					Optional[Any]=-1      ,				UpperCamelCase      :					List[str]=10_24      ,				UpperCamelCase      :					Optional[int]=25      ,				UpperCamelCase      :					Optional[Any]=5      ,				UpperCamelCase      :					Dict=True      ,				UpperCamelCase      :					Optional[Any]=25      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       int      					="""roberta"""







  def __init__(      self      :					int      ,				UpperCamelCase      :					Tuple=5_02_65      ,				UpperCamelCase      :					Any=7_68      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Dict=30_72      ,				UpperCamelCase      :					Any="gelu"      ,				UpperCamelCase      :					List[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=5_12      ,				UpperCamelCase      :					List[str]=2      ,				UpperCamelCase      :					Optional[Any]=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					str=1      ,				UpperCamelCase      :					int=0      ,				UpperCamelCase      :					Any=2      ,				UpperCamelCase      :					int="absolute"      ,				UpperCamelCase      :					int=True      ,				UpperCamelCase      :					List[Any]=None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase      ,				bos_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Any														=	vocab_size
    _snake_case   :				List[str]														=	hidden_size
    _snake_case   :				List[str]														=	num_hidden_layers
    _snake_case   :				Dict														=	num_attention_heads
    _snake_case   :				List[str]														=	hidden_act
    _snake_case   :				Union[str, Any]														=	intermediate_size
    _snake_case   :				Union[str, Any]														=	hidden_dropout_prob
    _snake_case   :				Optional[int]														=	attention_probs_dropout_prob
    _snake_case   :				Dict														=	max_position_embeddings
    _snake_case   :				Optional[int]														=	type_vocab_size
    _snake_case   :				Tuple														=	initializer_range
    _snake_case   :				int														=	layer_norm_eps
    _snake_case   :				Dict														=	position_embedding_type
    _snake_case   :				Union[str, Any]														=	use_cache
    _snake_case   :				str														=	classifier_dropout




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case   :				Optional[Any]														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case   :				Dict														=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]							)




