






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    str   		=   {"""vocab_file""": """vocab.json"""}

__lowerCamelCase      :    int   		=   {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

__lowerCamelCase      :    Dict   		=   {"""mgp-str""": 27}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



					def __init__(      self  :						int							,	__A  :						Optional[Any]							,	__A  :						int="[GO]"							,	__A  :						Union[str, Any]="[GO]"							,	__A  :						Optional[int]="[s]"							,	__A  :						Union[str, Any]="[GO]"							,	**__A  :						Optional[int] ):
						super().__init__(
						    unk_token=__A							,	bos_token=__A							,	eos_token=__A							,	pad_token=__A							,	**__A							,	)

						with open(__A							,	encoding="utf-8" ) as vocab_handle:
							snake_case__				:   str     =						json.load(__A )
						snake_case__				:   Optional[Any]     =						{v: k for k, v in self.vocab.items()}



					@property
					def  _lowercase  (      self  :						str ):
						return len(self.vocab )



					def  _lowercase  (      self  :						List[Any] ):
						return dict(self.vocab							,	**self.added_tokens_encoder )



					def  _lowercase  (      self  :						int							,	__A  :						Union[str, Any] ):
						snake_case__				:   List[Any]     =						[]
						for s in text:
							char_tokens.extend(__A )
						return char_tokens



					def  _lowercase  (      self  :						Any							,	__A  :						str ):
						return self.vocab.get(__A							,	self.vocab.get(self.unk_token ) )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						int ):
						return self.decoder.get(__A )






					def  _lowercase  (      self  :						Tuple							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not os.path.isdir(__A ):
							logger.error("Vocabulary path ({}) should be a directory".format(__A ) )
							return
						snake_case__				:   Optional[int]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

						with open(__A							,	"w"							,	encoding="utf-8" ) as f:
							f.write(json.dumps(self.vocab							,	indent=2							,	sort_keys=__A							,	ensure_ascii=__A ) + "\n" )

						return (vocab_file,)











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      :    Optional[int]   		=   """\
@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      :    str   		=   """\
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      :    str   		=   """
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 							SCREAMING_SNAKE_CASE__		(       datasets.Metric		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Dict ):
						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  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						List[str]							,	__A  :						int=None							,	__A  :						List[Any]=None							,	__A  :						Optional[int]=None							,	__A  :						List[Any]=None							,	__A  :						Union[str, Any]="auto"							,	__A  :						Optional[Any]=-1							,	__A  :						Optional[Any]=0.9							,	__A  :						Any=5							,	__A  :						List[Any]=5_0_0							,	__A  :						Tuple="gpt2-large"							,	__A  :						Optional[Any]=-1							,	__A  :						str=1_0_2_4							,	__A  :						Tuple=2_5							,	__A  :						str=5							,	__A  :						Optional[int]=True							,	__A  :						Any=2_5							,	):
						snake_case__				:   List[Any]     =						compute_mauve(
						    p_text=__A							,	q_text=__A							,	p_features=__A							,	q_features=__A							,	p_tokens=__A							,	q_tokens=__A							,	num_buckets=__A							,	pca_max_data=__A							,	kmeans_explained_var=__A							,	kmeans_num_redo=__A							,	kmeans_max_iter=__A							,	featurize_model_name=__A							,	device_id=__A							,	max_text_length=__A							,	divergence_curve_discretization_size=__A							,	mauve_scaling_factor=__A							,	verbose=__A							,	seed=__A							,	)
						return out



