



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


a_						:     Dict	     =		'\\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'

a_						:     Any	     =		'\\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'

a_						:     int	     =		'\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):
     """simple docstring"""



     def 						_a      (self    ):






           '''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 						_a      (self				,			__a				,			__a				,			__a=None				,			__a=None				,			__a=None				,			__a=None				,			__a="auto"				,			__a=-1				,			__a=0.9				,			__a=5				,			__a=5_00				,			__a="gpt2-large"				,			__a=-1				,			__a=10_24				,			__a=25				,			__a=5				,			__a=True				,			__a=25				,			):






           '''simple docstring'''



           lowerCamelCase								=		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




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_						:     int	     =		logging.get_logger(__name__)

a_						:     List[str]	     =		{'vocab_file': 'spiece.model'}

a_						:     Optional[Any]	     =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=False				,			__a=True				,			__a=False				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<sep>"				,			__a="<pad>"				,			__a="<cls>"				,			__a="<mask>"				,			__a=["<eop>", "<eod>"]				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else mask_token

           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__a				,			remove_space=__a				,			keep_accents=__a				,			bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			sep_token=__a				,			pad_token=__a				,			cls_token=__a				,			mask_token=__a				,			additional_special_tokens=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		3

           lowerCamelCase								=		do_lower_case
           lowerCamelCase								=		remove_space
           lowerCamelCase								=		keep_accents
           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )

           try:
                 import jieba
           except ModuleNotFoundError as error:
                 raise error.__class__(
                     "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                     "See https://pypi.org/project/jieba/ for installation."    )
           lowerCamelCase								=		jieba
           lowerCamelCase								=		str.maketrans(" \n"				,			"\u2582\u2583"    )







     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def 						_a      (self    ):






           '''simple docstring'''



           return len(self.sp_model    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if self.remove_space:
                 lowerCamelCase								=		" ".join(inputs.strip().split()    )
           else:
                 lowerCamelCase								=		inputs
           lowerCamelCase								=		outputs.replace("``"				,			"\""    ).replace("''"				,			"\""    )

           if not self.keep_accents:
                 lowerCamelCase								=		unicodedata.normalize("NFKD"				,			__a    )
                 lowerCamelCase								=		"".join([c for c in outputs if not unicodedata.combining(__a    )]    )
           if self.do_lower_case:
                 lowerCamelCase								=		outputs.lower()

           return outputs







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.preprocess_text(__a    )
           lowerCamelCase								=		self.sp_model.encode(__a				,			out_type=__a    )
           lowerCamelCase								=		[]
           for piece in pieces:
                 if len(__a    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
                       lowerCamelCase								=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__a				,			""    )    )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]    ) == 1:
                                   lowerCamelCase								=		cur_pieces[1:]
                             else:
                                   lowerCamelCase								=		cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]    )
                       new_pieces.extend(__a    )
                 else:
                       new_pieces.append(__a    )

           return new_pieces







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.PieceToId(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.IdToPiece(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"".join(__a    ).replace(__a				,			" "    ).strip()
           return out_string







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







     def 						_a      (self				,			__a				,			__a = None				,			__a = False    ):






           '''simple docstring'''



           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=__a				,			token_ids_a=__a				,			already_has_special_tokens=__a    )

           if token_ids_a is not None:
                 return ([0] * len(__a    )) + [1] + ([0] * len(__a    )) + [1, 1]
           return ([0] * len(__a    )) + [1, 1]







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[2]

           if token_ids_a is None:
                 return len(token_ids_a + sep    ) * [0] + cls_segment_id
           return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(__a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		super()._decode(*__a				,			**__a    )
           lowerCamelCase								=		text.replace(" "				,			""    ).replace("\u2582"				,			" "    ).replace("\u2583"				,			"\n"    )
           return text