


'''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


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










'''simple docstring'''







# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      List[str]       ):
       _A					:     Dict     = multiprocessing.Manager()
       _A					:     List[Any]     = manager.list()

       _A					:     Dict     = multiprocessing.Process(target=lowerCamelCase  ,args=(check_program, result, timeout)       )
       p.start()
       p.join(timeout=timeout + 1       )
       if p.is_alive():
              p.kill()

       if not result:
              result.append('timed out'       )

       return {
           "task_id": task_id,
           "passed": result[0] == "passed",
           "result": result[0],
           "completion_id": completion_id,
       }






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[Any]       ):
       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              _A					:     Any     = shutil.rmtree
              _A					:     Optional[int]     = os.rmdir
              _A					:     str     = os.chdir

              # Disable functionalities that can make destructive changes to the test.
              reliability_guard()

              # Run program.
              try:
                     _A					:     str     = {}
                     with swallow_io():
                            with time_limit(lowerCamelCase       ):
                                   exec(lowerCamelCase  ,lowerCamelCase       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F'failed: {e}'       )

              # Needed for cleaning up.
              _A					:     Optional[int]     = rmtree
              _A					:     Optional[Any]     = rmdir
              _A					:     Dict     = chdir






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      int       ):
       def signal_handler(lowerCamelCase					:      str  ,lowerCamelCase					:      Any       ):
              raise TimeoutException('Timed out!'       )

       signal.setitimer(signal.ITIMER_REAL  ,lowerCamelCase       )
       signal.signal(signal.SIGALRM  ,lowerCamelCase       )
       try:
              yield
       finally:
              signal.setitimer(signal.ITIMER_REAL  ,0       )






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       _A					:     Any     = WriteOnlyStringIO()
       with contextlib.redirect_stdout(lowerCamelCase       ):
              with contextlib.redirect_stderr(lowerCamelCase       ):
                     with redirect_stdin(lowerCamelCase       ):
                            yield






@contextlib.contextmanager
def 				lowerCAmelCase__	(    ):
       with tempfile.TemporaryDirectory() as dirname:
              with chdir(lowerCamelCase       ):
                     yield dirname


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    pass




class 			__lowerCamelCase			(	io.StringIO	):
    """simple docstring"""


    def        A   (  self      :							Tuple				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Dict):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							List[str]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           raise OSError


    def        A   (  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							str				,							**SCREAMING_SNAKE_CASE      :							Optional[int]):
           raise OSError


    def        A   (  self      :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE      :							Optional[int]				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           return False




class 			__lowerCamelCase			(	contextlib._RedirectStream	):  # type: ignore
    """simple docstring"""
    a													=   "stdin"






@contextlib.contextmanager
def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple       ):
       if root == ".":
              yield
              return
       _A					:     Any     = os.getcwd()
       os.chdir(lowerCamelCase       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]=None       ):

       if maximum_memory_bytes is not None:
              import resource

              resource.setrlimit(resource.RLIMIT_AS  ,(maximum_memory_bytes, maximum_memory_bytes)       )
              resource.setrlimit(resource.RLIMIT_DATA  ,(maximum_memory_bytes, maximum_memory_bytes)       )
              if not platform.uname().system == "Darwin":
                     resource.setrlimit(resource.RLIMIT_STACK  ,(maximum_memory_bytes, maximum_memory_bytes)       )

       faulthandler.disable()

       import builtins

       _A					:     List[Any]     = None
       _A					:     Dict     = None

       import os

       _A					:     Union[str, Any]     = '1'

       _A					:     int     = None
       _A					:     Optional[int]     = None
       _A					:     int     = None
       _A					:     Any     = None
       _A					:     Optional[int]     = None
       _A					:     Union[str, Any]     = None
       _A					:     List[Any]     = None
       _A					:     int     = None
       _A					:     List[Any]     = None
       _A					:     Tuple     = None
       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     Optional[int]     = None
       _A					:     Optional[Any]     = None
       _A					:     str     = None
       _A					:     Dict     = None
       _A					:     List[str]     = None
       _A					:     Union[str, Any]     = None
       _A					:     Union[str, Any]     = None
       _A					:     str     = None
       _A					:     str     = None
       _A					:     str     = None
       _A					:     Any     = None
       _A					:     Union[str, Any]     = None
       _A					:     str     = None
       _A					:     List[str]     = None
       _A					:     Union[str, Any]     = None

       import shutil

       _A					:     int     = None
       _A					:     Any     = None
       _A					:     List[Any]     = None

       import subprocess

       _A					:     Optional[Any]     = None  # type: ignore

       _A					:     List[Any]     = None

       import sys

       _A					:     Any     = None
       _A					:     Tuple     = None
       _A					:     str     = None
       _A					:     Tuple     = None
       _A					:     List[str]     = None






