


"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__SCREAMING_SNAKE_CASE							:      Any						=      logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:      Dict						=      R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'





class        __A       (snake_case__):





       '''simple docstring'''

       @add_start_docstrings(UpperCAmelCase_    )
       def __call__(			self							: Any						,    UpperCAmelCase_							: torch.LongTensor						,    UpperCAmelCase_							: torch.FloatTensor						,    **UpperCAmelCase_							: Optional[Any]    )   ->bool:



              """simple docstring"""



              raise NotImplementedError("""StoppingCriteria needs to be subclassed"""    )





class        __A       (snake_case__):





       '''simple docstring'''

       def __init__(			self							: Dict						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Optional[int] = None    )   ->Union[str, Any]:



              """simple docstring"""



              snake_case_          =						max_length
              snake_case_          =						max_position_embeddings



       @add_start_docstrings(UpperCAmelCase_    )
       def __call__(			self							: str						,    UpperCAmelCase_							: torch.LongTensor						,    UpperCAmelCase_							: torch.FloatTensor						,    **UpperCAmelCase_							: int    )   ->bool:



              """simple docstring"""



              snake_case_          =						input_ids.shape[-1]
              snake_case_          =						cur_len >= self.max_length
              if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
                     logger.warning_once(
                         """This is a friendly reminder - the current text generation call will exceed the model's predefined """
                         F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
                         """exceptions, performance degradation, or nothing at all."""    )
              return is_done





class        __A       (snake_case__):





       '''simple docstring'''

       def __init__(			self							: str						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: int    )   ->Union[str, Any]:



              """simple docstring"""



              warnings.warn(
                  """The class `MaxNewTokensCriteria` is deprecated. """
                  F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
                  """with `max_length = start_length + max_new_tokens` instead."""						,    UpperCAmelCase_						,    )
              snake_case_          =						start_length
              snake_case_          =						max_new_tokens
              snake_case_          =						start_length + max_new_tokens



       @add_start_docstrings(UpperCAmelCase_    )
       def __call__(			self							: Tuple						,    UpperCAmelCase_							: torch.LongTensor						,    UpperCAmelCase_							: torch.FloatTensor						,    **UpperCAmelCase_							: List[str]    )   ->bool:



              """simple docstring"""



              return input_ids.shape[-1] >= self.max_length





class        __A       (snake_case__):





       '''simple docstring'''

       def __init__(			self							: Any						,    UpperCAmelCase_							: float						,    UpperCAmelCase_							: Optional[float] = None    )   ->List[Any]:



              """simple docstring"""



              snake_case_          =						max_time
              snake_case_          =						time.time() if initial_timestamp is None else initial_timestamp



       @add_start_docstrings(UpperCAmelCase_    )
       def __call__(			self							: Tuple						,    UpperCAmelCase_							: torch.LongTensor						,    UpperCAmelCase_							: torch.FloatTensor						,    **UpperCAmelCase_							: Union[str, Any]    )   ->bool:



              """simple docstring"""



              return time.time() - self.initial_timestamp > self.max_time





class        __A       (snake_case__):





       '''simple docstring'''

       @add_start_docstrings(UpperCAmelCase_    )
       def __call__(			self							: Optional[int]						,    UpperCAmelCase_							: torch.LongTensor						,    UpperCAmelCase_							: torch.FloatTensor						,    **UpperCAmelCase_							: str    )   ->bool:



              """simple docstring"""



              return any(criteria(UpperCAmelCase_						,    UpperCAmelCase_    ) for criteria in self    )



       @property
       def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Optional[int]:



              """simple docstring"""



              for stopping_criterium in self:
                     if isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
                            return stopping_criterium.max_length
                     elif isinstance(UpperCAmelCase_						,    UpperCAmelCase_    ):
                            return stopping_criterium.max_length
              return None







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			StoppingCriteriaList:
       snake_case_          =						stopping_criteria.max_length
       snake_case_          =						deepcopy(_SCREAMING_SNAKE_CASE			)
       if stopping_max_length is not None and stopping_max_length != max_length:
              warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""			,	_SCREAMING_SNAKE_CASE			)
       elif stopping_max_length is None:
              new_stopping_criteria.append(MaxLengthCriteria(max_length=_SCREAMING_SNAKE_CASE			)			)
       return new_stopping_criteria







"""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							:      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'

__SCREAMING_SNAKE_CASE							:      Optional[int]						=      '\\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'

__SCREAMING_SNAKE_CASE							:      Tuple						=      '\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        __A       (datasets.Metric):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Tuple    )   ->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 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: Optional[Any]=None						,    UpperCAmelCase_							: Optional[Any]=None						,    UpperCAmelCase_							: List[Any]=None						,    UpperCAmelCase_							: Any=None						,    UpperCAmelCase_							: int="auto"						,    UpperCAmelCase_							: Dict=-1						,    UpperCAmelCase_							: Optional[Any]=0.9						,    UpperCAmelCase_							: Dict=5						,    UpperCAmelCase_							: Optional[int]=500						,    UpperCAmelCase_							: Any="gpt2-large"						,    UpperCAmelCase_							: Union[str, Any]=-1						,    UpperCAmelCase_							: Optional[Any]=1_024						,    UpperCAmelCase_							: Dict=25						,    UpperCAmelCase_							: Optional[Any]=5						,    UpperCAmelCase_							: int=True						,    UpperCAmelCase_							: Union[str, Any]=25						,    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						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



