





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


_snake_case  :    Tuple						    =  '\\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'

_snake_case  :    str						    =  '\\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'

_snake_case  :    List[str]						    =  '\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 				_UpperCAmelCase    (    datasets.Metric    ):

  """simple docstring"""


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








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






  @property
  def 				lowercase		(				self :		Tuple     )							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]     )






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

