





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


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

__lowerCamelCase      :    Dict   		=   {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"deberta-v2"



					def __init__(      self  :						Dict							,	__A  :						Dict=1_2_8_1_0_0							,	__A  :						Any=1_5_3_6							,	__A  :						Union[str, Any]=2_4							,	__A  :						Optional[int]=2_4							,	__A  :						Any=6_1_4_4							,	__A  :						List[str]="gelu"							,	__A  :						List[str]=0.1							,	__A  :						int=0.1							,	__A  :						Dict=5_1_2							,	__A  :						Optional[int]=0							,	__A  :						Union[str, Any]=0.0_2							,	__A  :						str=1e-7							,	__A  :						Union[str, Any]=False							,	__A  :						str=-1							,	__A  :						str=0							,	__A  :						Optional[Any]=True							,	__A  :						Union[str, Any]=None							,	__A  :						Any=0							,	__A  :						Any="gelu"							,	**__A  :						Optional[int]							,	):
						super().__init__(**__A )

						snake_case__				:   Optional[int]     =						hidden_size
						snake_case__				:   Union[str, Any]     =						num_hidden_layers
						snake_case__				:   Optional[int]     =						num_attention_heads
						snake_case__				:   Tuple     =						intermediate_size
						snake_case__				:   Union[str, Any]     =						hidden_act
						snake_case__				:   Any     =						hidden_dropout_prob
						snake_case__				:   Tuple     =						attention_probs_dropout_prob
						snake_case__				:   Tuple     =						max_position_embeddings
						snake_case__				:   List[Any]     =						type_vocab_size
						snake_case__				:   int     =						initializer_range
						snake_case__				:   List[Any]     =						relative_attention
						snake_case__				:   Dict     =						max_relative_positions
						snake_case__				:   Any     =						pad_token_id
						snake_case__				:   Any     =						position_biased_input

						# Backwards compatibility
						if type(__A ) == str:
							snake_case__				:   List[Any]     =						[x.strip() for x in pos_att_type.lower().split("|" )]

						snake_case__				:   List[str]     =						pos_att_type
						snake_case__				:   Tuple     =						vocab_size
						snake_case__				:   List[str]     =						layer_norm_eps

						snake_case__				:   List[str]     =						kwargs.get("pooler_hidden_size"							,	__A )
						snake_case__				:   Tuple     =						pooler_dropout
						snake_case__				:   str     =						pooler_hidden_act



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					@property
					def  _lowercase  (      self  :						str ):
						if self.task == "multiple-choice":
							snake_case__				:   int     =						{0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__				:   List[Any]     =						{0: "batch", 1: "sequence"}
						if self._config.type_vocab_size > 0:
							return OrderedDict(
							    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
						else:
							return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )



					@property
					def  _lowercase  (      self  :						Dict ):
						return 1_2






					def  _lowercase  (      self  :						List[Any]							,	__A  :						Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						int = -1							,	__A  :						bool = False							,	__A  :						Optional["TensorType"] = None							,	__A  :						int = 3							,	__A  :						int = 4_0							,	__A  :						int = 4_0							,	__A  :						"PreTrainedTokenizerBase" = None							,	):
						snake_case__				:   int     =						super().generate_dummy_inputs(preprocessor=__A							,	framework=__A )
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
							del dummy_inputs["token_type_ids"]
						return dummy_inputs











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



