



import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]			,							UpperCamelCase_					:							List[Any]						)->	Optional[Any]:
    A__					=       nn.functional.normalize(UpperCamelCase_						)
    A__					=       nn.functional.normalize(UpperCamelCase_						)
    return torch.mm(UpperCamelCase_			,							normalized_text_embeds.t()						)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					CLIPConfig

     UpperCamelCase__		     =					['''CLIPEncoderLayer''']




     def __init__(					self			,    a__):
         super().__init__(a__)

         A__					=       CLIPVisionModel(config.vision_config)
         A__					=       nn.Linear(config.vision_config.hidden_size			,    config.projection_dim			,    bias=a__)

         A__					=       nn.Parameter(torch.ones(1_7			,    config.projection_dim)			,    requires_grad=a__)
         A__					=       nn.Parameter(torch.ones(3			,    config.projection_dim)			,    requires_grad=a__)

         A__					=       nn.Parameter(torch.ones(1_7)			,    requires_grad=a__)
         A__					=       nn.Parameter(torch.ones(3)			,    requires_grad=a__)




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       self.vision_model(a__)[1]  # pooled_output
         A__					=       self.visual_projection(a__)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         A__					=       cosine_distance(a__			,    self.special_care_embeds).cpu().float().numpy()
         A__					=       cosine_distance(a__			,    self.concept_embeds).cpu().float().numpy()

         A__					=       []
         A__					=       image_embeds.shape[0]
         for i in range(a__):
             A__					=       {'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

             # increase this value to create a stronger `nfsw` filter
             # at the cost of increasing the possibility of filtering benign images
             A__					=       0.0

             for concept_idx in range(len(special_cos_dist[0])):
                 A__					=       special_cos_dist[i][concept_idx]
                 A__					=       self.special_care_embeds_weights[concept_idx].item()
                 A__					=       round(concept_cos - concept_threshold + adjustment			,    3)
                 if result_img["special_scores"][concept_idx] > 0:
                     result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]})
                     A__					=       0.0_1

             for concept_idx in range(len(cos_dist[0])):
                 A__					=       cos_dist[i][concept_idx]
                 A__					=       self.concept_embeds_weights[concept_idx].item()
                 A__					=       round(concept_cos - concept_threshold + adjustment			,    3)
                 if result_img["concept_scores"][concept_idx] > 0:
                     result_img["bad_concepts"].append(a__)

             result.append(a__)

         A__					=       [len(res['''bad_concepts''']) > 0 for res in result]

         return images, has_nsfw_concepts




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       self.vision_model(a__)[1]  # pooled_output
         A__					=       self.visual_projection(a__)

         A__					=       cosine_distance(a__			,    self.special_care_embeds)
         A__					=       cosine_distance(a__			,    self.concept_embeds)

         # increase this value to create a stronger `nsfw` filter
         # at the cost of increasing the possibility of filtering benign images
         A__					=       0.0

         A__					=       special_cos_dist - self.special_care_embeds_weights + adjustment
         # special_scores = special_scores.round(decimals=3)
         A__					=       torch.any(special_scores > 0			,    dim=1)
         A__					=       special_care * 0.0_1
         A__					=       special_adjustment.unsqueeze(1).expand(-1			,    cos_dist.shape[1])

         A__					=       (cos_dist - self.concept_embeds_weights) + special_adjustment
         # concept_scores = concept_scores.round(decimals=3)
         A__					=       torch.any(concept_scores > 0			,    dim=1)

         return images, has_nsfw_concepts





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


_lowercase    	=		"\\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"

_lowercase    	=		"\\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"

_lowercase    	=		"\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  ):




     def 		snake_case_		(					self):
         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 		snake_case_		(					self			,    a__			,    a__			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__="auto"			,    a__=-1			,    a__=0.9			,    a__=5			,    a__=5_0_0			,    a__="gpt2-large"			,    a__=-1			,    a__=1_0_2_4			,    a__=2_5			,    a__=5			,    a__=True			,    a__=2_5			,    ):
         A__					=       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
