"""simple docstring"""






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

from ...utils import logging


__lowerCAmelCase					: Optional[int]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			nn.functional.normalize(lowerCAmelCase__	)
      lowercase														=			nn.functional.normalize(lowerCAmelCase__	)
      return torch.mm(lowerCAmelCase__      ,	normalized_text_embeds.t()	)






class 				_A		( lowerCAmelCase			):
      snake_case__    :     List[Any]													=			CLIPConfig

      snake_case__    :     List[str]													=			['CLIPEncoderLayer']

      def __init__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            super().__init__(__lowerCAmelCase		)

            lowercase														=			CLIPVisionModel(config.vision_config		)
            lowercase														=			nn.Linear(config.vision_config.hidden_size	, config.projection_dim	, bias=__lowerCAmelCase		)

            lowercase														=			nn.Parameter(torch.ones(17	, config.projection_dim		)	, requires_grad=__lowerCAmelCase		)
            lowercase														=			nn.Parameter(torch.ones(3	, config.projection_dim		)	, requires_grad=__lowerCAmelCase		)

            lowercase														=			nn.Parameter(torch.ones(17		)	, requires_grad=__lowerCAmelCase		)
            lowercase														=			nn.Parameter(torch.ones(3		)	, requires_grad=__lowerCAmelCase		)

      @torch.no_grad()
      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.vision_model(__lowerCAmelCase		)[1]  # pooled_output
            lowercase														=			self.visual_projection(__lowerCAmelCase		)

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

            lowercase														=			[]
            lowercase														=			image_embeds.shape[0]
            for i in range(__lowerCAmelCase		):
                  lowercase														=			{"""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
                  lowercase														=			0.0

                  for concept_idx in range(len(special_cos_dist[0]		)		):
                        lowercase														=			special_cos_dist[i][concept_idx]
                        lowercase														=			self.special_care_embeds_weights[concept_idx].item()
                        lowercase														=			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]}		)
                              lowercase														=			0.0_1

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

                  result.append(__lowerCAmelCase		)

            lowercase														=			[len(res["""bad_concepts"""]		) > 0 for res in result]

            return images, has_nsfw_concepts







      @torch.no_grad()
      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.vision_model(__lowerCAmelCase		)[1]  # pooled_output
            lowercase														=			self.visual_projection(__lowerCAmelCase		)

            lowercase														=			cosine_distance(__lowerCAmelCase	, self.special_care_embeds		)
            lowercase														=			cosine_distance(__lowerCAmelCase	, self.concept_embeds		)

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

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

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

            return images, has_nsfw_concepts




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # 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 six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION			)
class 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """simple docstring"""



            return datasets.MetricInfo(
                description=_DESCRIPTION	, citation=_CITATION	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""string"""	, id="""sequence"""		),
                        """references""": datasets.Value("""string"""	, id="""sequence"""		),
                    }		)	, codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



