



"""simple docstring"""







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

from ...utils import logging


A     =      logging.get_logger(__name__)

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				Any:
							A    =       nn.functional.normalize(__lowerCamelCase      )
							A    =       nn.functional.normalize(__lowerCamelCase      )
							return torch.mm(__lowerCamelCase					,   normalized_text_embeds.t()      )






class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						Optional[Any]							=  CLIPConfig

		lowerCAmelCase_							:						Dict							=  ["""CLIPEncoderLayer"""]



		def __init__(			self	:     List[str]   ,				snake_case	:     Optional[int]					)      ->    str:






									'''simple docstring'''

									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(17   ,				config.projection_dim					)   ,				requires_grad=a__					)
									A    =       nn.Parameter(torch.ones(3   ,				config.projection_dim					)   ,				requires_grad=a__					)

									A    =       nn.Parameter(torch.ones(17					)   ,				requires_grad=a__					)
									A    =       nn.Parameter(torch.ones(3					)   ,				requires_grad=a__					)



		@torch.no_grad()
		def        A_		(			self	:     Any   ,				snake_case	:     List[Any]   ,				snake_case	:     int					)      ->    Any:






									'''simple docstring'''

									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    =       0.01

																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]

									for idx, has_nsfw_concept in enumerate(a__					):
																if has_nsfw_concept:
																							if torch.is_tensor(a__					) or torch.is_tensor(images[0]					):
																														A    =       torch.zeros_like(images[idx]					)  # black image
																							else:
																														A    =       np.zeros(images[idx].shape					)  # black image

									if any(a__					):
																logger.warning(
																    'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
																    ' Try again with a different prompt and/or seed.'					)

									return images, has_nsfw_concepts



		@torch.no_grad()
		def        A_		(			self	:     Tuple   ,				snake_case	:     Any   ,				snake_case	:     Any					)      ->    Tuple:






									'''simple docstring'''

									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 * 0.01
									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					)

									A    =       0.0  # black image

									return images, has_nsfw_concepts







from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				Any       =      '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return datasets.MetricInfo(
            description=_DESCRIPTION						,    citation=_CITATION						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(self._get_feature_types()       )						,    reference_urls=[
                '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
            ]						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value('''float'''       )       ),
             "references": datasets.Sequence(datasets.Value('''float'''       )       ),
         }
        else:
         return {
             "predictions": datasets.Value('''float'''       ),
             "references": datasets.Value('''float'''       ),
         }



       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






