


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     snake_case__  (      _A						,       _A						,       unittest.TestCase  ):
				__lowerCAmelCase		=      IFImgaImgPipeline
				__lowerCAmelCase		=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				__lowerCAmelCase		=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__lowerCAmelCase		=      PipelineTesterMixin.required_optional_params - {'''latents'''}
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return self._get_dummy_components()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=0      ):




										"""simple docstring"""




										if str(_UpperCAmelCase      ).startswith('mps'      ):
																__magic_name__				:							List[Any]				=      torch.manual_seed(_UpperCAmelCase      )
										else:
																__magic_name__				:							Dict				=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      floats_tensor((1, 3, 32, 32)     ,      rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )

										__magic_name__				:							str				=      {
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'output_type': 'numpy',
										}

										return inputs
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_save_load_optional_components()
				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()     ,      reason='XFormers attention is only available with CUDA and `xformers` installed'     ,      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )
				@unittest.skipIf(torch_device != 'cuda'     ,      reason='float16 requires CUDA'      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										super().test_save_load_floataa(expected_max_diff=1E-1      )
				@unittest.skipIf(torch_device != 'cuda'     ,      reason='float16 requires CUDA'      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										super().test_floataa_inference(expected_max_diff=1E-1      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_save_load_local()





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,      )






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

from ...utils import logging


snake_case__     							= logging.get_logger(__name__)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :List[str]				)       ->  Tuple:
						__magic_name__				:							List[str]				=      nn.functional.normalize(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      nn.functional.normalize(lowercase__				)
						return torch.mm(lowercase__      ,						normalized_text_embeds.t()				)



class     snake_case__  (      _A  ):
				__lowerCAmelCase		=      CLIPConfig

				__lowerCAmelCase		=      ['''CLIPEncoderLayer''']
				def __init__(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										super().__init__(_UpperCAmelCase      )

										__magic_name__				:							List[Any]				=      CLIPVisionModel(config.vision_config      )
										__magic_name__				:							Tuple				=      nn.Linear(config.vision_config.hidden_size     ,      config.projection_dim     ,      bias=_UpperCAmelCase      )

										__magic_name__				:							str				=      nn.Parameter(torch.ones(17     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(3     ,      config.projection_dim      )     ,      requires_grad=_UpperCAmelCase      )

										__magic_name__				:							Optional[Any]				=      nn.Parameter(torch.ones(17      )     ,      requires_grad=_UpperCAmelCase      )
										__magic_name__				:							str				=      nn.Parameter(torch.ones(3      )     ,      requires_grad=_UpperCAmelCase      )
				@torch.no_grad()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Union[str, Any]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							Any				=      self.visual_projection(_UpperCAmelCase      )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      ).cpu().float().numpy()
										__magic_name__				:							int				=      cosine_distance(_UpperCAmelCase     ,      self.concept_embeds      ).cpu().float().numpy()

										__magic_name__				:							str				=      []
										__magic_name__				:							List[Any]				=      image_embeds.shape[0]
										for i in range(_UpperCAmelCase      ):
																__magic_name__				:							Optional[Any]				=      {'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
																__magic_name__				:							Optional[int]				=      0.0

																for concept_idx in range(len(special_cos_dist[0]      )      ):
																						__magic_name__				:							Any				=      special_cos_dist[i][concept_idx]
																						__magic_name__				:							Tuple				=      self.special_care_embeds_weights[concept_idx].item()
																						__magic_name__				:							Dict				=      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]}      )
																												__magic_name__				:							str				=      0.01

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

																result.append(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      [len(res['bad_concepts']      ) > 0 for res in result]

										for idx, has_nsfw_concept in enumerate(_UpperCAmelCase      ):
																if has_nsfw_concept:
																						if torch.is_tensor(_UpperCAmelCase      ) or torch.is_tensor(images[0]      ):
																												__magic_name__				:							Union[str, Any]				=      torch.zeros_like(images[idx]      )  # black image
																						else:
																												__magic_name__				:							Optional[int]				=      np.zeros(images[idx].shape      )  # black image

										if any(_UpperCAmelCase      ):
																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  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      self.vision_model(_UpperCAmelCase      )[1]  # pooled_output
										__magic_name__				:							List[str]				=      self.visual_projection(_UpperCAmelCase      )

										__magic_name__				:							Union[str, Any]				=      cosine_distance(_UpperCAmelCase     ,      self.special_care_embeds      )
										__magic_name__				:							Optional[int]				=      cosine_distance(_UpperCAmelCase     ,      self.concept_embeds      )

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

										__magic_name__				:							Optional[int]				=      special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										__magic_name__				:							int				=      torch.any(special_scores > 0     ,      dim=1      )
										__magic_name__				:							Optional[Any]				=      special_care * 0.01
										__magic_name__				:							Any				=      special_adjustment.unsqueeze(1      ).expand(-1     ,      cos_dist.shape[1]      )

										__magic_name__				:							Optional[int]				=      (cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__magic_name__				:							Union[str, Any]				=      torch.any(concept_scores > 0     ,      dim=1      )

										__magic_name__				:							Dict				=      0.0  # black image

										return images, has_nsfw_concepts


