



import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									torch.manual_seed(0     )
									UpperCamelCase_			= UNetaDModel(
									    block_out_channels=(32, 64)  ,			layers_per_block=2  ,			sample_size=32  ,			in_channels=3  ,			out_channels=3  ,			down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,			up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,			)
									return model

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= 'cpu'
									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler()

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.Generator(device=snake_case__     ).manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"  ,			return_dict=snake_case__     )[0]

									UpperCamelCase_			= image[0, -3:, -3:, -1]
									UpperCamelCase_			= image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= np.array(
									    [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.dummy_uncond_unet
									UpperCamelCase_			= DDPMScheduler(prediction_type="sample"     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     ).images

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			num_inference_steps=2  ,			output_type="numpy"     )[0]

									UpperCamelCase_			= image[0, -3:, -3:, -1]
									UpperCamelCase_			= image_eps[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= 1e-2 if torch_device != 'mps' else 3e-2
									assert np.abs(image_slice.flatten() - image_eps_slice.flatten()     ).max() < tolerance




@slow
@require_torch_gpu
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= 'google/ddpm-cifar10-32'

									UpperCamelCase_			= UNetaDModel.from_pretrained(snake_case__     )
									UpperCamelCase_			= DDPMScheduler.from_pretrained(snake_case__     )

									UpperCamelCase_			= DDPMPipeline(unet=snake_case__  ,			scheduler=snake_case__     )
									ddpm.to(snake_case__     )
									ddpm.set_progress_bar_config(disable=snake_case__     )

									UpperCamelCase_			= torch.manual_seed(0     )
									UpperCamelCase_			= ddpm(generator=snake_case__  ,			output_type="numpy"     ).images

									UpperCamelCase_			= image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									UpperCamelCase_			= np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]     )
									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2





'''simple docstring'''







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

from ...utils import logging


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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.functional.normalize(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      nn.functional.normalize(SCREAMING_SNAKE_CASE     )
		return torch.mm(SCREAMING_SNAKE_CASE ,		normalized_text_embeds.t()     )





class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		int		=      CLIPConfig

			__snake_case  :		Optional[Any]		=      ['CLIPEncoderLayer']






			def __init__(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							CLIPConfig	):




					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      CLIPVisionModel(config.vision_config	)
					SCREAMING_SNAKE_CASE_						:Dict										=      nn.Linear(config.vision_config.hidden_size      ,       config.projection_dim      ,       bias=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      nn.Parameter(torch.ones(17      ,       config.projection_dim	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      nn.Parameter(torch.ones(3      ,       config.projection_dim	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      nn.Parameter(torch.ones(17	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      nn.Parameter(torch.ones(3	)      ,       requires_grad=SCREAMING_SNAKE_CASE	)






			@torch.no_grad()
			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.vision_model(SCREAMING_SNAKE_CASE	)[1]  # pooled_output
					SCREAMING_SNAKE_CASE_						:Dict										=      self.visual_projection(SCREAMING_SNAKE_CASE	)

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

					SCREAMING_SNAKE_CASE_						:str										=      []
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image_embeds.shape[0]
					for i in range(SCREAMING_SNAKE_CASE	):
							SCREAMING_SNAKE_CASE_						:List[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
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      0.0

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

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

							result.append(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      [len(res['bad_concepts']	) > 0 for res in result]

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

					if any(SCREAMING_SNAKE_CASE	):
							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 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.vision_model(SCREAMING_SNAKE_CASE	)[1]  # pooled_output
					SCREAMING_SNAKE_CASE_						:Tuple										=      self.visual_projection(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      cosine_distance(SCREAMING_SNAKE_CASE      ,       self.special_care_embeds	)
					SCREAMING_SNAKE_CASE_						:int										=      cosine_distance(SCREAMING_SNAKE_CASE      ,       self.concept_embeds	)

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

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

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

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0.0  # black image

					return images, has_nsfw_concepts
