
'''simple docstring'''





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class     a (					__magic_name__					,__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			StableDiffusionModelEditingPipeline
				_snake_case		     =			TEXT_TO_IMAGE_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_BATCH_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_IMAGE_PARAMS
				_snake_case		     =			TEXT_TO_IMAGE_IMAGE_PARAMS




				def 						__snake_case				(				self						:					Any   ):
										torch.manual_seed(0   )
										snake_case							:  List[str]			=     UNetaDConditionModel(
										    block_out_channels=(32, 64),     layers_per_block=2,     sample_size=32,     in_channels=4,     out_channels=4,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),     cross_attention_dim=32,     )
										snake_case							:  int			=     DDIMScheduler()
										torch.manual_seed(0   )
										snake_case							:  int			=     AutoencoderKL(
										    block_out_channels=[32, 64],     in_channels=3,     out_channels=3,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],     latent_channels=4,     )
										torch.manual_seed(0   )
										snake_case							:  List[Any]			=     CLIPTextConfig(
										    bos_token_id=0,     eos_token_id=2,     hidden_size=32,     intermediate_size=37,     layer_norm_eps=1e-05,     num_attention_heads=4,     num_hidden_layers=5,     pad_token_id=1,     vocab_size=10_00,     )
										snake_case							:  int			=     CLIPTextModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										snake_case							:  Union[str, Any]			=     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=0   ):
										snake_case							:  Union[str, Any]			=     torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     {
										    '''prompt''': '''A field of roses''',
										    '''generator''': generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    '''height''': None,
										    '''width''': None,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  int			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  Dict			=     self.get_dummy_components()
										snake_case							:  Dict			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Any			=     image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										snake_case							:  Union[str, Any]			=     np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]   )

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




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Union[str, Any]			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  Tuple			=     self.get_dummy_components()
										snake_case							:  Dict			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     '''french fries'''
										snake_case							:  Tuple			=     sd_pipe(**SCREAMING_SNAKE_CASE_,     negative_prompt=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     output.images
										snake_case							:  Dict			=     image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										snake_case							:  Tuple			=     np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]   )

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




				def 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  Union[str, Any]			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  int			=     self.get_dummy_components()
										snake_case							:  int			=     EulerAncestralDiscreteScheduler(
										    beta_start=0.00085,     beta_end=0.012,     beta_schedule='''scaled_linear'''   )
										snake_case							:  List[Any]			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  List[Any]			=     image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										snake_case							:  List[Any]			=     np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]   )

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




				def 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  Tuple			=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case							:  str			=     self.get_dummy_components()
										snake_case							:  Optional[int]			=     PNDMScheduler()
										snake_case							:  List[str]			=     StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     sd_pipe.to(SCREAMING_SNAKE_CASE_   )
										sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
										# the pipeline does not expect pndm so test if it raises error.
										with self.assertRaises(SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Any			=     sd_pipe(**SCREAMING_SNAKE_CASE_   ).images




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										super().test_inference_batch_single_identical(expected_max_diff=5e-3   )




				def 						__snake_case				(				self						:					List[str]   ):
										super().test_attention_slicing_forward_pass(expected_max_diff=5e-3   )




@slow
@require_torch_gpu
class     a (					unittest.TestCase     ):




				def 						__snake_case				(				self						:					Dict   ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=0   ):
										snake_case							:  List[Any]			=     torch.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     {
										    '''prompt''': '''A field of roses''',
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  int			=     '''CompVis/stable-diffusion-v1-4'''
										snake_case							:  Union[str, Any]			=     StableDiffusionModelEditingPipeline.from_pretrained(SCREAMING_SNAKE_CASE_,     safety_checker=SCREAMING_SNAKE_CASE_   )
										pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
										pipe.enable_attention_slicing()

										snake_case							:  str			=     self.get_inputs()
										snake_case							:  List[Any]			=     pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Tuple			=     image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										snake_case							:  Dict			=     np.array(
										    [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658]   )

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

										# make sure image changes after editing
										pipe.edit_model('''A pack of roses''',     '''A pack of blue roses'''   )

										snake_case							:  List[str]			=     pipe(**SCREAMING_SNAKE_CASE_   ).images
										snake_case							:  Optional[int]			=     image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										assert np.abs(expected_slice - image_slice   ).max() > 1e-1




				def 						__snake_case				(				self						:					int   ):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										snake_case							:  int			=     '''CompVis/stable-diffusion-v1-4'''
										snake_case							:  int			=     DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_,     subfolder='''scheduler'''   )
										snake_case							:  Tuple			=     StableDiffusionModelEditingPipeline.from_pretrained(
										    SCREAMING_SNAKE_CASE_,     scheduler=SCREAMING_SNAKE_CASE_,     safety_checker=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										snake_case							:  List[Any]			=     self.get_inputs()
										snake_case							:  Optional[int]			=     pipe(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     torch.cuda.max_memory_allocated()
										# make sure that less than 4.4 GB is allocated
										assert mem_bytes < 4.4 * 10**9





"""simple docstring"""






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    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					nn.functional.normalize(_lowerCAmelCase							)
     _a     						=					nn.functional.normalize(_lowerCAmelCase							)
     return torch.mm(_lowerCAmelCase,  normalized_text_embeds.t()							)


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						str     =		CLIPConfig

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


     def __init__(   self     ,					__UpperCAmelCase				)							->				List[Any]:
          super().__init__(__UpperCAmelCase				)

          _a     						=					CLIPVisionModel(config.vision_config				)
          _a     						=					nn.Linear(config.vision_config.hidden_size     ,					config.projection_dim     ,					bias=__UpperCAmelCase				)

          _a     						=					nn.Parameter(torch.ones(17     ,					config.projection_dim				)     ,					requires_grad=__UpperCAmelCase				)
          _a     						=					nn.Parameter(torch.ones(3     ,					config.projection_dim				)     ,					requires_grad=__UpperCAmelCase				)

          _a     						=					nn.Parameter(torch.ones(17				)     ,					requires_grad=__UpperCAmelCase				)
          _a     						=					nn.Parameter(torch.ones(3				)     ,					requires_grad=__UpperCAmelCase				)


     @torch.no_grad()
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:
          _a     						=					self.vision_model(__UpperCAmelCase				)[1]  # pooled_output
          _a     						=					self.visual_projection(__UpperCAmelCase				)

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

          _a     						=					[]
          _a     						=					image_embeds.shape[0]
          for i in range(__UpperCAmelCase				):
               _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(__UpperCAmelCase				)

               result.append(__UpperCAmelCase				)

          _a     						=					[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]				):
                         _a     						=					torch.zeros_like(images[idx]				)  # black image
                    else:
                         _a     						=					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				)							->				Any:
          _a     						=					self.vision_model(__UpperCAmelCase				)[1]  # pooled_output
          _a     						=					self.visual_projection(__UpperCAmelCase				)

          _a     						=					cosine_distance(__UpperCAmelCase     ,					self.special_care_embeds				)
          _a     						=					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
          _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