


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor


class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def __init__(    self:	List[Any]						,				a:	Tuple						,				a:	Dict=7						,				a:	Optional[int]=3						,				a:	int=18						,				a:	Tuple=30						,				a:	List[Any]=400						,				a:	List[str]=True						,				a:	Any=None						,				a:	Optional[int]=True						,				a:	Optional[int]=None						,				a:	Dict=True						,				a:	List[str]=[0.5, 0.5, 0.5]						,				a:	List[Any]=[0.5, 0.5, 0.5]						,				):
       __lowerCamelCase		:							Union[str, Any]		 =						size if size is not None else {'shortest_edge': 18}
       __lowerCamelCase		:							int		 =						crop_size if crop_size is not None else {'height': 18, 'width': 18}
       __lowerCamelCase		:							Any		 =						parent
       __lowerCamelCase		:							List[Any]		 =						batch_size
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							str		 =						image_size
       __lowerCamelCase		:							str		 =						min_resolution
       __lowerCamelCase		:							Optional[int]		 =						max_resolution
       __lowerCamelCase		:							Optional[Any]		 =						do_resize
       __lowerCamelCase		:							int		 =						size
       __lowerCamelCase		:							Optional[Any]		 =						do_center_crop
       __lowerCamelCase		:							Tuple		 =						crop_size
       __lowerCamelCase		:							Optional[int]		 =						do_normalize
       __lowerCamelCase		:							int		 =						image_mean
       __lowerCamelCase		:							str		 =						image_std




      def 					_snake_case     (    self:	int    ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class 	A_ (	A__ ,				unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       LevitImageProcessor if is_vision_available() else None




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Tuple		 =						LevitImageProcessingTester(self    )




      @property
      def 					_snake_case     (    self:	Tuple    ):
       return self.image_processor_tester.prepare_image_processor_dict()




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							str		 =						self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'image_mean'    )    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'image_std'    )    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'do_normalize'    )    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'do_resize'    )    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'do_center_crop'    )    )
       self.assertTrue(hasattr(lowerCamelCase__						,				'size'    )    )




      def 					_snake_case     (    self:	List[str]    ):
       __lowerCamelCase		:							Dict		 =						self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size						,				{'shortest_edge': 18}    )
       self.assertEqual(image_processor.crop_size						,				{'height': 18, 'width': 18}    )

       __lowerCamelCase		:							Dict		 =						self.image_processing_class.from_dict(self.image_processor_dict						,				size=42						,				crop_size=84    )
       self.assertEqual(image_processor.size						,				{'shortest_edge': 42}    )
       self.assertEqual(image_processor.crop_size						,				{'height': 84, 'width': 84}    )




      def 					_snake_case     (    self:	List[Any]    ):
       pass




      def 					_snake_case     (    self:	str    ):
       # Initialize image_processing
       __lowerCamelCase		:							List[str]		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       __lowerCamelCase		:							List[Any]		 =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCamelCase__    )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase__						,				Image.Image    )

       # Test not batched input
       __lowerCamelCase		:							Dict		 =						image_processing(image_inputs[0]						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)

       # Test batched
       __lowerCamelCase		:							str		 =						image_processing(lowerCamelCase__						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)




      def 					_snake_case     (    self:	Optional[int]    ):
       # Initialize image_processing
       __lowerCamelCase		:							Tuple		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       __lowerCamelCase		:							Any		 =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCamelCase__						,				numpify=lowerCamelCase__    )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase__						,				np.ndarray    )

       # Test not batched input
       __lowerCamelCase		:							str		 =						image_processing(image_inputs[0]						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)

       # Test batched
       __lowerCamelCase		:							int		 =						image_processing(lowerCamelCase__						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)




      def 					_snake_case     (    self:	List[Any]    ):
       # Initialize image_processing
       __lowerCamelCase		:							List[Any]		 =						self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       __lowerCamelCase		:							str		 =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCamelCase__						,				torchify=lowerCamelCase__    )
       for image in image_inputs:
        self.assertIsInstance(lowerCamelCase__						,				torch.Tensor    )

       # Test not batched input
       __lowerCamelCase		:							Dict		 =						image_processing(image_inputs[0]						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)

       # Test batched
       __lowerCamelCase		:							Union[str, Any]		 =						image_processing(lowerCamelCase__						,				return_tensors='pt'    ).pixel_values
       self.assertEqual(
           encoded_images.shape						,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,				)









import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if isinstance(SCREAMING_SNAKE_CASE__      ,	torch.Tensor						):
  return image
 elif isinstance(SCREAMING_SNAKE_CASE__      ,	PIL.Image.Image						):
  __lowerCamelCase		:							Union[str, Any]		 =						[image]

 if isinstance(image[0]      ,	PIL.Image.Image						):
  __lowerCamelCase		:							Any		 =						[np.array(i.resize((w, h)      ,	resample=PIL_INTERPOLATION['lanczos']						)						)[None, :] for i in image]
  __lowerCamelCase		:							Optional[int]		 =						np.concatenate(SCREAMING_SNAKE_CASE__      ,	axis=0						)
  __lowerCamelCase		:							str		 =						np.array(SCREAMING_SNAKE_CASE__						).astype(np.floataa						) / 255.0
  __lowerCamelCase		:							List[str]		 =						image.transpose(0      ,	3      ,	1      ,	2						)
  __lowerCamelCase		:							Union[str, Any]		 =						2.0 * image - 1.0
  __lowerCamelCase		:							Tuple		 =						torch.from_numpy(SCREAMING_SNAKE_CASE__						)
 elif isinstance(image[0]      ,	torch.Tensor						):
  __lowerCamelCase		:							str		 =						torch.cat(SCREAMING_SNAKE_CASE__      ,	dim=0						)
 return image
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=0.9_995						):
 if not isinstance(SCREAMING_SNAKE_CASE__      ,	np.ndarray						):
  __lowerCamelCase		:							List[str]		 =						True
  __lowerCamelCase		:							str		 =						va.device
  __lowerCamelCase		:							int		 =						va.cpu().numpy()
  __lowerCamelCase		:							List[str]		 =						va.cpu().numpy()

 __lowerCamelCase		:							str		 =						np.sum(va * va / (np.linalg.norm(SCREAMING_SNAKE_CASE__						) * np.linalg.norm(SCREAMING_SNAKE_CASE__						))						)
 if np.abs(SCREAMING_SNAKE_CASE__						) > DOT_THRESHOLD:
  __lowerCamelCase		:							Union[str, Any]		 =						(1 - t) * va + t * va
 else:
  __lowerCamelCase		:							List[Any]		 =						np.arccos(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Dict		 =						np.sin(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							str		 =						theta_a * t
  __lowerCamelCase		:							List[Any]		 =						np.sin(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							str		 =						np.sin(theta_a - theta_t						) / sin_theta_a
  __lowerCamelCase		:							List[Any]		 =						sin_theta_t / sin_theta_a
  __lowerCamelCase		:							Union[str, Any]		 =						sa * va + sa * va

 if inputs_are_torch:
  __lowerCamelCase		:							str		 =						torch.from_numpy(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)

 return va
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						F.normalize(SCREAMING_SNAKE_CASE__      ,	dim=-1						)
 __lowerCamelCase		:							Union[str, Any]		 =						F.normalize(SCREAMING_SNAKE_CASE__      ,	dim=-1						)
 return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for param in model.parameters():
  __lowerCamelCase		:							Any		 =						value



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      def __init__(    self:	Any						,				a:	AutoencoderKL						,				a:	CLIPTextModel						,				a:	CLIPModel						,				a:	CLIPTokenizer						,				a:	UNetaDConditionModel						,				a:	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]						,				a:	CLIPFeatureExtractor						,				a:	Union[str, Any]=None						,				a:	Union[str, Any]=None						,				a:	Union[str, Any]=None						,				):
       super().__init__()
       self.register_modules(
           vae=a						,				text_encoder=a						,				clip_model=a						,				tokenizer=a						,				unet=a						,				scheduler=a						,				feature_extractor=a						,				coca_model=a						,				coca_tokenizer=a						,				coca_transform=a						,				)
       __lowerCamelCase		:							Tuple		 =						(
           feature_extractor.size
           if isinstance(feature_extractor.size						,				a    )
           else feature_extractor.size['shortest_edge']
       )
       __lowerCamelCase		:							List[Any]		 =						transforms.Normalize(mean=feature_extractor.image_mean						,				std=feature_extractor.image_std    )
       set_requires_grad(self.text_encoder						,				a    )
       set_requires_grad(self.clip_model						,				a    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Optional[Union[str, int]] = "auto"    ):
       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        __lowerCamelCase		:							Any		 =						self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(a    )




      def 					_snake_case     (    self:	Dict    ):
       self.enable_attention_slicing(a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       set_requires_grad(self.vae						,				a    )




      def 					_snake_case     (    self:	List[Any]    ):
       set_requires_grad(self.vae						,				a    )




      def 					_snake_case     (    self:	int    ):
       set_requires_grad(self.unet						,				a    )




      def 					_snake_case     (    self:	int    ):
       set_requires_grad(self.unet						,				a    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Union[str, Any]						,				a:	List[str]						,				a:	List[Any]    ):
       # get the original timestep using init_timestep
       __lowerCamelCase		:							List[Any]		 =						min(int(num_inference_steps * strength    )						,				a    )

       __lowerCamelCase		:							str		 =						max(num_inference_steps - init_timestep						,				0    )
       __lowerCamelCase		:							List[Any]		 =						self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Optional[Any]						,				a:	Any						,				a:	Optional[int]						,				a:	Optional[Any]						,				a:	Union[str, Any]						,				a:	List[str]=None    ):
       if not isinstance(a						,				torch.Tensor    ):
        raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(a    )}'    )

       __lowerCamelCase		:							Union[str, Any]		 =						image.to(device=a						,				dtype=a    )

       if isinstance(a						,				a    ):
        __lowerCamelCase		:							str		 =						[
            self.vae.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(a    )
        ]
        __lowerCamelCase		:							Tuple		 =						torch.cat(a						,				dim=0    )
       else:
        __lowerCamelCase		:							List[Any]		 =						self.vae.encode(a    ).latent_dist.sample(a    )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       __lowerCamelCase		:							List[str]		 =						0.1_8_2_1_5 * init_latents
       __lowerCamelCase		:							Union[str, Any]		 =						init_latents.repeat_interleave(a						,				dim=0    )

       __lowerCamelCase		:							Optional[int]		 =						randn_tensor(init_latents.shape						,				generator=a						,				device=a						,				dtype=a    )

       # get latents
       __lowerCamelCase		:							Union[str, Any]		 =						self.scheduler.add_noise(a						,				a						,				a    )
       __lowerCamelCase		:							int		 =						init_latents

       return latents




      def 					_snake_case     (    self:	Optional[int]						,				a:	Any    ):
       __lowerCamelCase		:							List[Any]		 =						self.coca_transform(a    ).unsqueeze(0    )
       with torch.no_grad(), torch.cuda.amp.autocast():
        __lowerCamelCase		:							Any		 =						self.coca_model.generate(transformed_image.to(device=self.device						,				dtype=self.coca_model.dtype    )    )
       __lowerCamelCase		:							str		 =						self.coca_tokenizer.decode(generated[0].cpu().numpy()    )
       return generated.split('<end_of_text>'    )[0].replace('<start_of_text>'						,				''    ).rstrip(' .,'    )




      def 					_snake_case     (    self:	Any						,				a:	Tuple						,				a:	Tuple    ):
       __lowerCamelCase		:							Dict		 =						self.feature_extractor.preprocess(a    )
       __lowerCamelCase		:							Dict		 =						torch.from_numpy(clip_image_input['pixel_values'][0]    ).unsqueeze(0    ).to(self.device    ).half()
       __lowerCamelCase		:							List[str]		 =						self.clip_model.get_image_features(a    )
       __lowerCamelCase		:							Optional[Any]		 =						image_embeddings_clip / image_embeddings_clip.norm(p=2						,				dim=-1						,				keepdim=a    )
       __lowerCamelCase		:							Tuple		 =						image_embeddings_clip.repeat_interleave(a						,				dim=0    )
       return image_embeddings_clip




      @torch.enable_grad()
      def 					_snake_case     (    self:	str						,				a:	str						,				a:	int						,				a:	List[Any]						,				a:	str						,				a:	List[Any]						,				a:	Dict						,				a:	int						,				):
       __lowerCamelCase		:							Optional[Any]		 =						latents.detach().requires_grad_()

       __lowerCamelCase		:							str		 =						self.scheduler.scale_model_input(a						,				a    )

       # predict the noise residual
       __lowerCamelCase		:							Optional[int]		 =						self.unet(a						,				a						,				encoder_hidden_states=a    ).sample

       if isinstance(self.scheduler						,				(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
        __lowerCamelCase		:							str		 =						self.scheduler.alphas_cumprod[timestep]
        __lowerCamelCase		:							Dict		 =						1 - alpha_prod_t
        # compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __lowerCamelCase		:							Optional[int]		 =						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

        __lowerCamelCase		:							Optional[int]		 =						torch.sqrt(a    )
        __lowerCamelCase		:							int		 =						pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler						,				a    ):
        __lowerCamelCase		:							str		 =						self.scheduler.sigmas[index]
        __lowerCamelCase		:							List[Any]		 =						latents - sigma * noise_pred
       else:
        raise ValueError(F'scheduler type {type(self.scheduler    )} not supported'    )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       __lowerCamelCase		:							Optional[int]		 =						1 / 0.1_8_2_1_5 * sample
       __lowerCamelCase		:							Optional[Any]		 =						self.vae.decode(a    ).sample
       __lowerCamelCase		:							Tuple		 =						(image / 2 + 0.5).clamp(0						,				1    )

       __lowerCamelCase		:							Any		 =						transforms.Resize(self.feature_extractor_size    )(a    )
       __lowerCamelCase		:							Union[str, Any]		 =						self.normalize(a    ).to(latents.dtype    )

       __lowerCamelCase		:							Tuple		 =						self.clip_model.get_image_features(a    )
       __lowerCamelCase		:							List[str]		 =						image_embeddings_clip / image_embeddings_clip.norm(p=2						,				dim=-1						,				keepdim=a    )

       __lowerCamelCase		:							List[str]		 =						spherical_dist_loss(a						,				a    ).mean() * clip_guidance_scale

       __lowerCamelCase		:							Tuple		 =						-torch.autograd.grad(a						,				a    )[0]

       if isinstance(self.scheduler						,				a    ):
        __lowerCamelCase		:							Optional[int]		 =						latents.detach() + grads * (sigma**2)
        __lowerCamelCase		:							List[Any]		 =						noise_pred_original
       else:
        __lowerCamelCase		:							str		 =						noise_pred_original - torch.sqrt(a    ) * grads
       return noise_pred, latents




      @torch.no_grad()
      def __call__(    self:	Any						,				a:	Union[torch.FloatTensor, PIL.Image.Image]						,				a:	Union[torch.FloatTensor, PIL.Image.Image]						,				a:	Optional[str] = None						,				a:	Optional[str] = None						,				a:	Optional[int] = 512						,				a:	Optional[int] = 512						,				a:	float = 0.6						,				a:	Optional[int] = 50						,				a:	Optional[float] = 7.5						,				a:	Optional[int] = 1						,				a:	float = 0.0						,				a:	Optional[float] = 100						,				a:	Optional[torch.Generator] = None						,				a:	Optional[str] = "pil"						,				a:	bool = True						,				a:	float = 0.8						,				a:	float = 0.1						,				a:	float = 0.1						,				):
       if isinstance(a						,				a    ) and len(a    ) != batch_size:
        raise ValueError(F'You have passed {batch_size} batch_size, but only {len(a    )} generators.'    )

       if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'    )

       if isinstance(a						,				torch.Generator    ) and batch_size > 1:
        __lowerCamelCase		:							List[Any]		 =						[generator] + [None] * (batch_size - 1)

       __lowerCamelCase		:							Dict		 =						[
           ('model', self.coca_model is None),
           ('tokenizer', self.coca_tokenizer is None),
           ('transform', self.coca_transform is None),
       ]
       __lowerCamelCase		:							Any		 =						[x[0] for x in coca_is_none if x[1]]
       __lowerCamelCase		:							str		 =						', '.join(a    )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
        if len(a    ):
         raise ValueError(
             F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
             F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'    )
        __lowerCamelCase		:							Any		 =						self.get_image_description(a    )
       if style_prompt is None:
        if len(a    ):
         raise ValueError(
             F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
             F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'    )
        __lowerCamelCase		:							Tuple		 =						self.get_image_description(a    )

       # get prompt text embeddings for content and style
       __lowerCamelCase		:							int		 =						self.tokenizer(
           a						,				padding='max_length'						,				max_length=self.tokenizer.model_max_length						,				truncation=a						,				return_tensors='pt'						,				)
       __lowerCamelCase		:							Dict		 =						self.text_encoder(content_text_input.input_ids.to(self.device    )    )[0]

       __lowerCamelCase		:							Union[str, Any]		 =						self.tokenizer(
           a						,				padding='max_length'						,				max_length=self.tokenizer.model_max_length						,				truncation=a						,				return_tensors='pt'						,				)
       __lowerCamelCase		:							Any		 =						self.text_encoder(style_text_input.input_ids.to(self.device    )    )[0]

       __lowerCamelCase		:							List[Any]		 =						slerp(a						,				a						,				a    )

       # duplicate text embeddings for each generation per prompt
       __lowerCamelCase		:							Any		 =						text_embeddings.repeat_interleave(a						,				dim=0    )

       # set timesteps
       __lowerCamelCase		:							List[Any]		 =						'offset' in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
       __lowerCamelCase		:							Union[str, Any]		 =						{}
       if accepts_offset:
        __lowerCamelCase		:							Dict		 =						1

       self.scheduler.set_timesteps(a						,				**a    )
       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       self.scheduler.timesteps.to(self.device    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						self.get_timesteps(a						,				a						,				self.device    )
       __lowerCamelCase		:							Tuple		 =						timesteps[:1].repeat(a    )

       # Preprocess image
       __lowerCamelCase		:							Any		 =						preprocess(a						,				a						,				a    )
       __lowerCamelCase		:							str		 =						self.prepare_latents(
           a						,				a						,				a						,				text_embeddings.dtype						,				self.device						,				a    )

       __lowerCamelCase		:							Dict		 =						preprocess(a						,				a						,				a    )
       __lowerCamelCase		:							Optional[int]		 =						self.prepare_latents(
           a						,				a						,				a						,				text_embeddings.dtype						,				self.device						,				a    )

       __lowerCamelCase		:							int		 =						slerp(a						,				a						,				a    )

       if clip_guidance_scale > 0:
        __lowerCamelCase		:							List[str]		 =						self.get_clip_image_embeddings(a						,				a    )
        __lowerCamelCase		:							Union[str, Any]		 =						self.get_clip_image_embeddings(a						,				a    )
        __lowerCamelCase		:							Union[str, Any]		 =						slerp(
            a						,				a						,				a    )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       __lowerCamelCase		:							Tuple		 =						guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        __lowerCamelCase		:							Optional[int]		 =						content_text_input.input_ids.shape[-1]
        __lowerCamelCase		:							int		 =						self.tokenizer(['']						,				padding='max_length'						,				max_length=a						,				return_tensors='pt'    )
        __lowerCamelCase		:							List[Any]		 =						self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
        # duplicate unconditional embeddings for each generation per prompt
        __lowerCamelCase		:							List[Any]		 =						uncond_embeddings.repeat_interleave(a						,				dim=0    )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        __lowerCamelCase		:							int		 =						torch.cat([uncond_embeddings, text_embeddings]    )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       __lowerCamelCase		:							str		 =						(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       __lowerCamelCase		:							List[str]		 =						text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not work reproducibly on mps
         __lowerCamelCase		:							Tuple		 =						torch.randn(a						,				generator=a						,				device='cpu'						,				dtype=a    ).to(
             self.device    )
        else:
         __lowerCamelCase		:							List[Any]		 =						torch.randn(a						,				generator=a						,				device=self.device						,				dtype=a    )
       else:
        if latents.shape != latents_shape:
         raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'    )
        __lowerCamelCase		:							List[str]		 =						latents.to(self.device    )

       # scale the initial noise by the standard deviation required by the scheduler
       __lowerCamelCase		:							str		 =						latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       __lowerCamelCase		:							int		 =						'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
       __lowerCamelCase		:							Dict		 =						{}
       if accepts_eta:
        __lowerCamelCase		:							List[str]		 =						eta

       # check if the scheduler accepts generator
       __lowerCamelCase		:							Optional[int]		 =						'generator' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
       if accepts_generator:
        __lowerCamelCase		:							Optional[Any]		 =						generator

       with self.progress_bar(total=a    ):
        for i, t in enumerate(a    ):
         # expand the latents if we are doing classifier free guidance
         __lowerCamelCase		:							Optional[int]		 =						torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
         __lowerCamelCase		:							Union[str, Any]		 =						self.scheduler.scale_model_input(a						,				a    )

         # predict the noise residual
         __lowerCamelCase		:							Tuple		 =						self.unet(a						,				a						,				encoder_hidden_states=a    ).sample

         # perform classifier free guidance
         if do_classifier_free_guidance:
          __lowerCamelCase ,					__lowerCamelCase		:							str		 =						noise_pred.chunk(2    )
          __lowerCamelCase		:							List[str]		 =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         # perform clip guidance
         if clip_guidance_scale > 0:
          __lowerCamelCase		:							str		 =						(
              text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
          )
          __lowerCamelCase ,					__lowerCamelCase		:							int		 =						self.cond_fn(
              a						,				a						,				a						,				a						,				a						,				a						,				a						,				)

         # compute the previous noisy sample x_t -> x_t-1
         __lowerCamelCase		:							Tuple		 =						self.scheduler.step(a						,				a						,				a						,				**a    ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       __lowerCamelCase		:							List[Any]		 =						1 / 0.1_8_2_1_5 * latents
       __lowerCamelCase		:							Union[str, Any]		 =						self.vae.decode(a    ).sample

       __lowerCamelCase		:							Optional[int]		 =						(image / 2 + 0.5).clamp(0						,				1    )
       __lowerCamelCase		:							Any		 =						image.cpu().permute(0						,				2						,				3						,				1    ).numpy()

       if output_type == "pil":
        __lowerCamelCase		:							Union[str, Any]		 =						self.numpy_to_pil(a    )

       if not return_dict:
        return (image, None)

       return StableDiffusionPipelineOutput(images=a						,				nsfw_content_detected=a    )

