


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     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		Dict			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      if isinstance(UpperCAmelCase_			,  torch.Tensor						):
            return image
      elif isinstance(UpperCAmelCase_			,  PIL.Image.Image						):
            a						:Union[str, Any]										=	[image]

      if isinstance(image[0]			,  PIL.Image.Image						):
            a						:List[str]										=	[np.array(i.resize((w, h)			,  resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
            a						:Tuple										=	np.concatenate(UpperCAmelCase_			,  axis=0						)
            a						:Optional[int]										=	np.array(UpperCAmelCase_						).astype(np.floataa						) / 255.0
            a						:Optional[int]										=	image.transpose(0			,  3			,  1			,  2						)
            a						:Any										=	2.0 * image - 1.0
            a						:Optional[Any]										=	torch.from_numpy(UpperCAmelCase_						)
      elif isinstance(image[0]			,  torch.Tensor						):
            a						:Tuple										=	torch.cat(UpperCAmelCase_			,  dim=0						)
      return image
def     __lowerCamelCase						(			UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		str=0.9995						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_			,  np.ndarray						):
            a						:Tuple										=	True
            a						:Optional[Any]										=	va.device
            a						:Union[str, Any]										=	va.cpu().numpy()
            a						:str										=	va.cpu().numpy()

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

      if inputs_are_torch:
            a						:Optional[Any]										=	torch.from_numpy(UpperCAmelCase_						).to(UpperCAmelCase_						)

      return va
def     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





      a						:List[str]										=	F.normalize(UpperCAmelCase_			,  dim=-1						)
      a						:List[Any]										=	F.normalize(UpperCAmelCase_			,  dim=-1						)
      return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]			,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      for param in model.parameters():
            a						:Tuple										=	value







class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  _lowerCamelCase=None ,  ):
       super().__init__()
       self.register_modules(
           vae=_lowerCamelCase ,  text_encoder=_lowerCamelCase ,  clip_model=_lowerCamelCase ,  tokenizer=_lowerCamelCase ,  unet=_lowerCamelCase ,  scheduler=_lowerCamelCase ,  feature_extractor=_lowerCamelCase ,  coca_model=_lowerCamelCase ,  coca_tokenizer=_lowerCamelCase ,  coca_transform=_lowerCamelCase ,  )
       a						:int										=	(
           feature_extractor.size
           if isinstance(feature_extractor.size ,  _lowerCamelCase   )
           else feature_extractor.size['''shortest_edge''']
       )
       a						:int										=	transforms.Normalize(mean=feature_extractor.image_mean ,  std=feature_extractor.image_std   )
       set_requires_grad(self.text_encoder ,  _lowerCamelCase   )
       set_requires_grad(self.clip_model ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase = "auto"   ):
       if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             a						:Any										=	self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self.enable_attention_slicing(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       set_requires_grad(self.vae ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       set_requires_grad(self.vae ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       set_requires_grad(self.unet ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       set_requires_grad(self.unet ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       # get the original timestep using init_timestep
       a						:Dict										=	min(int(num_inference_steps * strength   ) ,  _lowerCamelCase   )

       a						:Dict										=	max(num_inference_steps - init_timestep ,  0   )
       a						:int										=	self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       if not isinstance(_lowerCamelCase ,  torch.Tensor   ):
             raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(_lowerCamelCase   )}'''   )

       a						:Optional[Any]										=	image.to(device=_lowerCamelCase ,  dtype=_lowerCamelCase   )

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

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       a						:Dict										=	0.1_8215 * init_latents
       a						:Optional[Any]										=	init_latents.repeat_interleave(_lowerCamelCase ,  dim=0   )

       a						:Any										=	randn_tensor(init_latents.shape ,  generator=_lowerCamelCase ,  device=_lowerCamelCase ,  dtype=_lowerCamelCase   )

       # get latents
       a						:Dict										=	self.scheduler.add_noise(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )
       a						:Optional[int]										=	init_latents

       return latents




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




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




 @torch.enable_grad()
 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  ):
       a						:Optional[Any]										=	latents.detach().requires_grad_()

       a						:Dict										=	self.scheduler.scale_model_input(_lowerCamelCase ,  _lowerCamelCase   )

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

       if isinstance(self.scheduler ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
             a						:Union[str, Any]										=	self.scheduler.alphas_cumprod[timestep]
             a						:Optional[int]										=	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
             a						:List[Any]										=	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

             a						:str										=	torch.sqrt(_lowerCamelCase   )
             a						:Optional[Any]										=	pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler ,  _lowerCamelCase   ):
             a						:Optional[Any]										=	self.scheduler.sigmas[index]
             a						: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
       a						:Tuple										=	1 / 0.1_8215 * sample
       a						:Optional[int]										=	self.vae.decode(_lowerCamelCase   ).sample
       a						:Tuple										=	(image / 2 + 0.5).clamp(0 ,  1   )

       a						:Dict										=	transforms.Resize(self.feature_extractor_size   )(_lowerCamelCase   )
       a						:str										=	self.normalize(_lowerCamelCase   ).to(latents.dtype   )

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

       a						:Tuple										=	spherical_dist_loss(_lowerCamelCase ,  _lowerCamelCase   ).mean() * clip_guidance_scale

       a						:List[Any]										=	-torch.autograd.grad(_lowerCamelCase ,  _lowerCamelCase   )[0]

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




 @torch.no_grad()
 def __call__(		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase = None ,  _lowerCamelCase = None ,  _lowerCamelCase = 512 ,  _lowerCamelCase = 512 ,  _lowerCamelCase = 0.6 ,  _lowerCamelCase = 50 ,  _lowerCamelCase = 7.5 ,  _lowerCamelCase = 1 ,  _lowerCamelCase = 0.0 ,  _lowerCamelCase = 100 ,  _lowerCamelCase = None ,  _lowerCamelCase = "pil" ,  _lowerCamelCase = True ,  _lowerCamelCase = 0.8 ,  _lowerCamelCase = 0.1 ,  _lowerCamelCase = 0.1 ,  ):
       if isinstance(_lowerCamelCase ,  _lowerCamelCase   ) and len(_lowerCamelCase   ) != batch_size:
             raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(_lowerCamelCase   )} 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(_lowerCamelCase ,  torch.Generator   ) and batch_size > 1:
             a						:Dict										=	[generator] + [None] * (batch_size - 1)

       a						:Tuple										=	[
           ('''model''', self.coca_model is None),
           ('''tokenizer''', self.coca_tokenizer is None),
           ('''transform''', self.coca_transform is None),
       ]
       a						:Dict										=	[x[0] for x in coca_is_none if x[1]]
       a						:Union[str, Any]										=	''', '''.join(_lowerCamelCase   )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
             if len(_lowerCamelCase   ):
                   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.'''   )
             a						:Optional[Any]										=	self.get_image_description(_lowerCamelCase   )
       if style_prompt is None:
             if len(_lowerCamelCase   ):
                   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.'''   )
             a						:Dict										=	self.get_image_description(_lowerCamelCase   )

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

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

       a						:Optional[Any]										=	slerp(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )

       # duplicate text embeddings for each generation per prompt
       a						:Optional[int]										=	text_embeddings.repeat_interleave(_lowerCamelCase ,  dim=0   )

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

       self.scheduler.set_timesteps(_lowerCamelCase ,  **_lowerCamelCase   )
       # 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   )

       a							,    a						:List[Any]										=	self.get_timesteps(_lowerCamelCase ,  _lowerCamelCase ,  self.device   )
       a						:List[str]										=	timesteps[:1].repeat(_lowerCamelCase   )

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

       a						:Tuple										=	preprocess(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )
       a						:Tuple										=	self.prepare_latents(
           _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  text_embeddings.dtype ,  self.device ,  _lowerCamelCase   )

       a						:List[str]										=	slerp(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )

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

       # 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.
       a						:str										=	guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
             a						:Tuple										=	content_text_input.input_ids.shape[-1]
             a						:Union[str, Any]										=	self.tokenizer([''''''] ,  padding='''max_length''' ,  max_length=_lowerCamelCase ,  return_tensors='''pt'''   )
             a						:int										=	self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
             # duplicate unconditional embeddings for each generation per prompt
             a						:List[Any]										=	uncond_embeddings.repeat_interleave(_lowerCamelCase ,  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
             a						:List[str]										=	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`.
       a						:List[str]										=	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       a						:str										=	text_embeddings.dtype
       if latents is None:
             if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   a						:List[Any]										=	torch.randn(_lowerCamelCase ,  generator=_lowerCamelCase ,  device='''cpu''' ,  dtype=_lowerCamelCase   ).to(
                       self.device   )
             else:
                   a						:Dict										=	torch.randn(_lowerCamelCase ,  generator=_lowerCamelCase ,  device=self.device ,  dtype=_lowerCamelCase   )
       else:
             if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
             a						:Union[str, Any]										=	latents.to(self.device   )

       # scale the initial noise by the standard deviation required by the scheduler
       a						: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]
       a						:Dict										=	'''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       a						:Tuple										=	{}
       if accepts_eta:
             a						:Optional[Any]										=	eta

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

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

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

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                         a							,    a						:Union[str, Any]										=	noise_pred.chunk(2   )
                         a						:Optional[int]										=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

                   # compute the previous noisy sample x_t -> x_t-1
                   a						:Union[str, Any]										=	self.scheduler.step(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  **_lowerCamelCase   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       a						:Union[str, Any]										=	1 / 0.1_8215 * latents
       a						:int										=	self.vae.decode(_lowerCamelCase   ).sample

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

       if output_type == "pil":
             a						:Optional[int]										=	self.numpy_to_pil(_lowerCamelCase   )

       if not return_dict:
             return (image, None)

       return StableDiffusionPipelineOutput(images=_lowerCamelCase ,  nsfw_content_detected=_lowerCamelCase   )









from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .multicontrolnet import MultiControlNetModel
     from .pipeline_controlnet import StableDiffusionControlNetPipeline
     from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
     from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
     from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





