



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     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Any]:
				if isinstance(_A	,				torch.Tensor ):
								return image
				elif isinstance(_A	,				PIL.Image.Image ):
								a__	:							str      						= [image]

				if isinstance(image[0]	,				PIL.Image.Image ):
								a__	:							str      						= [np.array(i.resize((w, h)	,				resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
								a__	:							Dict      						= np.concatenate(_A	,				axis=0 )
								a__	:							Dict      						= np.array(_A ).astype(np.floataa ) / 2_55.0
								a__	:							List[Any]      						= image.transpose(0	,				3	,				1	,				2 )
								a__	:							Tuple      						= 2.0 * image - 1.0
								a__	:							Any      						= torch.from_numpy(_A )
				elif isinstance(image[0]	,				torch.Tensor ):
								a__	:							Tuple      						= torch.cat(_A	,				dim=0 )
				return image






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=0.9_9_9_5 )	->						Optional[int]:
				if not isinstance(_A	,				np.ndarray ):
								a__	:							List[Any]      						= True
								a__	:							List[str]      						= va.device
								a__	:							Union[str, Any]      						= va.cpu().numpy()
								a__	:							List[Any]      						= va.cpu().numpy()

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

				if inputs_are_torch:
								a__	:							Dict      						= torch.from_numpy(_A ).to(_A )

				return va






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						int:
				a__	:							Optional[int]      						= F.normalize(_A	,				dim=-1 )
				a__	:							Union[str, Any]      						= F.normalize(_A	,				dim=-1 )
				return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						str:
				for param in model.parameters():
								a__	:							Tuple      						= value






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											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__	:							Tuple      						= (
											    feature_extractor.size
											    if isinstance(feature_extractor.size ,					lowerCamelCase_				)
											    else feature_extractor.size['''shortest_edge''']
											)
											a__	:							List[Any]      						= 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 		_A	(							self ,					__UpperCAmelCase = "auto"				):







											"""simple docstring"""



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



							def 		_A	(							self				):







											"""simple docstring"""



											self.enable_attention_slicing(lowerCamelCase_				)



							def 		_A	(							self				):







											"""simple docstring"""



											set_requires_grad(self.vae ,					lowerCamelCase_				)



							def 		_A	(							self				):







											"""simple docstring"""



											set_requires_grad(self.vae ,					lowerCamelCase_				)



							def 		_A	(							self				):







											"""simple docstring"""



											set_requires_grad(self.unet ,					lowerCamelCase_				)



							def 		_A	(							self				):







											"""simple docstring"""



											set_requires_grad(self.unet ,					lowerCamelCase_				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= min(int(num_inference_steps * strength				) ,					lowerCamelCase_				)

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

											return timesteps, num_inference_steps - t_start



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase=None				):







											"""simple docstring"""



											if not isinstance(lowerCamelCase_ ,					torch.Tensor				):
															raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase_				)}'				)

											a__	:							List[Any]      						= image.to(device=lowerCamelCase_ ,					dtype=lowerCamelCase_				)

											if isinstance(lowerCamelCase_ ,					lowerCamelCase_				):
															a__	:							Optional[int]      						= [
															    self.vae.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(lowerCamelCase_				)
															]
															a__	:							List[Any]      						= torch.cat(lowerCamelCase_ ,					dim=0				)
											else:
															a__	:							Any      						= self.vae.encode(lowerCamelCase_				).latent_dist.sample(lowerCamelCase_				)

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

											a__	:							str      						= randn_tensor(init_latents.shape ,					generator=lowerCamelCase_ ,					device=lowerCamelCase_ ,					dtype=lowerCamelCase_				)

											# get latents
											a__	:							List[str]      						= self.scheduler.add_noise(lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_				)
											a__	:							Dict      						= init_latents

											return latents



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= self.feature_extractor.preprocess(lowerCamelCase_				)
											a__	:							str      						= torch.from_numpy(clip_image_input["pixel_values"][0]				).unsqueeze(0				).to(self.device				).half()
											a__	:							Any      						= self.clip_model.get_image_features(lowerCamelCase_				)
											a__	:							Any      						= image_embeddings_clip / image_embeddings_clip.norm(p=2 ,					dim=-1 ,					keepdim=lowerCamelCase_				)
											a__	:							Dict      						= image_embeddings_clip.repeat_interleave(lowerCamelCase_ ,					dim=0				)
											return image_embeddings_clip



							@torch.enable_grad()
							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							int      						= latents.detach().requires_grad_()

											a__	:							Union[str, Any]      						= self.scheduler.scale_model_input(lowerCamelCase_ ,					lowerCamelCase_				)

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

											if isinstance(self.scheduler ,					(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
															a__	:							List[Any]      						= self.scheduler.alphas_cumprod[timestep]
															a__	:							List[Any]      						= 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__	:							List[str]      						= torch.sqrt(lowerCamelCase_				)
															a__	:							Any      						= pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler ,					lowerCamelCase_				):
															a__	:							Any      						= self.scheduler.sigmas[index]
															a__	:							Optional[int]      						= 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__	:							int      						= 1 / 0.1_8_2_1_5 * sample
											a__	:							Any      						= self.vae.decode(lowerCamelCase_				).sample
											a__	:							Tuple      						= (image / 2 + 0.5).clamp(0 ,					1				)

											a__	:							List[str]      						= transforms.Resize(self.feature_extractor_size				)(lowerCamelCase_				)
											a__	:							Optional[int]      						= self.normalize(lowerCamelCase_				).to(latents.dtype				)

											a__	:							int      						= self.clip_model.get_image_features(lowerCamelCase_				)
											a__	:							Dict      						= image_embeddings_clip / image_embeddings_clip.norm(p=2 ,					dim=-1 ,					keepdim=lowerCamelCase_				)

											a__	:							List[Any]      						= spherical_dist_loss(lowerCamelCase_ ,					lowerCamelCase_				).mean() * clip_guidance_scale

											a__	:							Union[str, Any]      						= -torch.autograd.grad(lowerCamelCase_ ,					lowerCamelCase_				)[0]

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




							@torch.no_grad()
							def __call__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 512 ,					__UpperCAmelCase = 512 ,					__UpperCAmelCase = 0.6 ,					__UpperCAmelCase = 50 ,					__UpperCAmelCase = 7.5 ,					__UpperCAmelCase = 1 ,					__UpperCAmelCase = 0.0 ,					__UpperCAmelCase = 100 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = "pil" ,					__UpperCAmelCase = True ,					__UpperCAmelCase = 0.8 ,					__UpperCAmelCase = 0.1 ,					__UpperCAmelCase = 0.1 ,					):







											"""simple docstring"""



											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__	:							Optional[Any]      						= [generator] + [None] * (batch_size - 1)

											a__	:							int      						= [
											    ('''model''', self.coca_model is None),
											    ('''tokenizer''', self.coca_tokenizer is None),
											    ('''transform''', self.coca_transform is None),
											]
											a__	:							List[str]      						= [x[0] for x in coca_is_none if x[1]]
											a__	:							List[str]      						= ''', '''.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__	:							Dict      						= 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__	:							int      						= self.get_image_description(lowerCamelCase_				)

											# get prompt text embeddings for content and style
											a__	:							Union[str, Any]      						= self.tokenizer(
											    lowerCamelCase_ ,					padding="max_length" ,					max_length=self.tokenizer.model_max_length ,					truncation=lowerCamelCase_ ,					return_tensors="pt" ,					)
											a__	:							Union[str, Any]      						= self.text_encoder(content_text_input.input_ids.to(self.device				)				)[0]

											a__	:							Optional[int]      						= self.tokenizer(
											    lowerCamelCase_ ,					padding="max_length" ,					max_length=self.tokenizer.model_max_length ,					truncation=lowerCamelCase_ ,					return_tensors="pt" ,					)
											a__	:							Optional[Any]      						= self.text_encoder(style_text_input.input_ids.to(self.device				)				)[0]

											a__	:							str      						= slerp(lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_				)

											# duplicate text embeddings for each generation per prompt
											a__	:							Union[str, Any]      						= text_embeddings.repeat_interleave(lowerCamelCase_ ,					dim=0				)

											# set timesteps
											a__	:							str      						= '''offset''' in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
											a__	:							Optional[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__	:							List[str]      						= self.get_timesteps(lowerCamelCase_ ,					lowerCamelCase_ ,					self.device				)
											a__	:							Optional[int]      						= timesteps[:1].repeat(lowerCamelCase_				)

											# Preprocess image
											a__	:							Any      						= preprocess(lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_				)
											a__	:							Optional[Any]      						= self.prepare_latents(
											    lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					text_embeddings.dtype ,					self.device ,					lowerCamelCase_				)

											a__	:							List[Any]      						= preprocess(lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_				)
											a__	:							Union[str, Any]      						= 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__	:							List[str]      						= self.get_clip_image_embeddings(lowerCamelCase_ ,					lowerCamelCase_				)
															a__	:							Any      						= self.get_clip_image_embeddings(lowerCamelCase_ ,					lowerCamelCase_				)
															a__	:							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__	:							List[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__	:							Optional[int]      						= 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[Any]      						= 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__	:							Dict      						= (batch_size, self.unet.config.in_channels, height // 8, width // 8)
											a__	:							List[Any]      						= text_embeddings.dtype
											if latents is None:
															if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			a__	:							str      						= 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__	:							List[Any]      						= latents.to(self.device				)

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

											# check if the scheduler accepts generator
											a__	:							int      						= '''generator''' in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
											if accepts_generator:
															a__	:							Tuple      						= generator

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

																			# predict the noise residual
																			a__	:							Union[str, Any]      						= self.unet(lowerCamelCase_ ,					lowerCamelCase_ ,					encoder_hidden_states=lowerCamelCase_				).sample

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

																			# perform clip guidance
																			if clip_guidance_scale > 0:
																							a__	:							Optional[Any]      						= (
																							    text_embeddings.chunk(2				)[1] if do_classifier_free_guidance else text_embeddings
																							)
																							a__	:							Optional[Any]      						= self.cond_fn(
																							    lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					lowerCamelCase_ ,					)

																			# compute the previous noisy sample x_t -> x_t-1
																			a__	:							Dict      						= 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__	:							Dict      						= 1 / 0.1_8_2_1_5 * latents
											a__	:							Tuple      						= self.vae.decode(lowerCamelCase_				).sample

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

											if output_type == "pil":
															a__	:							Dict      						= self.numpy_to_pil(lowerCamelCase_				)

											if not return_dict:
															return (image, None)

											return StableDiffusionPipelineOutput(images=lowerCamelCase_ ,					nsfw_content_detected=lowerCamelCase_				)



'''simple docstring'''

import baseaa







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   return baseaa.baaencode(string.encode('''utf-8'''     )     )







def __UpperCamelCase( _A       :					bytes     ):
   '''simple docstring'''
   return baseaa.baadecode(_A     ).decode('''utf-8'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[str]					=      'Hello World!'
  UpperCamelCase__  :						str					=      baseaa_encode(test)
  print(encoded)

  UpperCamelCase__  :						int					=      baseaa_decode(encoded)
  print(decoded)




