


'''simple docstring'''


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput





class 					lowercase__						(		nn.Module      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=1.0						):
											super().__init__()

											_SCREAMING_SNAKE_CASE				: Any  	=   cut_size
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   cut_power






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   pixel_values.shape[2:4]
											_SCREAMING_SNAKE_CASE				: Any  	=   min(__snake_case					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   min(__snake_case					,      __snake_case					,      self.cut_size						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   []
											for _ in range(__snake_case						):
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
															_SCREAMING_SNAKE_CASE				: str  	=   torch.randint(0					,      sideX - size + 1					,      ()						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   torch.randint(0					,      sideY - size + 1					,      ()						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(__snake_case					,      self.cut_size						)						)
											return torch.cat(__snake_case						)







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   F.normalize(SCREAMING_SNAKE_CASE__							,   dim=-1  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   F.normalize(SCREAMING_SNAKE_CASE__							,   dim=-1  )
				return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				for param in model.parameters():
								_SCREAMING_SNAKE_CASE				: int  	=   value







class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      ):
											super().__init__()
											self.register_modules(
											    vae=__snake_case					,      text_encoder=__snake_case					,      clip_model=__snake_case					,      tokenizer=__snake_case					,      unet=__snake_case					,      scheduler=__snake_case					,      feature_extractor=__snake_case					,      )

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   transforms.Normalize(mean=feature_extractor.image_mean					,      std=feature_extractor.image_std						)
											_SCREAMING_SNAKE_CASE				: str  	=   (
											    feature_extractor.size
											    if isinstance(feature_extractor.size					,      __snake_case						)
											    else feature_extractor.size["""shortest_edge"""]
											)
											_SCREAMING_SNAKE_CASE				: Dict  	=   MakeCutouts(self.cut_out_size						)

											set_requires_grad(self.text_encoder					,      __snake_case						)
											set_requires_grad(self.clip_model					,      __snake_case						)






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






							def UpperCAmelCase_			(					self						):
											self.enable_attention_slicing(__snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.vae					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.vae					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.unet					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											set_requires_grad(self.unet					,      __snake_case						)






							@torch.enable_grad()
							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case=True					,      ):
											_SCREAMING_SNAKE_CASE				: int  	=   latents.detach().requires_grad_()

											_SCREAMING_SNAKE_CASE				: Dict  	=   self.scheduler.scale_model_input(__snake_case					,      __snake_case						)

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

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

															_SCREAMING_SNAKE_CASE				: List[Any]  	=   torch.sqrt(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.scheduler.sigmas[index]
															_SCREAMING_SNAKE_CASE				: str  	=   latents - sigma * noise_pred
											else:
															raise ValueError(f"""scheduler type {type(self.scheduler						)} not supported"""						)

											_SCREAMING_SNAKE_CASE				: Any  	=   1 / self.vae.config.scaling_factor * sample
											_SCREAMING_SNAKE_CASE				: int  	=   self.vae.decode(__snake_case						).sample
											_SCREAMING_SNAKE_CASE				: str  	=   (image / 2 + 0.5).clamp(0					,      1						)

											if use_cutouts:
															_SCREAMING_SNAKE_CASE				: Any  	=   self.make_cutouts(__snake_case					,      __snake_case						)
											else:
															_SCREAMING_SNAKE_CASE				: Any  	=   transforms.Resize(self.cut_out_size						)(__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.normalize(__snake_case						).to(latents.dtype						)

											_SCREAMING_SNAKE_CASE				: str  	=   self.clip_model.get_image_features(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   image_embeddings_clip / image_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__snake_case						)

											if use_cutouts:
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   spherical_dist_loss(__snake_case					,      __snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   dists.view([num_cutouts, sample.shape[0], -1]						)
															_SCREAMING_SNAKE_CASE				: Any  	=   dists.sum(2						).mean(0						).sum() * clip_guidance_scale
											else:
															_SCREAMING_SNAKE_CASE				: Any  	=   spherical_dist_loss(__snake_case					,      __snake_case						).mean() * clip_guidance_scale

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   -torch.autograd.grad(__snake_case					,      __snake_case						)[0]

											if isinstance(self.scheduler					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Any  	=   latents.detach() + grads * (sigma**2)
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   noise_pred_original
											else:
															_SCREAMING_SNAKE_CASE				: Tuple  	=   noise_pred_original - torch.sqrt(__snake_case						) * grads
											return noise_pred, latents






							@torch.no_grad()
							def __call__(					self					,      __snake_case					,      __snake_case = 512					,      __snake_case = 512					,      __snake_case = 50					,      __snake_case = 7.5					,      __snake_case = 1					,      __snake_case = 0.0					,      __snake_case = 100					,      __snake_case = None					,      __snake_case = 4					,      __snake_case = True					,      __snake_case = None					,      __snake_case = None					,      __snake_case = "pil"					,      __snake_case = True					,      ):
											if isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: str  	=   1
											elif isinstance(__snake_case					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   len(__snake_case						)
											else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__snake_case						)}"""						)

											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}."""						)

											# get prompt text embeddings
											_SCREAMING_SNAKE_CASE				: Dict  	=   self.tokenizer(
											    __snake_case					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=__snake_case					,      return_tensors="""pt"""					,      )
											_SCREAMING_SNAKE_CASE				: List[str]  	=   self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
											# duplicate text embeddings for each generation per prompt
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   text_embeddings.repeat_interleave(__snake_case					,      dim=0						)

											if clip_guidance_scale > 0:
															if clip_prompt is not None:
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   self.tokenizer(
																			    __snake_case					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=__snake_case					,      return_tensors="""pt"""					,      ).input_ids.to(self.device						)
															else:
																			_SCREAMING_SNAKE_CASE				: int  	=   text_input.input_ids.to(self.device						)
															_SCREAMING_SNAKE_CASE				: str  	=   self.clip_model.get_text_features(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Dict  	=   text_embeddings_clip / text_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=__snake_case						)
															# duplicate text embeddings clip for each generation per prompt
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   text_embeddings_clip.repeat_interleave(__snake_case					,      dim=0						)

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

											# set timesteps
											_SCREAMING_SNAKE_CASE				: Tuple  	=   """offset""" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   {}
											if accepts_offset:
															_SCREAMING_SNAKE_CASE				: int  	=   1

											self.scheduler.set_timesteps(__snake_case					,      **__snake_case						)

											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   self.scheduler.timesteps.to(self.device						)

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

											# check if the scheduler accepts generator
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """generator""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
											if accepts_generator:
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   generator

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

															# predict the noise residual
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.unet(__snake_case					,      __snake_case					,      encoder_hidden_states=__snake_case						).sample

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

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

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

											# scale and decode the image latents with vae
											_SCREAMING_SNAKE_CASE				: Dict  	=   1 / self.vae.config.scaling_factor * latents
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.vae.decode(__snake_case						).sample

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

											if output_type == "pil":
															_SCREAMING_SNAKE_CASE				: Dict  	=   self.numpy_to_pil(__snake_case						)

											if not return_dict:
															return (image, None)

											return StableDiffusionPipelineOutput(images=__snake_case					,      nsfw_content_detected=__snake_case						)








'''simple docstring'''


def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				return (input_a, input_a).count(0  ) % 2







def     snake_case_     (						):







				"""simple docstring"""





				assert xor_gate(0							,   0  ) == 0
				assert xor_gate(0							,   1  ) == 1
				assert xor_gate(1							,   0  ) == 1
				assert xor_gate(1							,   1  ) == 0


if __name__ == "__main__":
				print(xor_gate(0, 0))
				print(xor_gate(0, 1))




