



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        __snake_case					(      nn.Module):







							'''simple docstring'''






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

											a__ 					=						cut_size
											a__ 					=						cut_power







							def _a					(							self     ,     a_     ,     a_						):
											a__					,	a__ 					=						pixel_values.shape[2:4]
											a__ 					=						min(_UpperCamelCase     ,     _UpperCamelCase						)
											a__ 					=						min(_UpperCamelCase     ,     _UpperCamelCase     ,     self.cut_size						)
											a__ 					=						[]
											for _ in range(_UpperCamelCase						):
															a__ 					=						int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
															a__ 					=						torch.randint(0     ,     sideX - size + 1     ,     ()						)
															a__ 					=						torch.randint(0     ,     sideY - size + 1     ,     ()						)
															a__ 					=						pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(_UpperCamelCase     ,     self.cut_size						)						)
											return torch.cat(_UpperCamelCase						)




def 	A_					(		__a   : int       ,						__a   : Any						):

				"""simple docstring"""
				a__ 					=						F.normalize(__UpperCamelCase       ,						dim=-1						)
				a__ 					=						F.normalize(__UpperCamelCase       ,						dim=-1						)
				return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)

def 	A_					(		__a   : Optional[Any]       ,						__a   : Union[str, Any]						):

				"""simple docstring"""
				for param in model.parameters():
								a__ 					=						value




class        __snake_case					(      __lowerCamelCase):







							'''simple docstring'''






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

											a__ 					=						transforms.Normalize(mean=feature_extractor.image_mean     ,     std=feature_extractor.image_std						)
											a__ 					=						(
											    feature_extractor.size
											    if isinstance(feature_extractor.size     ,     _UpperCamelCase						)
											    else feature_extractor.size["""shortest_edge"""]
											)
											a__ 					=						MakeCutouts(self.cut_out_size						)

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


							def _a					(							self     ,     a_ = "auto"						):





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


							def _a					(							self						):
											self.enable_attention_slicing(_UpperCamelCase						)


							def _a					(							self						):
											set_requires_grad(self.vae     ,     _UpperCamelCase						)


							def _a					(							self						):
											set_requires_grad(self.vae     ,     _UpperCamelCase						)


							def _a					(							self						):
											set_requires_grad(self.unet     ,     _UpperCamelCase						)


							def _a					(							self						):
											set_requires_grad(self.unet     ,     _UpperCamelCase						)


							@torch.enable_grad()
							def _a					(							self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_=True     ,     ):
											a__ 					=						latents.detach().requires_grad_()

											a__ 					=						self.scheduler.scale_model_input(_UpperCamelCase     ,     _UpperCamelCase						)

											# predict the noise residual
											a__ 					=						self.unet(_UpperCamelCase     ,     _UpperCamelCase     ,     encoder_hidden_states=_UpperCamelCase						).sample

											if isinstance(self.scheduler     ,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
															a__ 					=						self.scheduler.alphas_cumprod[timestep]
															a__ 					=						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__ 					=						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															a__ 					=						torch.sqrt(_UpperCamelCase						)
															a__ 					=						pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler     ,     _UpperCamelCase						):
															a__ 					=						self.scheduler.sigmas[index]
															a__ 					=						latents - sigma * noise_pred
											else:
															raise ValueError(F'''scheduler type {type(self.scheduler						)} not supported'''						)

											a__ 					=						1 / self.vae.config.scaling_factor * sample
											a__ 					=						self.vae.decode(_UpperCamelCase						).sample
											a__ 					=						(image / 2 + 0.5).clamp(0     ,     1						)

											if use_cutouts:
															a__ 					=						self.make_cutouts(_UpperCamelCase     ,     _UpperCamelCase						)
											else:
															a__ 					=						transforms.Resize(self.cut_out_size						)(_UpperCamelCase						)
											a__ 					=						self.normalize(_UpperCamelCase						).to(latents.dtype						)

											a__ 					=						self.clip_model.get_image_features(_UpperCamelCase						)
											a__ 					=						image_embeddings_clip / image_embeddings_clip.norm(p=2     ,     dim=-1     ,     keepdim=_UpperCamelCase						)

											if use_cutouts:
															a__ 					=						spherical_dist_loss(_UpperCamelCase     ,     _UpperCamelCase						)
															a__ 					=						dists.view([num_cutouts, sample.shape[0], -1]						)
															a__ 					=						dists.sum(2						).mean(0						).sum() * clip_guidance_scale
											else:
															a__ 					=						spherical_dist_loss(_UpperCamelCase     ,     _UpperCamelCase						).mean() * clip_guidance_scale

											a__ 					=						-torch.autograd.grad(_UpperCamelCase     ,     _UpperCamelCase						)[0]

											if isinstance(self.scheduler     ,     _UpperCamelCase						):
															a__ 					=						latents.detach() + grads * (sigma**2)
															a__ 					=						noise_pred_original
											else:
															a__ 					=						noise_pred_original - torch.sqrt(_UpperCamelCase						) * grads
											return noise_pred, latents







							@torch.no_grad()
							def __call__(							self     ,     a_     ,     a_ = 512     ,     a_ = 512     ,     a_ = 50     ,     a_ = 7.5     ,     a_ = 1     ,     a_ = 0.0     ,     a_ = 100     ,     a_ = None     ,     a_ = 4     ,     a_ = True     ,     a_ = None     ,     a_ = None     ,     a_ = "pil"     ,     a_ = True     ,     ):





											if isinstance(_UpperCamelCase     ,     _UpperCamelCase						):
															a__ 					=						1
											elif isinstance(_UpperCamelCase     ,     _UpperCamelCase						):
															a__ 					=						len(_UpperCamelCase						)
											else:
															raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase						)}'''						)

											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
											a__ 					=						self.tokenizer(
											    _UpperCamelCase     ,     padding="""max_length"""     ,     max_length=self.tokenizer.model_max_length     ,     truncation=_UpperCamelCase     ,     return_tensors="""pt"""     ,     )
											a__ 					=						self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
											# duplicate text embeddings for each generation per prompt
											a__ 					=						text_embeddings.repeat_interleave(_UpperCamelCase     ,     dim=0						)

											if clip_guidance_scale > 0:
															if clip_prompt is not None:
																			a__ 					=						self.tokenizer(
																			    _UpperCamelCase     ,     padding="""max_length"""     ,     max_length=self.tokenizer.model_max_length     ,     truncation=_UpperCamelCase     ,     return_tensors="""pt"""     ,     ).input_ids.to(self.device						)
															else:
																			a__ 					=						text_input.input_ids.to(self.device						)
															a__ 					=						self.clip_model.get_text_features(_UpperCamelCase						)
															a__ 					=						text_embeddings_clip / text_embeddings_clip.norm(p=2     ,     dim=-1     ,     keepdim=_UpperCamelCase						)
															# duplicate text embeddings clip for each generation per prompt
															a__ 					=						text_embeddings_clip.repeat_interleave(_UpperCamelCase     ,     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.
											a__ 					=						guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
															a__ 					=						text_input.input_ids.shape[-1]
															a__ 					=						self.tokenizer([""""""]     ,     padding="""max_length"""     ,     max_length=_UpperCamelCase     ,     return_tensors="""pt"""						)
															a__ 					=						self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
															# duplicate unconditional embeddings for each generation per prompt
															a__ 					=						uncond_embeddings.repeat_interleave(_UpperCamelCase     ,     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__ 					=						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__ 					=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
											a__ 					=						text_embeddings.dtype
											if latents is None:
															if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			a__ 					=						torch.randn(_UpperCamelCase     ,     generator=_UpperCamelCase     ,     device="""cpu"""     ,     dtype=_UpperCamelCase						).to(
																			    self.device						)
															else:
																			a__ 					=						torch.randn(_UpperCamelCase     ,     generator=_UpperCamelCase     ,     device=self.device     ,     dtype=_UpperCamelCase						)
											else:
															if latents.shape != latents_shape:
																			raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''						)
															a__ 					=						latents.to(self.device						)

											# set timesteps
											a__ 					=						"""offset""" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
											a__ 					=						{}
											if accepts_offset:
															a__ 					=						1

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

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

											# scale the initial noise by the standard deviation required by the scheduler
											a__ 					=						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__ 					=						"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
											a__ 					=						{}
											if accepts_eta:
															a__ 					=						eta

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

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

															# predict the noise residual
															a__ 					=						self.unet(_UpperCamelCase     ,     _UpperCamelCase     ,     encoder_hidden_states=_UpperCamelCase						).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																			a__					,	a__ 					=						noise_pred.chunk(2						)
																			a__ 					=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

															# compute the previous noisy sample x_t -> x_t-1
															a__ 					=						self.scheduler.step(_UpperCamelCase     ,     _UpperCamelCase     ,     _UpperCamelCase     ,     **_UpperCamelCase						).prev_sample

											# scale and decode the image latents with vae
											a__ 					=						1 / self.vae.config.scaling_factor * latents
											a__ 					=						self.vae.decode(_UpperCamelCase						).sample

											a__ 					=						(image / 2 + 0.5).clamp(0     ,     1						)
											a__ 					=						image.cpu().permute(0     ,     2     ,     3     ,     1						).numpy()

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

											if not return_dict:
															return (image, None)

											return StableDiffusionPipelineOutput(images=_UpperCamelCase     ,     nsfw_content_detected=_UpperCamelCase						)







from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class       __a					:


     """simple docstring"""


     _A				:    List[Any]									=  ("_front", "_back", "_len")
     @dataclass
     class       __a					:


          """simple docstring"""


          _A				:    Any									=  None
          _A				:    Deque._Node | None									=  None
          _A				:    Deque._Node | None									=  None






     class       __a					:


          """simple docstring"""


          _A				:    List[str]									=  ("_cur",)




          def __init__(	self			: int				,_UpperCamelCase			: Deque._Node | None						)		->   None:


              '''simple docstring'''





              SCREAMING_SNAKE_CASE__								=cur




          def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


              '''simple docstring'''





              return self




          def   __A				(	self			: Union[str, Any]						)		->   Any:


              '''simple docstring'''





              if self._cur is None:
                  # finished iterating
                  raise StopIteration
              SCREAMING_SNAKE_CASE__								=self._cur.val
              SCREAMING_SNAKE_CASE__								=self._cur.next_node

              return val






     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: Iterable[Any] | None = None						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=0

         if iterable is not None:
             # append every value to the deque
             for val in iterable:
                 self.append(_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=self._back
             SCREAMING_SNAKE_CASE__								=node  # assign new back to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: Any				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=self._front
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=node  # assign new front to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.append(_UpperCamelCase						)




     def   __A				(	self			: str				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.appendleft(_UpperCamelCase						)




     def   __A				(	self			: str						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._back
         SCREAMING_SNAKE_CASE__								=self._back.prev_node  # set new back
         # drop the last node - python will deallocate memory automatically
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Union[str, Any]						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=self._front.next_node  # set new front and drop the first node
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._front is None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         return self._len




     def __eq__(	self			: Union[str, Any]				,_UpperCamelCase			: object						)		->   bool:


         '''simple docstring'''





         if not isinstance(_UpperCamelCase				,_UpperCamelCase						):
             return NotImplemented

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=other._front

         # if the length of the dequeues are not the same, they are not equal
         if len(self						) != len(_UpperCamelCase						):
             return False

         while me is not None and oth is not None:
             # compare every value
             if me.val != oth.val:
                 return False
             SCREAMING_SNAKE_CASE__								=me.next_node
             SCREAMING_SNAKE_CASE__								=oth.next_node

         return True




     def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


         '''simple docstring'''





         return Deque._Iterator(self._front						)




     def __repr__(	self			: Optional[Any]						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         SCREAMING_SNAKE_CASE__								=self._front
         while aux is not None:
             # append the values in a list to display
             values_list.append(aux.val						)
             SCREAMING_SNAKE_CASE__								=aux.next_node

         return f"""[{", ".join(repr(_UpperCamelCase						) for val in values_list						)}]"""


if __name__ == "__main__":
    import doctest

    doctest.testmod()






