
"""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      _SCREAMING_SNAKE_CASE   (     nn.Module				):






			def __init__(   self			,  __A			,  __A=1.0			)     ->	Union[str, Any]:
						super().__init__()

						lowerCAmelCase_       :Any     	=    cut_size
						lowerCAmelCase_       :Any     	=    cut_power






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






def 		_snake_case			(      lowercase__						:    int ,	lowercase__						:    Union[str, Any]						)    ->							Dict:


			'''simple docstring'''





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






def 		_snake_case			(      lowercase__						:    int ,	lowercase__						:    Optional[int]						)    ->							List[str]:


			'''simple docstring'''





			for param in model.parameters():
						lowerCAmelCase_       :List[Any]     	=    value




class      _SCREAMING_SNAKE_CASE   (     A__				):






			def __init__(   self			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A			,  )     ->	Any:
						super().__init__()
						self.register_modules(
						    vae=__A			,  text_encoder=__A			,  clip_model=__A			,  tokenizer=__A			,  unet=__A			,  scheduler=__A			,  feature_extractor=__A			,  )

						lowerCAmelCase_       :Optional[int]     	=    transforms.Normalize(mean=feature_extractor.image_mean			,  std=feature_extractor.image_std			)
						lowerCAmelCase_       :Tuple     	=    (
						    feature_extractor.size
						    if isinstance(feature_extractor.size			,  __A			)
						    else feature_extractor.size["""shortest_edge"""]
						)
						lowerCAmelCase_       :Union[str, Any]     	=    MakeCutouts(self.cut_out_size			)

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






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






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						self.enable_attention_slicing(__A			)






			def 					__lowerCAmelCase					(   self			)     ->	Tuple:
						set_requires_grad(self.vae			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						set_requires_grad(self.vae			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						set_requires_grad(self.unet			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	str:
						set_requires_grad(self.unet			,  __A			)






			@torch.enable_grad()
			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A			,  __A=True			,  )     ->	Dict:
						lowerCAmelCase_       :List[Any]     	=    latents.detach().requires_grad_()

						lowerCAmelCase_       :int     	=    self.scheduler.scale_model_input(__A			,  __A			)

						# predict the noise residual
						lowerCAmelCase_       :Optional[Any]     	=    self.unet(__A			,  __A			,  encoder_hidden_states=__A			).sample

						if isinstance(self.scheduler			,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)			):
									lowerCAmelCase_       :Any     	=    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_       :Tuple     	=    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

						lowerCAmelCase_       :Any     	=    1 / self.vae.config.scaling_factor * sample
						lowerCAmelCase_       :str     	=    self.vae.decode(__A			).sample
						lowerCAmelCase_       :List[str]     	=    (image / 2 + 0.5).clamp(0			,  1			)

						if use_cutouts:
									lowerCAmelCase_       :Dict     	=    self.make_cutouts(__A			,  __A			)
						else:
									lowerCAmelCase_       :List[str]     	=    transforms.Resize(self.cut_out_size			)(__A			)
						lowerCAmelCase_       :Union[str, Any]     	=    self.normalize(__A			).to(latents.dtype			)

						lowerCAmelCase_       :Optional[int]     	=    self.clip_model.get_image_features(__A			)
						lowerCAmelCase_       :List[Any]     	=    image_embeddings_clip / image_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=__A			)

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

						lowerCAmelCase_       :List[Any]     	=    -torch.autograd.grad(__A			,  __A			)[0]

						if isinstance(self.scheduler			,  __A			):
									lowerCAmelCase_       :int     	=    latents.detach() + grads * (sigma**2)
									lowerCAmelCase_       :Any     	=    noise_pred_original
						else:
									lowerCAmelCase_       :int     	=    noise_pred_original - torch.sqrt(__A			) * 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			,  )     ->	Dict:
						if isinstance(__A			,  __A			):
									lowerCAmelCase_       :Optional[int]     	=    1
						elif isinstance(__A			,  __A			):
									lowerCAmelCase_       :Optional[Any]     	=    len(__A			)
						else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__A			)}"""			)

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

						if clip_guidance_scale > 0:
									if clip_prompt is not None:
												lowerCAmelCase_       :int     	=    self.tokenizer(
												    __A			,  padding="""max_length"""			,  max_length=self.tokenizer.model_max_length			,  truncation=__A			,  return_tensors="""pt"""			,  ).input_ids.to(self.device			)
									else:
												lowerCAmelCase_       :Optional[Any]     	=    text_input.input_ids.to(self.device			)
									lowerCAmelCase_       :Any     	=    self.clip_model.get_text_features(__A			)
									lowerCAmelCase_       :List[Any]     	=    text_embeddings_clip / text_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=__A			)
									# duplicate text embeddings clip for each generation per prompt
									lowerCAmelCase_       :Dict     	=    text_embeddings_clip.repeat_interleave(__A			,  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.
						lowerCAmelCase_       :Dict     	=    guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
									lowerCAmelCase_       :Optional[Any]     	=    text_input.input_ids.shape[-1]
									lowerCAmelCase_       :Dict     	=    self.tokenizer([""""""]			,  padding="""max_length"""			,  max_length=__A			,  return_tensors="""pt"""			)
									lowerCAmelCase_       :Any     	=    self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
									# duplicate unconditional embeddings for each generation per prompt
									lowerCAmelCase_       :int     	=    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_       :Dict     	=    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_       :Union[str, Any]     	=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						lowerCAmelCase_       :str     	=    text_embeddings.dtype
						if latents is None:
									if self.device.type == "mps":
												# randn does not work reproducibly on mps
												lowerCAmelCase_       :List[Any]     	=    torch.randn(__A			,  generator=__A			,  device="""cpu"""			,  dtype=__A			).to(
												    self.device			)
									else:
												lowerCAmelCase_       :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_       :Dict     	=    latents.to(self.device			)

						# set timesteps
						lowerCAmelCase_       :str     	=    """offset""" in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
						lowerCAmelCase_       :Dict     	=    {}
						if accepts_offset:
									lowerCAmelCase_       :List[str]     	=    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
						lowerCAmelCase_       :Optional[Any]     	=    self.scheduler.timesteps.to(self.device			)

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

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

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

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

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

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

									# compute the previous noisy sample x_t -> x_t-1
									lowerCAmelCase_       :Optional[Any]     	=    self.scheduler.step(__A			,  __A			,  __A			,  **__A			).prev_sample

						# scale and decode the image latents with vae
						lowerCAmelCase_       :Any     	=    1 / self.vae.config.scaling_factor * 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_       :Optional[Any]     	=    self.numpy_to_pil(__A			)

						if not return_dict:
									return (image, None)

						return StableDiffusionPipelineOutput(images=__A			,  nsfw_content_detected=__A			)








"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase 					=    TypeVar('T')




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     A__				):
			pass




class      _SCREAMING_SNAKE_CASE   (     Generic[T]				):






			def __init__(   self			,  __A = 10			)     ->	Optional[int]:
						lowerCAmelCase_       :list[T]     	=    []
						lowerCAmelCase_       :int     	=    limit






			def __bool__(   self			)     ->	bool:
						return bool(self.stack			)






			def __str__(   self			)     ->	str:
						return str(self.stack			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	None:
						if len(self.stack			) >= self.limit:
									raise StackOverflowError
						self.stack.append(__A			)






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack.pop()






			def 					__lowerCAmelCase					(   self			)     ->	T:
						if not self.stack:
									raise StackUnderflowError
						return self.stack[-1]






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return not bool(self.stack			)






			def 					__lowerCAmelCase					(   self			)     ->	bool:
						return self.size() == self.limit






			def 					__lowerCAmelCase					(   self			)     ->	int:
						return len(self.stack			)






			def __contains__(   self			,  __A			)     ->	bool:
						return item in self.stack






def 		_snake_case			(      )    ->							None:


			'''simple docstring'''





			lowerCAmelCase_       :Stack[int]     	=    Stack(1_0						)
			assert bool(lowercase__						) is False
			assert stack.is_empty() is True
			assert stack.is_full() is False
			assert str(lowercase__						) == "[]"

			try:
						lowerCAmelCase_       :Optional[Any]     	=    stack.pop()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			try:
						lowerCAmelCase_       :Optional[int]     	=    stack.peek()
						raise AssertionError  # This should not happen
			except StackUnderflowError:
						assert True  # This should happen

			for i in range(1_0						):
						assert stack.size() == i
						stack.push(lowercase__						)

			assert bool(lowercase__						)
			assert not stack.is_empty()
			assert stack.is_full()
			assert str(lowercase__						) == str(list(range(1_0						)						)						)
			assert stack.pop() == 9
			assert stack.peek() == 8

			stack.push(1_0_0						)
			assert str(lowercase__						) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_0_0]						)

			try:
						stack.push(2_0_0						)
						raise AssertionError  # This should not happen
			except StackOverflowError:
						assert True  # This should happen

			assert not stack.is_empty()
			assert stack.size() == 1_0

			assert 5 in stack
			assert 5_5 not in stack


if __name__ == "__main__":
				test_stack()






