


"""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):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=1.0)   ->	List[str]:







								'''simple docstring'''




								super().__init__()

								snake_case__  :						int		=      cut_size
								snake_case__  :						Dict		=      cut_power







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




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



def 			A__		(					_UpperCAmelCase    :      Tuple				,    _UpperCAmelCase    :      str	)							->	Optional[int]:

	'''simple docstring'''


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



def 			A__		(					_UpperCAmelCase    :      Union[str, Any]				,    _UpperCAmelCase    :      Optional[int]	)							->	Dict:

	'''simple docstring'''


	for param in model.parameters():
		snake_case__  :						Dict		=      value



class 			SCREAMING_SNAKE_CASE_  (   __A):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	Optional[int]:







								'''simple docstring'''




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

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

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




							def        UpperCAmelCase (						self	,					lowerCamelCase__ = "auto")   ->	List[str]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								self.enable_attention_slicing(lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								set_requires_grad(self.vae	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								set_requires_grad(self.vae	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								set_requires_grad(self.unet	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								set_requires_grad(self.unet	,					lowerCamelCase__)




							@torch.enable_grad()
							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=True	,					)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[str]		=      latents.detach().requires_grad_()

								snake_case__  :						int		=      self.scheduler.scale_model_input(lowerCamelCase__	,					lowerCamelCase__)

								# predict the noise residual
								snake_case__  :						List[str]		=      self.unet(lowerCamelCase__	,					lowerCamelCase__	,					encoder_hidden_states=lowerCamelCase__).sample

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

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

								snake_case__  :						Optional[int]		=      1 / self.vae.config.scaling_factor * sample
								snake_case__  :						Tuple		=      self.vae.decode(lowerCamelCase__).sample
								snake_case__  :						Any		=      (image / 2 + 0.5).clamp(0	,					1)

								if use_cutouts:
									snake_case__  :						Tuple		=      self.make_cutouts(lowerCamelCase__	,					lowerCamelCase__)
								else:
									snake_case__  :						int		=      transforms.Resize(self.cut_out_size)(lowerCamelCase__)
								snake_case__  :						List[str]		=      self.normalize(lowerCamelCase__).to(latents.dtype)

								snake_case__  :						List[str]		=      self.clip_model.get_image_features(lowerCamelCase__)
								snake_case__  :						int		=      image_embeddings_clip / image_embeddings_clip.norm(p=2	,					dim=-1	,					keepdim=lowerCamelCase__)

								if use_cutouts:
									snake_case__  :						str		=      spherical_dist_loss(lowerCamelCase__	,					lowerCamelCase__)
									snake_case__  :						int		=      dists.view([num_cutouts, sample.shape[0], -1])
									snake_case__  :						str		=      dists.sum(2).mean(0).sum() * clip_guidance_scale
								else:
									snake_case__  :						Dict		=      spherical_dist_loss(lowerCamelCase__	,					lowerCamelCase__).mean() * clip_guidance_scale

								snake_case__  :						Any		=      -torch.autograd.grad(lowerCamelCase__	,					lowerCamelCase__)[0]

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







							@torch.no_grad()
							def __call__(						self	,					lowerCamelCase__	,					lowerCamelCase__ = 512	,					lowerCamelCase__ = 512	,					lowerCamelCase__ = 50	,					lowerCamelCase__ = 7.5	,					lowerCamelCase__ = 1	,					lowerCamelCase__ = 0.0	,					lowerCamelCase__ = 100	,					lowerCamelCase__ = None	,					lowerCamelCase__ = 4	,					lowerCamelCase__ = True	,					lowerCamelCase__ = None	,					lowerCamelCase__ = None	,					lowerCamelCase__ = "pil"	,					lowerCamelCase__ = True	,					)   ->	List[Any]:







								'''simple docstring'''




								if isinstance(lowerCamelCase__	,					lowerCamelCase__):
									snake_case__  :						Union[str, Any]		=      1
								elif isinstance(lowerCamelCase__	,					lowerCamelCase__):
									snake_case__  :						Dict		=      len(lowerCamelCase__)
								else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__)}""")

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

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

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

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

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

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

									# predict the noise residual
									snake_case__  :						str		=      self.unet(lowerCamelCase__	,					lowerCamelCase__	,					encoder_hidden_states=lowerCamelCase__).sample

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

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

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

								# scale and decode the image latents with vae
								snake_case__  :						Optional[Any]		=      1 / self.vae.config.scaling_factor * latents
								snake_case__  :						Optional[Any]		=      self.vae.decode(lowerCamelCase__).sample

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

								if output_type == "pil":
									snake_case__  :						str		=      self.numpy_to_pil(lowerCamelCase__)

								if not return_dict:
									return (image, None)

								return StableDiffusionPipelineOutput(images=lowerCamelCase__	,					nsfw_content_detected=lowerCamelCase__)











'''simple docstring'''
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]     ):



    """simple docstring"""




    __lowercase     							=		(boundary[1] - boundary[0]) / steps
    __lowercase     							=		boundary[0]
    __lowercase     							=		boundary[1]
    __lowercase     							=		make_points(a__       ,a__       ,a__     )
    __lowercase     							=		0.0
    y += (h / 3.0) * f(a__     )
    __lowercase     							=		2
    for i in x_i:
        y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a__     )
        cnt += 1
    y += (h / 3.0) * f(a__     )
    return y
def 							snake_case_    (  a__						:      Optional[Any]       ,a__						:      List[str]       ,a__						:      Optional[Any]     ):



    """simple docstring"""




    __lowercase     							=		a + h
    while x < (b - h):
        yield x
        __lowercase     							=		x + h
def 							snake_case_    (  a__						:      List[Any]     ):  # enter your function here



    """simple docstring"""




    __lowercase     							=		(x - 0) * (x - 0)
    return y
def 							snake_case_    (  ):



    """simple docstring"""




    __lowercase     							=		0.0  # Lower bound of integration
    __lowercase     							=		1.0  # Upper bound of integration
    __lowercase     							=		1_0.0  # define number of steps or resolution
    __lowercase     							=		[a, b]  # define boundary of integration
    __lowercase     							=		method_a(a__       ,a__     )
    print(f'y = {y}'     )


if __name__ == "__main__":
  main()




