code: 
  
  
  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 			a      (      nn.Module   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=1.0							)->			List[str]:
  						'''simple docstring'''
  
  
  						super().__init__()
  
  						A__     :							Union[str, Any]   				=cut_size
  						A__     :							Optional[int]   				=cut_power
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__				,  A__     :							Any   				=pixel_values.shape[2:4]
  						A__     :							Optional[Any]   				=min(a_	,     a_							)
  						A__     :							Dict   				=min(a_	,     a_	,     self.cut_size							)
  						A__     :							int   				=[]
  						for _ in range(a_							):
  										A__     :							str   				=int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
  										A__     :							int   				=torch.randint(0	,     sideX - size + 1	,     ()							)
  										A__     :							Optional[int]   				=torch.randint(0	,     sideY - size + 1	,     ()							)
  										A__     :							Optional[int]   				=pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
  										cutouts.append(F.adaptive_avg_poolad(a_	,     self.cut_size							)							)
  						return torch.cat(a_							)
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Union[str, Any]:
  				A__     :							str   				=F.normalize(UpperCamelCase__,	dim=-1	)
  				A__     :							Dict   				=F.normalize(UpperCamelCase__,	dim=-1	)
  				return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Dict:
  
  
  
  
  
  
  				for param in model.parameters():
  								A__     :							Tuple   				=value
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     )->			Optional[Any]:
  						'''simple docstring'''
  
  
  						super().__init__()
  						self.register_modules(
  						    vae=a_	,     text_encoder=a_	,     clip_model=a_	,     tokenizer=a_	,     unet=a_	,     scheduler=a_	,     feature_extractor=a_	,     )
  
  						A__     :							Dict   				=transforms.Normalize(mean=feature_extractor.image_mean	,     std=feature_extractor.image_std							)
  						A__     :							str   				=(
  						    feature_extractor.size
  						    if isinstance(feature_extractor.size	,     a_							)
  						    else feature_extractor.size['''shortest_edge''']
  						)
  						A__     :							Optional[Any]   				=MakeCutouts(self.cut_out_size							)
  
  						set_requires_grad(self.text_encoder	,     a_							)
  						set_requires_grad(self.clip_model	,     a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase = "auto"							)->			str:
  						'''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(a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						self.enable_attention_slicing(a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						set_requires_grad(self.vae	,     a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						set_requires_grad(self.vae	,     a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						set_requires_grad(self.unet	,     a_							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						set_requires_grad(self.unet	,     a_							)
  
  
  
  
  
  
  		@torch.enable_grad()
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=True	,     )->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=latents.detach().requires_grad_()
  
  						A__     :							int   				=self.scheduler.scale_model_input(a_	,     a_							)
  
  						# predict the noise residual
  						A__     :							Union[str, Any]   				=self.unet(a_	,     a_	,     encoder_hidden_states=a_							).sample
  
  						if isinstance(self.scheduler	,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
  										A__     :							Union[str, Any]   				=self.scheduler.alphas_cumprod[timestep]
  										A__     :							Optional[int]   				=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__     :							Union[str, Any]   				=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
  
  										A__     :							Dict   				=torch.sqrt(a_							)
  										A__     :							List[str]   				=pred_original_sample * (fac) + latents * (1 - fac)
  						elif isinstance(self.scheduler	,     a_							):
  										A__     :							str   				=self.scheduler.sigmas[index]
  										A__     :							str   				=latents - sigma * noise_pred
  						else:
  										raise ValueError(F'scheduler type {type(self.scheduler							)} not supported'							)
  
  						A__     :							Optional[int]   				=1 / self.vae.config.scaling_factor * sample
  						A__     :							Dict   				=self.vae.decode(a_							).sample
  						A__     :							Union[str, Any]   				=(image / 2 + 0.5).clamp(0	,     1							)
  
  						if use_cutouts:
  										A__     :							str   				=self.make_cutouts(a_	,     a_							)
  						else:
  										A__     :							Dict   				=transforms.Resize(self.cut_out_size							)(a_							)
  						A__     :							Union[str, Any]   				=self.normalize(a_							).to(latents.dtype							)
  
  						A__     :							Optional[Any]   				=self.clip_model.get_image_features(a_							)
  						A__     :							Dict   				=image_embeddings_clip / image_embeddings_clip.norm(p=2	,     dim=-1	,     keepdim=a_							)
  
  						if use_cutouts:
  										A__     :							Union[str, Any]   				=spherical_dist_loss(a_	,     a_							)
  										A__     :							List[str]   				=dists.view([num_cutouts, sample.shape[0], -1]							)
  										A__     :							int   				=dists.sum(2							).mean(0							).sum() * clip_guidance_scale
  						else:
  										A__     :							int   				=spherical_dist_loss(a_	,     a_							).mean() * clip_guidance_scale
  
  						A__     :							Union[str, Any]   				=-torch.autograd.grad(a_	,     a_							)[0]
  
  						if isinstance(self.scheduler	,     a_							):
  										A__     :							Any   				=latents.detach() + grads * (sigma**2)
  										A__     :							Dict   				=noise_pred_original
  						else:
  										A__     :							Optional[Any]   				=noise_pred_original - torch.sqrt(a_							) * grads
  						return noise_pred, latents
  
  
  
  
  
  
  		@torch.no_grad()
  		def __call__(  self	,     __UpperCamelCase	,     __UpperCamelCase = 5_12	,     __UpperCamelCase = 5_12	,     __UpperCamelCase = 50	,     __UpperCamelCase = 7.5	,     __UpperCamelCase = 1	,     __UpperCamelCase = 0.0	,     __UpperCamelCase = 1_00	,     __UpperCamelCase = None	,     __UpperCamelCase = 4	,     __UpperCamelCase = True	,     __UpperCamelCase = None	,     __UpperCamelCase = None	,     __UpperCamelCase = "pil"	,     __UpperCamelCase = True	,     )->			str:
  						'''simple docstring'''
  
  
  						if isinstance(a_	,     a_							):
  										A__     :							List[str]   				=1
  						elif isinstance(a_	,     a_							):
  										A__     :							Union[str, 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
  						A__     :							Optional[int]   				=self.tokenizer(
  						    a_	,     padding='''max_length'''	,     max_length=self.tokenizer.model_max_length	,     truncation=a_	,     return_tensors='''pt'''	,     )
  						A__     :							Optional[int]   				=self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
  						# duplicate text embeddings for each generation per prompt
  						A__     :							Optional[int]   				=text_embeddings.repeat_interleave(a_	,     dim=0							)
  
  						if clip_guidance_scale > 0:
  										if clip_prompt is not None:
  														A__     :							str   				=self.tokenizer(
  														    a_	,     padding='''max_length'''	,     max_length=self.tokenizer.model_max_length	,     truncation=a_	,     return_tensors='''pt'''	,     ).input_ids.to(self.device							)
  										else:
  														A__     :							Tuple   				=text_input.input_ids.to(self.device							)
  										A__     :							Tuple   				=self.clip_model.get_text_features(a_							)
  										A__     :							int   				=text_embeddings_clip / text_embeddings_clip.norm(p=2	,     dim=-1	,     keepdim=a_							)
  										# duplicate text embeddings clip for each generation per prompt
  										A__     :							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.
  						A__     :							Dict   				=guidance_scale > 1.0
  						# get unconditional embeddings for classifier free guidance
  						if do_classifier_free_guidance:
  										A__     :							int   				=text_input.input_ids.shape[-1]
  										A__     :							str   				=self.tokenizer(['''''']	,     padding='''max_length'''	,     max_length=a_	,     return_tensors='''pt'''							)
  										A__     :							Dict   				=self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
  										# duplicate unconditional embeddings for each generation per prompt
  										A__     :							List[Any]   				=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
  										A__     :							List[str]   				=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__     :							str   				=(batch_size * num_images_per_prompt, 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(a_	,     generator=a_	,     device='''cpu'''	,     dtype=a_							).to(
  														    self.device							)
  										else:
  														A__     :							List[str]   				=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}'							)
  										A__     :							Any   				=latents.to(self.device							)
  
  						# set timesteps
  						A__     :							Dict   				='''offset''' in set(inspect.signature(self.scheduler.set_timesteps							).parameters.keys()							)
  						A__     :							List[str]   				={}
  						if accepts_offset:
  										A__     :							Optional[int]   				=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
  						A__     :							Optional[Any]   				=self.scheduler.timesteps.to(self.device							)
  
  						# scale the initial noise by the standard deviation required by the scheduler
  						A__     :							List[Any]   				=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__     :							Optional[Any]   				='''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
  						A__     :							List[str]   				={}
  						if accepts_eta:
  										A__     :							str   				=eta
  
  						# check if the scheduler accepts generator
  						A__     :							int   				='''generator''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
  						if accepts_generator:
  										A__     :							Union[str, Any]   				=generator
  
  						for i, t in enumerate(self.progress_bar(a_							)							):
  										# expand the latents if we are doing classifier free guidance
  										A__     :							Any   				=torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
  										A__     :							Union[str, Any]   				=self.scheduler.scale_model_input(a_	,     a_							)
  
  										# predict the noise residual
  										A__     :							int   				=self.unet(a_	,     a_	,     encoder_hidden_states=a_							).sample
  
  										# perform classifier free guidance
  										if do_classifier_free_guidance:
  														A__				,  A__     :							Tuple   				=noise_pred.chunk(2							)
  														A__     :							Dict   				=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  										# perform clip guidance
  										if clip_guidance_scale > 0:
  														A__     :							List[str]   				=(
  														    text_embeddings.chunk(2							)[1] if do_classifier_free_guidance else text_embeddings
  														)
  														A__				,  A__     :							List[str]   				=self.cond_fn(
  														    a_	,     a_	,     a_	,     a_	,     a_	,     a_	,     a_	,     a_	,     a_	,     )
  
  										# compute the previous noisy sample x_t -> x_t-1
  										A__     :							str   				=self.scheduler.step(a_	,     a_	,     a_	,     **a_							).prev_sample
  
  						# scale and decode the image latents with vae
  						A__     :							List[Any]   				=1 / self.vae.config.scaling_factor * latents
  						A__     :							Optional[int]   				=self.vae.decode(a_							).sample
  
  						A__     :							Optional[int]   				=(image / 2 + 0.5).clamp(0	,     1							)
  						A__     :							Any   				=image.cpu().permute(0	,     2	,     3	,     1							).numpy()
  
  						if output_type == "pil":
  										A__     :							int   				=self.numpy_to_pil(a_							)
  
  						if not return_dict:
  										return (image, None)
  
  						return StableDiffusionPipelineOutput(images=a_	,     nsfw_content_detected=a_							)
  
  
  
  
style_context: 
  
  
  import argparse
  import copy
  import json
  import multiprocessing
  import os
  import time
  
  from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
  from huggingface_hub import ModelFilter, hf_api
  
  import transformers
  from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
  from transformers.image_processing_utils import BaseImageProcessor
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Tuple:
  				A__     :							List[Any]   				=set()
  				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
  				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
  								A__     :							Optional[int]   				=getattr(transformers.models.auto,	snake_case_,	snake_case_	)
  								if module is None:
  												continue
  								# all mappings in a single auto modeling file
  								A__     :							Optional[int]   				=[
  								    x
  								    for x in dir(snake_case_	)
  								    if x.endswith('''_MAPPING_NAMES'''	)
  								    and (x.startswith('''MODEL_'''	) or x.startswith('''TF_MODEL_'''	) or x.startswith('''FLAX_MODEL_'''	))
  								]
  								for name in mapping_names:
  												A__     :							Any   				=getattr(snake_case_,	snake_case_	)
  												if mapping is not None:
  																for v in mapping.values():
  																				if isinstance(snake_case_,	(list, tuple)	):
  																								model_names.update(snake_case_	)
  																				elif isinstance(snake_case_,	snake_case_	):
  																								model_names.add(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[Any]:
  				# All model names defined in auto mappings
  				A__     :							int   				=set(get_all_model_names()	)
  
  				with open('''tests/utils/tiny_model_summary.json'''	) as fp:
  								A__     :							Union[str, Any]   				=json.load(snake_case_	)
  				A__     :							int   				=set()
  				for model_base_name in tiny_model_info:
  								tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']	)
  
  				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
  				A__     :							Optional[Any]   				=model_names.difference(snake_case_	)
  				for model_name in copy.copy(snake_case_	):
  								if not model_name.startswith('''TF'''	) and f'TF{model_name}' in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  								elif model_name.startswith('''TF'''	) and model_name[2:] in not_on_hub:
  												tiny_models_names.remove(snake_case_	)
  
  				return sorted(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
  				A__     :							Dict   				=COMPOSITE_MODELS.values()
  
  				# All tiny model base names on Hub
  				A__     :							Union[str, Any]   				=get_all_model_names()
  				A__     :							Optional[Any]   				=hf_api.list_models(
  				    filter=ModelFilter(
  				        author='''hf-internal-testing''',	)	)
  				A__     :							str   				=set()
  				for x in models:
  								A__     :							List[Any]   				=x.modelId
  								A__				,  A__     :							List[str]   				=model.split('''/'''	)
  								if not model.startswith('''tiny-random-'''	):
  												continue
  								A__     :							str   				=model.replace('''tiny-random-''',	''''''	)
  								if not model[0].isupper():
  												continue
  								if model not in model_names and model not in special_models:
  												continue
  								_models.add(snake_case_	)
  
  				A__     :							Dict   				=sorted(_models	)
  				# All tiny model names on Hub
  				A__     :							Optional[int]   				={}
  				for model in models:
  								A__     :							Optional[int]   				=f'hf-internal-testing/tiny-random-{model}'
  								A__     :							str   				=model.split('''-'''	)[0]
  								try:
  												A__     :							int   				=hf_api.repo_info(snake_case_	)
  												A__     :							Optional[int]   				={
  												    '''tokenizer_classes''': set(),
  												    '''processor_classes''': set(),
  												    '''model_classes''': set(),
  												    '''sha''': repo_info.sha,
  												}
  								except Exception:
  												continue
  								try:
  												time.sleep(1	)
  												A__     :							List[str]   				=AutoTokenizer.from_pretrained(snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoTokenizer.from_pretrained(snake_case_,	use_fast=snake_case_	)
  												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=AutoImageProcessor.from_pretrained(snake_case_	)
  												content["processor_classes"].add(img_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Optional[int]   				=AutoFeatureExtractor.from_pretrained(snake_case_	)
  												if not isinstance(snake_case_,	snake_case_	):
  																content["processor_classes"].add(feat_p.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							Tuple   				=getattr(snake_case_,	snake_case_	)
  												A__     :							Tuple   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  								try:
  												time.sleep(1	)
  												A__     :							int   				=getattr(snake_case_,	f'TF{model}'	)
  												A__     :							Union[str, Any]   				=model_class.from_pretrained(snake_case_	)
  												content["model_classes"].add(m.__class__.__name__	)
  								except Exception:
  												pass
  
  								A__     :							List[str]   				=sorted(content['''tokenizer_classes''']	)
  								A__     :							Tuple   				=sorted(content['''processor_classes''']	)
  								A__     :							Optional[Any]   				=sorted(content['''model_classes''']	)
  
  								A__     :							List[Any]   				=content
  								with open(os.path.join(snake_case_,	'''hub_tiny_model_summary.json'''	),	'''w'''	) as fp:
  												json.dump(snake_case_,	snake_case_,	ensure_ascii=snake_case_,	indent=4	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Any				   =			argparse.ArgumentParser()
  						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
  						__lowerCamelCase       :       int				   =			parser.parse_args()
  
  						# This has to be `spawn` to avoid hanging forever!
  						multiprocessing.set_start_method("spawn")
  
  						__lowerCamelCase       :       Any				   =			"tiny_models"
  						__lowerCamelCase       :       Optional[int]				   =			True
  						__lowerCamelCase       :       int				   =			None
  						__lowerCamelCase       :       str				   =			get_tiny_model_names_from_repo()
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       List[Any]				   =			True
  						__lowerCamelCase       :       Optional[Any]				   =			"hf-internal-testing"
  
  						create_tiny_models(
  						    output_path,
  						    all,
  						    model_types,
  						    models_to_skip,
  						    no_check,
  						    upload,
  						    organization,
  						    token=os.environ.get("TOKEN", None),
  						    num_workers=args.num_workers,
  						)
  
  
  
  