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 		_snake_case      (			nn.Module				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a	,    a=1.0					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
  
     _A           =    cut_size
     _A           =    cut_power
  
  
  
   def lowercase_		(						self	,    a	,    a					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A    ,   _A           =    pixel_values.shape[2:4]
     _A           =    min(a	,    a					)
     _A           =    min(a	,    a	,    self.cut_size					)
     _A           =    []
     for _ in range(a					):
       _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(a	,    self.cut_size					)					)
     return torch.cat(a					)
  
  
  
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Optional[Any]:
    _A           =    F.normalize(__snake_case						,				dim=-1							)
    _A           =    F.normalize(__snake_case						,				dim=-1							)
    return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Union[str, Any]:
    for param in model.parameters():
      _A           =    value
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    )	->							Tuple:
  
  
  
  
     """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           =    transforms.Normalize(mean=feature_extractor.image_mean	,    std=feature_extractor.image_std					)
     _A           =    (
         feature_extractor.size
         if isinstance(feature_extractor.size	,    a					)
         else feature_extractor.size['''shortest_edge''']
     )
     _A           =    MakeCutouts(self.cut_out_size					)
  
     set_requires_grad(self.text_encoder	,    a					)
     set_requires_grad(self.clip_model	,    a					)
   def lowercase_		(						self	,    a = "auto"					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     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(a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     self.enable_attention_slicing(a					)
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     set_requires_grad(self.vae	,    a					)
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     set_requires_grad(self.vae	,    a					)
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     set_requires_grad(self.unet	,    a					)
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     set_requires_grad(self.unet	,    a					)
   @torch.enable_grad()
   def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a=True	,    )	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    latents.detach().requires_grad_()
  
     _A           =    self.scheduler.scale_model_input(a	,    a					)
  
     # predict the noise residual
     _A           =    self.unet(a	,    a	,    encoder_hidden_states=a					).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(a					)
       _A           =    pred_original_sample * (fac) + latents * (1 - fac)
     elif isinstance(self.scheduler	,    a					):
       _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(a					).sample
     _A           =    (image / 2 + 0.5).clamp(0	,    1					)
  
     if use_cutouts:
       _A           =    self.make_cutouts(a	,    a					)
     else:
       _A           =    transforms.Resize(self.cut_out_size					)(a					)
     _A           =    self.normalize(a					).to(latents.dtype					)
  
     _A           =    self.clip_model.get_image_features(a					)
     _A           =    image_embeddings_clip / image_embeddings_clip.norm(p=2	,    dim=-1	,    keepdim=a					)
  
     if use_cutouts:
       _A           =    spherical_dist_loss(a	,    a					)
       _A           =    dists.view([num_cutouts, sample.shape[0], -1]					)
       _A           =    dists.sum(2					).mean(0					).sum() * clip_guidance_scale
     else:
       _A           =    spherical_dist_loss(a	,    a					).mean() * clip_guidance_scale
  
     _A           =    -torch.autograd.grad(a	,    a					)[0]
  
     if isinstance(self.scheduler	,    a					):
       _A           =    latents.detach() + grads * (sigma**2)
       _A           =    noise_pred_original
     else:
       _A           =    noise_pred_original - torch.sqrt(a					) * grads
     return noise_pred, latents
  
  
  
   @torch.no_grad()
   def __call__(						self	,    a	,    a = 5_1_2	,    a = 5_1_2	,    a = 5_0	,    a = 7.5	,    a = 1	,    a = 0.0	,    a = 1_0_0	,    a = None	,    a = 4	,    a = True	,    a = None	,    a = None	,    a = "pil"	,    a = True	,    )	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if isinstance(a	,    a					):
       _A           =    1
     elif isinstance(a	,    a					):
       _A           =    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           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=a	,    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(a	,    dim=0					)
  
     if clip_guidance_scale > 0:
       if clip_prompt is not None:
         _A           =    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           =    text_input.input_ids.to(self.device					)
       _A           =    self.clip_model.get_text_features(a					)
       _A           =    text_embeddings_clip / text_embeddings_clip.norm(p=2	,    dim=-1	,    keepdim=a					)
       # duplicate text embeddings clip for each generation per prompt
       _A           =    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           =    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=a	,    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(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           =    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(a	,    generator=a	,    device='''cpu'''	,    dtype=a					).to(
             self.device					)
       else:
         _A           =    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           =    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(a	,    **a					)
  
     # 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(a					)					):
       # 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(a	,    a					)
  
       # predict the noise residual
       _A           =    self.unet(a	,    a	,    encoder_hidden_states=a					).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(
             a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    )
  
       # compute the previous noisy sample x_t -> x_t-1
       _A           =    self.scheduler.step(a	,    a	,    a	,    **a					).prev_sample
  
     # scale and decode the image latents with vae
     _A           =    1 / self.vae.config.scaling_factor * latents
     _A           =    self.vae.decode(a					).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(a					)
  
     if not return_dict:
       return (image, None)
  
     return StableDiffusionPipelineOutput(images=a	,    nsfw_content_detected=a					)
style_context: 
  
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{}
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt_decoder'''
   def __init__(						self	,    a=0	,    a=0					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
     _A           =    vocab_size
     _A           =    bos_token_id
  
  
  
  
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			'''fsmt'''
   lowerCamelCase_          =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
   def __init__(						self	,    a=["en", "de"]	,    a=4_2_0_2_4	,    a=4_2_0_2_4	,    a="relu"	,    a=1_0_2_4	,    a=2_0_0	,    a=1_0_2_4	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=4_0_9_6	,    a=1_2	,    a=1_6	,    a=0.0	,    a=0.0	,    a=0.1	,    a=0.0	,    a=0.02	,    a=2	,    a=True	,    a=True	,    a=False	,    a=5	,    a=1.0	,    a=False	,    a=True	,    a=1	,    a=0	,    a=2	,    a=2	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    langs
     _A           =    src_vocab_size
     _A           =    tgt_vocab_size
     _A           =    d_model  # encoder_embed_dim and decoder_embed_dim
  
     _A           =    encoder_ffn_dim
     _A           =    _A           =    encoder_layers
     _A           =    encoder_attention_heads
     _A           =    encoder_layerdrop
     _A           =    decoder_layerdrop
     _A           =    decoder_ffn_dim
     _A           =    decoder_layers
     _A           =    decoder_attention_heads
     _A           =    max_position_embeddings
     _A           =    init_std  # Normal(0, this parameter)
     _A           =    activation_function
  
     _A           =    DecoderConfig(vocab_size=a	,    bos_token_id=a					)
     if "decoder" in common_kwargs:
       del common_kwargs["decoder"]
  
     _A           =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
     # 3 Types of Dropout
     _A           =    attention_dropout
     _A           =    activation_dropout
     _A           =    dropout
  
     _A           =    use_cache
     super().__init__(
         pad_token_id=a	,    bos_token_id=a	,    eos_token_id=a	,    decoder_start_token_id=a	,    is_encoder_decoder=a	,    tie_word_embeddings=a	,    forced_eos_token_id=a	,    max_length=a	,    num_beams=a	,    length_penalty=a	,    early_stopping=a	,    **a	,    )
  
  
  
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    copy.deepcopy(self.__dict__					)
     _A           =    self.decoder.to_dict()
     _A           =    self.__class__.model_type
     return output