
'''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 			__A					(			nn.Module      ):
 def __init__( self      :     int					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     List[str]=1.0							):



   """simple docstring"""





   super().__init__()

   __A		:  Optional[Any]       				=		cut_size
   __A		:  List[str]       				=		cut_power






 def 		lowercase_( self      :     str					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  int       				=		pixel_values.shape[2:4]
   __A		:  Any       				=		min(lowerCAmelCase__					, lowerCAmelCase__							)
   __A		:  str       				=		min(lowerCAmelCase__					, lowerCAmelCase__					, self.cut_size							)
   __A		:  Union[str, Any]       				=		[]
   for _ in range(lowerCAmelCase__							):
     __A		:  Tuple       				=		int(torch.rand([]							) ** self.cut_power * (max_size - min_size) + min_size							)
     __A		:  Union[str, Any]       				=		torch.randint(0					, sideX - size + 1					, ()							)
     __A		:  Any       				=		torch.randint(0					, sideY - size + 1					, ()							)
     __A		:  List[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_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]			)					->							Union[str, Any]:





  """simple docstring"""

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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]      ,  __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  for param in model.parameters():
    __A		:  Any       				=		value







class 			__A					(			SCREAMING_SNAKE_CASE__      ):
 def __init__( self      :     Any					, lowerCamelCase      :     Dict					, lowerCamelCase      :     str					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[Any]					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Dict					, ):



   """simple docstring"""





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

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

   set_requires_grad(self.text_encoder					, lowerCAmelCase__							)
   set_requires_grad(self.clip_model					, lowerCAmelCase__							)
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     int = "auto"							):



   """simple docstring"""





   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     __A		:  List[str]       				=		self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(lowerCAmelCase__							)
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   self.enable_attention_slicing(lowerCAmelCase__							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   set_requires_grad(self.vae					, lowerCAmelCase__							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   set_requires_grad(self.vae					, lowerCAmelCase__							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   set_requires_grad(self.unet					, lowerCAmelCase__							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   set_requires_grad(self.unet					, lowerCAmelCase__							)
 @torch.enable_grad()
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     Dict					, lowerCamelCase      :     str					, lowerCamelCase      :     int					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Dict					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     int					, lowerCamelCase      :     str					, lowerCamelCase      :     Dict=True					, ):



   """simple docstring"""





   __A		:  Dict       				=		latents.detach().requires_grad_()

   __A		:  Optional[Any]       				=		self.scheduler.scale_model_input(lowerCAmelCase__					, lowerCAmelCase__							)

   # predict the noise residual
   __A		:  Dict       				=		self.unet(lowerCAmelCase__					, lowerCAmelCase__					, encoder_hidden_states=lowerCAmelCase__							).sample

   if isinstance(self.scheduler					, (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)							):
     __A		:  Optional[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		:  Tuple       				=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

   __A		:  List[str]       				=		-torch.autograd.grad(lowerCAmelCase__					, lowerCAmelCase__							)[0]

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






 @torch.no_grad()
 def __call__( self      :     Union[str, Any]					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Dict = 5_12					, lowerCamelCase      :     Any = 5_12					, lowerCamelCase      :     Optional[Any] = 50					, lowerCamelCase      :     List[str] = 7.5					, lowerCamelCase      :     Dict = 1					, lowerCamelCase      :     Union[str, Any] = 0.0					, lowerCamelCase      :     Tuple = 1_00					, lowerCamelCase      :     List[Any] = None					, lowerCamelCase      :     Dict = 4					, lowerCamelCase      :     List[str] = True					, lowerCamelCase      :     Tuple = None					, lowerCamelCase      :     Any = None					, lowerCamelCase      :     List[str] = "pil"					, lowerCamelCase      :     Optional[int] = True					, ):



   """simple docstring"""





   if isinstance(lowerCAmelCase__					, lowerCAmelCase__							):
     __A		:  str       				=		1
   elif isinstance(lowerCAmelCase__					, lowerCAmelCase__							):
     __A		:  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
   __A		:  Dict       				=		self.tokenizer(
       lowerCAmelCase__					, padding="""max_length"""					, max_length=self.tokenizer.model_max_length					, truncation=lowerCAmelCase__					, return_tensors="""pt"""					, )
   __A		:  List[str]       				=		self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
   # duplicate text embeddings for each generation per prompt
   __A		:  str       				=		text_embeddings.repeat_interleave(lowerCAmelCase__					, dim=0							)

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

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

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

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

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

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

     # perform classifier free guidance
     if do_classifier_free_guidance:
       __A		:  List[Any]       				=		noise_pred.chunk(2							)
       __A		:  List[Any]       				=		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		:  Optional[Any]       				=		self.cond_fn(
           lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, )

     # compute the previous noisy sample x_t -> x_t-1
     __A		:  Tuple       				=		self.scheduler.step(lowerCAmelCase__					, lowerCAmelCase__					, lowerCAmelCase__					, **lowerCAmelCase__							).prev_sample

   # scale and decode the image latents with vae
   __A		:  Tuple       				=		1 / self.vae.config.scaling_factor * latents
   __A		:  Union[str, Any]       				=		self.vae.decode(lowerCAmelCase__							).sample

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

   if output_type == "pil":
     __A		:  Dict       				=		self.numpy_to_pil(lowerCAmelCase__							)

   if not return_dict:
     return (image, None)

   return StableDiffusionPipelineOutput(images=lowerCAmelCase__					, nsfw_content_detected=lowerCAmelCase__							)









"""simple docstring"""
import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Union[str, Any]						)    ->       int:
 return EnvironmentCommand()







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  parser.add_parser("env"					,     help="Print relevant system environment info."   )
  download_parser.set_defaults(func=lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Any					      =  {
      "`datasets` version": version,
      "Platform": platform.platform(),
      "Python version": platform.python_version(),
      "Huggingface_hub version": huggingface_hub.__version__,
      "PyArrow version": pyarrow.__version__,
      "Pandas version": pandas.__version__,
  }

  print("\nCopy-and-paste the text below in your GitHub issue.\n"   )
  print(self.format_dict(lowerCAmelCase__   )   )

  return info






 @staticmethod
 def lowercase_      (lowerCAmelCase__   ):

  '''simple docstring'''







  return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]   ) + "\n"



