





"""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 	__snake_case      (	nn.Module  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase=1.0						):



     '''simple docstring'''




     super().__init__()

     __A					:		Optional[Any]						=  cut_size
     __A					:		Optional[int]						=  cut_power







 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




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







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						Tuple		)     ->Tuple:



    '''simple docstring'''







    __A					:		Tuple						=  F.normalize(snake_case_       ,dim=-1		)
    __A					:		Dict						=  F.normalize(snake_case_       ,dim=-1		)
    return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)







def 	__lowercase  (     snake_case_						:						Optional[Any]       ,snake_case_						:						Optional[Any]		)     ->Union[str, Any]:



    '''simple docstring'''







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






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



     '''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					:		Dict						=  (
         feature_extractor.size
         if isinstance(feature_extractor.size      ,		__lowerCamelCase						)
         else feature_extractor.size['''shortest_edge''']
     )
     __A					:		Optional[int]						=  MakeCutouts(self.cut_out_size						)

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

 def       UpperCamelCase__(					self      ,		__lowerCamelCase = "auto"						):



     '''simple docstring'''




     if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         __A					:		Any						=  self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     self.enable_attention_slicing(__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     set_requires_grad(self.vae      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     set_requires_grad(self.vae      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     set_requires_grad(self.unet      ,		__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     set_requires_grad(self.unet      ,		__lowerCamelCase						)

 @torch.enable_grad()
 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase=True      ,		):



     '''simple docstring'''




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

     __A					:		str						=  self.scheduler.scale_model_input(__lowerCamelCase      ,		__lowerCamelCase						)

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

     if isinstance(self.scheduler      ,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
         __A					:		Dict						=  self.scheduler.alphas_cumprod[timestep]
         __A					:		List[Any]						=  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					:		Optional[int]						=  (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					:		Union[str, Any]						=  self.scheduler.sigmas[index]
         __A					:		List[str]						=  latents - sigma * noise_pred
     else:
         raise ValueError(F"""scheduler type {type(self.scheduler						)} not supported"""						)

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

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

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

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

     __A					:		int						=  -torch.autograd.grad(__lowerCamelCase      ,		__lowerCamelCase						)[0]

     if isinstance(self.scheduler      ,		__lowerCamelCase						):
         __A					:		int						=  latents.detach() + grads * (sigma**2)
         __A					:		str						=  noise_pred_original
     else:
         __A					:		Optional[Any]						=  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      ,		):



     '''simple docstring'''




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

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

     # set timesteps
     __A					:		Any						=  '''offset''' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
     __A					:		Optional[Any]						=  {}
     if accepts_offset:
         __A					:		int						=  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					:		int						=  self.scheduler.timesteps.to(self.device						)

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

     # check if the scheduler accepts generator
     __A					:		Optional[Any]						=  '''generator''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
     if accepts_generator:
         __A					:		Dict						=  generator

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

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

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

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

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

     # scale and decode the image latents with vae
     __A					:		List[str]						=  1 / self.vae.config.scaling_factor * latents
     __A					:		List[str]						=  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					:		str						=  self.numpy_to_pil(__lowerCamelCase						)

     if not return_dict:
         return (image, None)

     return StableDiffusionPipelineOutput(images=__lowerCamelCase      ,		nsfw_content_detected=__lowerCamelCase						)











"""simple docstring"""



def 	__lowercase  (     snake_case_						:						list		)     ->list:



    '''simple docstring'''







    for insert_index, insert_value in enumerate(collection[1:]		):
        __A					:		Any						=  insert_index
        while insert_index >= 0 and insert_value < collection[insert_index]:
            __A					:		List[Any]						=  collection[insert_index]
            insert_index -= 1
        if insert_index != temp_index:
            __A					:		str						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(f'''{insertion_sort(unsorted) = }''')




