



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 	snake_case_			(SCREAMING_SNAKE_CASE__	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase = 0.93  ,lowercase = True  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		mixing_coeff
             UpperCAmelCase_	:						Tuple					   =		leapfrog_steps

             super().__init__()
             self.register_modules(
                 vae=_UpperCAmelCase  ,text_encoder=_UpperCAmelCase  ,tokenizer=_UpperCAmelCase  ,unet=_UpperCAmelCase  ,scheduler=_UpperCAmelCase  ,)

             UpperCAmelCase_	:						Tuple					   =		2 ** (len(self.vae.config.block_out_channels) - 1)
             UpperCAmelCase_	:						Dict					   =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)



       def 			A_ (  self  ,lowercase  ,lowercase = None  ,lowercase = False):

             """simple docstring"""





             UpperCAmelCase_	:						Dict					   =		self.tokenizer(
                 _UpperCAmelCase  ,padding="max_length"  ,max_length=self.tokenizer.model_max_length  ,truncation=_UpperCAmelCase  ,return_tensors="pt"  ,)

             UpperCAmelCase_	:						Any					   =		self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state

             UpperCAmelCase_	:						Tuple					   =		prompt_embeds.to(dtype=self.text_encoder.dtype  ,device=self.device)

             if do_classifier_free_guidance:
                   UpperCAmelCase_	:						int					   =		"" if negative_prompt is None else negative_prompt

                   UpperCAmelCase_	:						Tuple					   =		self.tokenizer(
                       _UpperCAmelCase  ,padding="max_length"  ,max_length=self.tokenizer.model_max_length  ,truncation=_UpperCAmelCase  ,return_tensors="pt"  ,)

                   UpperCAmelCase_	:						Dict					   =		self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state

                   UpperCAmelCase_	:						Optional[int]					   =		torch.cat([negative_prompt_embeds, prompt_embeds])

             return prompt_embeds



       def 			A_ (  self  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             UpperCAmelCase_	:						List[str]					   =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]



       def 			A_ (  self  ,lowercase  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[Any]					   =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             UpperCAmelCase_	:						str					   =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						Union[str, Any]					   =		int(_UpperCAmelCase)

             UpperCAmelCase_	:						Optional[int]					   =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             UpperCAmelCase_	:						str					   =		self._get_alpha_and_beta(_UpperCAmelCase)
             UpperCAmelCase_	:						List[Any]					   =		self._get_alpha_and_beta(_UpperCAmelCase)

             UpperCAmelCase_	:						Tuple					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
             UpperCAmelCase_	:						Dict					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             UpperCAmelCase_	:						Any					   =		(base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             UpperCAmelCase_	:						Union[str, Any]					   =		self._get_alpha_and_beta(_UpperCAmelCase)
             UpperCAmelCase_	:						Union[str, Any]					   =		self._get_alpha_and_beta(_UpperCAmelCase)

             UpperCAmelCase_	:						Any					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
             UpperCAmelCase_	:						int					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             UpperCAmelCase_	:						Tuple					   =		a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype)



       @torch.no_grad()
       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             UpperCAmelCase_	:						str					   =		1 / self.vae.config.scaling_factor * latents
             UpperCAmelCase_	:						Tuple					   =		self.vae.decode(_UpperCAmelCase).sample
             UpperCAmelCase_	:						Optional[Any]					   =		(image / 2 + 0.5).clamp(0  ,1)
             return image



       @torch.no_grad()
       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase  ,lowercase = None  ,):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		guidance_scale > 1.0

             UpperCAmelCase_	:						List[Any]					   =		image.to(device=self.device  ,dtype=text_embeds.dtype)
             UpperCAmelCase_	:						List[str]					   =		self.vae.encode(_UpperCAmelCase).latent_dist.sample(_UpperCAmelCase)

             UpperCAmelCase_	:						Tuple					   =		self.vae.config.scaling_factor * latent

             UpperCAmelCase_	:						Tuple					   =		[latent.clone(), latent.clone()]

             for i, t in tqdm(enumerate(_UpperCAmelCase)  ,total=len(_UpperCAmelCase)):
                   UpperCAmelCase_	:						str					   =		self.noise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1])

                   # j - model_input index, k - base index
                   for j in range(2):
                         UpperCAmelCase_	:						Optional[Any]					   =		j ^ 1

                         if self.leapfrog_steps:
                               if i % 2 == 0:
                                     UpperCAmelCase_	:						int					   =		j, k

                         UpperCAmelCase_	:						Dict					   =		coupled_latents[j]
                         UpperCAmelCase_	:						Dict					   =		coupled_latents[k]

                         UpperCAmelCase_	:						Optional[Any]					   =		torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                         UpperCAmelCase_	:						int					   =		self.unet(_UpperCAmelCase  ,_UpperCAmelCase  ,encoder_hidden_states=_UpperCAmelCase).sample

                         if do_classifier_free_guidance:
                               UpperCAmelCase_	:						Optional[Any]					   =		noise_pred.chunk(2)
                               UpperCAmelCase_	:						Tuple					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                         UpperCAmelCase_	:						Tuple					   =		self.noise_step(
                             base=_UpperCAmelCase  ,model_input=_UpperCAmelCase  ,model_output=_UpperCAmelCase  ,timestep=_UpperCAmelCase  ,)

                         UpperCAmelCase_	:						List[Any]					   =		model_input

             return coupled_latents



       @torch.no_grad()
       def __call__(  self  ,lowercase  ,lowercase  ,lowercase  ,lowercase = 3.0  ,lowercase = 50  ,lowercase = 0.8  ,lowercase = None  ,lowercase = None  ,lowercase = "pil"  ,):

             """simple docstring"""





             UpperCAmelCase_	:						List[str]					   =		guidance_scale > 1.0

             UpperCAmelCase_	:						Optional[Any]					   =		self.image_processor.preprocess(_UpperCAmelCase)

             UpperCAmelCase_	:						Tuple					   =		self._encode_prompt(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase)
             UpperCAmelCase_	:						List[Any]					   =		self._encode_prompt(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase)

             self.scheduler.set_timesteps(_UpperCAmelCase  ,self.device)

             UpperCAmelCase_	:						List[Any]					   =		num_inference_steps - int(num_inference_steps * strength)
             UpperCAmelCase_	:						Tuple					   =		self.scheduler.timesteps[t_limit:]
             UpperCAmelCase_	:						Union[str, Any]					   =		fwd_timesteps.flip(0)

             UpperCAmelCase_	:						List[str]					   =		self.prepare_latents(_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase  ,_UpperCAmelCase)

             for i, t in tqdm(enumerate(_UpperCAmelCase)  ,total=len(_UpperCAmelCase)):
                   # j - model_input index, k - base index
                   for k in range(2):
                         UpperCAmelCase_	:						List[Any]					   =		k ^ 1

                         if self.leapfrog_steps:
                               if i % 2 == 1:
                                     UpperCAmelCase_	:						Dict					   =		j, k

                         UpperCAmelCase_	:						List[Any]					   =		coupled_latents[j]
                         UpperCAmelCase_	:						Optional[int]					   =		coupled_latents[k]

                         UpperCAmelCase_	:						List[str]					   =		torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input

                         UpperCAmelCase_	:						str					   =		self.unet(_UpperCAmelCase  ,_UpperCAmelCase  ,encoder_hidden_states=_UpperCAmelCase).sample

                         if do_classifier_free_guidance:
                               UpperCAmelCase_	:						List[Any]					   =		noise_pred.chunk(2)
                               UpperCAmelCase_	:						Union[str, Any]					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                         UpperCAmelCase_	:						Union[str, Any]					   =		self.denoise_step(
                             base=_UpperCAmelCase  ,model_input=_UpperCAmelCase  ,model_output=_UpperCAmelCase  ,timestep=_UpperCAmelCase  ,)

                         UpperCAmelCase_	:						List[Any]					   =		model_input

                   UpperCAmelCase_	:						Dict					   =		self.denoise_mixing_layer(x=coupled_latents[0]  ,y=coupled_latents[1])

             # either one is fine
             UpperCAmelCase_	:						List[Any]					   =		coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                   UpperCAmelCase_	:						List[str]					   =		(
                       F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
                       "`pil`, `np`, `pt`, `latent`"
                   )
                   deprecate("Unsupported output_type"  ,"1.0.0"  ,_UpperCAmelCase  ,standard_warn=_UpperCAmelCase)
                   UpperCAmelCase_	:						int					   =		"np"

             if output_type == "latent":
                   UpperCAmelCase_	:						str					   =		final_latent
             else:
                   UpperCAmelCase_	:						Optional[Any]					   =		self.decode_latents(_UpperCAmelCase)
                   UpperCAmelCase_	:						Optional[int]					   =		self.image_processor.postprocess(_UpperCAmelCase  ,output_type=_UpperCAmelCase)

             return image






'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):

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


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

				testmod()

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



