
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 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case = 0.93		,   snake_case = True		,   ):
		lowercase					  =   mixing_coeff
		lowercase					  =   leapfrog_steps

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

		lowercase					  =   2 ** (len(self.vae.config.block_out_channels	) - 1)
		lowercase					  =   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case = None		,   snake_case = False	):
		lowercase					  =   self.tokenizer(
		    snake_case		,   padding='max_length'		,   max_length=self.tokenizer.model_max_length		,   truncation=snake_case		,   return_tensors='pt'		,   )

		lowercase					  =   self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

		lowercase					  =   prompt_embeds.to(dtype=self.text_encoder.dtype		,   device=self.device	)

		if do_classifier_free_guidance:
			lowercase					  =   '' if negative_prompt is None else negative_prompt

			lowercase					  =   self.tokenizer(
			    snake_case		,   padding='max_length'		,   max_length=self.tokenizer.model_max_length		,   truncation=snake_case		,   return_tensors='pt'		,   )

			lowercase					  =   self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

			lowercase					  =   torch.cat([negative_prompt_embeds, prompt_embeds]	)

		return prompt_embeds





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		lowercase					  =   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
		lowercase					  =   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

		return [x, y]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		lowercase					  =   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
		lowercase					  =   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

		return [x, y]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		# as self.alphas_cumprod is always in cpu
		lowercase					  =   int(snake_case	)

		lowercase					  =   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

		return alpha_prod, 1 - alpha_prod





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   ):
		lowercase					  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

		lowercase  , lowercase					  =   self._get_alpha_and_beta(snake_case	)
		lowercase  , lowercase					  =   self._get_alpha_and_beta(snake_case	)

		lowercase					  =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
		lowercase					  =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

		lowercase					  =   (base - b_t * model_output) / a_t

		return model_input, next_model_input.to(base.dtype	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   ):
		lowercase					  =   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

		lowercase  , lowercase					  =   self._get_alpha_and_beta(snake_case	)
		lowercase  , lowercase					  =   self._get_alpha_and_beta(snake_case	)

		lowercase					  =   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
		lowercase					  =   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
		lowercase					  =   a_t * base + b_t * model_output

		return model_input, next_model_input.to(base.dtype	)





	@torch.no_grad()
	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   1 / self.vae.config.scaling_factor * latents
		lowercase					  =   self.vae.decode(snake_case	).sample
		lowercase					  =   (image / 2 + 0.5).clamp(0		,   1	)
		return image





	@torch.no_grad()
	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   ):
		lowercase					  =   guidance_scale > 1.0

		lowercase					  =   image.to(device=self.device		,   dtype=text_embeds.dtype	)
		lowercase					  =   self.vae.encode(snake_case	).latent_dist.sample(snake_case	)

		lowercase					  =   self.vae.config.scaling_factor * latent

		lowercase					  =   [latent.clone(), latent.clone()]

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

			# j - model_input index, k - base index
			for j in range(2	):
				lowercase					  =   j ^ 1

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

				lowercase					  =   coupled_latents[j]
				lowercase					  =   coupled_latents[k]

				lowercase					  =   torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

				lowercase					  =   self.unet(snake_case		,   snake_case		,   encoder_hidden_states=snake_case	).sample

				if do_classifier_free_guidance:
					lowercase  , lowercase					  =   noise_pred.chunk(2	)
					lowercase					  =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				lowercase  , lowercase					  =   self.noise_step(
				    base=snake_case		,   model_input=snake_case		,   model_output=snake_case		,   timestep=snake_case		,   )

				lowercase					  =   model_input

		return coupled_latents





	@torch.no_grad()
	def __call__(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case = 3.0		,   snake_case = 50		,   snake_case = 0.8		,   snake_case = None		,   snake_case = None		,   snake_case = "pil"		,   ):
		lowercase					  =   guidance_scale > 1.0

		lowercase					  =   self.image_processor.preprocess(snake_case	)

		lowercase					  =   self._encode_prompt(snake_case		,   snake_case		,   snake_case	)
		lowercase					  =   self._encode_prompt(snake_case		,   snake_case		,   snake_case	)

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

		lowercase					  =   num_inference_steps - int(num_inference_steps * strength	)
		lowercase					  =   self.scheduler.timesteps[t_limit:]
		lowercase					  =   fwd_timesteps.flip(0	)

		lowercase					  =   self.prepare_latents(snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	)

		for i, t in tqdm(enumerate(snake_case	)		,   total=len(snake_case	)	):
			# j - model_input index, k - base index
			for k in range(2	):
				lowercase					  =   k ^ 1

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

				lowercase					  =   coupled_latents[j]
				lowercase					  =   coupled_latents[k]

				lowercase					  =   torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

				lowercase					  =   self.unet(snake_case		,   snake_case		,   encoder_hidden_states=snake_case	).sample

				if do_classifier_free_guidance:
					lowercase  , lowercase					  =   noise_pred.chunk(2	)
					lowercase					  =   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				lowercase  , lowercase					  =   self.denoise_step(
				    base=snake_case		,   model_input=snake_case		,   model_output=snake_case		,   timestep=snake_case		,   )

				lowercase					  =   model_input

			lowercase					  =   self.denoise_mixing_layer(x=coupled_latents[0]		,   y=coupled_latents[1]	)

		# either one is fine
		lowercase					  =   coupled_latents[0]

		if output_type not in ["latent", "pt", "np", "pil"]:
			lowercase					  =   (
			    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'		,   snake_case		,   standard_warn=snake_case	)
			lowercase					  =   'np'

		if output_type == "latent":
			lowercase					  =   final_latent
		else:
			lowercase					  =   self.decode_latents(snake_case	)
			lowercase					  =   self.image_processor.postprocess(snake_case		,   output_type=snake_case	)

		return image



def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   [701, 301, 132, 57, 23, 10, 4, 1]
	for gap in gaps:
		for i in range(__SCREAMING_SNAKE_CASE			,							len(__SCREAMING_SNAKE_CASE							)							):
			lowercase					  =   collection[i]
			lowercase					  =   i
			while j >= gap and collection[j - gap] > insert_value:
				lowercase					  =   collection[j - gap]
				j -= gap
			if j != i:
				lowercase					  =   insert_value
	return collection


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

	testmod()
	UpperCAmelCase     				=			input('''Enter numbers separated by a comma:\n''').strip()
	UpperCAmelCase     				=			[int(item) for item in user_input.split(''',''')]
	print(shell_sort(unsorted))

