
"""simple docstring"""




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 						lowercase(SCREAMING_SNAKE_CASE__					):



     def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 0.93    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Dict:







         """simple docstring"""



         a__    = mixing_coeff
         a__    = leapfrog_steps

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

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



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False      )		->    Any:







         """simple docstring"""



         a__    = self.tokenizer(
             __SCREAMING_SNAKE_CASE    ,							padding='max_length'    ,							max_length=self.tokenizer.model_max_length    ,							truncation=__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'    ,							)

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

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

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

             a__    = self.tokenizer(
                 __SCREAMING_SNAKE_CASE    ,							padding='max_length'    ,							max_length=self.tokenizer.model_max_length    ,							truncation=__SCREAMING_SNAKE_CASE    ,							return_tensors='pt'    ,							)

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

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

         return prompt_embeds



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Any:







         """simple docstring"""



         a__    = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         a__    = self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         a__    = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = int(__SCREAMING_SNAKE_CASE      )

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

         return alpha_prod, 1 - alpha_prod



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    Dict:







         """simple docstring"""



         a__    = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         a__  ,    a__    = self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE      )
         a__  ,    a__    = self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE      )

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

         a__    = (base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype      )



     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    Tuple:







         """simple docstring"""



         a__    = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         a__  ,    a__    = self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE      )
         a__  ,    a__    = self._get_alpha_and_beta(__SCREAMING_SNAKE_CASE      )

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

         return model_input, next_model_input.to(base.dtype      )



     @torch.no_grad()
     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE      )		->    Any:







         """simple docstring"""



         a__    = 1 / self.vae.config.scaling_factor * latents
         a__    = self.vae.decode(__SCREAMING_SNAKE_CASE      ).sample
         a__    = (image / 2 + 0.5).clamp(0    ,							1      )
         return image



     @torch.no_grad()
     def  lowercase__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							)		->    List[Any]:







         """simple docstring"""



         a__    = guidance_scale > 1.0

         a__    = image.to(device=self.device    ,							dtype=text_embeds.dtype      )
         a__    = self.vae.encode(__SCREAMING_SNAKE_CASE      ).latent_dist.sample(__SCREAMING_SNAKE_CASE      )

         a__    = self.vae.config.scaling_factor * latent

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

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

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

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

                 a__    = coupled_latents[j]
                 a__    = coupled_latents[k]

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

                 a__    = self.unet(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE      ).sample

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

                 a__  ,    a__    = self.noise_step(
                     base=__SCREAMING_SNAKE_CASE    ,							model_input=__SCREAMING_SNAKE_CASE    ,							model_output=__SCREAMING_SNAKE_CASE    ,							timestep=__SCREAMING_SNAKE_CASE    ,							)

                 a__    = model_input

         return coupled_latents




     @torch.no_grad()
     def __call__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 3.0    ,							__SCREAMING_SNAKE_CASE = 5_0    ,							__SCREAMING_SNAKE_CASE = 0.8    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "pil"    ,							)		->    Optional[int]:







         """simple docstring"""



         a__    = guidance_scale > 1.0

         a__    = self.image_processor.preprocess(__SCREAMING_SNAKE_CASE      )

         a__    = self._encode_prompt(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         a__    = self._encode_prompt(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )

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

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

         a__    = self.prepare_latents(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )

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

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

                 a__    = coupled_latents[j]
                 a__    = coupled_latents[k]

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

                 a__    = self.unet(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE      ).sample

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

                 a__  ,    a__    = self.denoise_step(
                     base=__SCREAMING_SNAKE_CASE    ,							model_input=__SCREAMING_SNAKE_CASE    ,							model_output=__SCREAMING_SNAKE_CASE    ,							timestep=__SCREAMING_SNAKE_CASE    ,							)

                 a__    = model_input

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

         # either one is fine
         a__    = coupled_latents[0]

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

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

         return image




"""simple docstring"""




a		:					str    	=					(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
a		:					tuple    	=					(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    a__    = 0
    a__    = 0
    a__    = n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    a__    = n
    while temp > 0:
        a__    = temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False

    # Init a "histogram" of the digits
    a__    = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    a__    = 0
    a__    = 0
    a__    = n
    while temp > 0:
        a__  ,    a__    = divmod(UpperCamelCase       ,		10    )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(UpperCamelCase       ,		range(len(UpperCamelCase    )    )    ):
        total += cnt * i**digit_total

    return n == total



def 						__magic_name__		(				UpperCamelCase    :		int    )    ->	bool:
    if not isinstance(UpperCamelCase       ,		UpperCamelCase    ) or n < 1:
        return False
    a__    = len(str(UpperCamelCase    )    )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(UpperCamelCase    ) ** expo for i in str(UpperCamelCase    )    )



def 						__magic_name__		(				)    ->	List[Any]:
    a__    = int(input('Enter an integer to see if it is an Armstrong number: '    ).strip()    )
    print(f'{num} is {"" if armstrong_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if narcissistic_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )
    print(f'{num} is {"" if pluperfect_number(UpperCamelCase    ) else "not "}an Armstrong number.'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


