






"""simple docstring"""





from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class  __lowerCamelCase     (	_a	):

   def __init__(       self			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					)		->							str:
       super().__init__()

       self.register_modules(vae=snake_case_			,					text_encoder=snake_case_			,					tokenizer=snake_case_			,					unet=snake_case_			,					scheduler=snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Tuple:
       with torch.no_grad():
           UpperCamelCase__								=     self.vae.encode(tfms.ToTensor()(snake_case_   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
           UpperCamelCase__								=     0.18_215 * latent.latent_dist.sample()
       return latent

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       UpperCamelCase__								=     (1 / 0.18_215) * latent
       with torch.no_grad():
           UpperCamelCase__								=     self.vae.decode(snake_case_   ).sample
       UpperCamelCase__								=     (img / 2 + 0.5).clamp(0			,					1   )
       UpperCamelCase__								=     img.detach().cpu().permute(0			,					2			,					3			,					1   ).numpy()
       UpperCamelCase__								=     (img * 255).round().astype('uint8'   )
       return Image.fromarray(img[0]   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       UpperCamelCase__								=     self.tokenizer(
           snake_case_			,					padding='max_length'			,					max_length=self.tokenizer.model_max_length			,					truncation=snake_case_			,					return_tensors='pt'			,					)

       UpperCamelCase__								=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]

       UpperCamelCase__								=     self.tokenizer(
           ''			,					padding='max_length'			,					max_length=self.tokenizer.model_max_length			,					truncation=snake_case_			,					return_tensors='pt'			,					)

       UpperCamelCase__								=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

       return torch.cat([uncond_embedding, text_embedding]   )







   def __call__(       self			,					snake_case_			,					snake_case_			,					snake_case_ = 0.3			,					snake_case_ = 0.6			,					snake_case_ = 0.5			,					snake_case_ = 42			,					snake_case_ = 50			,					snake_case_ = 7.5			,					)		->							Image.Image:
       UpperCamelCase__								=     steps - int(kmin * steps   )
       UpperCamelCase__								=     steps - int(kmax * steps   )

       UpperCamelCase__								=     self.prep_text(snake_case_   )

       self.scheduler.set_timesteps(snake_case_   )

       UpperCamelCase__							,    UpperCamelCase__								=     img.size
       UpperCamelCase__								=     self.encode(snake_case_   )

       torch.manual_seed(snake_case_   )
       UpperCamelCase__								=     torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)			,					).to(self.device   )

       UpperCamelCase__								=     self.scheduler.add_noise(
           snake_case_			,					snake_case_			,					timesteps=self.scheduler.timesteps[tmax]			,					)

       UpperCamelCase__								=     torch.cat([latents] * 2   )

       UpperCamelCase__								=     self.scheduler.scale_model_input(snake_case_			,					self.scheduler.timesteps[tmax]   )

       with torch.no_grad():
           UpperCamelCase__								=     self.unet(
               snake_case_			,					self.scheduler.timesteps[tmax]			,					encoder_hidden_states=snake_case_			,					).sample

       UpperCamelCase__							,    UpperCamelCase__								=     pred.chunk(2   )
       UpperCamelCase__								=     pred_uncond + guidance_scale * (pred_text - pred_uncond)

       UpperCamelCase__								=     self.scheduler.step(snake_case_			,					self.scheduler.timesteps[tmax]			,					snake_case_   ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
           if i > tmax:
               if i < tmin:  # layout generation phase
                   UpperCamelCase__								=     self.scheduler.add_noise(
                       snake_case_			,					snake_case_			,					timesteps=snake_case_			,					)

                   UpperCamelCase__								=     (mix_factor * latents) + (
                       1 - mix_factor
                   ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                   UpperCamelCase__								=     torch.cat([input] * 2   )

               else:  # content generation phase
                   UpperCamelCase__								=     torch.cat([latents] * 2   )

               UpperCamelCase__								=     self.scheduler.scale_model_input(snake_case_			,					snake_case_   )

               with torch.no_grad():
                   UpperCamelCase__								=     self.unet(
                       snake_case_			,					snake_case_			,					encoder_hidden_states=snake_case_			,					).sample

               UpperCamelCase__							,    UpperCamelCase__								=     pred.chunk(2   )
               UpperCamelCase__								=     pred_uncond + guidance_scale * (pred_text - pred_uncond)

               UpperCamelCase__								=     self.scheduler.step(snake_case_			,					snake_case_			,					snake_case_   ).prev_sample

       return self.decode(snake_case_   )












"""simple docstring"""





from math import gcd







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE = 1_20_00       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     0
    for d in range(max_d + 1       ):
        for n in range(d // 3 + 1			,						(d + 1) // 2       ):
            if gcd(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
  print(F"""{solution() = }""")




