




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  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       AutoencoderKL       ,				snake_case    :       CLIPTextModel       ,				snake_case    :       CLIPTokenizer       ,				snake_case    :       UNetaDConditionModel       ,				snake_case    :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]       ,				):
          super().__init__()

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







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Union[str, Any] ):
          with torch.no_grad():
              __UpperCamelCase     						=  self.vae.encode(tfms.ToTensor()(snake_case ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
              __UpperCamelCase     						=  0.1_82_15 * latent.latent_dist.sample()
          return latent







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[Any] ):
          __UpperCamelCase     						=  (1 / 0.1_82_15) * 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 					snake_case		(   self    :       int       ,				snake_case    :       Any ):
          __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    :       Dict       ,				snake_case    :       Image.Image       ,				snake_case    :       str       ,				snake_case    :       float = 0.3       ,				snake_case    :       float = 0.6       ,				snake_case    :       float = 0.5       ,				snake_case    :       int = 42       ,				snake_case    :       int = 50       ,				snake_case    :       float = 7.5       ,				):
          __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 )












import random

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Any       ,				snake_case    :       float       ,				snake_case    :       float ):
          __UpperCamelCase     						=  x
          __UpperCamelCase     						=  y







      def 					snake_case		(   self    :       List[Any] ):
          return (self.x**2 + self.y**2) <= 1







      @classmethod
      def 					snake_case		(   cls    :       List[str] ):
          return cls(x=random.random()       ,				y=random.random() )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		float:

    """simple docstring"""


    if number_of_simulations < 1:
        raise ValueError('''At least one simulation is necessary to estimate PI.'''					)

    __UpperCamelCase     						=  0
    for _ in range(lowercase_					):
        __UpperCamelCase     						=  Point.random_unit_square()

        if random_point.is_in_unit_circle():
            number_in_unit_circle += 1

    return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
 # import doctest

 # doctest.testmod()
 from math import pi

 a_      						=   "Please enter the desired number of Monte Carlo simulations: "
 a_      						=   estimate_pi(int(input(prompt).strip()))
 print(f"""An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}""")






