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 			UpperCamelCase					(			snake_case__			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_					,					A_					,					A_					,					A_					,					)			->					Any:






           """simple docstring"""
           super().__init__()

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



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[int]:






           """simple docstring"""







           with torch.no_grad():
                 _lowerCamelCase           =					self.vae.encode(tfms.ToTensor()(A_    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
                 _lowerCamelCase           =					0.18215 * latent.latent_dist.sample()
           return latent



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					(1 / 0.18215) * latent
           with torch.no_grad():
                 _lowerCamelCase           =					self.vae.decode(A_    ).sample
           _lowerCamelCase           =					(img / 2 + 0.5).clamp(0					,					1    )
           _lowerCamelCase           =					img.detach().cpu().permute(0					,					2					,					3					,					1    ).numpy()
           _lowerCamelCase           =					(img * 2_55).round().astype('''uint8'''    )
           return Image.fromarray(img[0]    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.tokenizer(
               A_					,					padding='''max_length'''					,					max_length=self.tokenizer.model_max_length					,					truncation=A_					,					return_tensors='''pt'''					,					)

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

           _lowerCamelCase           =					self.tokenizer(
               ''''''					,					padding='''max_length'''					,					max_length=self.tokenizer.model_max_length					,					truncation=A_					,					return_tensors='''pt'''					,					)

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

           return torch.cat([uncond_embedding, text_embedding]    )





     def __call__(       self					,					A_					,					A_					,					A_ = 0.3					,					A_ = 0.6					,					A_ = 0.5					,					A_ = 42					,					A_ = 50					,					A_ = 7.5					,					)			->					int:






           """simple docstring"""
           _lowerCamelCase           =					steps - int(kmin * steps    )
           _lowerCamelCase           =					steps - int(kmax * steps    )

           _lowerCamelCase           =					self.prep_text(A_    )

           self.scheduler.set_timesteps(A_    )

           _lowerCamelCase     ,       _lowerCamelCase           =					img.size
           _lowerCamelCase           =					self.encode(A_    )

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

           _lowerCamelCase           =					self.scheduler.add_noise(
               A_					,					A_					,					timesteps=self.scheduler.timesteps[tmax]					,					)

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

           _lowerCamelCase           =					self.scheduler.scale_model_input(A_					,					self.scheduler.timesteps[tmax]    )

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

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

           _lowerCamelCase           =					self.scheduler.step(A_					,					self.scheduler.timesteps[tmax]					,					A_    ).prev_sample

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

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

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

                       _lowerCamelCase           =					self.scheduler.scale_model_input(A_					,					A_    )

                       with torch.no_grad():
                             _lowerCamelCase           =					self.unet(
                                 A_					,					A_					,					encoder_hidden_states=A_					,					).sample

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

                       _lowerCamelCase           =					self.scheduler.step(A_					,					A_					,					A_    ).prev_sample

           return self.decode(A_    )







from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
