


'''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 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




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

										self.register_modules(vae=_lowerCamelCase	,				text_encoder=_lowerCamelCase	,				tokenizer=_lowerCamelCase	,				unet=_lowerCamelCase	,				scheduler=_lowerCamelCase  )
						def 					__A					( self				:					int	,				_lowerCamelCase				:					Dict  )							->					List[Any]:
										with torch.no_grad():
														__magic_name__	  = self.vae.encode(tfms.ToTensor()(_lowerCamelCase  ).unsqueeze(0  ).to(self.device  ) * 2 - 1  )
														__magic_name__	  = 0.18_215 * latent.latent_dist.sample()
										return latent
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					List[Any]  )							->					List[Any]:
										__magic_name__	  = (1 / 0.18_215) * latent
										with torch.no_grad():
														__magic_name__	  = self.vae.decode(_lowerCamelCase  ).sample
										__magic_name__	  = (img / 2 + 0.5).clamp(0	,				1  )
										__magic_name__	  = img.detach().cpu().permute(0	,				2	,				3	,				1  ).numpy()
										__magic_name__	  = (img * 2_55).round().astype("uint8"  )
										return Image.fromarray(img[0]  )
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int  )							->					Dict:
										__magic_name__	  = self.tokenizer(
										    _lowerCamelCase	,				padding="max_length"	,				max_length=self.tokenizer.model_max_length	,				truncation=_lowerCamelCase	,				return_tensors="pt"	,				)

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

										__magic_name__	  = self.tokenizer(
										    ""	,				padding="max_length"	,				max_length=self.tokenizer.model_max_length	,				truncation=_lowerCamelCase	,				return_tensors="pt"	,				)

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

										return torch.cat([uncond_embedding, text_embedding]  )

						def __call__( self				:					str	,				_lowerCamelCase				:					Image.Image	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					float = 0.3	,				_lowerCamelCase				:					float = 0.6	,				_lowerCamelCase				:					float = 0.5	,				_lowerCamelCase				:					int = 42	,				_lowerCamelCase				:					int = 50	,				_lowerCamelCase				:					float = 7.5	,				)							->					Image.Image:
										__magic_name__	  = steps - int(kmin * steps  )
										__magic_name__	  = steps - int(kmax * steps  )

										__magic_name__	  = self.prep_text(_lowerCamelCase  )

										self.scheduler.set_timesteps(_lowerCamelCase  )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

										return self.decode(_lowerCamelCase  )







'''simple docstring'''
import numpy as np

__magic_name__   :   str  							=[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]  )							->					None:
										__magic_name__	  = np.array(_lowerCamelCase  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					str  )							->					np.ndarray:
										__magic_name__  ,      __magic_name__	  = np.where(letter == self.SQUARE  )
										__magic_name__	  = np.concatenate([indexa + 1, indexa + 1]  )
										return indexes
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					int  )							->					str:
										return self.SQUARE[indexa - 1, indexa - 1]
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.lower()
										__magic_name__	  = message.replace("j"	,				"i"  )

										__magic_name__	  = ""
										for letter_index in range(len(_lowerCamelCase  )  ):
														if message[letter_index] != " ":
																		__magic_name__	  = self.letter_to_numbers(message[letter_index]  )
																		__magic_name__	  = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
														elif message[letter_index] == " ":
																		__magic_name__	  = encoded_message + " "

										return encoded_message

						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					str  )							->					str:
										__magic_name__	  = message.replace(" "	,				"  "  )
										__magic_name__	  = ""
										for numbers_index in range(int(len(_lowerCamelCase  ) / 2  )  ):
														if message[numbers_index * 2] != " ":
																		__magic_name__	  = message[numbers_index * 2]
																		__magic_name__	  = message[numbers_index * 2 + 1]

																		__magic_name__	  = self.numbers_to_letter(int(_lowerCamelCase  )	,				int(_lowerCamelCase  )  )
																		__magic_name__	  = decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		__magic_name__	  = decoded_message + " "

										return decoded_message



