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 					_snake_case						(						_lowercase				):
					def __init__(  self:				str	,  __lowerCamelCase:				AutoencoderKL	,  __lowerCamelCase:				CLIPTextModel	,  __lowerCamelCase:				CLIPTokenizer	,  __lowerCamelCase:				UNetaDConditionModel	,  __lowerCamelCase:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]	,  )    ->				Union[str, Any]:
												super().__init__()

												self.register_modules(vae=__lowerCamelCase	,  text_encoder=__lowerCamelCase	,  tokenizer=__lowerCamelCase	,  unet=__lowerCamelCase	,  scheduler=__lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[str]	,  __lowerCamelCase:				Optional[int]     )    ->				int:
												with torch.no_grad():
																			__UpperCAmelCase						:			Union[str, Any]         =   self.vae.encode(tfms.ToTensor()(__lowerCamelCase     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
																			__UpperCAmelCase						:			Any         =   0.1_82_15 * latent.latent_dist.sample()
												return latent
					def    _lowerCamelCase (  self:				Any	,  __lowerCamelCase:				Union[str, Any]     )    ->				List[Any]:
												__UpperCAmelCase						:			List[str]         =   (1 / 0.1_82_15) * latent
												with torch.no_grad():
																			__UpperCAmelCase						:			Union[str, Any]         =   self.vae.decode(__lowerCamelCase     ).sample
												__UpperCAmelCase						:			str         =   (img / 2 + 0.5).clamp(0	,  1     )
												__UpperCAmelCase						:			Tuple         =   img.detach().cpu().permute(0	,  2	,  3	,  1     ).numpy()
												__UpperCAmelCase						:			Tuple         =   (img * 2_55).round().astype("uint8"     )
												return Image.fromarray(img[0]     )
					def    _lowerCamelCase (  self:				Union[str, Any]	,  __lowerCamelCase:				List[Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Tuple         =   self.tokenizer(
												    __lowerCamelCase	,  padding="max_length"	,  max_length=self.tokenizer.model_max_length	,  truncation=__lowerCamelCase	,  return_tensors="pt"	,  )

												__UpperCAmelCase						:			Tuple         =   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

												__UpperCAmelCase						:			Tuple         =   self.tokenizer(
												    ""	,  padding="max_length"	,  max_length=self.tokenizer.model_max_length	,  truncation=__lowerCamelCase	,  return_tensors="pt"	,  )

												__UpperCAmelCase						:			Union[str, Any]         =   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

												return torch.cat([uncond_embedding, text_embedding]     )





					def __call__(  self:				List[Any]	,  __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:
												__UpperCAmelCase						:			List[Any]         =   steps - int(kmin * steps     )
												__UpperCAmelCase						:			int         =   steps - int(kmax * steps     )

												__UpperCAmelCase						:			str         =   self.prep_text(__lowerCamelCase     )

												self.scheduler.set_timesteps(__lowerCamelCase     )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   img.size
												__UpperCAmelCase						:			Tuple         =   self.encode(__lowerCamelCase     )

												torch.manual_seed(__lowerCamelCase     )
												__UpperCAmelCase						:			List[str]         =   torch.randn(
												    (1, self.unet.config.in_channels, height // 8, width // 8)	,  ).to(self.device     )

												__UpperCAmelCase						:			int         =   self.scheduler.add_noise(
												    __lowerCamelCase	,  __lowerCamelCase	,  timesteps=self.scheduler.timesteps[tmax]	,  )

												__UpperCAmelCase						:			Dict         =   torch.cat([latents] * 2     )

												__UpperCAmelCase						:			int         =   self.scheduler.scale_model_input(__lowerCamelCase	,  self.scheduler.timesteps[tmax]     )

												with torch.no_grad():
																			__UpperCAmelCase						:			Tuple         =   self.unet(
																			    __lowerCamelCase	,  self.scheduler.timesteps[tmax]	,  encoder_hidden_states=__lowerCamelCase	,  ).sample

												__UpperCAmelCase    ,	__UpperCAmelCase						:			List[str]         =   pred.chunk(2     )
												__UpperCAmelCase						:			List[str]         =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

												__UpperCAmelCase						:			Tuple         =   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
																																	__UpperCAmelCase						:			Optional[Any]         =   self.scheduler.add_noise(
																																	    __lowerCamelCase	,  __lowerCamelCase	,  timesteps=__lowerCamelCase	,  )

																																	__UpperCAmelCase						:			Union[str, Any]         =   (mix_factor * latents) + (
																																	    1 - mix_factor
																																	) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																																	__UpperCAmelCase						:			Dict         =   torch.cat([input] * 2     )

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

																										__UpperCAmelCase						:			Union[str, Any]         =   self.scheduler.scale_model_input(__lowerCamelCase	,  __lowerCamelCase     )

																										with torch.no_grad():
																																	__UpperCAmelCase						:			List[str]         =   self.unet(
																																	    __lowerCamelCase	,  __lowerCamelCase	,  encoder_hidden_states=__lowerCamelCase	,  ).sample

																										__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   pred.chunk(2     )
																										__UpperCAmelCase						:			Optional[Any]         =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																										__UpperCAmelCase						:			Optional[int]         =   self.scheduler.step(__lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase     ).prev_sample

												return self.decode(__lowerCamelCase     )





import numpy as np
import requests




def   _UpperCamelCase   (			)						->   Tuple:
							__UpperCAmelCase						:			Tuple         =   requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"							)
							__UpperCAmelCase						:			List[Any]         =   response.text.splitlines()
							__UpperCAmelCase						:			Any         =   []
							for item in lines:
														__UpperCAmelCase						:			Any         =   item.split(","							)
														data.append(snake_case__							)
							data.pop(0							)  # This is for removing the labels from the list
							__UpperCAmelCase						:			List[str]         =   np.matrix(snake_case__							)
							return dataset




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[Any]:
							__UpperCAmelCase						:			Tuple         =   len_data

							__UpperCAmelCase						:			int         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.dot(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[Any]         =   theta - (alpha / n) * sum_grad
							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			Any         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.sum(np.square(snake_case__							)							)
							__UpperCAmelCase						:			List[Any]         =   sum_elem / (2 * len_data)
							return error




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			Any         =   10_0000
							__UpperCAmelCase						:			Dict         =   0.000_1550

							__UpperCAmelCase						:			str         =   data_x.shape[1]
							__UpperCAmelCase						:			Any         =   data_x.shape[0] - 1

							__UpperCAmelCase						:			List[str]         =   np.zeros((1, no_features)							)

							for i in range(0,    snake_case__							):
														__UpperCAmelCase						:			Union[str, Any]         =   run_steep_gradient_descent(snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														__UpperCAmelCase						:			Any         =   sum_of_square_error(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														print(f'''At Iteration {i + 1} - Error is {error:.5f}'''							)

							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			str         =   sum(abs(y - predicted_y[i]							) for i, y in enumerate(snake_case__							)							)
							return total / len(snake_case__							)




def   _UpperCamelCase   (			)						->   List[str]:
							__UpperCAmelCase						:			str         =   collect_dataset()

							__UpperCAmelCase						:			List[Any]         =   data.shape[0]
							__UpperCAmelCase						:			Union[str, Any]         =   np.c_[np.ones(snake_case__							), data[:, :-1]].astype(snake_case__							)
							__UpperCAmelCase						:			int         =   data[:, -1].astype(snake_case__							)

							__UpperCAmelCase						:			List[Any]         =   run_linear_regression(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[str]         =   theta.shape[1]
							print("Resultant Feature vector : "							)
							for i in range(0,    snake_case__							):
														print(f'''{theta[0, i]:.5f}'''							)


if __name__ == "__main__":
	main()




