






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__snake_case			:			int			   =   logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				torch.FloatTensor
						_SCREAMING_SNAKE_CASE		:				Optional[torch.FloatTensor]            =    None







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Optional[Any]=0.999			,				UpperCamelCase_				:    Any="cosine"			,				)     ->      int:

						"""simple docstring"""



						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCamelCase_				:    Union[str, Any]						):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCamelCase_				:    Union[str, Any]						):
																		return math.exp(t * -12.0						)

						else:
												raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"						)

						lowerCAmelCase__			  =  []
						for i in range(UpperCamelCase_						):
												lowerCAmelCase__			  =  i / num_diffusion_timesteps
												lowerCAmelCase__			  =  (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCamelCase_						) / alpha_bar_fn(UpperCamelCase_						)			,				UpperCamelCase_						)						)
						return torch.tensor(UpperCamelCase_			,				dtype=torch.floataa						)


class 			__SCREAMING_SNAKE_CASE    (      __lowercase						,    __lowercase):
						_SCREAMING_SNAKE_CASE		:				str            =    [e.name for e in KarrasDiffusionSchedulers]
						_SCREAMING_SNAKE_CASE		:				Any            =    1






						@register_to_config
						def __init__(		self   ,  _UpperCamelCase = 10_00   ,  _UpperCamelCase = 0.00_01   ,  _UpperCamelCase = 0.02   ,  _UpperCamelCase = "linear"   ,  _UpperCamelCase = None   ,  _UpperCamelCase = "epsilon"   ,  _UpperCamelCase = "linspace"   ,  _UpperCamelCase = 0   ,  ):






												"""simple docstring"""
												if trained_betas is not None:
																		lowerCAmelCase__			  =  torch.tensor(_UpperCamelCase   ,  dtype=torch.floataa      )
												elif beta_schedule == "linear":
																		lowerCAmelCase__			  =  torch.linspace(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  dtype=torch.floataa      )
												elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		lowerCAmelCase__			  =  (
																		    torch.linspace(beta_start**0.5   ,  beta_end**0.5   ,  _UpperCamelCase   ,  dtype=torch.floataa      ) ** 2
																		)
												elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		lowerCAmelCase__			  =  betas_for_alpha_bar(_UpperCamelCase      )
												else:
																		raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

												lowerCAmelCase__			  =  1.0 - self.betas
												lowerCAmelCase__			  =  torch.cumprod(self.alphas   ,  dim=0      )

												lowerCAmelCase__			  =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												lowerCAmelCase__			  =  np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      )

												# setable values
												lowerCAmelCase__			  =  None
												lowerCAmelCase__			  =  np.linspace(0   ,  num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )[::-1].copy()
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      )
												lowerCAmelCase__			  =  False






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												# standard deviation of the initial noise distribution
												if self.config.timestep_spacing in ["linspace", "trailing"]:
																		return self.sigmas.max()

												return (self.sigmas.max() ** 2 + 1) ** 0.5






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																		lowerCAmelCase__			  =  timestep.to(self.timesteps.device      )
												lowerCAmelCase__			  =  (self.timesteps == timestep).nonzero().item()
												lowerCAmelCase__			  =  self.sigmas[step_index]
												lowerCAmelCase__			  =  sample / ((sigma**2 + 1) ** 0.5)
												lowerCAmelCase__			  =  True
												return sample






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  num_inference_steps

												# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
												if self.config.timestep_spacing == "linspace":
																		lowerCAmelCase__			  =  np.linspace(0   ,  self.config.num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase      )[
																		    ::-1
																		].copy()
												elif self.config.timestep_spacing == "leading":
																		lowerCAmelCase__			  =  self.config.num_train_timesteps // self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		lowerCAmelCase__			  =  (np.arange(0   ,  _UpperCamelCase      ) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase      )
																		timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																		lowerCAmelCase__			  =  self.config.num_train_timesteps / self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		lowerCAmelCase__			  =  (np.arange(self.config.num_train_timesteps   ,  0   ,  -step_ratio      )).round().copy().astype(_UpperCamelCase      )
																		timesteps -= 1
												else:
																		raise ValueError(
																		    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

												lowerCAmelCase__			  =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												lowerCAmelCase__			  =  np.interp(_UpperCamelCase   ,  np.arange(0   ,  len(_UpperCamelCase      )      )   ,  _UpperCamelCase      )
												lowerCAmelCase__			  =  np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
												lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )
												if str(_UpperCamelCase      ).startswith('mps'      ):
																		# mps does not support float64
																		lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(_UpperCamelCase   ,  dtype=torch.floataa      )
												else:
																		lowerCAmelCase__			  =  torch.from_numpy(_UpperCamelCase      ).to(device=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = None   ,  _UpperCamelCase = True   ,  ):






												"""simple docstring"""

												if (
												    isinstance(_UpperCamelCase   ,  _UpperCamelCase      )
												    or isinstance(_UpperCamelCase   ,  torch.IntTensor      )
												    or isinstance(_UpperCamelCase   ,  torch.LongTensor      )
												):
																		raise ValueError(
																		    (
																		        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																		        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
																		        ' one of the `scheduler.timesteps` as a timestep.'
																		    )   ,  )

												if not self.is_scale_input_called:
																		logger.warning(
																		    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
																		    'See `StableDiffusionPipeline` for a usage example.'      )

												if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																		lowerCAmelCase__			  =  timestep.to(self.timesteps.device      )

												lowerCAmelCase__			  =  (self.timesteps == timestep).nonzero().item()
												lowerCAmelCase__			  =  self.sigmas[step_index]

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																		lowerCAmelCase__			  =  sample - sigma * model_output
												elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		lowerCAmelCase__			  =  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
												elif self.config.prediction_type == "sample":
																		raise NotImplementedError('prediction_type not implemented yet: sample'      )
												else:
																		raise ValueError(
																		    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

												lowerCAmelCase__			  =  self.sigmas[step_index]
												lowerCAmelCase__			  =  self.sigmas[step_index + 1]
												lowerCAmelCase__			  =  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
												lowerCAmelCase__			  =  (sigma_to**2 - sigma_up**2) ** 0.5

												# 2. Convert to an ODE derivative
												lowerCAmelCase__			  =  (sample - pred_original_sample) / sigma

												lowerCAmelCase__			  =  sigma_down - sigma

												lowerCAmelCase__			  =  sample + derivative * dt

												lowerCAmelCase__			  =  model_output.device
												lowerCAmelCase__			  =  randn_tensor(model_output.shape   ,  dtype=model_output.dtype   ,  device=_UpperCamelCase   ,  generator=_UpperCamelCase      )

												lowerCAmelCase__			  =  prev_sample + noise * sigma_up

												if not return_dict:
																		return (prev_sample,)

												return EulerAncestralDiscreteSchedulerOutput(
												    prev_sample=_UpperCamelCase   ,  pred_original_sample=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  ):






												"""simple docstring"""
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												lowerCAmelCase__			  =  self.sigmas.to(device=original_samples.device   ,  dtype=original_samples.dtype      )
												if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCamelCase      ):
																		# mps does not support float64
																		lowerCAmelCase__			  =  self.timesteps.to(original_samples.device   ,  dtype=torch.floataa      )
																		lowerCAmelCase__			  =  timesteps.to(original_samples.device   ,  dtype=torch.floataa      )
												else:
																		lowerCAmelCase__			  =  self.timesteps.to(original_samples.device      )
																		lowerCAmelCase__			  =  timesteps.to(original_samples.device      )

												lowerCAmelCase__			  =  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

												lowerCAmelCase__			  =  sigmas[step_indices].flatten()
												while len(sigma.shape      ) < len(original_samples.shape      ):
																		lowerCAmelCase__			  =  sigma.unsqueeze(-1      )

												lowerCAmelCase__			  =  original_samples + noise * sigma
												return noisy_samples







						def __len__(		self      ):






												"""simple docstring"""
												return self.config.num_train_timesteps













import numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





