






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				CommonSchedulerState
						_SCREAMING_SNAKE_CASE		:				jnp.ndarray

						# setable values
						_SCREAMING_SNAKE_CASE		:				jnp.ndarray
						_SCREAMING_SNAKE_CASE		:				jnp.ndarray
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    None






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  ):






												"""simple docstring"""
												return cls(
												    common=_UpperCamelCase   ,  final_alpha_cumprod=_UpperCamelCase   ,  init_noise_sigma=_UpperCamelCase   ,  timesteps=_UpperCamelCase   ,  )




@dataclass
class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				DDIMSchedulerState




class 			__SCREAMING_SNAKE_CASE    (      __lowercase						,    __lowercase):
						_SCREAMING_SNAKE_CASE		:				Optional[int]            =    [e.name for e in FlaxKarrasDiffusionSchedulers]

						_SCREAMING_SNAKE_CASE		:				jnp.dtype






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return True






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






												"""simple docstring"""
												lowerCAmelCase__			  =  dtype






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												if common is None:
																		lowerCAmelCase__			  =  CommonSchedulerState.create(self      )

												# At every step in ddim, we are looking into the previous alphas_cumprod
												# For the final step, there is no previous alphas_cumprod because we are already at 0
												# `set_alpha_to_one` decides whether we set this parameter simply to one or
												# whether we use the final alpha of the "non-previous" one.
												lowerCAmelCase__			  =  (
												    jnp.array(1.0   ,  dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
												)

												# standard deviation of the initial noise distribution
												lowerCAmelCase__			  =  jnp.array(1.0   ,  dtype=self.dtype      )

												lowerCAmelCase__			  =  jnp.arange(0   ,  self.config.num_train_timesteps      ).round()[::-1]

												return DDIMSchedulerState.create(
												    common=_UpperCamelCase   ,  final_alpha_cumprod=_UpperCamelCase   ,  init_noise_sigma=_UpperCamelCase   ,  timesteps=_UpperCamelCase   ,  )






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






												"""simple docstring"""
												return sample






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






												"""simple docstring"""
												lowerCAmelCase__			  =  self.config.num_train_timesteps // num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# rounding to avoid issues when num_inference_step is power of 3
												lowerCAmelCase__			  =  (jnp.arange(0   ,  _UpperCamelCase      ) * step_ratio).round()[::-1] + self.config.steps_offset

												return state.replace(
												    num_inference_steps=_UpperCamelCase   ,  timesteps=_UpperCamelCase   ,  )






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






												"""simple docstring"""
												lowerCAmelCase__			  =  state.common.alphas_cumprod[timestep]
												lowerCAmelCase__			  =  jnp.where(
												    prev_timestep >= 0   ,  state.common.alphas_cumprod[prev_timestep]   ,  state.final_alpha_cumprod      )
												lowerCAmelCase__			  =  1 - alpha_prod_t
												lowerCAmelCase__			  =  1 - alpha_prod_t_prev

												lowerCAmelCase__			  =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = 0.0   ,  _UpperCamelCase = True   ,  ):






												"""simple docstring"""
												if state.num_inference_steps is None:
																		raise ValueError(
																		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

												# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
												# Ideally, read DDIM paper in-detail understanding

												# Notation (<variable name> -> <name in paper>
												# - pred_noise_t -> e_theta(x_t, t)
												# - pred_original_sample -> f_theta(x_t, t) or x_0
												# - std_dev_t -> sigma_t
												# - eta -> η
												# - pred_sample_direction -> "direction pointing to x_t"
												# - pred_prev_sample -> "x_t-1"

												# 1. get previous step value (=t-1)
												lowerCAmelCase__			  =  timestep - self.config.num_train_timesteps // state.num_inference_steps

												lowerCAmelCase__			  =  state.common.alphas_cumprod
												lowerCAmelCase__			  =  state.final_alpha_cumprod

												# 2. compute alphas, betas
												lowerCAmelCase__			  =  alphas_cumprod[timestep]
												lowerCAmelCase__			  =  jnp.where(prev_timestep >= 0   ,  alphas_cumprod[prev_timestep]   ,  _UpperCamelCase      )

												lowerCAmelCase__			  =  1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																		lowerCAmelCase__			  =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		lowerCAmelCase__			  =  model_output
												elif self.config.prediction_type == "sample":
																		lowerCAmelCase__			  =  model_output
																		lowerCAmelCase__			  =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																		lowerCAmelCase__			  =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		lowerCAmelCase__			  =  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																		raise ValueError(
																		    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																		    ' `v_prediction`'      )

												# 4. compute variance: "sigma_t(η)" -> see formula (16)
												# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
												lowerCAmelCase__			  =  self._get_variance(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
												lowerCAmelCase__			  =  eta * variance ** 0.5

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowerCAmelCase__			  =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowerCAmelCase__			  =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																		return (prev_sample, state)

												return FlaxDDIMSchedulerOutput(prev_sample=_UpperCamelCase   ,  state=_UpperCamelCase      )






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






												"""simple docstring"""
												return add_noise_common(state.common   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )






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






												"""simple docstring"""
												return get_velocity_common(state.common   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )







						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()





