




"""simple docstring"""







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 SchedulerMixin


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






@dataclass
class  SCREAMING_SNAKE_CASE__   (   lowerCamelCase__   ):
						_a        =  42






class  SCREAMING_SNAKE_CASE__   (   lowerCamelCase__  ,			lowerCamelCase__   ):
						_a        =  1


						@register_to_config
						def __init__(							self			:     str  ,  lowerCAmelCase			:     Any = 40  ,  lowerCAmelCase			:     Tuple = 0.002  ,  lowerCAmelCase			:     Any = 80.0  ,  lowerCAmelCase			:     List[Any] = 0.5  ,  lowerCAmelCase			:     Dict = 1.0  ,  lowerCAmelCase			:     List[Any] = 7.0  ,  lowerCAmelCase			:     Any = True  ,  ):
										lowerCAmelCase		=			sigma_max

										lowerCAmelCase		=			np.linspace(0  ,  1  ,  lowercase_   )
										lowerCAmelCase		=			self._convert_to_karras(lowercase_   )
										lowerCAmelCase		=			self.sigma_to_t(lowercase_   )

										# setable values
										lowerCAmelCase		=			None
										lowerCAmelCase		=			torch.from_numpy(lowercase_   )
										lowerCAmelCase		=			torch.from_numpy(lowercase_   )
										lowerCAmelCase		=			False
										lowerCAmelCase		=			False


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     Optional[Any]  ,  lowerCAmelCase			:     Union[str, Any]=None   ):






										if schedule_timesteps is None:
														lowerCAmelCase		=			self.timesteps

										lowerCAmelCase		=			(schedule_timesteps == timestep).nonzero()
										return indices.item()


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     List[Any]   ):






										if isinstance(lowercase_  ,  torch.Tensor   ):
														lowerCAmelCase		=			timestep.to(self.timesteps.device   )
										lowerCAmelCase		=			self.index_for_timestep(lowercase_   )
										lowerCAmelCase		=			self.sigmas[step_idx]

										lowerCAmelCase		=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										lowerCAmelCase		=			True
										return sample


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     Any   ):






										if not isinstance(lowercase_  ,  np.ndarray   ):
														lowerCAmelCase		=			np.array(lowercase_  ,  dtype=np.floataa   )

										lowerCAmelCase		=			1000 * 0.25 * np.log(sigmas + 1e-44   )

										return timesteps


						def 					__lowercase	(							self			:     List[str]  ,  lowerCAmelCase			:     Optional[int] = None  ,  lowerCAmelCase			:     Tuple = None  ,  lowerCAmelCase			:     Union[str, Any] = None  ,  ):






										if num_inference_steps is None and timesteps is None:
														raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""   )

										if num_inference_steps is not None and timesteps is not None:
														raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""   )

										# Follow DDPMScheduler custom timesteps logic
										if timesteps is not None:
														for i in range(1  ,  len(lowercase_   )   ):
																		if timesteps[i] >= timesteps[i - 1]:
																						raise ValueError("""`timesteps` must be in descending order."""   )

														if timesteps[0] >= self.config.num_train_timesteps:
																		raise ValueError(
																		    f'''`timesteps` must start before `self.config.train_timesteps`:'''
																		    f''' {self.config.num_train_timesteps}.'''   )

														lowerCAmelCase		=			np.array(lowercase_  ,  dtype=np.intaa   )
														lowerCAmelCase		=			True
										else:
														if num_inference_steps > self.config.num_train_timesteps:
																		raise ValueError(
																		    f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																		    f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																		    f''' maximal {self.config.num_train_timesteps} timesteps.'''   )

														lowerCAmelCase		=			num_inference_steps

														lowerCAmelCase		=			self.config.num_train_timesteps // self.num_inference_steps
														lowerCAmelCase		=			(np.arange(0  ,  lowercase_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
														lowerCAmelCase		=			False

										# Map timesteps to Karras sigmas directly for multistep sampling
										# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
										lowerCAmelCase		=			self.config.num_train_timesteps
										lowerCAmelCase		=			timesteps[::-1].copy()
										lowerCAmelCase		=			ramp / (num_train_timesteps - 1)
										lowerCAmelCase		=			self._convert_to_karras(lowercase_   )
										lowerCAmelCase		=			self.sigma_to_t(lowercase_   )

										lowerCAmelCase		=			np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
										lowerCAmelCase		=			torch.from_numpy(lowercase_   ).to(device=lowercase_   )

										if str(lowercase_   ).startswith("""mps"""   ):
														# mps does not support float64
														lowerCAmelCase		=			torch.from_numpy(lowercase_   ).to(lowercase_  ,  dtype=torch.floataa   )
										else:
														lowerCAmelCase		=			torch.from_numpy(lowercase_   ).to(device=lowercase_   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			self.config.sigma_min
										lowerCAmelCase		=			self.config.sigma_max

										lowerCAmelCase		=			self.config.rho
										lowerCAmelCase		=			sigma_min ** (1 / rho)
										lowerCAmelCase		=			sigma_max ** (1 / rho)
										lowerCAmelCase		=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas


						def 					__lowercase	(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			self.config.sigma_data

										lowerCAmelCase		=			sigma_data**2 / (sigma**2 + sigma_data**2)
										lowerCAmelCase		=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out


						def 					__lowercase	(							self			:     Any  ,  lowerCAmelCase			:     Optional[int]   ):
										lowerCAmelCase		=			self.config.sigma_min
										lowerCAmelCase		=			self.config.sigma_data

										lowerCAmelCase		=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										lowerCAmelCase		=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out


						def 					__lowercase	(							self			:     Any  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     Any = None  ,  lowerCAmelCase			:     str = True  ,  ):






										if (
										    isinstance(lowercase_  ,  lowercase_   )
										    or isinstance(lowercase_  ,  torch.IntTensor   )
										    or isinstance(lowercase_  ,  torch.LongTensor   )
										):
														raise ValueError(
														    (
														        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
														        f''' `{self.__class__}.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(lowercase_  ,  torch.Tensor   ):
														lowerCAmelCase		=			timestep.to(self.timesteps.device   )

										lowerCAmelCase		=			self.config.sigma_min
										lowerCAmelCase		=			self.config.sigma_max

										lowerCAmelCase		=			self.index_for_timestep(lowercase_   )

										# sigma_next corresponds to next_t in original implementation
										lowerCAmelCase		=			self.sigmas[step_index]
										if step_index + 1 < self.config.num_train_timesteps:
														lowerCAmelCase		=			self.sigmas[step_index + 1]
										else:
														# Set sigma_next to sigma_min
														lowerCAmelCase		=			self.sigmas[-1]

										# Get scalings for boundary conditions
										lowerCAmelCase	,  lowerCAmelCase		=			self.get_scalings_for_boundary_condition(lowercase_   )

										# 1. Denoise model output using boundary conditions
										lowerCAmelCase		=			c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
														lowerCAmelCase		=			denoised.clamp(-1  ,  1   )

										# 2. Sample z ~ N(0, s_noise^2 * I)
										# Noise is not used for onestep sampling.
										if len(self.timesteps   ) > 1:
														lowerCAmelCase		=			randn_tensor(
														    model_output.shape  ,  dtype=model_output.dtype  ,  device=model_output.device  ,  generator=lowercase_   )
										else:
														lowerCAmelCase		=			torch.zeros_like(lowercase_   )
										lowerCAmelCase		=			noise * self.config.s_noise

										lowerCAmelCase		=			sigma_next.clamp(min=lowercase_  ,  max=lowercase_   )

										# 3. Return noisy sample
										# tau = sigma_hat, eps = sigma_min
										lowerCAmelCase		=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

										if not return_dict:
														return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Union[str, Any]  ,  lowerCAmelCase			:     Tuple  ,  ):
										lowerCAmelCase		=			self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype   )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_   ):
														# 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			:     List[str]   ):
										return self.config.num_train_timesteps









"""simple docstring"""







import numpy as np
import requests





def 							lowercase   ()       ->					Optional[int]:


				'''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(snake_case__	)
				data.pop(0	)  # This is for removing the labels from the list
				lowerCAmelCase		=			np.matrix(snake_case__	)
				return dataset





def 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	int ,    snake_case__				:	List[str] ,    snake_case__				:	Tuple ,    snake_case__				:	Union[str, Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			len_data

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





def 							lowercase   (snake_case__				:	Any ,    snake_case__				:	int ,    snake_case__				:	Any ,    snake_case__				:	Dict	)       ->					List[str]:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	Optional[Any] ,    snake_case__				:	List[Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			100_000
				lowerCAmelCase		=			0.0_001_550

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

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

				for i in range(0 ,    snake_case__	):
								lowerCAmelCase		=			run_steep_gradient_descent(snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__ ,    snake_case__	)
								lowerCAmelCase		=			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 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	Optional[int]	)       ->					List[str]:


				'''simple docstring'''






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





def 							lowercase   ()       ->					Optional[int]:


				'''simple docstring'''






				lowerCAmelCase		=			collect_dataset()

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

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


if __name__ == "__main__":
		main()





