






'''simple docstring'''





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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class      UpperCamelCase (				lowerCAmelCase__ ):







		"""simple docstring"""



		snake_case    =   42
		snake_case    =   42


class      UpperCamelCase (				lowerCAmelCase__			,       lowerCAmelCase__ ):







		"""simple docstring"""



		snake_case    =   1
		@register_to_config
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       List[str] = 2_0_0_0						,_SCREAMING_SNAKE_CASE		:       Any = 0.15						,_SCREAMING_SNAKE_CASE		:       Tuple = 0.01						,_SCREAMING_SNAKE_CASE		:       int = 1_3_4_8.0						,_SCREAMING_SNAKE_CASE		:       Any = 1E-5						,_SCREAMING_SNAKE_CASE		:       Optional[int] = 1						,)    ->   List[str]:


						'''simple docstring'''
						A	 =      sigma_max

						# setable values
						A	 =      None

						self.set_sigmas(_lowerCamelCase						,_lowerCamelCase						,_lowerCamelCase						,_lowerCamelCase		)
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[Any] = None		)    ->   torch.FloatTensor:


						'''simple docstring'''
						return sample
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Tuple = None						,_SCREAMING_SNAKE_CASE		:       str = None		)    ->   List[str]:


						'''simple docstring'''
						A	 =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

						A	 =      torch.linspace(1						,_lowerCamelCase						,_lowerCamelCase						,device=_lowerCamelCase		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       List[str] = None						,_SCREAMING_SNAKE_CASE		:       List[str] = None						,_SCREAMING_SNAKE_CASE		:       List[str] = None		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      sigma_min if sigma_min is not None else self.config.sigma_min
						A	 =      sigma_max if sigma_max is not None else self.config.sigma_max
						A	 =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if self.timesteps is None:
										self.set_timesteps(_lowerCamelCase						,_lowerCamelCase		)

						A	 =      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
						A	 =      torch.exp(torch.linspace(math.log(_lowerCamelCase		)						,math.log(_lowerCamelCase		)						,_lowerCamelCase		)		)
						A	 =      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)
		def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[str]:


						'''simple docstring'''
						return torch.where(
						    timesteps == 0						,torch.zeros_like(t.to(timesteps.device		)		)						,self.discrete_sigmas[timesteps - 1].to(timesteps.device		)						,)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Tuple = None						,_SCREAMING_SNAKE_CASE		:       str = True						,)    ->   Union[SdeVeOutput, Tuple]:


						'''simple docstring'''





						if self.timesteps is None:
										raise ValueError(
										    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'		)

						A	 =      timestep * torch.ones(
						    sample.shape[0]						,device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
						A	 =      (timestep * (len(self.timesteps		) - 1)).long()

						# mps requires indices to be in the same device, so we use cpu as is the default with cuda
						A	 =      timesteps.to(self.discrete_sigmas.device		)

						A	 =      self.discrete_sigmas[timesteps].to(sample.device		)
						A	 =      self.get_adjacent_sigma(_lowerCamelCase						,_lowerCamelCase		).to(sample.device		)
						A	 =      torch.zeros_like(_lowerCamelCase		)
						A	 =      (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						A	 =      diffusion.flatten()
						while len(diffusion.shape		) < len(sample.shape		):
										A	 =      diffusion.unsqueeze(-1		)
						A	 =      drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						A	 =      randn_tensor(
						    sample.shape						,layout=sample.layout						,generator=_lowerCamelCase						,device=sample.device						,dtype=sample.dtype		)
						A	 =      sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						A	 =      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
										return (prev_sample, prev_sample_mean)

						return SdeVeOutput(prev_sample=_lowerCamelCase						,prev_sample_mean=_lowerCamelCase		)
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Tuple = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = True						,)    ->   Union[SchedulerOutput, Tuple]:


						'''simple docstring'''





						if self.timesteps is None:
										raise ValueError(
										    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'		)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						A	 =      randn_tensor(sample.shape						,layout=sample.layout						,generator=_lowerCamelCase		).to(sample.device		)

						# compute step size from the model_output, the noise, and the snr
						A	 =      torch.norm(model_output.reshape(model_output.shape[0]						,-1		)						,dim=-1		).mean()
						A	 =      torch.norm(noise.reshape(noise.shape[0]						,-1		)						,dim=-1		).mean()
						A	 =      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						A	 =      step_size * torch.ones(sample.shape[0]		).to(sample.device		)
						# self.repeat_scalar(step_size, sample.shape[0])

						# compute corrected sample: model_output term and noise term
						A	 =      step_size.flatten()
						while len(step_size.shape		) < len(sample.shape		):
										A	 =      step_size.unsqueeze(-1		)
						A	 =      sample + step_size * model_output
						A	 =      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
										return (prev_sample,)

						return SchedulerOutput(prev_sample=_lowerCamelCase		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[Any]						,)    ->   torch.FloatTensor:


						'''simple docstring'''
						A	 =      timesteps.to(original_samples.device		)
						A	 =      self.discrete_sigmas.to(original_samples.device		)[timesteps]
						A	 =      (
						    noise * sigmas[:, None, None, None]
						    if noise is not None
						    else torch.randn_like(_lowerCamelCase		) * sigmas[:, None, None, None]
						)
						A	 =      noise + original_samples
						return noisy_samples







		def __len__(  self		:       str		)    ->   Union[str, Any]:


						'''simple docstring'''
						return self.config.num_train_timesteps













import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





