


import os
from collections.abc import Iterator







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str = "."    )     ->		Iterator[str]:

       """simple docstring"""

       for dir_path, dir_names, filenames in os.walk(__A    ):
              a_ :		Dict							=  [d for d in dir_names if d != 'scripts' and d[0] not in '._']
              for filename in filenames:
                     if filename == "__init__.py":
                            continue
                     if os.path.splitext(__A    )[1] in (".py", ".ipynb"):
                            yield os.path.join(__A  ,   __A    ).lstrip('./'    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]    )     ->		Optional[Any]:

       """simple docstring"""

       return F"""{i * "  "}*""" if i else "\n##"







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  old_path.split(os.sep    )
       for i, new_part in enumerate(new_path.split(os.sep    )    ):
              if (i + 1 > len(__A    ) or old_parts[i] != new_part) and new_part:
                     print(F"""{md_prefix(__A    )} {new_part.replace("_"  ,   " "    ).title()}"""    )
       return new_path







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str = "."    )     ->		None:

       """simple docstring"""

       a_ :		Dict							=  ''
       for filepath in sorted(good_file_paths(__A    )    ):
              a_ :		str							=  os.path.split(__A    )
              if filepath != old_path:
                     a_ :		List[Any]							=  print_path(__A  ,   __A    )
              a_ :		Dict							=  (filepath.count(os.sep    ) + 1) if filepath else 0
              a_ :		int							=  F"""{filepath}/{filename}""".replace(' '  ,   '%20'    )
              a_ :		Optional[Any]							=  os.path.splitext(filename.replace('_'  ,   ' '    ).title()    )[0]
              print(F"""{md_prefix(__A    )} [{filename}]({url})"""    )


if __name__ == "__main__":
   print_directory_md('.')






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


