






"""simple docstring"""



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						(				UpperCamelCase       ,		UpperCamelCase		):
							snake_case_     =							1


							@register_to_config
							def __init__(			self      :						Any			,    snake_case      :						Any=2000			,    snake_case      :						Union[str, Any]=0.1			,    snake_case      :						List[Any]=20			,    snake_case      :						int=1e-3							):
													'''simple docstring'''





													A__						:  Any		  =  None
													A__						:  Tuple		  =  None
													A__						:  List[str]		  =  None


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Any			,    snake_case      :						Union[str, torch.device] = None							):
													'''simple docstring'''





													A__						:  Dict		  =  torch.linspace(1			,    self.config.sampling_eps			,    snake_case			,    device=snake_case							)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Optional[int]			,    snake_case      :						Tuple			,    snake_case      :						List[Any]			,    snake_case      :						str=None							):
													'''simple docstring'''





													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__						:  int		  =  (
													    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
													)
													A__						:  List[str]		  =  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
													A__						:  Tuple		  =  std.flatten()
													while len(std.shape							) < len(score.shape							):
																			A__						:  Optional[int]		  =  std.unsqueeze(-1							)
													A__						:  Tuple		  =  -score / std

													# compute
													A__						:  Optional[int]		  =  -1.0 / len(self.timesteps							)

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

													A__						:  Optional[Any]		  =  torch.sqrt(snake_case							)
													A__						:  int		  =  drift - diffusion**2 * score
													A__						:  Tuple		  =  x + drift * dt

													# add noise
													A__						:  List[str]		  =  randn_tensor(x.shape			,    layout=x.layout			,    generator=snake_case			,    device=x.device			,    dtype=x.dtype							)
													A__						:  int		  =  x_mean + diffusion * math.sqrt(-dt							) * noise

													return x, x_mean


							def __len__(			self      :						Dict							):
													'''simple docstring'''





													return self.config.num_train_timesteps














"""simple docstring"""



import os

try:
						from .build_directory_md import good_file_paths
except ImportError:
						from build_directory_md import good_file_paths  # type: ignore

A_    =							list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_    =							[file for file in filepaths if file != file.lower()]
if upper_files:
						print(F'{len(upper_files)} files contain uppercase characters:')
						print('''\n'''.join(upper_files) + '''\n''')

A_    =							[file for file in filepaths if ''' ''' in file]
if space_files:
						print(F'{len(space_files)} files contain space characters:')
						print('''\n'''.join(space_files) + '''\n''')

A_    =							[file for file in filepaths if '''-''' in file]
if hyphen_files:
						print(F'{len(hyphen_files)} files contain hyphen characters:')
						print('''\n'''.join(hyphen_files) + '''\n''')

A_    =							[file for file in filepaths if os.sep not in file]
if nodir_files:
						print(F'{len(nodir_files)} files are not in a directory:')
						print('''\n'''.join(nodir_files) + '''\n''')

A_    =							len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
						import sys

						sys.exit(bad_files)






