






# 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 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  1







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase=2_0_0_0       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=2_0       ,					__lowerCAmelCase=1E-3     ):
								"""simple docstring"""
								__magic_name__		:str        =      None
								__magic_name__		:Optional[Any]        =      None
								__magic_name__		:int        =      None







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:int        =      torch.linspace(1       ,					self.config.sampling_eps       ,					__lowerCAmelCase       ,					device=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=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
								__magic_name__		:Union[str, Any]        =      (
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								__magic_name__		:Optional[Any]        =      torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								__magic_name__		:int        =      std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												__magic_name__		:Tuple        =      std.unsqueeze(-1     )
								__magic_name__		:Tuple        =      -score / std

								# compute
								__magic_name__		:Dict        =      -1.0 / len(self.timesteps     )

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

								__magic_name__		:Optional[int]        =      torch.sqrt(__lowerCAmelCase     )
								__magic_name__		:str        =      drift - diffusion**2 * score
								__magic_name__		:int        =      x + drift * dt

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

								return x, x_mean







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






