
# 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__    ):

							"""simple docstring"""






							_snake_case     		=  1



							@register_to_config
							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=2000     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=20     ,	SCREAMING_SNAKE_CASE_=1E-3			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			None
													__UpperCamelCase       =			None
													__UpperCamelCase       =			None



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			torch.linspace(1     ,	self.config.sampling_eps     ,	__lowercase     ,	device=__lowercase			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None			)->   Dict:





													'''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
													__UpperCamelCase       =			(
													    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
													)
													__UpperCamelCase       =			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
													__UpperCamelCase       =			std.flatten()
													while len(std.shape			) < len(score.shape			):
																			__UpperCamelCase       =			std.unsqueeze(-1			)
													__UpperCamelCase       =			-score / std

													# compute
													__UpperCamelCase       =			-1.0 / len(self.timesteps			)

													__UpperCamelCase       =			self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
													__UpperCamelCase       =			beta_t.flatten()
													while len(beta_t.shape			) < len(x.shape			):
																			__UpperCamelCase       =			beta_t.unsqueeze(-1			)
													__UpperCamelCase       =			-0.5 * beta_t * x

													__UpperCamelCase       =			torch.sqrt(__lowercase			)
													__UpperCamelCase       =			drift - diffusion**2 * score
													__UpperCamelCase       =			x + drift * dt

													# add noise
													__UpperCamelCase       =			randn_tensor(x.shape     ,	layout=x.layout     ,	generator=__lowercase     ,	device=x.device     ,	dtype=x.dtype			)
													__UpperCamelCase       =			x_mean + diffusion * math.sqrt(-dt			) * noise

													return x, x_mean



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





													'''simple docstring'''






													return self.config.num_train_timesteps






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




