
"""simple docstring"""







import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    **SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE							,    **SCREAMING_SNAKE_CASE )
							__snake_case     			=       AutoModelForSeqaSeqLM.from_config(SCREAMING_SNAKE_CASE )
							model.save_pretrained(SCREAMING_SNAKE_CASE )
							AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE ).save_pretrained(SCREAMING_SNAKE_CASE )
							return model


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


"""simple docstring"""







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

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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
												if self.timesteps is None:
																			self.set_timesteps(snake_case_							,						snake_case_    )

												__snake_case     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (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
												__snake_case     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (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
												__snake_case     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       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=snake_case_							,						prev_sample_mean=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												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
												__snake_case     			=       randn_tensor(sample.shape							,						layout=sample.layout							,						generator=snake_case_    ).to(sample.device    )

												# compute step size from the model_output, the noise, and the snr
												__snake_case     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       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
												__snake_case     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





					def __len__(				self   :							Tuple    ):
												return self.config.num_train_timesteps
