


"""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 						snake_case_(				a__	):
     __UpperCamelCase     =    42
     __UpperCamelCase     =    42



class 						snake_case_(				a__							,    a__	):
     __UpperCamelCase     =    1






     @register_to_config
     def __init__(		self     :    str    , UpperCamelCase_     :    int = 2_0_0_0    , UpperCamelCase_     :    float = 0.15    , UpperCamelCase_     :    float = 0.01    , UpperCamelCase_     :    float = 1_348.0    , UpperCamelCase_     :    float = 1E-5    , UpperCamelCase_     :    int = 1    , ):
           # standard deviation of the initial noise distribution
           lowerCAmelCase    :		Union[str, Any]				=      sigma_max

           # setable values
           lowerCAmelCase    :		Dict				=      None

           self.set_sigmas(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    Optional[int] = None ):
           return sample






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

           lowerCAmelCase    :		Dict				=      torch.linspace(1    , UpperCamelCase_    , UpperCamelCase_    , device=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    int    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    float = None ):
           lowerCAmelCase    :		Optional[Any]				=      sigma_min if sigma_min is not None else self.config.sigma_min
           lowerCAmelCase    :		Union[str, Any]				=      sigma_max if sigma_max is not None else self.config.sigma_max
           lowerCAmelCase    :		int				=      sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if self.timesteps is None:
                 self.set_timesteps(UpperCamelCase_    , UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
           lowerCAmelCase    :		Any				=      torch.exp(torch.linspace(math.log(UpperCamelCase_ )    , math.log(UpperCamelCase_ )    , UpperCamelCase_ ) )
           lowerCAmelCase    :		Dict				=      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[Any] ):
           return torch.where(
               timesteps == 0    , torch.zeros_like(t.to(timesteps.device ) )    , self.discrete_sigmas[timesteps - 1].to(timesteps.device )    , )






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

           lowerCAmelCase    :		Optional[int]				=      timestep * torch.ones(
               sample.shape[0]    , device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
           lowerCAmelCase    :		Any				=      (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
           lowerCAmelCase    :		int				=      timesteps.to(self.discrete_sigmas.device )

           lowerCAmelCase    :		Union[str, Any]				=      self.discrete_sigmas[timesteps].to(sample.device )
           lowerCAmelCase    :		Any				=      self.get_adjacent_sigma(UpperCamelCase_    , UpperCamelCase_ ).to(sample.device )
           lowerCAmelCase    :		Union[str, Any]				=      torch.zeros_like(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      (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
           lowerCAmelCase    :		List[str]				=      diffusion.flatten()
           while len(diffusion.shape ) < len(sample.shape ):
                 lowerCAmelCase    :		List[Any]				=      diffusion.unsqueeze(-1 )
           lowerCAmelCase    :		int				=      drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           lowerCAmelCase    :		str				=      randn_tensor(
               sample.shape    , layout=sample.layout    , generator=UpperCamelCase_    , device=sample.device    , dtype=sample.dtype )
           lowerCAmelCase    :		Optional[Any]				=      sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           lowerCAmelCase    :		Union[str, Any]				=      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=UpperCamelCase_    , prev_sample_mean=UpperCamelCase_ )






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

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

           if not return_dict:
                 return (prev_sample,)

           return SchedulerOutput(prev_sample=UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    torch.FloatTensor    , UpperCamelCase_     :    torch.FloatTensor    , ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           lowerCAmelCase    :		List[Any]				=      timesteps.to(original_samples.device )
           lowerCAmelCase    :		Optional[int]				=      self.discrete_sigmas.to(original_samples.device )[timesteps]
           lowerCAmelCase    :		str				=      (
               noise * sigmas[:, None, None, None]
               if noise is not None
               else torch.randn_like(UpperCamelCase_ ) * sigmas[:, None, None, None]
           )
           lowerCAmelCase    :		int				=      noise + original_samples
           return noisy_samples






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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






