

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						1





			@register_to_config
			def __init__(		self					,  __snake_case = 1000					,  __snake_case = None					)   -> List[str]:




						'''simple docstring'''
						# set `betas`, `alphas`, `timesteps`
						self.set_timesteps(__snake_case					)

						# standard deviation of the initial noise distribution
						__a        =1.0

						# For now we only support F-PNDM, i.e. the runge-kutta method
						# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
						# mainly at formula (9), (12), (13) and the Algorithm 2.
						__a        =4

						# running values
						__a        =[]





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case = None					)   -> str:




						'''simple docstring'''
						__a        =num_inference_steps
						__a        =torch.linspace(1					,  0					,  num_inference_steps + 1					)[:-1]
						__a        =torch.cat([steps, torch.tensor([0.0]					)]					)

						if self.config.trained_betas is not None:
									__a        =torch.tensor(self.config.trained_betas					,  dtype=torch.floataa					)
						else:
									__a        =torch.sin(steps * math.pi / 2					) ** 2

						__a        =(1.0 - self.betas**2) ** 0.5

						__a        =(torch.atana(self.betas					,  self.alphas					) / math.pi * 2)[:-1]
						__a        =timesteps.to(__snake_case					)

						__a        =[]





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = True					,  )   -> Union[SchedulerOutput, Tuple]:




						'''simple docstring'''
						if self.num_inference_steps is None:
									raise ValueError(
									    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'					)

						__a        =(self.timesteps == timestep).nonzero().item()
						__a        =timestep_index + 1

						__a        =sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
						self.ets.append(__snake_case					)

						if len(self.ets					) == 1:
									__a        =self.ets[-1]
						elif len(self.ets					) == 2:
									__a        =(3 * self.ets[-1] - self.ets[-2]) / 2
						elif len(self.ets					) == 3:
									__a        =(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
						else:
									__a        =(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

						__a        =self._get_prev_sample(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  *__snake_case					,  **__snake_case					)   -> torch.FloatTensor:




						'''simple docstring'''
						return sample





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =self.alphas[timestep_index]
						__a        =self.betas[timestep_index]

						__a        =self.alphas[prev_timestep_index]
						__a        =self.betas[prev_timestep_index]

						__a        =(sample - sigma * ets) / max(__snake_case					,  1e-8					)
						__a        =next_alpha * pred + ets * next_sigma

						return prev_sample





			def __len__(		self					)   -> Dict:




						'''simple docstring'''
						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					)






