


"""simple docstring"""




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 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Tuple								=     1







		@register_to_config
		def __init__(	self						,       lowerCAmelCase__ = 1_000						,       lowerCAmelCase__ = None				)     ->							int:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(lowerCAmelCase__				)

								# standard deviation of the initial noise distribution
								SCREAMING_SNAKE_CASE													=					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.
								SCREAMING_SNAKE_CASE													=					4

								# running values
								SCREAMING_SNAKE_CASE													=					[]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					num_inference_steps
								SCREAMING_SNAKE_CASE													=					torch.linspace(1						,       0						,       num_inference_steps + 1				)[:-1]
								SCREAMING_SNAKE_CASE													=					torch.cat([steps, torch.tensor([0.0]				)]				)

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

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

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

								SCREAMING_SNAKE_CASE													=					[]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = True						,       )     ->							Union[SchedulerOutput, Tuple]:
								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'				)

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

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

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

								SCREAMING_SNAKE_CASE													=					self._get_prev_sample(lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)

								if not return_dict:
														return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       *lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							torch.FloatTensor:
								return sample







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.alphas[timestep_index]
								SCREAMING_SNAKE_CASE													=					self.betas[timestep_index]

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

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

								return prev_sample







		def __len__(	self				)     ->							str:
								return self.config.num_train_timesteps






"""simple docstring"""




__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image'''])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

__UpperCamelCase						=      frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

__UpperCamelCase						=      frozenset(['''example_image''', '''image''', '''mask_image'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''class_labels'''])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(['''batch_size'''])

__UpperCamelCase						=      frozenset([])

__UpperCamelCase						=      frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

__UpperCamelCase						=      frozenset(['''prompt''', '''negative_prompt'''])
__UpperCamelCase						=      frozenset(['''input_tokens'''])

__UpperCamelCase						=      frozenset(['''input_tokens'''])


