"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42
					a   	=		None



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		2



					@register_to_config
					def __init__(						self			:				Any   , _snake_case			:				float = 0.02   , _snake_case			:				float = 100   , _snake_case			:				float = 1.007   , _snake_case			:				float = 80   , _snake_case			:				float = 0.05   , _snake_case			:				float = 50   , )							->       Optional[Any]:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None  # sigma(t_i)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				Union[str, torch.device] = None		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				num_inference_steps
									SCREAMING_SNAKE_CASE__		  =				np.arange(0   , self.num_inference_steps		)[::-1].copy()
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(_snake_case		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in self.timesteps
									]
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(_snake_case   , dtype=torch.floataa   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				Optional[torch.Generator] = None		)							->       Tuple[torch.FloatTensor, float]:
									if self.config.s_min <= sigma <= self.config.s_max:
													SCREAMING_SNAKE_CASE__		  =				min(self.config.s_churn / self.num_inference_steps   , 2**0.5 - 1		)
									else:
													SCREAMING_SNAKE_CASE__		  =				0

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE__		  =				self.config.s_noise * randn_tensor(sample.shape   , generator=_snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				sigma + gamma * sigma
									SCREAMING_SNAKE_CASE__		  =				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				float   , _snake_case			:				torch.FloatTensor   , _snake_case			:				bool = True   , )							->       Union[KarrasVeOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE__		  =				(sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE__		  =				sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
													return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_snake_case   , derivative=_snake_case   , pred_original_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				float   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				bool = True   , )							->       Union[KarrasVeOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE__		  =				(sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE__		  =				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
													return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_snake_case   , derivative=_snake_case   , pred_original_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Dict   , _snake_case			:				Any		)							->       Union[str, Any]:
									raise NotImplementedError()





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A   					=    {
    'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['VisionEncoderDecoderModel']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['TFVisionEncoderDecoderModel']

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    ['FlaxVisionEncoderDecoderModel']

if TYPE_CHECKING:
						from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




