


'''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	(						lowercase__							):



					'''simple docstring'''







					A_     :      torch.FloatTensor
					A_     :      torch.FloatTensor
					A_     :      Optional[torch.FloatTensor]				   =    None







class _lowerCamelCase	(						lowercase__		,					lowercase__							):



					'''simple docstring'''







					A_     :      Optional[int]				   =    2







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

						# setable values
						__magic_name__	:   int					     =					None
						__magic_name__	:   np.IntTensor					     =					None
						__magic_name__	:   torch.FloatTensor					     =					None  # sigma(t_i)







					def        __lowerCAmelCase		( self		:						Any  ,					_A		:						torch.FloatTensor  ,					_A		:						Optional[int] = None      )  ->			torch.FloatTensor:
						return sample







					def        __lowerCAmelCase		( self		:						str  ,					_A		:						int  ,					_A		:						Union[str, torch.device] = None      )  ->			int:
						__magic_name__	:   Tuple					     =					num_inference_steps
						__magic_name__	:   Optional[Any]					     =					np.arange(0  ,					self.num_inference_steps      )[::-1].copy()
						__magic_name__	:   Any					     =					torch.from_numpy(_A      ).to(_A      )
						__magic_name__	:   Optional[int]					     =					[
						    (
						        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
						]
						__magic_name__	:   Tuple					     =					torch.tensor(_A  ,					dtype=torch.floataa  ,					device=_A      )







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

						# sample eps ~ N(0, S_noise^2 * I)
						__magic_name__	:   Optional[Any]					     =					self.config.s_noise * randn_tensor(sample.shape  ,					generator=_A      ).to(sample.device      )
						__magic_name__	:   Dict					     =					sigma + gamma * sigma
						__magic_name__	:   Union[str, Any]					     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat







					def        __lowerCAmelCase		( self		:						Any  ,					_A		:						torch.FloatTensor  ,					_A		:						float  ,					_A		:						float  ,					_A		:						torch.FloatTensor  ,					_A		:						bool = True  ,					)  ->			Union[KarrasVeOutput, Tuple]:
						__magic_name__	:   List[Any]					     =					sample_hat + sigma_hat * model_output
						__magic_name__	:   int					     =					(sample_hat - pred_original_sample) / sigma_hat
						__magic_name__	:   Any					     =					sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
							return (sample_prev, derivative)

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







					def        __lowerCAmelCase		( self		:						str  ,					_A		:						torch.FloatTensor  ,					_A		:						float  ,					_A		:						float  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.FloatTensor  ,					_A		:						bool = True  ,					)  ->			Union[KarrasVeOutput, Tuple]:
						__magic_name__	:   Dict					     =					sample_prev + sigma_prev * model_output
						__magic_name__	:   Any					     =					(sample_prev - pred_original_sample) / sigma_prev
						__magic_name__	:   int					     =					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=_A  ,					derivative=_A  ,					pred_original_sample=_A      )







					def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						str  ,					_A		:						Any  ,					_A		:						str      )  ->			str:
						raise NotImplementedError()



'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase				:str        =      {
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

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

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

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase				:Optional[Any]        =      ['''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

							lowerCAmelCase				:Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)