
'''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       __A(				a__       ):
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    42
					SCREAMING_SNAKE_CASE			    =    None





class       __A(				a__     ,		a__       ):
					SCREAMING_SNAKE_CASE			    =    2

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any = 0.02   ,					__UpperCamelCase					:     Optional[Any] = 1_0_0   ,					__UpperCamelCase					:     Any = 1.007   ,					__UpperCamelCase					:     Dict = 8_0   ,					__UpperCamelCase					:     List[str] = 0.05   ,					__UpperCamelCase					:     Optional[int] = 5_0   ,					):
												# standard deviation of the initial noise distribution
												lowerCamelCase_							  =					sigma_max

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

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str = None   ):
												return sample

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any = None   ):
												lowerCamelCase_							  =					num_inference_steps
												lowerCamelCase_							  =					np.arange(0   ,					self.num_inference_steps   )[::-1].copy()
												lowerCamelCase_							  =					torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
												lowerCamelCase_							  =					[
												    (
												        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
												]
												lowerCamelCase_							  =					torch.tensor(lowerCAmelCase__   ,					dtype=torch.floataa   ,					device=lowerCAmelCase__   )

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int = None   ):
												if self.config.s_min <= sigma <= self.config.s_max:
																			lowerCamelCase_							  =					min(self.config.s_churn / self.num_inference_steps   ,					2**0.5 - 1   )
												else:
																			lowerCamelCase_							  =					0

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

												return sample_hat, sigma_hat

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Any = True   ,					):
												lowerCamelCase_							  =					sample_hat + sigma_hat * model_output
												lowerCamelCase_							  =					(sample_hat - pred_original_sample) / sigma_hat
												lowerCamelCase_							  =					sample_hat + (sigma_prev - sigma_hat) * derivative

												if not return_dict:
																			return (sample_prev, derivative)

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

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any] = True   ,					):
												lowerCamelCase_							  =					sample_prev + sigma_prev * model_output
												lowerCamelCase_							  =					(sample_prev - pred_original_sample) / sigma_prev
												lowerCamelCase_							  =					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=lowerCAmelCase__   ,					derivative=lowerCAmelCase__   ,					pred_original_sample=lowerCAmelCase__   )



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Dict   ):
												raise NotImplementedError()







from typing import TYPE_CHECKING

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


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





