




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase   :	Tuple       =    logging.get_logger(__name__)

_lowerCamelCase   :	str       =    {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}
class 							__snake_case	(_UpperCamelCase    ,						_UpperCamelCase		):
				lowerCAmelCase__      						=  '''resnet'''
				lowerCAmelCase__      						=  ['''basic''', '''bottleneck''']



				def __init__(   self   :		Union[str, Any]		,						_UpperCAmelCase   :		Dict=3		,						_UpperCAmelCase   :		Tuple=64		,						_UpperCAmelCase   :		Optional[Any]=[256, 512, 1024, 2048]		,						_UpperCAmelCase   :		Optional[int]=[3, 4, 6, 3]		,						_UpperCAmelCase   :		Union[str, Any]="bottleneck"		,						_UpperCAmelCase   :		Tuple="relu"		,						_UpperCAmelCase   :		Optional[int]=False		,						_UpperCAmelCase   :		int=None		,						_UpperCAmelCase   :		Tuple=None		,						**_UpperCAmelCase   :		List[str]		,						)	->     Dict:





								'''simple docstring'''




								super().__init__(**lowercase__   )
								if layer_type not in self.layer_types:
												raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types   )}"   )
								_lowerCAmelCase     :     List[Any]              =   num_channels
								_lowerCAmelCase     :     List[Any]              =   embedding_size
								_lowerCAmelCase     :     List[str]              =   hidden_sizes
								_lowerCAmelCase     :     Optional[Any]              =   depths
								_lowerCAmelCase     :     int              =   layer_type
								_lowerCAmelCase     :     str              =   hidden_act
								_lowerCAmelCase     :     Dict              =   downsample_in_first_stage
								_lowerCAmelCase     :     Union[str, Any]              =   ['''stem'''] + [f"stage{idx}" for idx in range(1		,						len(lowercase__   ) + 1   )]
								_lowerCAmelCase     :     Dict              =   get_aligned_output_features_output_indices(
								    out_features=lowercase__		,						out_indices=lowercase__		,						stage_names=self.stage_names   )






class 							__snake_case	(_UpperCamelCase		):
				lowerCAmelCase__      						=  version.parse("1.11"		)



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		int   )	->     str:





								'''simple docstring'''




								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								    ]   )



				@property
				def    SCREAMING_SNAKE_CASE							(   self   :		Dict   )	->     List[str]:





								'''simple docstring'''




								return 1E-3





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   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

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


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        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
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

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

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

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


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	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=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

