



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_ ):
 lowerCAmelCase__							       =	4_2
 lowerCAmelCase__							       =	4_2
 lowerCAmelCase__							       =	None



class 			__lowerCAmelCase  (						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_ ):
 lowerCAmelCase__							       =	2




 @register_to_config
 def __init__(    self						,					__UpperCAmelCase = 0.02						,					__UpperCAmelCase = 100						,					__UpperCAmelCase = 1.007						,					__UpperCAmelCase = 80						,					__UpperCAmelCase = 0.05						,					__UpperCAmelCase = 50						,					):



    '''simple docstring'''
    # standard deviation of the initial noise distribution
    __lowerCamelCase    				=     sigma_max

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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    return sample




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    __lowerCamelCase    				=     num_inference_steps
    __lowerCamelCase    				=     np.arange(0						,					self.num_inference_steps					)[::-1].copy()
    __lowerCamelCase    				=     torch.from_numpy(__snake_case					).to(__snake_case					)
    __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(__snake_case						,					dtype=torch.floataa						,					device=__snake_case					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    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=__snake_case					).to(sample.device					)
    __lowerCamelCase    				=     sigma + gamma * sigma
    __lowerCamelCase    				=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = True						,					):



    '''simple docstring'''
    __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=__snake_case						,					derivative=__snake_case						,					pred_original_sample=__snake_case					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = True						,					):



    '''simple docstring'''
    __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=__snake_case						,					derivative=__snake_case						,					pred_original_sample=__snake_case					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    raise NotImplementedError()














"""simple docstring"""





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"efficientnet"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int = 3     ,		__snake_case  :      int = 6_0_0     ,		__snake_case  :      float = 2.0     ,		__snake_case  :      float = 3.1     ,		__snake_case  :      int = 8     ,		__snake_case  :      List[int] = [3, 3, 5, 3, 5, 5, 3]     ,		__snake_case  :      List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,		__snake_case  :      List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,		__snake_case  :      List[int] = []     ,		__snake_case  :      List[int] = [1, 2, 2, 2, 1, 2, 1]     ,		__snake_case  :      List[int] = [1, 2, 2, 3, 3, 4, 1]     ,		__snake_case  :      List[int] = [1, 6, 6, 6, 6, 6, 6]     ,		__snake_case  :      float = 0.25     ,		__snake_case  :      str = "swish"     ,		__snake_case  :      int = 2_5_6_0     ,		__snake_case  :      str = "mean"     ,		__snake_case  :      float = 0.02     ,		__snake_case  :      float = 0.001     ,		__snake_case  :      float = 0.99     ,		__snake_case  :      float = 0.5     ,		__snake_case  :      float = 0.2     ,		**__snake_case  :      List[Any]     ,		)			->							List[Any]:
											super().__init__(**__snake_case					)

											__magic_name__:	str							=       num_channels
											__magic_name__:	List[str]							=       image_size
											__magic_name__:	List[str]							=       width_coefficient
											__magic_name__:	Optional[Any]							=       depth_coefficient
											__magic_name__:	Tuple							=       depth_divisor
											__magic_name__:	Dict							=       kernel_sizes
											__magic_name__:	int							=       in_channels
											__magic_name__:	str							=       out_channels
											__magic_name__:	Dict							=       depthwise_padding
											__magic_name__:	Union[str, Any]							=       strides
											__magic_name__:	Dict							=       num_block_repeats
											__magic_name__:	Tuple							=       expand_ratios
											__magic_name__:	List[str]							=       squeeze_expansion_ratio
											__magic_name__:	Any							=       hidden_act
											__magic_name__:	Tuple							=       hidden_dim
											__magic_name__:	int							=       pooling_type
											__magic_name__:	int							=       initializer_range
											__magic_name__:	List[str]							=       batch_norm_eps
											__magic_name__:	str							=       batch_norm_momentum
											__magic_name__:	List[str]							=       dropout_rate
											__magic_name__:	Dict							=       drop_connect_rate
											__magic_name__:	Optional[Any]							=       sum(__snake_case					) * 4



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				version.parse("1.11"			)

					@property
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Mapping[str, Mapping[int, str]]:
											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											    ]					)






					@property
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							float:
											return 1E-5



