


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


UpperCAmelCase_				:			Any   =	logging.get_logger(__name__)

UpperCAmelCase_				:			Tuple   =	{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Optional[int]					       =						'''resnet'''
      snake_case__    :					Dict					       =						['''basic''', '''bottleneck''']
      def __init__(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Any=3					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=6_4					,							SCREAMING_SNAKE_CASE__    :	int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=[3, 4, 6, 3]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="bottleneck"					,							SCREAMING_SNAKE_CASE__    :	Tuple="relu"					,							SCREAMING_SNAKE_CASE__    :	Dict=False					,							SCREAMING_SNAKE_CASE__    :	Dict=None					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							**SCREAMING_SNAKE_CASE__    :	List[str]					,							)		->      Dict:
             super().__init__(**SCREAMING_SNAKE_CASE__					)
             if layer_type not in self.layer_types:
                    raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types					)}"""					)
             a_ :		int							=  num_channels
             a_ :		Optional[int]							=  embedding_size
             a_ :		List[str]							=  hidden_sizes
             a_ :		Tuple							=  depths
             a_ :		int							=  layer_type
             a_ :		Dict							=  hidden_act
             a_ :		Any							=  downsample_in_first_stage
             a_ :		int							=  ['stem'] + [F"""stage{idx}""" for idx in range(1					,							len(SCREAMING_SNAKE_CASE__					) + 1					)]
             a_							,		a_ :		str							=  get_aligned_output_features_output_indices(
                 out_features=SCREAMING_SNAKE_CASE__					,							out_indices=SCREAMING_SNAKE_CASE__					,							stage_names=self.stage_names					)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Dict					       =						version.parse('''1.11'''      )
      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Mapping[str, Mapping[int, str]]:
             return OrderedDict(
                 [
                     ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                 ]					)

      @property
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      float:
             return 1E-3






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


