






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase : int						   =       logging.get_logger(__name__)

lowerCamelCase : Optional[Any]						   =       {
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			Any									=      '''cvt'''



					def __init__(  self				:	Optional[Any]		,     __a				:	Optional[int]=3		,     __a				:	Optional[Any]=[7, 3, 3]		,     __a				:	List[Any]=[4, 2, 2]		,     __a				:	Union[str, Any]=[2, 1, 1]		,     __a				:	Dict=[64, 192, 384]		,     __a				:	str=[1, 3, 6]		,     __a				:	List[str]=[1, 2, 10]		,     __a				:	Optional[int]=[4.0, 4.0, 4.0]		,     __a				:	List[str]=[0.0, 0.0, 0.0]		,     __a				:	Any=[0.0, 0.0, 0.0]		,     __a				:	Dict=[0.0, 0.0, 0.1]		,     __a				:	Union[str, Any]=[True, True, True]		,     __a				:	int=[False, False, True]		,     __a				:	Optional[int]=["dw_bn", "dw_bn", "dw_bn"]		,     __a				:	Optional[int]=[3, 3, 3]		,     __a				:	Dict=[1, 1, 1]		,     __a				:	Tuple=[2, 2, 2]		,     __a				:	List[str]=[1, 1, 1]		,     __a				:	Optional[Any]=[1, 1, 1]		,     __a				:	Any=0.02		,     __a				:	List[str]=1E-12		,     **__a				:	str		,     )			->  Optional[Any]:


												"""simple docstring"""

												super().__init__(**__a							)
												__lowercase      :							Any				=     num_channels
												__lowercase      :							Union[str, Any]				=     patch_sizes
												__lowercase      :							int				=     patch_stride
												__lowercase      :							List[str]				=     patch_padding
												__lowercase      :							List[Any]				=     embed_dim
												__lowercase      :							Dict				=     num_heads
												__lowercase      :							Optional[int]				=     depth
												__lowercase      :							str				=     mlp_ratio
												__lowercase      :							Union[str, Any]				=     attention_drop_rate
												__lowercase      :							Optional[int]				=     drop_rate
												__lowercase      :							Any				=     drop_path_rate
												__lowercase      :							Optional[int]				=     qkv_bias
												__lowercase      :							Union[str, Any]				=     cls_token
												__lowercase      :							int				=     qkv_projection_method
												__lowercase      :							List[Any]				=     kernel_qkv
												__lowercase      :							Optional[int]				=     padding_kv
												__lowercase      :							str				=     stride_kv
												__lowercase      :							List[str]				=     padding_q
												__lowercase      :							List[str]				=     stride_q
												__lowercase      :							List[Any]				=     initializer_range
												__lowercase      :							int				=     layer_norm_eps







# 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 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												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
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

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

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps