





'''simple docstring'''


# 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_		(		__magic_name__    ,__magic_name__		):
 __lowerCamelCase    :				Dict								=       1




 @register_to_config
 def __init__(						self      ,  _lowerCAmelCase=2000      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=20      ,  _lowerCAmelCase=1E-3		)     ->				List[str]:
     _lowerCAmelCase						      =   None
     _lowerCAmelCase						      =   None
     _lowerCAmelCase						      =   None




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Any:
     _lowerCAmelCase						      =   torch.linspace(1      ,  self.config.sampling_eps      ,  _lowerCAmelCase      ,  device=_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None		)     ->				List[Any]:
     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
     _lowerCAmelCase						      =   (
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     _lowerCAmelCase						      =   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
     _lowerCAmelCase						      =   std.flatten()
     while len(std.shape		) < len(score.shape		):
         _lowerCAmelCase						      =   std.unsqueeze(-1		)
     _lowerCAmelCase						      =   -score / std

     # compute
     _lowerCAmelCase						      =   -1.0 / len(self.timesteps		)

     _lowerCAmelCase						      =   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
     _lowerCAmelCase						      =   beta_t.flatten()
     while len(beta_t.shape		) < len(x.shape		):
         _lowerCAmelCase						      =   beta_t.unsqueeze(-1		)
     _lowerCAmelCase						      =   -0.5 * beta_t * x

     _lowerCAmelCase						      =   torch.sqrt(_lowerCAmelCase		)
     _lowerCAmelCase						      =   drift - diffusion**2 * score
     _lowerCAmelCase						      =   x + drift * dt

     # add noise
     _lowerCAmelCase						      =   randn_tensor(x.shape      ,  layout=x.layout      ,  generator=_lowerCAmelCase      ,  device=x.device      ,  dtype=x.dtype		)
     _lowerCAmelCase						      =   x_mean + diffusion * math.sqrt(-dt		) * noise

     return x, x_mean





 def __len__(						self		)     ->				Optional[int]:
     return self.config.num_train_timesteps









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "swinv2"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=32      ,  **_lowerCAmelCase      ,  )     ->				Tuple:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
     _lowerCAmelCase						      =   (0, 0, 0, 0)


