
"""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	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ):
     lowercase		     =		1





     @register_to_config
     def __init__(			self			,  __UpperCAmelCase=2000			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=20			,  __UpperCAmelCase=1E-3   ):

      '''simple docstring'''




      __UpperCamelCase       						= None
      __UpperCamelCase       						= None
      __UpperCamelCase       						= None





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

      '''simple docstring'''




      __UpperCamelCase       						= torch.linspace(1			,  self.config.sampling_eps			,  __UpperCAmelCase			,  device=__UpperCAmelCase   )





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

      '''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
      __UpperCamelCase       						= (
          -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      __UpperCamelCase       						= torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
      __UpperCamelCase       						= std.flatten()
      while len(std.shape   ) < len(score.shape   ):
       __UpperCamelCase       						= std.unsqueeze(-1   )
      __UpperCamelCase       						= -score / std

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

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

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

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

      return x, x_mean







     def __len__(			self   ):

      '''simple docstring'''




      return self.config.num_train_timesteps





"""simple docstring"""

from typing import List

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


UpperCamelCase			:       Tuple									=				logging.get_logger(__name__)

UpperCamelCase			:       List[str]									=				{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"efficientformer"





     def __init__(			self			,  __UpperCAmelCase = [3, 2, 6, 4]			,  __UpperCAmelCase = [48, 96, 224, 448]			,  __UpperCAmelCase = [True, True, True, True]			,  __UpperCAmelCase = 448			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 7			,  __UpperCAmelCase = 5			,  __UpperCAmelCase = 8			,  __UpperCAmelCase = 4			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 16			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 3			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = 0.0			,  __UpperCAmelCase = 1			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1E-5			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase = 1E-12			,  __UpperCAmelCase = 224			,  __UpperCAmelCase = 1E-05			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= mlp_expansion_ratio
      __UpperCamelCase       						= downsamples
      __UpperCamelCase       						= dim
      __UpperCamelCase       						= key_dim
      __UpperCamelCase       						= attention_ratio
      __UpperCamelCase       						= resolution
      __UpperCamelCase       						= pool_size
      __UpperCamelCase       						= downsample_patch_size
      __UpperCamelCase       						= downsample_stride
      __UpperCamelCase       						= downsample_pad
      __UpperCamelCase       						= drop_path_rate
      __UpperCamelCase       						= num_metaad_blocks
      __UpperCamelCase       						= distillation
      __UpperCamelCase       						= use_layer_scale
      __UpperCamelCase       						= layer_scale_init_value
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= batch_norm_eps



