



# 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 (     lowerCamelCase__       ,						lowerCamelCase__       ):
				__lowerCamelCase =				1




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



									"""simple docstring"""





									_lowerCAmelCase						=    None
									_lowerCAmelCase						=    None
									_lowerCAmelCase						=    None




				def 							snake_case   (							self						,				_snake_case						,				_snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase						=    torch.linspace(1						,				self.config.sampling_eps						,				_snake_case						,				device=_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case=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
									_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(_snake_case		)
									_lowerCAmelCase						=    drift - diffusion**2 * score
									_lowerCAmelCase						=    x + drift * dt

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

									return x, x_mean




				def __len__(							self		):



									"""simple docstring"""





									return self.config.num_train_timesteps










from typing import List

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			"efficientformer"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    List[int] = [3, 2, 6, 4]						,lowerCamelCase__						:    List[int] = [48, 96, 224, 448]						,lowerCamelCase__						:    List[bool] = [True, True, True, True]						,lowerCamelCase__						:    int = 448						,lowerCamelCase__						:    int = 32						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    int = 7						,lowerCamelCase__						:    int = 5						,lowerCamelCase__						:    int = 8						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 16						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 2						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    float = 1e-5						,lowerCamelCase__						:    str = "gelu"						,lowerCamelCase__						:    float = 0.02						,lowerCamelCase__						:    float = 1e-1_2						,lowerCamelCase__						:    int = 224						,lowerCamelCase__						:    float = 1e-0_5						,**lowerCamelCase__						:    str						,)     ->		None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

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


