






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    {
							    "sld_warmup_steps": 15,
							    "sld_guidance_scale": 20,
							    "sld_threshold": 0.0,
							    "sld_momentum_scale": 0.0,
							    "sld_mom_beta": 0.0,
							}
							A_       =    {
							    "sld_warmup_steps": 10,
							    "sld_guidance_scale": 1_000,
							    "sld_threshold": 0.01,
							    "sld_momentum_scale": 0.3,
							    "sld_mom_beta": 0.4,
							}
							A_       =    {
							    "sld_warmup_steps": 7,
							    "sld_guidance_scale": 2_000,
							    "sld_threshold": 0.025,
							    "sld_momentum_scale": 0.5,
							    "sld_mom_beta": 0.7,
							}
							A_       =    {
							    "sld_warmup_steps": 0,
							    "sld_guidance_scale": 5_000,
							    "sld_threshold": 1.0,
							    "sld_momentum_scale": 0.5,
							    "sld_mom_beta": 0.7,
							}





@dataclass
class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    42
							A_       =    42
							A_       =    42
							A_       =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
				from .safety_checker import SafeStableDiffusionSafetyChecker











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


__A          = logging.get_logger(__name__)

__A          = {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       initializer_range
												_A									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



