

import logging

from transformers.configuration_utils import PretrainedConfig


a		=logging.getLogger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[Any]						      =      '''masked_bert'''
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			List[Any]=3_0_5_2_2   ,SCREAMING_SNAKE_CASE__		:			str=7_6_8   ,SCREAMING_SNAKE_CASE__		:			List[str]=1_2   ,SCREAMING_SNAKE_CASE__		:			List[str]=1_2   ,SCREAMING_SNAKE_CASE__		:			str=3_0_7_2   ,SCREAMING_SNAKE_CASE__		:			Tuple="gelu"   ,SCREAMING_SNAKE_CASE__		:			Tuple=0.1   ,SCREAMING_SNAKE_CASE__		:			Any=0.1   ,SCREAMING_SNAKE_CASE__		:			Any=5_1_2   ,SCREAMING_SNAKE_CASE__		:			List[str]=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=0.02   ,SCREAMING_SNAKE_CASE__		:			Any=1E-12   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=0   ,SCREAMING_SNAKE_CASE__		:			Optional[int]="topK"   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]="constant"   ,SCREAMING_SNAKE_CASE__		:			str=0.0   ,**SCREAMING_SNAKE_CASE__		:			Tuple   ,):
								super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = vocab_size
								__lowerCamelCase				:				Any					       = hidden_size
								__lowerCamelCase				:				Optional[Any]					       = num_hidden_layers
								__lowerCamelCase				:				Any					       = num_attention_heads
								__lowerCamelCase				:				List[Any]					       = hidden_act
								__lowerCamelCase				:				Optional[Any]					       = intermediate_size
								__lowerCamelCase				:				Optional[Any]					       = hidden_dropout_prob
								__lowerCamelCase				:				Optional[int]					       = attention_probs_dropout_prob
								__lowerCamelCase				:				Union[str, Any]					       = max_position_embeddings
								__lowerCamelCase				:				List[Any]					       = type_vocab_size
								__lowerCamelCase				:				Optional[Any]					       = initializer_range
								__lowerCamelCase				:				str					       = layer_norm_eps
								__lowerCamelCase				:				int					       = pruning_method
								__lowerCamelCase				:				str					       = mask_init
								__lowerCamelCase				:				Union[str, Any]					       = mask_scale






'''simple docstring'''




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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)





@dataclass
class  A				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		42


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_if import IFPipeline
      from .pipeline_if_imgaimg import IFImgaImgPipeline
      from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
      from .pipeline_if_inpainting import IFInpaintingPipeline
      from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
      from .pipeline_if_superresolution import IFSuperResolutionPipeline
      from .safety_checker import IFSafetyChecker
      from .watermark import IFWatermarker




