





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			List[Any]     =    "vit_mae"


	def __init__(self       ,_lowerCamelCase=768       ,_lowerCamelCase=12       ,_lowerCamelCase=12       ,_lowerCamelCase=3072       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-1_2       ,_lowerCamelCase=224       ,_lowerCamelCase=16       ,_lowerCamelCase=3       ,_lowerCamelCase=True       ,_lowerCamelCase=16       ,_lowerCamelCase=512       ,_lowerCamelCase=8       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.7_5       ,_lowerCamelCase=False       ,**_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)

				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_size
				__lowercase			  =							hidden_act
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_probs_dropout_prob
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							image_size
				__lowercase			  =							patch_size
				__lowercase			  =							num_channels
				__lowercase			  =							qkv_bias
				__lowercase			  =							decoder_num_attention_heads
				__lowercase			  =							decoder_hidden_size
				__lowercase			  =							decoder_num_hidden_layers
				__lowercase			  =							decoder_intermediate_size
				__lowercase			  =							mask_ratio
				__lowercase			  =							norm_pix_loss












'''simple docstring'''



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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[List[PIL.Image.Image], np.ndarray]
	a      :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





