'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}


class 	a__     (				_lowercase	):
	__magic_name__		:    List[str]	    =     "vit_msn"


	def __init__(self : Optional[Any],					__UpperCAmelCase : Dict=768,					__UpperCAmelCase : List[Any]=12,					__UpperCAmelCase : str=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Dict="gelu",					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : Tuple=0.0,					__UpperCAmelCase : List[str]=0.02,					__UpperCAmelCase : str=1e-06,					__UpperCAmelCase : Optional[int]=224,					__UpperCAmelCase : Union[str, Any]=16,					__UpperCAmelCase : Optional[Any]=3,					__UpperCAmelCase : Optional[Any]=True,					**__UpperCAmelCase : Dict,					)     ->			List[str]:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						Dict      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						Any      					= initializer_range
					SCREAMING_SNAKE_CASE  :						List[Any]      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						int      					= image_size
					SCREAMING_SNAKE_CASE  :						int      					= patch_size
					SCREAMING_SNAKE_CASE  :						Dict      					= num_channels
					SCREAMING_SNAKE_CASE  :						Tuple      					= qkv_bias









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




