


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='donut-swin'

		lowerCamelCase__				={
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		}



		def __init__(self				,       a_=2_24				,       a_=4				,       a_=3				,       a_=96				,       a_=[2, 2, 6, 2]				,       a_=[3, 6, 12, 24]				,       a_=7				,       a_=4.0				,       a_=True				,       a_=0.0				,       a_=0.0				,       a_=0.1				,       a_="gelu"				,       a_=False				,       a_=0.02				,       a_=1E-5				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						int						=				image_size
			__snake_case	:						Optional[Any]						=				patch_size
			__snake_case	:						Any						=				num_channels
			__snake_case	:						List[Any]						=				embed_dim
			__snake_case	:						List[str]						=				depths
			__snake_case	:						str						=				len(a_					)
			__snake_case	:						Optional[Any]						=				num_heads
			__snake_case	:						str						=				window_size
			__snake_case	:						int						=				mlp_ratio
			__snake_case	:						Any						=				qkv_bias
			__snake_case	:						Union[str, Any]						=				hidden_dropout_prob
			__snake_case	:						Dict						=				attention_probs_dropout_prob
			__snake_case	:						Optional[int]						=				drop_path_rate
			__snake_case	:						Any						=				hidden_act
			__snake_case	:						List[Any]						=				use_absolute_embeddings
			__snake_case	:						List[str]						=				layer_norm_eps
			__snake_case	:						Optional[int]						=				initializer_range
			# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
			# this indicates the channel dimension after the last stage of the model
			__snake_case	:						Any						=				int(embed_dim * 2 ** (len(a_					) - 1)					)









"""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





