





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


_snake_case     :		Union[str, Any]       						= logging.get_logger(__name__)

_snake_case     :		Optional[Any]       						= {
    '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   (	lowercase_ ):
					UpperCamelCase      =							'''donut-swin'''

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


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:int=2_24     ,       __UpperCamelCase			:List[str]=4     ,       __UpperCamelCase			:Optional[int]=3     ,       __UpperCamelCase			:Dict=96     ,       __UpperCamelCase			:List[Any]=[2, 2, 6, 2]     ,       __UpperCamelCase			:Dict=[3, 6, 12, 24]     ,       __UpperCamelCase			:Dict=7     ,       __UpperCamelCase			:List[str]=4.0     ,       __UpperCamelCase			:Any=True     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:List[str]=0.0     ,       __UpperCamelCase			:int=0.1     ,       __UpperCamelCase			:int="gelu"     ,       __UpperCamelCase			:Optional[int]=False     ,       __UpperCamelCase			:List[Any]=0.02     ,       __UpperCamelCase			:str=1e-5     ,       **__UpperCamelCase			:Any     ,       ):
												super().__init__(**__UpperCamelCase		)

												A		=  image_size
												A		=  patch_size
												A		=  num_channels
												A		=  embed_dim
												A		=  depths
												A		=  len(__UpperCamelCase		)
												A		=  num_heads
												A		=  window_size
												A		=  mlp_ratio
												A		=  qkv_bias
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  drop_path_rate
												A		=  hidden_act
												A		=  use_absolute_embeddings
												A		=  layer_norm_eps
												A		=  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
												A		=  int(embed_dim * 2 ** (len(__UpperCamelCase		) - 1)		)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


