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


A__           =							logging.get_logger(__name__)

A__           =							{
    """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   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'donut-swin'

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







							def __init__(				self     :    Optional[int]	,					__snake_case     :    List[str]=224	,					__snake_case     :    Optional[Any]=4	,					__snake_case     :    List[Any]=3	,					__snake_case     :    Optional[Any]=96	,					__snake_case     :    Dict=[2, 2, 6, 2]	,					__snake_case     :    Optional[Any]=[3, 6, 12, 24]	,					__snake_case     :    str=7	,					__snake_case     :    Dict=4.0	,					__snake_case     :    List[str]=True	,					__snake_case     :    Optional[Any]=0.0	,					__snake_case     :    List[Any]=0.0	,					__snake_case     :    List[str]=0.1	,					__snake_case     :    List[str]="gelu"	,					__snake_case     :    List[str]=False	,					__snake_case     :    int=0.0_2	,					__snake_case     :    List[Any]=1e-5	,					**__snake_case     :    Dict	,					):
										super().__init__(**__snake_case		)

										lowerCamelCase						:Optional[Any]						  =     image_size
										lowerCamelCase						:Union[str, Any]						  =     patch_size
										lowerCamelCase						:Optional[Any]						  =     num_channels
										lowerCamelCase						:Dict						  =     embed_dim
										lowerCamelCase						:Optional[int]						  =     depths
										lowerCamelCase						:List[Any]						  =     len(__snake_case		)
										lowerCamelCase						:Any						  =     num_heads
										lowerCamelCase						:int						  =     window_size
										lowerCamelCase						:Dict						  =     mlp_ratio
										lowerCamelCase						:str						  =     qkv_bias
										lowerCamelCase						:int						  =     hidden_dropout_prob
										lowerCamelCase						:List[Any]						  =     attention_probs_dropout_prob
										lowerCamelCase						:List[Any]						  =     drop_path_rate
										lowerCamelCase						:Union[str, Any]						  =     hidden_act
										lowerCamelCase						:str						  =     use_absolute_embeddings
										lowerCamelCase						:Dict						  =     layer_norm_eps
										lowerCamelCase						: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
										lowerCamelCase						:Optional[int]						  =     int(embed_dim * 2 ** (len(__snake_case		) - 1)		)





from typing import TYPE_CHECKING

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


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



