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


lowercase__          =logging.get_logger(__name__)

lowercase__          ={
    '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   ):
   _SCREAMING_SNAKE_CASE				:							int     	=				"donut-swin"

   _SCREAMING_SNAKE_CASE				:							Dict     	=				{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }
   def __init__(self			: Optional[int] ,    snake_case_			: Tuple=2_2_4 ,    snake_case_			: Any=4 ,    snake_case_			: Optional[Any]=3 ,    snake_case_			: Optional[Any]=9_6 ,    snake_case_			: str=[2, 2, 6, 2] ,    snake_case_			: Optional[Any]=[3, 6, 1_2, 2_4] ,    snake_case_			: Optional[int]=7 ,    snake_case_			: List[str]=4.0 ,    snake_case_			: Optional[int]=True ,    snake_case_			: str=0.0 ,    snake_case_			: int=0.0 ,    snake_case_			: Union[str, Any]=0.1 ,    snake_case_			: Tuple="gelu" ,    snake_case_			: Optional[Any]=False ,    snake_case_			: Tuple=0.02 ,    snake_case_			: List[Any]=1E-5 ,    **snake_case_			: Optional[Any] ,    ):
    super().__init__(**snake_case_       )

    __a			:  Optional[int]		    =  image_size
    __a			:  Tuple		    =  patch_size
    __a			:  Tuple		    =  num_channels
    __a			:  List[Any]		    =  embed_dim
    __a			:  List[Any]		    =  depths
    __a			:  Dict		    =  len(snake_case_       )
    __a			:  Union[str, Any]		    =  num_heads
    __a			:  Optional[Any]		    =  window_size
    __a			:  Dict		    =  mlp_ratio
    __a			:  Dict		    =  qkv_bias
    __a			:  List[str]		    =  hidden_dropout_prob
    __a			:  Any		    =  attention_probs_dropout_prob
    __a			:  Dict		    =  drop_path_rate
    __a			:  int		    =  hidden_act
    __a			:  Any		    =  use_absolute_embeddings
    __a			:  Optional[Any]		    =  layer_norm_eps
    __a			:  str		    =  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			:  str		    =  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,
)


lowercase__          ={
    '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:
      lowercase__          =['LayoutLMv2TokenizerFast']

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          '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

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
