



"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


lowerCamelCase      	=						logging.get_logger(__name__)

lowerCamelCase      	=						{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class 				lowercase__       (   lowerCamelCase_				):







       '''simple docstring'''


       UpperCamelCase						=					'''layoutlmv3'''



       def __init__(							self      :  str		,    _UpperCAmelCase      :  str=50265		,    _UpperCAmelCase      :  Any=768		,    _UpperCAmelCase      :  Union[str, Any]=12		,    _UpperCAmelCase      :  str=12		,    _UpperCAmelCase      :  Dict=3072		,    _UpperCAmelCase      :  Tuple="gelu"		,    _UpperCAmelCase      :  str=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  List[Any]=512		,    _UpperCAmelCase      :  List[str]=2		,    _UpperCAmelCase      :  List[Any]=0.02		,    _UpperCAmelCase      :  Optional[int]=1e-5		,    _UpperCAmelCase      :  List[str]=1		,    _UpperCAmelCase      :  Tuple=0		,    _UpperCAmelCase      :  Optional[int]=2		,    _UpperCAmelCase      :  str=1024		,    _UpperCAmelCase      :  List[Any]=128		,    _UpperCAmelCase      :  Optional[Any]=128		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  Optional[Any]=32		,    _UpperCAmelCase      :  Optional[int]=128		,    _UpperCAmelCase      :  Any=64		,    _UpperCAmelCase      :  int=256		,    _UpperCAmelCase      :  str=True		,    _UpperCAmelCase      :  Union[str, Any]=True		,    _UpperCAmelCase      :  Tuple=True		,    _UpperCAmelCase      :  Optional[int]=224		,    _UpperCAmelCase      :  int=3		,    _UpperCAmelCase      :  str=16		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  int		,    )			->						Any:


             '''simple docstring'''




             super().__init__(
                 vocab_size=__snake_case		,    hidden_size=__snake_case		,    num_hidden_layers=__snake_case		,    num_attention_heads=__snake_case		,    intermediate_size=__snake_case		,    hidden_act=__snake_case		,    hidden_dropout_prob=__snake_case		,    attention_probs_dropout_prob=__snake_case		,    max_position_embeddings=__snake_case		,    type_vocab_size=__snake_case		,    initializer_range=__snake_case		,    layer_norm_eps=__snake_case		,    pad_token_id=__snake_case		,    bos_token_id=__snake_case		,    eos_token_id=__snake_case		,    **__snake_case		,    )
             UpperCAmelCase_      			=							max_ad_position_embeddings
             UpperCAmelCase_      			=							coordinate_size
             UpperCAmelCase_      			=							shape_size
             UpperCAmelCase_      			=							has_relative_attention_bias
             UpperCAmelCase_      			=							rel_pos_bins
             UpperCAmelCase_      			=							max_rel_pos
             UpperCAmelCase_      			=							has_spatial_attention_bias
             UpperCAmelCase_      			=							rel_ad_pos_bins
             UpperCAmelCase_      			=							max_rel_ad_pos
             UpperCAmelCase_      			=							text_embed
             UpperCAmelCase_      			=							visual_embed
             UpperCAmelCase_      			=							input_size
             UpperCAmelCase_      			=							num_channels
             UpperCAmelCase_      			=							patch_size
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   lowerCamelCase_				):







       '''simple docstring'''


       UpperCamelCase						=					version.parse('''1.12'''				)



       @property
       def 					lowercase__    (							self      :  int   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task in ["question-answering", "sequence-classification"]:
                   return OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "sequence"}),
                           ("attention_mask", {0: "batch", 1: "sequence"}),
                           ("bbox", {0: "batch", 1: "sequence"}),
                           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                       ]   )
             else:
                   return OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "sequence"}),
                           ("bbox", {0: "batch", 1: "sequence"}),
                           ("attention_mask", {0: "batch", 1: "sequence"}),
                           ("pixel_values", {0: "batch", 1: "num_channels"}),
                       ]   )



       @property
       def 					lowercase__    (							self      :  Optional[Any]   )			->						float:


             '''simple docstring'''




             return 1e-5



       @property
       def 					lowercase__    (							self      :  List[Any]   )			->						int:


             '''simple docstring'''




             return 12







       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  "ProcessorMixin"		,    _UpperCAmelCase      :  int = -1		,    _UpperCAmelCase      :  int = -1		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  Optional["TensorType"] = None		,    _UpperCAmelCase      :  int = 3		,    _UpperCAmelCase      :  int = 40		,    _UpperCAmelCase      :  int = 40		,    )			->						Mapping[str, Any]:


             '''simple docstring'''




             setattr(processor.image_processor		,    "apply_ocr"		,    __snake_case   )

             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             UpperCAmelCase_      			=							compute_effective_axis_dimension(
                 __snake_case		,    fixed_dimension=OnnxConfig.default_fixed_batch		,    num_token_to_add=0   )
             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             UpperCAmelCase_      			=							processor.tokenizer.num_special_tokens_to_add(__snake_case   )
             UpperCAmelCase_      			=							compute_effective_axis_dimension(
                 __snake_case		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=__snake_case   )
             # Generate dummy inputs according to compute batch and sequence
             UpperCAmelCase_      			=							[[" ".join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

             # Generate dummy bounding boxes
             UpperCAmelCase_      			=							[[[48, 84, 73, 128]]] * batch_size

             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
             UpperCAmelCase_      			=							self._generate_dummy_images(__snake_case		,    __snake_case		,    __snake_case		,    __snake_case   )

             UpperCAmelCase_      			=							dict(
                 processor(
                     __snake_case		,    text=__snake_case		,    boxes=__snake_case		,    return_tensors=__snake_case		,    )   )

             return inputs









"""simple docstring"""





from collections.abc import Iterable
from typing import Generic, TypeVar

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


if __name__ == "__main__":
  from doctest import testmod

  testmod()




