






"""simple docstring"""


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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


_UpperCamelCase   :						List[Any]        = logging.get_logger(__name__)

_UpperCamelCase   :						List[str]        = {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}






class __a   (   snake_case_				):
  __lowercase    :    List[Any]						=					'layoutlm'



  def __init__(self  :		int				,			lowercase_  :		Any=30_522				,			lowercase_  :		Union[str, Any]=768				,			lowercase_  :		Optional[int]=12				,			lowercase_  :		List[Any]=12				,			lowercase_  :		str=3_072				,			lowercase_  :		Union[str, Any]="gelu"				,			lowercase_  :		List[Any]=0.1				,			lowercase_  :		int=0.1				,			lowercase_  :		Optional[Any]=512				,			lowercase_  :		Dict=2				,			lowercase_  :		List[str]=0.02				,			lowercase_  :		Optional[int]=1E-12				,			lowercase_  :		Union[str, Any]=0				,			lowercase_  :		Optional[int]="absolute"				,			lowercase_  :		List[str]=True				,			lowercase_  :		Union[str, Any]=1_024				,			**lowercase_  :		List[str]				,			)		->							List[Any]:
   super().__init__(pad_token_id=lowercase_				,			**lowercase_   )
   __A	:       Tuple       =	vocab_size
   __A	:       Union[str, Any]       =	hidden_size
   __A	:       Tuple       =	num_hidden_layers
   __A	:       Optional[Any]       =	num_attention_heads
   __A	:       Optional[Any]       =	hidden_act
   __A	:       Tuple       =	intermediate_size
   __A	:       List[Any]       =	hidden_dropout_prob
   __A	:       List[Any]       =	attention_probs_dropout_prob
   __A	:       Union[str, Any]       =	max_position_embeddings
   __A	:       Tuple       =	type_vocab_size
   __A	:       Dict       =	initializer_range
   __A	:       Any       =	layer_norm_eps
   __A	:       int       =	position_embedding_type
   __A	:       Dict       =	use_cache
   __A	:       Union[str, Any]       =	max_ad_position_embeddings






class __a   (   snake_case_				):



  def __init__(self  :		int				,			lowercase_  :		PretrainedConfig				,			lowercase_  :		str = "default"				,			lowercase_  :		List[PatchingSpec] = None				,			)		->							Dict:
   super().__init__(lowercase_				,			task=lowercase_				,			patching_specs=lowercase_   )
   __A	:       Optional[int]       =	config.max_ad_position_embeddings - 1



  @property
  def 						A_				(self  :		Optional[int]   )		->							Mapping[str, Mapping[int, str]]:
   return OrderedDict(
       [
           ('input_ids', {0: 'batch', 1: 'sequence'}),
           ('bbox', {0: 'batch', 1: 'sequence'}),
           ('attention_mask', {0: 'batch', 1: 'sequence'}),
           ('token_type_ids', {0: 'batch', 1: 'sequence'}),
       ]   )



  def 						A_				(self  :		Union[str, Any]				,			lowercase_  :		PreTrainedTokenizer				,			lowercase_  :		int = -1				,			lowercase_  :		int = -1				,			lowercase_  :		bool = False				,			lowercase_  :		Optional[TensorType] = None				,			)		->							Mapping[str, Any]:
   __A	:       int       =	super().generate_dummy_inputs(
       lowercase_				,			batch_size=lowercase_				,			seq_length=lowercase_				,			is_pair=lowercase_				,			framework=lowercase_   )

   # Generate a dummy bbox
   __A	:       Optional[Any]       =	[48, 84, 73, 128]

   if not framework == TensorType.PYTORCH:
    raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'   )

   if not is_torch_available():
    raise ValueError('Cannot generate dummy inputs without PyTorch installed.'   )
   import torch

   __A	:       Optional[Any]       =	input_dict['input_ids'].shape
   __A	:       Union[str, Any]       =	torch.tensor([*[box] * seq_length]   ).tile(lowercase_				,			1				,			1   )
   return input_dict













"""simple docstring"""


from __future__ import annotations

from typing import Any






class __a   :



  def __init__(self  :		Optional[Any]				,			lowercase_  :		Any				,			lowercase_  :		Any   )		->							None:  # noqa: A002
   __A	:       Optional[Any]       =	item
   __A	:       Union[str, Any]       =	next






class __a   :



  def __init__(self  :		Tuple   )		->							None:
   __A	:       Node | None       =	None
   __A	:       Dict       =	0



  def 						A_				(self  :		str				,			lowercase_  :		Any   )		->							None:
   __A	:       Dict       =	Node(lowercase_				,			self.head   )
   self.size += 1



  def 						A_				(self  :		Optional[int]   )		->							Any:
   # Switched 'self.is_empty()' to 'self.head is None'
   # because mypy was considering the possibility that 'self.head'
   # can be None in below else part and giving error
   if self.head is None:
    return None
   else:
    __A	:       Dict       =	self.head.item
    __A	:       List[str]       =	self.head.next
    self.size -= 1
    return item



  def 						A_				(self  :		List[str]   )		->							bool:
   return self.head is None



  def __str__(self  :		Any   )		->							str:
   if self.is_empty():
    return ""
   else:
    __A	:       List[str]       =	self.head
    __A	:       Union[str, Any]       =	''
    __A	:       list[str]       =	[]
    while iterate:
     item_list.append(str(iterate.item   )   )
     __A	:       List[str]       =	iterate.next

    __A	:       str       =	' --> '.join(lowercase_   )

    return item_str



  def __len__(self  :		Union[str, Any]   )		->							int:
   return self.size





