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


SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Optional[int]						=					{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'layoutlmv3'




  def __init__(      self     :	Dict     , a_     :	List[Any]=5_0265     , a_     :	Optional[int]=768     , a_     :	List[Any]=12     , a_     :	Any=12     , a_     :	List[str]=3072     , a_     :	int="gelu"     , a_     :	Any=0.1     , a_     :	int=0.1     , a_     :	Optional[Any]=512     , a_     :	Any=2     , a_     :	Optional[int]=0.02     , a_     :	Tuple=1e-5     , a_     :	Any=1     , a_     :	Tuple=0     , a_     :	Union[str, Any]=2     , a_     :	List[Any]=1024     , a_     :	Dict=128     , a_     :	Tuple=128     , a_     :	int=True     , a_     :	Optional[Any]=32     , a_     :	List[Any]=128     , a_     :	Union[str, Any]=64     , a_     :	Tuple=256     , a_     :	Union[str, Any]=True     , a_     :	Optional[Any]=True     , a_     :	List[Any]=True     , a_     :	List[str]=224     , a_     :	Union[str, Any]=3     , a_     :	Dict=16     , a_     :	Tuple=None     , **a_     :	str     , )->       Optional[int]:




       """simple docstring"""
       super().__init__(
           vocab_size=a_     , hidden_size=a_     , num_hidden_layers=a_     , num_attention_heads=a_     , intermediate_size=a_     , hidden_act=a_     , hidden_dropout_prob=a_     , attention_probs_dropout_prob=a_     , max_position_embeddings=a_     , type_vocab_size=a_     , initializer_range=a_     , layer_norm_eps=a_     , pad_token_id=a_     , bos_token_id=a_     , eos_token_id=a_     , **a_     , )
       SCREAMING_SNAKE_CASE__				:				Tuple			=		max_ad_position_embeddings
       SCREAMING_SNAKE_CASE__				:				List[str]			=		coordinate_size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		shape_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		has_relative_attention_bias
       SCREAMING_SNAKE_CASE__				:				str			=		rel_pos_bins
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		max_rel_pos
       SCREAMING_SNAKE_CASE__				:				Any			=		has_spatial_attention_bias
       SCREAMING_SNAKE_CASE__				:				Tuple			=		rel_ad_pos_bins
       SCREAMING_SNAKE_CASE__				:				int			=		max_rel_ad_pos
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		text_embed
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		visual_embed
       SCREAMING_SNAKE_CASE__				:				Any			=		input_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		num_channels
       SCREAMING_SNAKE_CASE__				:				List[str]			=		patch_size
       SCREAMING_SNAKE_CASE__				:				int			=		classifier_dropout




class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     version.parse('1.12'						)




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




       """simple docstring"""
       # The order of inputs is different for question answering and sequence classification
       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     :	Union[str, Any]				)->       float:




       """simple docstring"""
       return 1e-5




  @property
  def 	__lowercase(      self     :	Optional[int]				)->       int:




       """simple docstring"""
       return 12




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




       """simple docstring"""
       setattr(processor.image_processor     , 'apply_ocr'     , a_				)

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       SCREAMING_SNAKE_CASE__				:				Tuple			=		compute_effective_axis_dimension(
           a_     , 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
       SCREAMING_SNAKE_CASE__				:				Dict			=		processor.tokenizer.num_special_tokens_to_add(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		compute_effective_axis_dimension(
           a_     , fixed_dimension=OnnxConfig.default_fixed_sequence     , num_token_to_add=a_				)
       # Generate dummy inputs according to compute batch and sequence
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[[' '.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[[[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)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self._generate_dummy_images(a_     , a_     , a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		dict(
           processor(
               a_     , text=a_     , boxes=a_     , return_tensors=a_     , )				)

       return inputs




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



