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 :	List[Any]							     =		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""layoutlmv3"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=5_02_65     ,							__SCREAMING_SNAKE_CASE :      List[str]=7_68     ,							__SCREAMING_SNAKE_CASE :      List[str]=12     ,							__SCREAMING_SNAKE_CASE :      Dict=12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      List[Any]="gelu"     ,							__SCREAMING_SNAKE_CASE :      str=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=5_12     ,							__SCREAMING_SNAKE_CASE :      List[str]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=1E-5     ,							__SCREAMING_SNAKE_CASE :      Dict=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Dict=2     ,							__SCREAMING_SNAKE_CASE :      Any=10_24     ,							__SCREAMING_SNAKE_CASE :      Dict=1_28     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1_28     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=32     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=1_28     ,							__SCREAMING_SNAKE_CASE :      List[str]=64     ,							__SCREAMING_SNAKE_CASE :      Any=2_56     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      List[str]=True     ,							__SCREAMING_SNAKE_CASE :      Tuple=True     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2_24     ,							__SCREAMING_SNAKE_CASE :      List[str]=3     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=16     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   super().__init__(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							hidden_size=__SCREAMING_SNAKE_CASE     ,							num_hidden_layers=__SCREAMING_SNAKE_CASE     ,							num_attention_heads=__SCREAMING_SNAKE_CASE     ,							intermediate_size=__SCREAMING_SNAKE_CASE     ,							hidden_act=__SCREAMING_SNAKE_CASE     ,							hidden_dropout_prob=__SCREAMING_SNAKE_CASE     ,							attention_probs_dropout_prob=__SCREAMING_SNAKE_CASE     ,							max_position_embeddings=__SCREAMING_SNAKE_CASE     ,							type_vocab_size=__SCREAMING_SNAKE_CASE     ,							initializer_range=__SCREAMING_SNAKE_CASE     ,							layer_norm_eps=__SCREAMING_SNAKE_CASE     ,							pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)
   __a       =  max_ad_position_embeddings
   __a       =  coordinate_size
   __a       =  shape_size
   __a       =  has_relative_attention_bias
   __a       =  rel_pos_bins
   __a       =  max_rel_pos
   __a       =  has_spatial_attention_bias
   __a       =  rel_ad_pos_bins
   __a       =  max_rel_ad_pos
   __a       =  text_embed
   __a       =  visual_embed
   __a       =  input_size
   __a       =  num_channels
   __a       =  patch_size
   __a       =  classifier_dropout
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			version.parse("""1.12""" )

  @property
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   # 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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   return 1E-5

  @property
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   return 12

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      "ProcessorMixin"     ,							__SCREAMING_SNAKE_CASE :      int = -1     ,							__SCREAMING_SNAKE_CASE :      int = -1     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      Optional["TensorType"] = None     ,							__SCREAMING_SNAKE_CASE :      int = 3     ,							__SCREAMING_SNAKE_CASE :      int = 40     ,							__SCREAMING_SNAKE_CASE :      int = 40     ,							):
   setattr(processor.image_processor     ,							"apply_ocr"     ,							__SCREAMING_SNAKE_CASE       )

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

   # Generate dummy bounding boxes
   __a       =  [[[48, 84, 73, 1_28]]] * 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)
   __a       =  self._generate_dummy_images(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  dict(
       processor(
           __SCREAMING_SNAKE_CASE     ,							text=__SCREAMING_SNAKE_CASE     ,							boxes=__SCREAMING_SNAKE_CASE     ,							return_tensors=__SCREAMING_SNAKE_CASE     ,							)       )

   return inputs



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


