





"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''MarkupLMFeatureExtractor'''
					UpperCamelCase						=				('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
					UpperCamelCase						=				True


					def __call__(   self     :Optional[int]	,			__UpperCamelCase     :Optional[int]=None	,			__UpperCamelCase     :Dict=None	,			__UpperCamelCase     :Any=None	,			__UpperCamelCase     :Dict=None	,			__UpperCamelCase     :Dict=None	,			__UpperCamelCase     :bool = True	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Union[bool, str, TruncationStrategy] = None	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :int = 0	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :bool = True	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			**__UpperCamelCase     :str	,			):
												# first, create nodes and xpaths
												if self.parse_html:
																			if html_strings is None:
																										raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"						)

																			if nodes is not None or xpaths is not None or node_labels is not None:
																										raise ValueError(
																										    "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"						)

																			A						=    self.feature_extractor(__UpperCamelCase						)
																			A						=    features["nodes"]
																			A						=    features["xpaths"]
												else:
																			if html_strings is not None:
																										raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."						)
																			if nodes is None or xpaths is None:
																										raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"						)

        # # second, apply the tokenizer
												if questions is not None and self.parse_html:
																			if isinstance(__UpperCamelCase	,			__UpperCamelCase						):
																										A						=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

												A						=    self.tokenizer(
												    text=questions if questions is not None else nodes	,			text_pair=nodes if questions is not None else None	,			xpaths=__UpperCamelCase	,			node_labels=__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			padding=__UpperCamelCase	,			truncation=__UpperCamelCase	,			max_length=__UpperCamelCase	,			stride=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_token_type_ids=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			return_overflowing_tokens=__UpperCamelCase	,			return_special_tokens_mask=__UpperCamelCase	,			return_offsets_mapping=__UpperCamelCase	,			return_length=__UpperCamelCase	,			verbose=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase	,			)

												return encoded_inputs


					def       lowerCamelCase			(   self     :Tuple	,			*__UpperCamelCase     :Tuple	,			**__UpperCamelCase     :Optional[Any]						):
												return self.tokenizer.batch_decode(*__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]	,			*__UpperCamelCase     :List[Any]	,			**__UpperCamelCase     :Any						):
												return self.tokenizer.decode(*__UpperCamelCase	,			**__UpperCamelCase						)


					@property
					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.tokenizer.model_input_names
												return tokenizer_input_names









"""simple docstring"""
from __future__ import annotations

from queue import Queue






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							a ^= b
							b ^= a
							a ^= b
							return a, b






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    1
							while (1 << j) < max_node:
														for i in range(1   ,    max_node + 1			):
																					A						=    parent[j - 1][parent[j - 1][i]]
														j += 1
							return parent






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							# u must be deeper in the tree than v
							if level[u] < level[v]:
														A,      A						=    swap(UpperCamelCase   ,    UpperCamelCase			)
							# making depth of u same as depth of v
							for i in range(18   ,    -1   ,    -1			):
														if level[u] - (1 << i) >= level[v]:
																					A						=    parent[i][u]
    # at the same depth if u==v that mean lca is found
							if u == v:
														return u
							# moving both nodes upwards till lca in found
							for i in range(18   ,    -1   ,    -1			):
														if parent[i][u] not in [0, parent[i][v]]:
																					A,      A						=    parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
							return parent[0][u]






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = 1   ,    ):
							A						=    0
							A						=    Queue(maxsize=UpperCamelCase			)
							q.put(UpperCamelCase			)
							while q.qsize() != 0:
														A						=    q.get()
														for v in graph[u]:
																					if level[v] == -1:
																												A						=    level[u] + 1
																												q.put(UpperCamelCase			)
																												A						=    u
							return level, parent






def    A__							(     ):
							A						=    13
							# initializing with 0
							A						=    [[0 for _ in range(max_node + 10			)] for _ in range(20			)]
							# initializing with -1 which means every node is unvisited
							A						=    [-1 for _ in range(max_node + 10			)]
							A						=    {
							    1: [2, 3, 4],
							    2: [5],
							    3: [6, 7],
							    4: [8],
							    5: [9, 10],
							    6: [11],
							    7: [],
							    8: [12, 13],
							    9: [],
							    10: [],
							    11: [],
							    12: [],
							    13: [],
							}
							A,      A						=    breadth_first_search(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    1			)
							A						=    create_sparse(UpperCamelCase   ,    UpperCamelCase			)
							print("LCA of node 1 and 3 is: "   ,    lowest_common_ancestor(1   ,    3   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 5 and 6 is: "   ,    lowest_common_ancestor(5   ,    6   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 7 and 11 is: "   ,    lowest_common_ancestor(7   ,    11   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 6 and 7 is: "   ,    lowest_common_ancestor(6   ,    7   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 4 and 12 is: "   ,    lowest_common_ancestor(4   ,    12   ,    UpperCamelCase   ,    UpperCamelCase			)			)
							print("LCA of node 8 and 8 is: "   ,    lowest_common_ancestor(8   ,    8   ,    UpperCamelCase   ,    UpperCamelCase			)			)


if __name__ == "__main__":
		main()


