





"""simple docstring"""
from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				DPRContextEncoderTokenizer
					UpperCamelCase						=				DPRContextEncoderTokenizerFast
					UpperCamelCase						=				True







@require_tokenizers
class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				DPRQuestionEncoderTokenizer
					UpperCamelCase						=				DPRQuestionEncoderTokenizerFast
					UpperCamelCase						=				True







@require_tokenizers
class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				DPRReaderTokenizer
					UpperCamelCase						=				DPRReaderTokenizerFast
					UpperCamelCase						=				True


					@slow
					def       lowerCamelCase			(   self     :Dict						):
												A						=    self.tokenizer_class.from_pretrained("bert-base-uncased"						)

												A						=    tokenizer.encode("question sequence"	,			add_special_tokens=__UpperCamelCase						)
												A						=    tokenizer.encode("title sequence"	,			add_special_tokens=__UpperCamelCase						)
												A						=    tokenizer.encode("text sequence " * 4	,			add_special_tokens=__UpperCamelCase						)
												A						=    [[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
												A						=    BatchEncoding({"input_ids": input_ids}						)

												A						=    [[0] * len(input_ids[0]						)]
												A						=    [[0] * len(input_ids[0]						)]
												A						=    [0]
												A						=    DPRReaderOutput(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)

												A,      A						=    8, 9
												A						=    10
												A						=    10
												A						=    tokenizer.decode_best_spans(__UpperCamelCase	,			__UpperCamelCase						)
												self.assertEqual(predicted_spans[0].start_index	,			__UpperCamelCase						)
												self.assertEqual(predicted_spans[0].end_index	,			__UpperCamelCase						)
												self.assertEqual(predicted_spans[0].doc_id	,			0						)


					@slow
					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.tokenizer_class.from_pretrained("bert-base-uncased"						)

												A						=    tokenizer.encode("question sequence"	,			add_special_tokens=__UpperCamelCase						)
												A						=    tokenizer.encode("title sequence"	,			add_special_tokens=__UpperCamelCase						)
												A						=    tokenizer.encode("text sequence"	,			add_special_tokens=__UpperCamelCase						)
												A						=    [1_01] + text_a + [1_02] + text_a + [1_02] + text_a
												A						=    tokenizer(questions=["question sequence"]	,			titles=["title sequence"]	,			texts=["text sequence"]						)
												self.assertIn("input_ids"	,			__UpperCamelCase						)
												self.assertIn("attention_mask"	,			__UpperCamelCase						)
												self.assertListEqual(encoded_input["input_ids"][0]	,			__UpperCamelCase						)









"""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()


