


from __future__ import annotations

from queue import Queue
def   snake_case_    (			__lowercase      ,							__lowercase							):
   a ^= b
   b ^= a
   a ^= b
   return a, b
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Tuple							= 1
   while (1 << j) < max_node:
      for i in range(1      ,							max_node + 1							):
         UpperCAmelCase_				:   Optional[int]							= parent[j - 1][parent[j - 1][i]]
      j += 1
   return parent
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):





   if level[u] < level[v]:
      UpperCAmelCase_		,   UpperCAmelCase_				:   Union[str, Any]							= swap(__snake_case      ,							__snake_case							)
   # making depth of u same as depth of v
   for i in range(1_8      ,							-1      ,							-1							):
      if level[u] - (1 << i) >= level[v]:
         UpperCAmelCase_				:   Dict							= 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(1_8      ,							-1      ,							-1							):
      if parent[i][u] not in [0, parent[i][v]]:
         UpperCAmelCase_		,   UpperCAmelCase_				:   Any							= parent[i][u], parent[i][v]
      # returning longest common ancestor of u,v
   return parent[0][u]
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = 1      ,							):
   UpperCAmelCase_				:   Tuple							= 0
   UpperCAmelCase_				:   List[str]							= Queue(maxsize=__snake_case							)
   q.put(__snake_case							)
   while q.qsize() != 0:
      UpperCAmelCase_				:   List[str]							= q.get()
      for v in graph[u]:
         if level[v] == -1:
            UpperCAmelCase_				:   Dict							= level[u] + 1
            q.put(__snake_case							)
            UpperCAmelCase_				:   str							= u
   return level, parent
def   snake_case_    (			):
   UpperCAmelCase_				:   Dict							= 1_3
   # initializing with 0
   UpperCAmelCase_				:   Optional[int]							= [[0 for _ in range(max_node + 1_0							)] for _ in range(2_0							)]
   # initializing with -1 which means every node is unvisited
   UpperCAmelCase_				:   Optional[int]							= [-1 for _ in range(max_node + 1_0							)]
   UpperCAmelCase_				:   Tuple							= {
       1: [2, 3, 4],
       2: [5],
       3: [6, 7],
       4: [8],
       5: [9, 1_0],
       6: [1_1],
       7: [],
       8: [1_2, 1_3],
       9: [],
       1_0: [],
       1_1: [],
       1_2: [],
       1_3: [],
   }
   UpperCAmelCase_		,   UpperCAmelCase_				:   List[Any]							= breadth_first_search(__snake_case      ,							__snake_case      ,							__snake_case      ,							__snake_case      ,							1							)
   UpperCAmelCase_				:   List[str]							= create_sparse(__snake_case      ,							__snake_case							)
   print('''LCA of node 1 and 3 is: '''      ,							lowest_common_ancestor(1      ,							3      ,							__snake_case      ,							__snake_case							)							)
   print('''LCA of node 5 and 6 is: '''      ,							lowest_common_ancestor(5      ,							6      ,							__snake_case      ,							__snake_case							)							)
   print('''LCA of node 7 and 11 is: '''      ,							lowest_common_ancestor(7      ,							1_1      ,							__snake_case      ,							__snake_case							)							)
   print('''LCA of node 6 and 7 is: '''      ,							lowest_common_ancestor(6      ,							7      ,							__snake_case      ,							__snake_case							)							)
   print('''LCA of node 4 and 12 is: '''      ,							lowest_common_ancestor(4      ,							1_2      ,							__snake_case      ,							__snake_case							)							)
   print('''LCA of node 8 and 8 is: '''      ,							lowest_common_ancestor(8      ,							8      ,							__snake_case      ,							__snake_case							)							)


if __name__ == "__main__":
       main()







"""simple docstring"""



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor_slow.tokenizer.get_vocab()				,						tokenizer_slow.get_vocab()			)
      self.assertEqual(processor_fast.tokenizer.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertEqual(tokenizer_slow.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertIsInstance(processor_slow.tokenizer				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor_slow.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertEqual(processor_fast.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertIsInstance(processor_slow.image_processor				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase			):
        processor()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						processor.model_input_names			)


