

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer






class 	__magic_name__    (			lowerCAmelCase_      ):





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =tempfile.mkdtemp()
						__a        =5

						# Realm tok
						__a        =[
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'test',
						    'question',
						    'this',
						    'is',
						    'the',
						    'first',
						    'second',
						    'third',
						    'fourth',
						    'fifth',
						    'record',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						__a        =os.path.join(self.tmpdirname					,  'realm_tokenizer'					)
						os.makedirs(__snake_case					,  exist_ok=__snake_case					)
						__a        =os.path.join(__snake_case					,  VOCAB_FILES_NAMES['vocab_file']					)
						with open(self.vocab_file					,  'w'					,  encoding='utf-8'					) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

						__a        =os.path.join(self.tmpdirname					,  'realm_block_records'					)
						os.makedirs(__snake_case					,  exist_ok=__snake_case					)





			def 		__magic_name__    (		self					)   -> RealmTokenizer:




						'''simple docstring'''
						return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname					,  'realm_tokenizer'					)					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						shutil.rmtree(self.tmpdirname					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						__a        =RealmConfig(num_block_records=self.num_block_records					)
						return config





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =Dataset.from_dict(
						    {
						        'id': ['0', '1'],
						        'question': ['foo', 'bar'],
						        'answers': [['Foo', 'Bar'], ['Bar']],
						    }					)
						return dataset





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =np.array(
						    [
						        b'This is the first record',
						        b'This is the second record',
						        b'This is the third record',
						        b'This is the fourth record',
						        b'This is the fifth record',
						        b'This is a longer longer longer record',
						    ]					,  dtype=__snake_case					,  )
						return block_records





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =RealmRetriever(
						    block_records=self.get_dummy_block_records()					,  tokenizer=self.get_tokenizer()					,  )
						return retriever





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.get_config()
						__a        =self.get_dummy_retriever()
						__a        =retriever.tokenizer

						__a        =np.array([0, 3]					,  dtype='long'					)
						__a        =tokenizer(['Test question']					).input_ids
						__a        =tokenizer(
						    ['the fourth']					,  add_special_tokens=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  ).input_ids
						__a        =config.reader_seq_len

						__a      ,    __a      ,    __a      ,    __a        =retriever(
						    __snake_case					,  __snake_case					,  answer_ids=__snake_case					,  max_length=__snake_case					,  return_tensors='np'					)

						self.assertEqual(len(__snake_case					)					,  2					)
						self.assertEqual(len(__snake_case					)					,  2					)
						self.assertEqual(len(__snake_case					)					,  2					)
						self.assertEqual(concat_inputs.input_ids.shape					,  (2, 10)					)
						self.assertEqual(concat_inputs.attention_mask.shape					,  (2, 10)					)
						self.assertEqual(concat_inputs.token_type_ids.shape					,  (2, 10)					)
						self.assertEqual(concat_inputs.special_tokens_mask.shape					,  (2, 10)					)
						self.assertEqual(
						    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]					)					,  ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']					,  )
						self.assertEqual(
						    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]					)					,  ['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']					,  )





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =self.get_config()
						__a        =self.get_dummy_retriever()
						__a        =retriever.tokenizer

						__a        =np.array([0, 3, 5]					,  dtype='long'					)
						__a        =tokenizer(['Test question']					).input_ids
						__a        =tokenizer(
						    ['the fourth', 'longer longer']					,  add_special_tokens=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  ).input_ids
						__a        =config.reader_seq_len

						__a      ,    __a      ,    __a      ,    __a        =retriever(
						    __snake_case					,  __snake_case					,  answer_ids=__snake_case					,  max_length=__snake_case					,  return_tensors='np'					)

						self.assertEqual([False, True, True]					,  __snake_case					)
						self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]					,  __snake_case					)
						self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.get_dummy_retriever()
						retriever.save_pretrained(os.path.join(self.tmpdirname					,  'realm_block_records'					)					)

						# Test local path
						__a        =retriever.from_pretrained(os.path.join(self.tmpdirname					,  'realm_block_records'					)					)
						self.assertEqual(retriever.block_records[0]					,  b'This is the first record'					)

						# Test mocked remote path
						with patch('transformers.models.realm.retrieval_realm.hf_hub_download'					) as mock_hf_hub_download:
									__a        =os.path.join(
									    os.path.join(self.tmpdirname					,  'realm_block_records'					)					,  _REALM_BLOCK_RECORDS_FILENAME					)
									__a        =RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa'					)

						self.assertEqual(retriever.block_records[0]					,  b'This is the first record'					)









# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






