






'''simple docstring'''
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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 def        __magic_name__		( self		:							str )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE__       :			Dict													=5

       # Realm tok
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[
           '''[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''',
       ]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=os.path.join(self.tmpdirname   , '''realm_tokenizer''' )
       os.makedirs(__lowercase   , exist_ok=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=os.path.join(__lowercase   , 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] ) )

       SCREAMING_SNAKE_CASE__       :			Any													=os.path.join(self.tmpdirname   , '''realm_block_records''' )
       os.makedirs(__lowercase   , exist_ok=__lowercase )




 def        __magic_name__		( self		:							Tuple )		->  RealmTokenizer:
       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   , '''realm_tokenizer''' ) )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Tuple:
       shutil.rmtree(self.tmpdirname )




 def        __magic_name__		( self		:							List[Any] )		->  int:
       SCREAMING_SNAKE_CASE__       :			Any													=RealmConfig(num_block_records=self.num_block_records )
       return config




 def        __magic_name__		( self		:							Optional[int] )		->  int:
       SCREAMING_SNAKE_CASE__       :			Any													=Dataset.from_dict(
           {
               '''id''': ['''0''', '''1'''],
               '''question''': ['''foo''', '''bar'''],
               '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
           } )
       return dataset




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=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=__lowercase   , )
       return block_records




 def        __magic_name__		( self		:							Any )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=RealmRetriever(
           block_records=self.get_dummy_block_records()   , tokenizer=self.get_tokenizer()   , )
       return retriever




 def        __magic_name__		( self		:							Optional[int] )		->  int:
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.get_config()
       SCREAMING_SNAKE_CASE__       :			Dict													=self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__       :			str													=retriever.tokenizer

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=np.array([0, 3]   , dtype='''long''' )
       SCREAMING_SNAKE_CASE__       :			int													=tokenizer(['''Test question'''] ).input_ids
       SCREAMING_SNAKE_CASE__       :			int													=tokenizer(
           ['''the fourth''']   , add_special_tokens=__lowercase   , return_token_type_ids=__lowercase   , return_attention_mask=__lowercase   , ).input_ids
       SCREAMING_SNAKE_CASE__       :			int													=config.reader_seq_len

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=retriever(
           __lowercase   , __lowercase   , answer_ids=__lowercase   , max_length=__lowercase   , return_tensors='''np''' )

       self.assertEqual(len(__lowercase )   , 2 )
       self.assertEqual(len(__lowercase )   , 2 )
       self.assertEqual(len(__lowercase )   , 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		:							str )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.get_config()
       SCREAMING_SNAKE_CASE__       :			Tuple													=self.get_dummy_retriever()
       SCREAMING_SNAKE_CASE__       :			Tuple													=retriever.tokenizer

       SCREAMING_SNAKE_CASE__       :			Dict													=np.array([0, 3, 5]   , dtype='''long''' )
       SCREAMING_SNAKE_CASE__       :			Any													=tokenizer(['''Test question'''] ).input_ids
       SCREAMING_SNAKE_CASE__       :			Tuple													=tokenizer(
           ['''the fourth''', '''longer longer''']   , add_special_tokens=__lowercase   , return_token_type_ids=__lowercase   , return_attention_mask=__lowercase   , ).input_ids
       SCREAMING_SNAKE_CASE__       :			Tuple													=config.reader_seq_len

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[str]													=retriever(
           __lowercase   , __lowercase   , answer_ids=__lowercase   , max_length=__lowercase   , return_tensors='''np''' )

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




 def        __magic_name__		( self		:							List[Any] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Dict													=self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname   , '''realm_block_records''' ) )

       # Test local path
       SCREAMING_SNAKE_CASE__       :			int													=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:
             SCREAMING_SNAKE_CASE__       :			str													=os.path.join(
                 os.path.join(self.tmpdirname   , '''realm_block_records''' )   , _REALM_BLOCK_RECORDS_FILENAME )
             SCREAMING_SNAKE_CASE__       :			Tuple													=RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )

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







'''simple docstring'''
from __future__ import annotations

import os
from collections.abc import Mapping

a_					=	tuple[int, int]






class        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							Any   , __lowercase		:							set[int]   , __lowercase		:							Mapping[EdgeT, int] )		->  None:
       SCREAMING_SNAKE_CASE__       :			set[int]													=vertices
       SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={
           (min(__lowercase ), max(__lowercase )): weight for edge, weight in edges.items()
       }




 def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							EdgeT   , __lowercase		:							int )		->  None:
       self.vertices.add(edge[0] )
       self.vertices.add(edge[1] )
       SCREAMING_SNAKE_CASE__       :			List[str]													=weight




 def        __magic_name__		( self		:							Optional[Any] )		->  Graph:
       SCREAMING_SNAKE_CASE__       :			Graph													=Graph({min(self.vertices )}   , {} )
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int
       SCREAMING_SNAKE_CASE__       :			EdgeT
       SCREAMING_SNAKE_CASE__       :			int

       while len(subgraph.vertices ) < len(self.vertices ):
             SCREAMING_SNAKE_CASE__       :			Any													=max(self.edges.values() ) + 1
             for edge, weight in self.edges.items():
                   if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                         if weight < min_weight:
                               SCREAMING_SNAKE_CASE__       :			List[str]													=edge
                               SCREAMING_SNAKE_CASE__       :			Any													=weight

             subgraph.add_edge(__lowercase   , __lowercase )

       return subgraph





def 	_a(					UpperCamelCase__			:  str = "p107_network.txt"							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
      SCREAMING_SNAKE_CASE__       :			str													=os.path.join(UpperCamelCase__,		UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			dict[EdgeT, int]													={}
      SCREAMING_SNAKE_CASE__       :			list[str]
      SCREAMING_SNAKE_CASE__       :			int
      SCREAMING_SNAKE_CASE__       :			int

      with open(UpperCamelCase__							) as f:
            SCREAMING_SNAKE_CASE__       :			Any													=f.read().strip().split('''\n'''							)

      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[line.split(''','''							) for line in data]

      for edgea in range(1,		len(UpperCamelCase__							)							):
            for edgea in range(UpperCamelCase__							):
                  if adjaceny_matrix[edgea][edgea] != "-":
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=int(adjaceny_matrix[edgea][edgea]							)

      SCREAMING_SNAKE_CASE__       :			Graph													=Graph(set(range(len(UpperCamelCase__							)							)							),		UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Graph													=graph.prims_algorithm()

      SCREAMING_SNAKE_CASE__       :			int													=sum(graph.edges.values()							)
      SCREAMING_SNAKE_CASE__       :			int													=sum(subgraph.edges.values()							)

      return initial_total - optimal_total


if __name__ == "__main__":
  print(F'''{solution() = }''')