


'''simple docstring'''



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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
      import faiss





@require_faiss
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Union[str, Any]														=						tempfile.mkdtemp()
         __lowerCamelCase  :      List[str]														=						8

         # DPR tok
         __lowerCamelCase  :      List[str]														=						[
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __lowerCamelCase  :      Dict														=						os.path.join(self.tmpdirname     ,	'dpr_tokenizer'      )
         os.makedirs(SCREAMING_SNAKE_CASE_     ,	exist_ok=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	DPR_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]      )      )

         # BART tok
         __lowerCamelCase  :      List[str]														=						[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         __lowerCamelCase  :      Dict														=						dict(zip(SCREAMING_SNAKE_CASE_     ,	range(len(SCREAMING_SNAKE_CASE_      )      )      )      )
         __lowerCamelCase  :      Optional[int]														=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __lowerCamelCase  :      List[Any]														=						{'unk_token': '<unk>'}

         __lowerCamelCase  :      int														=						os.path.join(self.tmpdirname     ,	'bart_tokenizer'      )
         os.makedirs(SCREAMING_SNAKE_CASE_     ,	exist_ok=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Any														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	BART_VOCAB_FILES_NAMES['vocab_file']      )
         __lowerCamelCase  :      Tuple														=						os.path.join(SCREAMING_SNAKE_CASE_     ,	BART_VOCAB_FILES_NAMES['merges_file']      )
         with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_      ) + '\n'      )
         with open(self.merges_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              fp.write('\n'.join(SCREAMING_SNAKE_CASE_      )      )







    def  lowercase_							(				self      )							->	DPRQuestionEncoderTokenizer:
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'dpr_tokenizer'      )      )







    def  lowercase_							(				self      )							->	DPRContextEncoderTokenizer:
         return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'dpr_tokenizer'      )      )







    def  lowercase_							(				self      )							->	BartTokenizer:
         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,	'bart_tokenizer'      )      )







    def  lowercase_							(				self      )							->	Dict:
         shutil.rmtree(self.tmpdirname      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      int														=						Dataset.from_dict(
             {
                 'id': ['0', '1'],
                 'text': ['foo', 'bar'],
                 'title': ['Foo', 'Bar'],
                 'embeddings': [np.ones(self.retrieval_vector_size      ), 2 * np.ones(self.retrieval_vector_size      )],
             }      )
         dataset.add_faiss_index('embeddings'     ,	string_factory='Flat'     ,	metric_type=faiss.METRIC_INNER_PRODUCT      )
         return dataset







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[Any]														=						self.get_dummy_dataset()
         __lowerCamelCase  :      Any														=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size     ,	question_encoder=DPRConfig().to_dict()     ,	generator=BartConfig().to_dict()     ,	)
         with patch('transformers.models.rag.retrieval_rag.load_dataset'      ) as mock_load_dataset:
              __lowerCamelCase  :      int														=						dataset
              __lowerCamelCase  :      str														=						RagRetriever(
                  SCREAMING_SNAKE_CASE_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	)
         return retriever







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      int														=						self.get_dummy_dataset()
         __lowerCamelCase  :      str														=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size     ,	question_encoder=DPRConfig().to_dict()     ,	generator=BartConfig().to_dict()     ,	index_name='custom'     ,	)
         if from_disk:
              __lowerCamelCase  :      Optional[Any]														=						os.path.join(self.tmpdirname     ,	'dataset'      )
              __lowerCamelCase  :      Any														=						os.path.join(self.tmpdirname     ,	'index.faiss'      )
              dataset.get_index('embeddings'      ).save(os.path.join(self.tmpdirname     ,	'index.faiss'      )      )
              dataset.drop_index('embeddings'      )
              dataset.save_to_disk(os.path.join(self.tmpdirname     ,	'dataset'      )      )
              del dataset
              __lowerCamelCase  :      int														=						RagRetriever(
                  SCREAMING_SNAKE_CASE_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	)
         else:
              __lowerCamelCase  :      Optional[Any]														=						RagRetriever(
                  SCREAMING_SNAKE_CASE_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()     ,	index=CustomHFIndex(config.retrieval_vector_size     ,	SCREAMING_SNAKE_CASE_      )     ,	)
         return retriever







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						Dataset.from_dict(
             {
                 'id': ['0', '1'],
                 'text': ['foo', 'bar'],
                 'title': ['Foo', 'Bar'],
                 'embeddings': [np.ones(self.retrieval_vector_size + 1      ), 2 * np.ones(self.retrieval_vector_size + 1      )],
             }      )
         dataset.add_faiss_index('embeddings'     ,	string_factory='Flat'     ,	metric_type=faiss.METRIC_INNER_PRODUCT      )

         __lowerCamelCase  :      Optional[int]														=						os.path.join(self.tmpdirname     ,	'hf_bert_base.hnswSQ8_correct_phi_128.c_index'      )
         dataset.save_faiss_index('embeddings'     ,	index_file_name + '.index.dpr'      )
         pickle.dump(dataset['id']     ,	open(index_file_name + '.index_meta.dpr'     ,	'wb'      )      )

         __lowerCamelCase  :      Optional[int]														=						os.path.join(self.tmpdirname     ,	'psgs_w100.tsv.pkl'      )
         __lowerCamelCase  :      int														=						{sample['id']: [sample['text'], sample['title']] for sample in dataset}
         pickle.dump(SCREAMING_SNAKE_CASE_     ,	open(SCREAMING_SNAKE_CASE_     ,	'wb'      )      )

         __lowerCamelCase  :      Optional[int]														=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size     ,	question_encoder=DPRConfig().to_dict()     ,	generator=BartConfig().to_dict()     ,	index_name='legacy'     ,	index_path=self.tmpdirname     ,	)
         __lowerCamelCase  :      Optional[Any]														=						RagRetriever(
             SCREAMING_SNAKE_CASE_     ,	question_encoder_tokenizer=self.get_dpr_tokenizer()     ,	generator_tokenizer=self.get_bart_tokenizer()      )
         return retriever







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Optional[Any]														=						1
         __lowerCamelCase  :      Optional[int]														=						self.get_dummy_canonical_hf_index_retriever()
         __lowerCamelCase  :      Tuple														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=SCREAMING_SNAKE_CASE_      )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	2      )
         self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
         self.assertEqual(len(doc_dicts[0]['id']      )     ,	SCREAMING_SNAKE_CASE_      )
         self.assertEqual(doc_dicts[0]['id'][0]     ,	'1'      )  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]['id'][0]     ,	'0'      )  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()     ,	[[1], [0]]      )







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      str														=						self.get_dummy_canonical_hf_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
              with patch('transformers.models.rag.retrieval_rag.load_dataset'      ) as mock_load_dataset:
                   __lowerCamelCase  :      Tuple														=						self.get_dummy_dataset()
                   retriever.save_pretrained(SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Dict														=						RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_      )
                   self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Optional[Any]														=						np.array(
                       [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
                   __lowerCamelCase  :      int														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=1      )
                   self.assertTrue(out is not None      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      str														=						1
         __lowerCamelCase  :      Tuple														=						self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=SCREAMING_SNAKE_CASE_      )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	2      )
         self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
         self.assertEqual(len(doc_dicts[0]['id']      )     ,	SCREAMING_SNAKE_CASE_      )
         self.assertEqual(doc_dicts[0]['id'][0]     ,	'1'      )  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]['id'][0]     ,	'0'      )  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()     ,	[[1], [0]]      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      int														=						self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_      )
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[str]														=						RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Optional[Any]														=						np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              __lowerCamelCase  :      Dict														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=1      )
              self.assertTrue(out is not None      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      str														=						1
         __lowerCamelCase  :      Optional[Any]														=						self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      List[Any]														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=SCREAMING_SNAKE_CASE_      )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	2      )
         self.assertEqual(sorted(doc_dicts[0]      )     ,	['embeddings', 'id', 'text', 'title']      )
         self.assertEqual(len(doc_dicts[0]['id']      )     ,	SCREAMING_SNAKE_CASE_      )
         self.assertEqual(doc_dicts[0]['id'][0]     ,	'1'      )  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]['id'][0]     ,	'0'      )  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()     ,	[[1], [0]]      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Tuple														=						self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_      )
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              __lowerCamelCase  :      Tuple														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=1      )
              self.assertTrue(out is not None      )







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						1
         __lowerCamelCase  :      Tuple														=						self.get_dummy_legacy_index_retriever()
         __lowerCamelCase  :      List[Any]														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=SCREAMING_SNAKE_CASE_      )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	2      )
         self.assertEqual(sorted(doc_dicts[0]      )     ,	['text', 'title']      )
         self.assertEqual(len(doc_dicts[0]['text']      )     ,	SCREAMING_SNAKE_CASE_      )
         self.assertEqual(doc_dicts[0]['text'][0]     ,	'bar'      )  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]['text'][0]     ,	'foo'      )  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()     ,	[[1], [0]]      )







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      Union[str, Any]														=						self.get_dummy_legacy_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Union[str, Any]														=						np.array(
                  [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
              __lowerCamelCase  :      List[str]														=						retriever.retrieve(SCREAMING_SNAKE_CASE_     ,	n_docs=1      )
              self.assertTrue(out is not None      )







    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def  lowercase_							(				self      )							->	List[Any]:
         import torch

         __lowerCamelCase  :      Union[str, Any]														=						1
         __lowerCamelCase  :      Union[str, Any]														=						self.get_dummy_canonical_hf_index_retriever()
         __lowerCamelCase  :      int														=						[[5, 7], [10, 11]]
         __lowerCamelCase  :      Optional[int]														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase  :      Optional[int]														=						retriever(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Dict														=						(
             out['context_input_ids'],
             out['context_attention_mask'],
             out['retrieved_doc_embeds'],
         )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	np.ndarray      )

         __lowerCamelCase  :      Dict														=						retriever(
             SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'     ,	)
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      int														=						(  # noqa: F841
             out['context_input_ids'],
             out['context_attention_mask'],
             out['retrieved_doc_embeds'],
             out['doc_ids'],
         )
         self.assertEqual(retrieved_doc_embeds.shape     ,	(2, n_docs, self.retrieval_vector_size)      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor      )
         self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor      )







    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Dict														=						self.get_dpr_ctx_encoder_tokenizer()
         __lowerCamelCase  :      str														=						1
         __lowerCamelCase  :      int														=						self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_      )
         retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						[[5, 7], [10, 11]]
         __lowerCamelCase  :      List[Any]														=						np.array(
             [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]     ,	dtype=np.floataa      )
         __lowerCamelCase  :      str														=						retriever(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	prefix=retriever.config.generator.prefix     ,	n_docs=SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             len(SCREAMING_SNAKE_CASE_      )     ,	6      )  # check whether the retriever output consist of 6 attributes including tokenized docs
         self.assertEqual(
             all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')      )     ,	SCREAMING_SNAKE_CASE_      )  # check for doc token related keys in dictionary.










'''simple docstring'''



from __future__ import annotations

import os
from collections.abc import Mapping

A__						:   Optional[Any]				=    tuple[int, int]





class   UpperCAmelCase_    :





    """simple docstring"""






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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	None:
         self.vertices.add(edge[0]      )
         self.vertices.add(edge[1]      )
         __lowerCamelCase  :      Union[str, Any]														=						weight







    def  lowercase_							(				self      )							->	Graph:
         __lowerCamelCase  :      Graph														=						Graph({min(self.vertices      )}     ,	{}      )
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int
         __lowerCamelCase  :      EdgeT
         __lowerCamelCase  :      int

         while len(subgraph.vertices      ) < len(self.vertices      ):
              __lowerCamelCase  :      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:
                             __lowerCamelCase  :      Optional[int]														=						edge
                             __lowerCamelCase  :      List[str]														=						weight

              subgraph.add_edge(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )

         return subgraph







def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "p107_network.txt"				)	->						int:
     __lowerCamelCase  :      str														=						os.path.abspath(os.path.dirname(UpperCAmelCase_				)				)
     __lowerCamelCase  :      str														=						os.path.join(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      dict[EdgeT, int]														=						{}
     __lowerCamelCase  :      list[str]
     __lowerCamelCase  :      int
     __lowerCamelCase  :      int

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

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

     for edgea in range(1 ,       len(UpperCAmelCase_				)				):
          for edgea in range(UpperCAmelCase_				):
               if adjaceny_matrix[edgea][edgea] != "-":
                    __lowerCamelCase  :      int														=						int(adjaceny_matrix[edgea][edgea]				)

     __lowerCamelCase  :      Graph														=						Graph(set(range(len(UpperCAmelCase_				)				)				) ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Graph														=						graph.prims_algorithm()

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

     return initial_total - optimal_total


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






