





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 						SCREAMING_SNAKE_CASE__							(			_UpperCAmelCase    ):


      def   a					(self			: Tuple ):





         """simple docstring"""



         __snake_case							=  tempfile.mkdtemp()
         __snake_case							=  8

         # DPR tok
         __snake_case							=  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             '[PAD]',
             '[MASK]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __snake_case							=  os.path.join(self.tmpdirname							,     '''dpr_tokenizer''' )
         os.makedirs(SCREAMING_SNAKE_CASE_							,     exist_ok=SCREAMING_SNAKE_CASE_ )
         __snake_case							=  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
         __snake_case							=  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         __snake_case							=  dict(zip(SCREAMING_SNAKE_CASE_							,     range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
         __snake_case							=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __snake_case							=  {'unk_token': '<unk>'}

         __snake_case							=  os.path.join(self.tmpdirname							,     '''bart_tokenizer''' )
         os.makedirs(SCREAMING_SNAKE_CASE_							,     exist_ok=SCREAMING_SNAKE_CASE_ )
         __snake_case							=  os.path.join(SCREAMING_SNAKE_CASE_							,     BART_VOCAB_FILES_NAMES['''vocab_file'''] )
         __snake_case							=  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   a					(self			: Any ):





         """simple docstring"""



         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,     '''dpr_tokenizer''' ) )


      def   a					(self			: Union[str, Any] ):





         """simple docstring"""



         return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,     '''dpr_tokenizer''' ) )


      def   a					(self			: Any ):





         """simple docstring"""



         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,     '''bart_tokenizer''' ) )


      def   a					(self			: Optional[Any] ):





         """simple docstring"""



         shutil.rmtree(self.tmpdirname )


      def   a					(self			: str ):





         """simple docstring"""



         __snake_case							=  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   a					(self			: Optional[int] ):





         """simple docstring"""



         __snake_case							=  self.get_dummy_dataset()
         __snake_case							=  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:
            __snake_case							=  dataset
            __snake_case							=  RagRetriever(
                SCREAMING_SNAKE_CASE_							,     question_encoder_tokenizer=self.get_dpr_tokenizer()							,     generator_tokenizer=self.get_bart_tokenizer()							,     )
         return retriever


      def   a					(self			: Union[str, Any]							,     a__			: Optional[Any] ):





         """simple docstring"""



         __snake_case							=  self.get_dummy_dataset()
         __snake_case							=  RagConfig(
             retrieval_vector_size=self.retrieval_vector_size							,     question_encoder=DPRConfig().to_dict()							,     generator=BartConfig().to_dict()							,     index_name='''custom'''							,     )
         if from_disk:
            __snake_case							=  os.path.join(self.tmpdirname							,     '''dataset''' )
            __snake_case							=  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
            __snake_case							=  RagRetriever(
                SCREAMING_SNAKE_CASE_							,     question_encoder_tokenizer=self.get_dpr_tokenizer()							,     generator_tokenizer=self.get_bart_tokenizer()							,     )
         else:
            __snake_case							=  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   a					(self			: str ):





         """simple docstring"""



         __snake_case							=  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 )

         __snake_case							=  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''' ) )

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

         __snake_case							=  RagConfig(
             retrieval_vector_size=self.retrieval_vector_size							,     question_encoder=DPRConfig().to_dict()							,     generator=BartConfig().to_dict()							,     index_name='''legacy'''							,     index_path=self.tmpdirname							,     )
         __snake_case							=  RagRetriever(
             SCREAMING_SNAKE_CASE_							,     question_encoder_tokenizer=self.get_dpr_tokenizer()							,     generator_tokenizer=self.get_bart_tokenizer() )
         return retriever


      def   a					(self			: Dict ):





         """simple docstring"""



         __snake_case							=  1
         __snake_case							=  self.get_dummy_canonical_hf_index_retriever()
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  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   a					(self			: Tuple ):





         """simple docstring"""



         __snake_case							=  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:
               __snake_case							=  self.get_dummy_dataset()
               retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
               __snake_case							=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
               self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_ )
               __snake_case							=  np.array(
                   [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
               __snake_case							=  retriever.retrieve(SCREAMING_SNAKE_CASE_							,     n_docs=1 )
               self.assertTrue(out is not None )


      def   a					(self			: str ):





         """simple docstring"""



         __snake_case							=  1
         __snake_case							=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  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   a					(self			: int ):





         """simple docstring"""



         __snake_case							=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
         with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
            __snake_case							=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
            self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_ )
            __snake_case							=  np.array(
                [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
            __snake_case							=  retriever.retrieve(SCREAMING_SNAKE_CASE_							,     n_docs=1 )
            self.assertTrue(out is not None )


      def   a					(self			: List[Any] ):





         """simple docstring"""



         __snake_case							=  1
         __snake_case							=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  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   a					(self			: Any ):





         """simple docstring"""



         __snake_case							=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
         with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
            __snake_case							=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
            self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_ )
            __snake_case							=  np.array(
                [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
            __snake_case							=  retriever.retrieve(SCREAMING_SNAKE_CASE_							,     n_docs=1 )
            self.assertTrue(out is not None )


      def   a					(self			: Dict ):





         """simple docstring"""



         __snake_case							=  1
         __snake_case							=  self.get_dummy_legacy_index_retriever()
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  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   a					(self			: Optional[Any] ):





         """simple docstring"""



         __snake_case							=  self.get_dummy_legacy_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(SCREAMING_SNAKE_CASE_ )
            __snake_case							=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_ )
            self.assertIsInstance(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_ )
            __snake_case							=  np.array(
                [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
            __snake_case							=  retriever.retrieve(SCREAMING_SNAKE_CASE_							,     n_docs=1 )
            self.assertTrue(out is not None )


      @require_torch
      @require_tokenizers
      @require_sentencepiece
      def   a					(self			: Tuple ):





         """simple docstring"""



         import torch

         __snake_case							=  1
         __snake_case							=  self.get_dummy_canonical_hf_index_retriever()
         __snake_case							=  [[5, 7], [10, 11]]
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  retriever(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     prefix=retriever.config.generator.prefix							,     n_docs=SCREAMING_SNAKE_CASE_ )
         __snake_case							=  (
             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 )

         __snake_case							=  retriever(
             SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     prefix=retriever.config.generator.prefix							,     n_docs=SCREAMING_SNAKE_CASE_							,     return_tensors='''pt'''							,     )
         __snake_case							=  (  # 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   a					(self			: int ):





         """simple docstring"""



         __snake_case							=  self.get_dpr_ctx_encoder_tokenizer()
         __snake_case							=  1
         __snake_case							=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_ )
         retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_ )

         __snake_case							=  [[5, 7], [10, 11]]
         __snake_case							=  np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,     dtype=np.floataa )
         __snake_case							=  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() = }''')






