
'''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 ( lowerCAmelCase__    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							tempfile.mkdtemp()
       a_      :	Optional[int]      =							8

       # DPR tok
       a_      :	Optional[Any]      =							[
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""dpr_tokenizer""" )
       os.makedirs(lowerCAmelCase_							,		exist_ok=lowerCAmelCase_ )
       a_      :	str      =							os.path.join(lowerCAmelCase_							,		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
       a_      :	Optional[Any]      =							[
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       a_      :	str      =							dict(zip(lowerCAmelCase_							,		range(len(lowerCAmelCase_ ) ) ) )
       a_      :	Any      =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       a_      :	Union[str, Any]      =							{"""unk_token""": """<unk>"""}

       a_      :	List[str]      =							os.path.join(self.tmpdirname							,		"""bart_tokenizer""" )
       os.makedirs(lowerCAmelCase_							,		exist_ok=lowerCAmelCase_ )
       a_      :	str      =							os.path.join(lowerCAmelCase_							,		BART_VOCAB_FILES_NAMES["""vocab_file"""] )
       a_      :	Tuple      =							os.path.join(lowerCAmelCase_							,		BART_VOCAB_FILES_NAMES["""merges_file"""] )
       with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8""" ) as fp:
         fp.write(json.dumps(lowerCAmelCase_ ) + """\n""" )
       with open(self.merges_file							,		"""w"""							,		encoding="""utf-8""" ) as fp:
         fp.write("""\n""".join(lowerCAmelCase_ ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.get_dummy_dataset()
       a_      :	int      =							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:
         a_      :	List[str]      =							dataset
         a_      :	Any      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
       return retriever


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							self.get_dummy_dataset()
       a_      :	Tuple      =							RagConfig(
           retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""custom"""							,		)
       if from_disk:
         a_      :	Union[str, Any]      =							os.path.join(self.tmpdirname							,		"""dataset""" )
         a_      :	List[str]      =							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
         a_      :	str      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
       else:
         a_      :	Union[str, Any]      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		index=CustomHFIndex(config.retrieval_vector_size							,		lowerCAmelCase_ )							,		)
       return retriever


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	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 )

       a_      :	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""" ) )

       a_      :	int      =							os.path.join(self.tmpdirname							,		"""psgs_w100.tsv.pkl""" )
       a_      :	str      =							{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
       pickle.dump(lowerCAmelCase_							,		open(lowerCAmelCase_							,		"""wb""" ) )

       a_      :	List[str]      =							RagConfig(
           retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""legacy"""							,		index_path=self.tmpdirname							,		)
       a_      :	Tuple      =							RagRetriever(
           lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer() )
       return retriever


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							1
       a_      :	int      =							self.get_dummy_canonical_hf_index_retriever()
       a_      :	Optional[Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      , a_      , a_      :	Dict      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							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:
           a_      :	str      =							self.get_dummy_dataset()
           retriever.save_pretrained(lowerCAmelCase_ )
           a_      :	Optional[Any]      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
           self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
           a_      :	List[Any]      =							np.array(
               [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
           a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
           self.assertTrue(out is not None )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							1
       a_      :	str      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      , a_      , a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							1
       a_      :	int      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       a_      :	int      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      , a_      , a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							1
       a_      :	List[str]      =							self.get_dummy_legacy_index_retriever()
       a_      :	Union[str, Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      , a_      , a_      :	Any      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""text"""] )							,		lowerCAmelCase_ )
       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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.get_dummy_legacy_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCAmelCase_ )
         a_      :	Dict      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
         self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
         a_      :	int      =							np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
         a_      :	Union[str, Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
         self.assertTrue(out is not None )


     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       import torch

       a_      :	List[Any]      =							1
       a_      :	Dict      =							self.get_dummy_canonical_hf_index_retriever()
       a_      :	Optional[Any]      =							[[5, 7], [10, 11]]
       a_      :	Tuple      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Optional[int]      =							retriever(lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_ )
       a_      , a_      , a_      :	List[Any]      =							(
           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(lowerCAmelCase_							,		lowerCAmelCase_ )
       self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
       self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       a_      :	Dict      =							retriever(
           lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_							,		return_tensors="""pt"""							,		)
       a_      , a_      , a_      , a_      :	str      =							(  # 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(lowerCAmelCase_							,		torch.Tensor )
       self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )
       self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_dpr_ctx_encoder_tokenizer()
       a_      :	Optional[Any]      =							1
       a_      :	str      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       retriever.set_ctx_encoder_tokenizer(lowerCAmelCase_ )

       a_      :	List[Any]      =							[[5, 7], [10, 11]]
       a_      :	Optional[Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	int      =							retriever(lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_ )

       self.assertEqual(
           len(lowerCAmelCase_ )							,		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""") )							,		lowerCAmelCase_ )  # check for doc token related keys in dictionary.




'''simple docstring'''

def 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


