
'''simple docstring'''



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

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
      from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
      UpperCamelCase__ :						Union[str, Any]    							=					None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      UpperCamelCase__ :						Optional[int]    							=					None
      UpperCamelCase__ :						Optional[int]    							=					None
      UpperCamelCase__ :						List[str]    							=					None
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       List[str]:




   """simple docstring"""

   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE       	=			unittest.skip("""test requires Datasets, Faiss, psutil"""  )(SCREAMING_SNAKE_CASE_  )
   return test_case







@require_distributed_retrieval
class 				_a			(SCREAMING_SNAKE_CASE_):





       """simple docstring"""




       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			tempfile.mkdtemp()
          _SCREAMING_SNAKE_CASE       	=			8

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

          _SCREAMING_SNAKE_CASE       	=			os.path.join(self.tmpdirname    ,							"""bart_tokenizer""" )
          os.makedirs(SCREAMING_SNAKE_CASE_    ,							exist_ok=SCREAMING_SNAKE_CASE_ )
          _SCREAMING_SNAKE_CASE       	=			os.path.join(SCREAMING_SNAKE_CASE_    ,							BART_VOCAB_FILES_NAMES["""vocab_file"""] )
          _SCREAMING_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     UpperCamelCase			(							self )      ->     DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,							"""dpr_tokenizer""" ) )






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






       def     UpperCamelCase			(							self )      ->     List[Any]:
          shutil.rmtree(self.tmpdirname )






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_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     UpperCamelCase			(							self    ,							A__    ,							A__=1_23_45 )      ->     RagPyTorchDistributedRetriever:
          _SCREAMING_SNAKE_CASE       	=			self.get_dummy_dataset()
          _SCREAMING_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:
             _SCREAMING_SNAKE_CASE       	=			dataset
             _SCREAMING_SNAKE_CASE       	=			RagPyTorchDistributedRetriever(
                 SCREAMING_SNAKE_CASE_    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							)
             if init_retrieval:
                retriever.init_retrieval(SCREAMING_SNAKE_CASE_ )
          return retriever






       def     UpperCamelCase			(							self    ,							A__ )      ->     RagRayDistributedRetriever:
          ray.init(local_mode=SCREAMING_SNAKE_CASE_ )
          _SCREAMING_SNAKE_CASE       	=			RagConfig(
              retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							)
          _SCREAMING_SNAKE_CASE       	=			ray.remote(SCREAMING_SNAKE_CASE_ )
          _SCREAMING_SNAKE_CASE       	=			[remote_cls.remote() for _ in range(1 )]
          with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
             _SCREAMING_SNAKE_CASE       	=			self.get_dummy_dataset()
             _SCREAMING_SNAKE_CASE       	=			RagRayDistributedRetriever(
                 SCREAMING_SNAKE_CASE_    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=SCREAMING_SNAKE_CASE_    ,							)
             if init_retrieval:
                retriever.init_retrieval()
          return retriever






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__=1_23_45 )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.get_dummy_dataset()
          _SCREAMING_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:
             _SCREAMING_SNAKE_CASE       	=			os.path.join(self.tmpdirname    ,							"""dataset""" )
             _SCREAMING_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
             _SCREAMING_SNAKE_CASE       	=			RagPyTorchDistributedRetriever(
                 SCREAMING_SNAKE_CASE_    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							)
          else:
             _SCREAMING_SNAKE_CASE       	=			RagPyTorchDistributedRetriever(
                 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_ )    ,							)
          if init_retrieval:
             retriever.init_retrieval(SCREAMING_SNAKE_CASE_ )
          return retriever






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Tuple:
          ray.init(local_mode=SCREAMING_SNAKE_CASE_ )
          _SCREAMING_SNAKE_CASE       	=			self.get_dummy_dataset()
          _SCREAMING_SNAKE_CASE       	=			RagConfig(
              retrieval_vector_size=self.retrieval_vector_size    ,							question_encoder=DPRConfig().to_dict()    ,							generator=BartConfig().to_dict()    ,							index_name="""custom"""    ,							)
          _SCREAMING_SNAKE_CASE       	=			ray.remote(SCREAMING_SNAKE_CASE_ )
          _SCREAMING_SNAKE_CASE       	=			[remote_cls.remote() for _ in range(1 )]
          if from_disk:
             _SCREAMING_SNAKE_CASE       	=			os.path.join(self.tmpdirname    ,							"""dataset""" )
             _SCREAMING_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
             _SCREAMING_SNAKE_CASE       	=			RagRayDistributedRetriever(
                 SCREAMING_SNAKE_CASE_    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=SCREAMING_SNAKE_CASE_    ,							index=CustomHFIndex.load_from_disk(
                     vector_size=config.retrieval_vector_size    ,							dataset_path=config.passages_path    ,							index_path=config.index_path    ,							)    ,							)
          else:
             _SCREAMING_SNAKE_CASE       	=			RagRayDistributedRetriever(
                 SCREAMING_SNAKE_CASE_    ,							question_encoder_tokenizer=self.get_dpr_tokenizer()    ,							generator_tokenizer=self.get_bart_tokenizer()    ,							retrieval_workers=SCREAMING_SNAKE_CASE_    ,							index=CustomHFIndex(config.retrieval_vector_size    ,							SCREAMING_SNAKE_CASE_ )    ,							)
          if init_retrieval:
             retriever.init_retrieval()
          return retriever






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     None:
          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_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     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )

          self.distributed_retriever_check(
              self.get_dummy_pytorch_distributed_retriever(init_retrieval=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_ )






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=SCREAMING_SNAKE_CASE_    ,							from_disk=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_    ,							)






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=SCREAMING_SNAKE_CASE_    ,							from_disk=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_    ,							)






       @require_ray
       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )

          self.distributed_retriever_check(
              self.get_dummy_ray_distributed_retriever(init_retrieval=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_ )
          ray.shutdown()






       @require_ray
       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )
          with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
             self.distributed_retriever_check(
                 self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE_    ,							from_disk=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_    ,							)
          ray.shutdown()






       @require_ray
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			1
          _SCREAMING_SNAKE_CASE       	=			np.array(
              [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]    ,							dtype=np.floataa )

          self.distributed_retriever_check(
              self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE_    ,							from_disk=SCREAMING_SNAKE_CASE_ )    ,							SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_ )
          ray.shutdown()








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   print("""\nVertex Distance"""  )
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if dist[i] != float("""inf"""  ):
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , int(dist[i]  )      , end="""\t"""  )
      else:
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , """INF"""      , end="""\t"""  )
      print()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			float("""inf"""  )
   _SCREAMING_SNAKE_CASE       	=			-1
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if (not vset[i]) and mdist[i] < min_val:
         _SCREAMING_SNAKE_CASE       	=			i
         _SCREAMING_SNAKE_CASE       	=			mdist[i]
   return min_ind
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			[float("""inf"""  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			[False for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			0.0

   for _ in range(v - 1  ):
      _SCREAMING_SNAKE_CASE       	=			min_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			True

      for i in range(SCREAMING_SNAKE_CASE_  ):
         if (
             (not vset[i])
             and graph[u][i] != float("""inf"""  )
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _SCREAMING_SNAKE_CASE       	=			mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					int(input("Enter number of vertices: ").strip())
     UpperCamelCase__ :						Tuple    							=					int(input("Enter number of edges: ").strip())

     UpperCamelCase__ :						Optional[Any]    							=					[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          UpperCamelCase__ :						int    							=					0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          UpperCamelCase__ :						Union[str, Any]    							=					int(input("Enter source:").strip())
          UpperCamelCase__ :						Tuple    							=					int(input("Enter destination:").strip())
          UpperCamelCase__ :						Any    							=					float(input("Enter weight:").strip())
          UpperCamelCase__ :						List[Any]    							=					weight

     UpperCamelCase__ :						Optional[Any]    							=					int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)






