



"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A:     Union[str, Any]										=	2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
A:     int										=	5_0
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
A:     Tuple										=	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      Tuple			=			len([g for position, g in enumerate(UpperCamelCase		) if g == main_target[position]]		)
 return (item, float(UpperCamelCase		))

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			random.randint(0    ,	len(UpperCamelCase		) - 1		)
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 UpperCAmelCase			:      List[str]			=			parent_a[:random_slice] + parent_a[random_slice:]
 return (child_a, child_a)

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]		):
 UpperCAmelCase			:      str			=			list(UpperCamelCase		)
 if random.uniform(0    ,	1		) < MUTATION_PROBABILITY:
  UpperCAmelCase			:      int			=			random.choice(UpperCamelCase		)
 return "".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	tuple[str, float]    ,	UpperCamelCase		:	list[tuple[str, float]]    ,	UpperCamelCase		:	list[str]    ,	):
 UpperCAmelCase			:      Optional[Any]			=			[]
 # Generate more children proportionally to the fitness score.
 UpperCAmelCase			:      Optional[Any]			=			int(parent_a[1] * 100		) + 1
 UpperCAmelCase			:      List[str]			=			10 if child_n >= 10 else child_n
 for _ in range(UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			population_score[random.randint(0    ,	UpperCamelCase		)][0]

  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			crossover(parent_a[0]    ,	UpperCamelCase		)
  # Append new string to the population list.
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
  pop.append(mutate(UpperCamelCase    ,	UpperCamelCase		)		)
 return pop

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	list[str]    ,	UpperCamelCase		:	bool = True		):

 # Verify if N_POPULATION is bigger than N_SELECTED
 if N_POPULATION < N_SELECTED:
  UpperCAmelCase			:      Dict			=			F"{N_POPULATION} must be bigger than {N_SELECTED}"
  raise ValueError(UpperCamelCase		)
 # Verify that the target contains no genes besides the ones inside genes variable.
 UpperCAmelCase			:      str			=			sorted({c for c in target if c not in genes}		)
 if not_in_genes_list:
  UpperCAmelCase			:      Optional[Any]			=			F"{not_in_genes_list} is not in genes list, evolution cannot converge"
  raise ValueError(UpperCamelCase		)

 # Generate random starting population.
 UpperCAmelCase			:      Optional[int]			=			[]
 for _ in range(UpperCamelCase		):
  population.append("""""".join([random.choice(UpperCamelCase		) for i in range(len(UpperCamelCase		)		)]		)		)

 # Just some logs to know what the algorithms is doing.
 UpperCAmelCase			,	UpperCAmelCase			:      Any			=			0, 0

 # This loop will end when we find a perfect match for our target.
 while True:
  generation += 1
  total_population += len(UpperCamelCase		)

  # Random population created. Now it's time to evaluate.

  # Adding a bit of concurrency can make everything faster,
  #
  # import concurrent.futures
  # population_score: list[tuple[str, float]] = []
  # with concurrent.futures.ThreadPoolExecutor(
  #                                   max_workers=NUM_WORKERS) as executor:
  #     futures = {executor.submit(evaluate, item) for item in population}
  #     concurrent.futures.wait(futures)
  #     population_score = [item.result() for item in futures]
  #
  # but with a simple algorithm like this, it will probably be slower.
  # We just need to call evaluate for every item inside the population.
  UpperCAmelCase			:      str			=			[evaluate(UpperCamelCase    ,	UpperCamelCase		) for item in population]

  # Check if there is a matching evolution.
  UpperCAmelCase			:      Union[str, Any]			=			sorted(UpperCamelCase    ,	key=lambda UpperCamelCase		: x[1]    ,	reverse=UpperCamelCase		)
  if population_score[0][0] == target:
   return (generation, total_population, population_score[0][0])

  # Print the best result every 10 generation.
  # Just to know that the algorithm is working.
  if debug and generation % 10 == 0:
   print(
       F"\nGeneration: {generation}"
       F"\nTotal Population:{total_population}"
       F"\nBest score: {population_score[0][1]}"
       F"\nBest string: {population_score[0][0]}"		)

  # Flush the old population, keeping some of the best evolutions.
  # Keeping this avoid regression of evolution.
  UpperCAmelCase			:      Tuple			=			population[: int(N_POPULATION / 3		)]
  population.clear()
  population.extend(UpperCamelCase		)
  # Normalize population score to be between 0 and 1.
  UpperCAmelCase			:      List[str]			=			[
      (item, score / len(UpperCamelCase		)) for item, score in population_score
  ]

  # This is selection
  for i in range(UpperCamelCase		):
   population.extend(select(population_score[int(UpperCamelCase		)]    ,	UpperCamelCase    ,	UpperCamelCase		)		)
   # Check if the population has already reached the maximum value and if so,
   # break the cycle.  If this check is disabled, the algorithm will take
   # forever to compute large strings, but will also calculate small strings in
   # a far fewer generations.
   if len(UpperCamelCase		) > N_POPULATION:
    break


if __name__ == "__main__":
      A:     Union[str, Any]										=	(
          "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
      )
      A:     Dict										=	list(
          " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
          "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
      )
      A							,   A							,   A:     List[Any]										=	basic(target_str, genes_list)
      print(
          f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
      )





"""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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			tempfile.mkdtemp()
    UpperCAmelCase			:      str			=			8

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

    UpperCAmelCase			:      int			=			os.path.join(self.tmpdirname   ,  """bart_tokenizer"""		)
    os.makedirs(_SCREAMING_SNAKE_CASE   ,  exist_ok=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Union[str, Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRQuestionEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRContextEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   BartTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    shutil.rmtree(self.tmpdirname		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_dataset()
    UpperCAmelCase			:      List[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:
     UpperCAmelCase			:      Tuple			=			dataset
     UpperCAmelCase			:      List[str]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.get_dummy_dataset()
    UpperCAmelCase			:      Any			=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name="""custom"""   ,  )
    if from_disk:
     UpperCAmelCase			:      str			=			os.path.join(self.tmpdirname   ,  """dataset"""		)
     UpperCAmelCase			:      Union[str, 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
     UpperCAmelCase			:      List[Any]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    else:
     UpperCAmelCase			:      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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			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		)

    UpperCAmelCase			:      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"""		)		)

    UpperCAmelCase			:      Dict			=			os.path.join(self.tmpdirname   ,  """psgs_w100.tsv.pkl"""		)
    UpperCAmelCase			:      Any			=			{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
    pickle.dump(_SCREAMING_SNAKE_CASE   ,  open(_SCREAMING_SNAKE_CASE   ,  """wb"""		)		)

    UpperCAmelCase			:      Dict			=			RagConfig(
        retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name="""legacy"""   ,  index_path=self.tmpdirname   ,  )
    UpperCAmelCase			:      str			=			RagRetriever(
        _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()		)
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      Dict			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Union[str, 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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[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		)
     UpperCAmelCase			:      Union[str, Any]			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Optional[int]			=			np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
     UpperCAmelCase			:      List[str]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
     self.assertTrue(out is not None		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			1
    UpperCAmelCase			:      Optional[Any]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_legacy_index_retriever()
    UpperCAmelCase			:      Any			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      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]		)   ,  ["""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 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




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

   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    import torch

    UpperCAmelCase			:      Dict			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      List[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Union[str, Any]			=			retriever(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      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(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  np.ndarray		)

    UpperCAmelCase			:      Any			=			retriever(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""   ,  )
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			(  # 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 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.get_dpr_ctx_encoder_tokenizer()
    UpperCAmelCase			:      Any			=			1
    UpperCAmelCase			:      List[str]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Tuple			=			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.
