






'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload


class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							Collection[float] | None = None      ):






       '''simple docstring'''






       if components is None:
          lowercase_						:     Union[str, Any]							     =  []
       lowercase_						:     List[str]							     =  list(a      )




    def __len__(      self	:							Any      ):






       '''simple docstring'''






       return len(self.__components      )




    def __str__(      self	:							str      ):






       '''simple docstring'''






       return "(" + ",".join(map(a	,  self.__components      )      ) + ")"




    def __add__(      self	:							int	,  a	:							Vector      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  len(self      )
       if size == len(a      ):
          lowercase_						:     Optional[int]							     =  [self.__components[i] + other.component(a      ) for i in range(a      )]
          return Vector(a      )
       else:
          raise Exception("must have the same size"      )




    def __sub__(      self	:							Dict	,  a	:							Vector      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  len(self      )
       if size == len(a      ):
          lowercase_						:     Optional[Any]							     =  [self.__components[i] - other.component(a      ) for i in range(a      )]
          return Vector(a      )
       else:  # error case
          raise Exception("must have the same size"      )




    @overload
    def __mul__(      self	:							Optional[Any]	,  a	:							float      ):






       '''simple docstring'''






       ...




    @overload
    def __mul__(      self	:							Union[str, Any]	,  a	:							Vector      ):






       '''simple docstring'''






       ...




    def __mul__(      self	:							int	,  a	:							float | Vector      ):






       '''simple docstring'''






       if isinstance(a	,  (float, int)      ):
          lowercase_						:     Union[str, Any]							     =  [c * other for c in self.__components]
          return Vector(a      )
       elif isinstance(a	,  a      ) and len(self      ) == len(a      ):
          lowercase_						:     Tuple							     =  len(self      )
          lowercase_						:     Dict							     =  [self.__components[i] * other.component(a      ) for i in range(a      )]
          return sum(a      )
       else:  # error case
          raise Exception("invalid operand!"      )




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






       '''simple docstring'''






       return Vector(self.__components      )




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							int      ):






       '''simple docstring'''






       if isinstance(a	,  a      ) and -len(self.__components      ) <= i < len(self.__components      ):
          return self.__components[i]
       else:
          raise Exception("index out of range"      )




    def 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							int	,  a	:							float      ):






       '''simple docstring'''






       assert -len(self.__components      ) <= pos < len(self.__components      )
       lowercase_						:     Any							     =  value




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       if len(self.__components      ) == 0:
          raise Exception("Vector is empty"      )
       lowercase_						:     Optional[int]							     =  [c**2 for c in self.__components]
       return math.sqrt(sum(a      )      )





    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							Vector	,  a	:							bool = False      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self * other
       lowercase_						:     List[str]							     =  self.euclidean_length() * other.euclidean_length()
       if deg:
          return math.degrees(math.acos(num / den      )      )
       else:
          return math.acos(num / den      )




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   assert isinstance(_UpperCamelCase    ,		_UpperCamelCase				)
   return Vector([0] * dimension				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   assert isinstance(_UpperCamelCase    ,		_UpperCamelCase				) and (isinstance(_UpperCamelCase    ,		_UpperCamelCase				))
   lowercase_						:     List[Any]							     =  [0] * dimension
   lowercase_						:     List[Any]							     =  1
   return Vector(_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   assert (
       isinstance(_UpperCamelCase    ,		_UpperCamelCase				)
       and isinstance(_UpperCamelCase    ,		_UpperCamelCase				)
       and (isinstance(_UpperCamelCase    ,		(int, float)				))
   )
   return x * scalar + y




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   random.seed(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  [random.randint(_UpperCamelCase    ,		_UpperCamelCase				) for _ in range(_UpperCamelCase				)]
   return Vector(_UpperCamelCase				)


class 							_UpperCAmelCase    :




    def __init__(      self	:							List[str]	,  a	:							list[list[float]]	,  a	:							int	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  matrix
       lowercase_						:     Any							     =  w
       lowercase_						:     List[Any]							     =  h




    def __str__(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  ""
       for i in range(self.__height      ):
          ans += "|"
          for j in range(self.__width      ):
             if j < self.__width - 1:
                ans += str(self.__matrix[i][j]      ) + ","
             else:
                ans += str(self.__matrix[i][j]      ) + "|\n"
       return ans




    def __add__(      self	:							Optional[Any]	,  a	:							Matrix      ):






       '''simple docstring'''






       if self.__width == other.width() and self.__height == other.height():
          lowercase_						:     str							     =  []
          for i in range(self.__height      ):
             lowercase_						:     List[Any]							     =  [
                 self.__matrix[i][j] + other.component(a	,  a      )
                 for j in range(self.__width      )
             ]
             matrix.append(a      )
          return Matrix(a	,  self.__width	,  self.__height      )
       else:
          raise Exception("matrix must have the same dimension!"      )




    def __sub__(      self	:							int	,  a	:							Matrix      ):






       '''simple docstring'''






       if self.__width == other.width() and self.__height == other.height():
          lowercase_						:     str							     =  []
          for i in range(self.__height      ):
             lowercase_						:     int							     =  [
                 self.__matrix[i][j] - other.component(a	,  a      )
                 for j in range(self.__width      )
             ]
             matrix.append(a      )
          return Matrix(a	,  self.__width	,  self.__height      )
       else:
          raise Exception("matrices must have the same dimension!"      )




    @overload
    def __mul__(      self	:							Optional[int]	,  a	:							float      ):






       '''simple docstring'''






       ...




    @overload
    def __mul__(      self	:							List[Any]	,  a	:							Vector      ):






       '''simple docstring'''






       ...




    def __mul__(      self	:							Union[str, Any]	,  a	:							float | Vector      ):






       '''simple docstring'''






       if isinstance(a	,  a      ):  # matrix-vector
          if len(a      ) == self.__width:
             lowercase_						:     List[Any]							     =  zero_vector(self.__height      )
             for i in range(self.__height      ):
                lowercase_						:     Tuple							     =  [
                    self.__matrix[i][j] * other.component(a      )
                    for j in range(self.__width      )
                ]
                ans.change_component(a	,  sum(a      )      )
             return ans
          else:
             raise Exception(
                 "vector must have the same size as the "
                 "number of columns of the matrix!"      )
       elif isinstance(a	,  (int, float)      ):  # matrix-scalar
          lowercase_						:     List[str]							     =  [
              [self.__matrix[i][j] * other for j in range(self.__width      )]
              for i in range(self.__height      )
          ]
          return Matrix(a	,  self.__width	,  self.__height      )
       return None




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       return self.__height




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






       '''simple docstring'''






       return self.__width




    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int	,  a	:							int      ):






       '''simple docstring'''






       if 0 <= x < self.__height and 0 <= y < self.__width:
          return self.__matrix[x][y]
       else:
          raise Exception("change_component: indices out of bounds"      )




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							int	,  a	:							int	,  a	:							float      ):






       '''simple docstring'''






       if 0 <= x < self.__height and 0 <= y < self.__width:
          lowercase_						:     Any							     =  value
       else:
          raise Exception("change_component: indices out of bounds"      )




    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							int	,  a	:							int      ):






       '''simple docstring'''






       if self.__height != self.__width:
          raise Exception("Matrix is not square"      )
       lowercase_						:     Dict							     =  self.__matrix[:x] + self.__matrix[x + 1 :]
       for i in range(len(a      )      ):
          lowercase_						:     Any							     =  minor[i][:y] + minor[i][y + 1 :]
       return Matrix(a	,  self.__width - 1	,  self.__height - 1      ).determinant()




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							int	,  a	:							int      ):






       '''simple docstring'''






       if self.__height != self.__width:
          raise Exception("Matrix is not square"      )
       if 0 <= x < self.__height and 0 <= y < self.__width:
          return (-1) ** (x + y) * self.minor(a	,  a      )
       else:
          raise Exception("Indices out of bounds"      )





    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       if self.__height != self.__width:
          raise Exception("Matrix is not square"      )
       if self.__height < 1:
          raise Exception("Matrix has no element"      )
       elif self.__height == 1:
          return self.__matrix[0][0]
       elif self.__height == 2:
          return (
              self.__matrix[0][0] * self.__matrix[1][1]
              - self.__matrix[0][1] * self.__matrix[1][0]
          )
       else:
          lowercase_						:     Optional[Any]							     =  [
              self.__matrix[0][y] * self.cofactor(0	,  a      ) for y in range(self.__width      )
          ]
          return sum(a      )




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     list[list[float]]							     =  [[0] * n for _ in range(_UpperCamelCase				)]
   return Matrix(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)





def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   random.seed(_UpperCamelCase				)
   lowercase_						:     list[list[float]]							     =  [
       [random.randint(_UpperCamelCase    ,		_UpperCamelCase				) for _ in range(_UpperCamelCase				)] for _ in range(_UpperCamelCase				)
   ]
   return Matrix(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)












'''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    (		snake_case      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  tempfile.mkdtemp()
       lowercase_						:     str							     =  8

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

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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  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	:							List[Any]      ):






       '''simple docstring'''






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




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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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      )

       lowercase_						:     str							     =  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"      )      )

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

       lowercase_						:     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	,  )
       lowercase_						:     str							     =  RagRetriever(
           a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()      )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     int							     =  self.get_dummy_legacy_index_retriever()
       lowercase_						:     Dict							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["text"]      )	,  a      )
       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	:							Optional[int]      ):






       '''simple docstring'''






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




    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       import torch

       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     str							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Optional[int]							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     str							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     List[str]							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Union[str, 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(a	,  a      )
       self.assertIsInstance(a	,  a      )
       self.assertIsInstance(a	,  np.ndarray      )

       lowercase_						:     Tuple							     =  retriever(
           a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a	,  return_tensors="pt"	,  )
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  (  # 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(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.get_dpr_ctx_encoder_tokenizer()
       lowercase_						:     Tuple							     =  1
       lowercase_						:     int							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       retriever.set_ctx_encoder_tokenizer(a      )

       lowercase_						:     Tuple							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     Optional[Any]							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     Dict							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )

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




