


"""simple docstring"""



from __future__ import annotations

__snake_case         =					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						int		,							lowercase						:						List[str]		,							lowercase						:						int		,							lowercase						:						Optional[int]		,							)			->		tuple[list[list[int]], list[list[int]]]:


    """simple docstring"""





    snake_case					:   Tuple         =					[
        [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
    ]  # the reference grid
    snake_case					:   List[Any]         =					1
    snake_case					:   Optional[Any]         =					[
        [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
    ]  # the action grid

    snake_case					:   Any         =					init[0]
    snake_case					:   List[str]         =					init[1]
    snake_case					:   Any         =					0
    snake_case					:   Union[str, Any]         =					g + heuristic[x][y]  # cost from starting cell to destination cell
    snake_case					:   Dict         =					[[f, g, x, y]]

    snake_case					:   str         =					False  # flag that is set when search is complete
    snake_case					:   List[Any]         =					False  # flag set if we can't find expand

    while not found and not resign:
        if len(_SCREAMING_SNAKE_CASE				) == 0:
            raise ValueError("Algorithm is unable to find solution"				)
        else:  # to choose the least costliest action so as to move closer to the goal
            cell.sort()
            cell.reverse()
            snake_case					:   Any         =					cell.pop()
            snake_case					:   Any         =					next_cell[2]
            snake_case					:   List[str]         =					next_cell[3]
            snake_case					:   Optional[int]         =					next_cell[1]

            if x == goal[0] and y == goal[1]:
                snake_case					:   str         =					True
            else:
                for i in range(len(_SCREAMING_SNAKE_CASE				)				):  # to try out different valid actions
                    snake_case					:   Optional[int]         =					x + DIRECTIONS[i][0]
                    snake_case					:   Optional[Any]         =					y + DIRECTIONS[i][1]
                    if xa >= 0 and xa < len(_SCREAMING_SNAKE_CASE				) and ya >= 0 and ya < len(grid[0]				):
                        if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                            snake_case					:   List[Any]         =					g + cost
                            snake_case					:   int         =					ga + heuristic[xa][ya]
                            cell.append([fa, ga, xa, ya]				)
                            snake_case					:   Dict         =					1
                            snake_case					:   List[Any]         =					i
    snake_case					:   str         =					[]
    snake_case					:   Tuple         =					goal[0]
    snake_case					:   List[str]         =					goal[1]
    invpath.append([x, y]				)  # we get the reverse path from here
    while x != init[0] or y != init[1]:
        snake_case					:   Any         =					x - DIRECTIONS[action[x][y]][0]
        snake_case					:   Union[str, Any]         =					y - DIRECTIONS[action[x][y]][1]
        snake_case					:   int         =					xa
        snake_case					:   List[Any]         =					ya
        invpath.append([x, y]				)

    snake_case					:   str         =					[]
    for i in range(len(_SCREAMING_SNAKE_CASE				)				):
        path.append(invpath[len(_SCREAMING_SNAKE_CASE				) - 1 - i]				)
    return path, action


if __name__ == "__main__":
       __snake_case         =					[
           [0, 1, 0, 0, 0, 0],
           [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
           [0, 1, 0, 0, 0, 0],
           [0, 1, 0, 0, 1, 0],
           [0, 0, 0, 0, 1, 0],
       ]

       __snake_case         =					[0, 0]
       # all coordinates are given in format [y,x]
       __snake_case         =					[len(grid) - 1, len(grid[0]) - 1]
       __snake_case         =					1

       # the cost map which pushes the path closer to the goal
       __snake_case         =					[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
       for i in range(len(grid)):
              for j in range(len(grid[0])):
                     __snake_case         =					abs(i - goal[0]) + abs(j - goal[1])
                     if grid[i][j] == 1:
                            # added extra penalty in the heuristic map
                            __snake_case         =					99

       __snake_case         =					search(grid, init, goal, cost, heuristic)

       print("""ACTION MAP""")
       for i in range(len(action)):
              print(action[i])

       for i in range(len(path)):
              print(path[i])









"""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 _lowerCAmelCase       (   snake_case_			):







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					tempfile.mkdtemp()
       snake_case					:   Union[str, Any]         =					8

       # DPR tok
       snake_case					:   str         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   Union[str, Any]         =					os.path.join(self.tmpdirname		,			"dpr_tokenizer"		)
       os.makedirs(UpperCamelCase__		,			exist_ok=UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					os.path.join(UpperCamelCase__		,			DPR_VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

       # BART tok
       snake_case					:   Union[str, Any]         =					[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       snake_case					:   Tuple         =					dict(zip(UpperCamelCase__		,			range(len(UpperCamelCase__		)		)		)		)
       snake_case					:   List[Any]         =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case					:   Optional[Any]         =					{"unk_token": "<unk>"}

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







   def 				lowerCamelCase	(			self		)			->	DPRQuestionEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	DPRContextEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	BartTokenizer:

       '''simple docstring'''




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







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

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   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		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   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:
           snake_case					:   int         =					dataset
           snake_case					:   int         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       return retriever







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   Optional[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		)

       snake_case					:   Dict         =					os.path.join(self.tmpdirname		,			"hf_bert_base.hnswSQ8_correct_phi_128.c_index"		)
       dataset.save_faiss_index("embeddings"		,			index_file_name + ".index.dpr"		)
       pickle.dump(dataset["id"]		,			open(index_file_name + ".index_meta.dpr"		,			"wb"		)		)

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

       snake_case					:   List[Any]         =					RagConfig(
           retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name="legacy"		,			index_path=self.tmpdirname		,			)
       snake_case					:   Dict         =					RagRetriever(
           UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
       return retriever







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_canonical_hf_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
           with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
               snake_case					:   List[str]         =					self.get_dummy_dataset()
               retriever.save_pretrained(UpperCamelCase__		)
               snake_case					:   Union[str, Any]         =					RagRetriever.from_pretrained(UpperCamelCase__		)
               self.assertIsInstance(UpperCamelCase__		,			UpperCamelCase__		)
               snake_case					:   Union[str, Any]         =					np.array(
                   [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
               snake_case					:   List[Any]         =					retriever.retrieve(UpperCamelCase__		,			n_docs=1		)
               self.assertTrue(out is not None		)







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

       '''simple docstring'''




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

       '''simple docstring'''




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







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

       '''simple docstring'''




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

       '''simple docstring'''




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







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

       '''simple docstring'''




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

       '''simple docstring'''




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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       import torch

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

       snake_case					:   Tuple         =					retriever(
           UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		,			return_tensors="pt"		,			)
       snake_case      ,snake_case      ,snake_case      ,snake_case					:   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(UpperCamelCase__		,			torch.Tensor		)
       self.assertIsInstance(UpperCamelCase__		,			torch.Tensor		)
       self.assertIsInstance(UpperCamelCase__		,			torch.Tensor		)







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					self.get_dpr_ctx_encoder_tokenizer()
       snake_case					:   Union[str, Any]         =					1
       snake_case					:   List[str]         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       retriever.set_ctx_encoder_tokenizer(UpperCamelCase__		)

       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   Dict         =					retriever(UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		)

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





