






"""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:
			a :				Any      					=		None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			a :				Any      					=		None
			a :				Any      					=		None
			a :				List[Any]      					=		None





def 						lowercase__(A						)				->Dict:




						"""simple docstring"""



						if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
												lowercase__				:      List[str]=       unittest.skip("test requires Datasets, Faiss, psutil"						)(A						)
						return test_case







@require_distributed_retrieval
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       tempfile.mkdtemp()
												lowercase__				:      int=       8

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

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,  "dpr_tokenizer"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,  "bart_tokenizer"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												shutil.rmtree(self.tmpdirname			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      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 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__=12345			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.get_dummy_dataset()
												lowercase__				:      Optional[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:
																		lowercase__				:      int=       dataset
																		lowercase__				:      List[str]=       RagPyTorchDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  )
																		if init_retrieval:
																								retriever.init_retrieval(snake_case__			)
												return retriever




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												# Have to run in local mode because sys.path modifications at top of
												# file are not propogated to remote workers.
												# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
												ray.init(local_mode=snake_case__			)
												lowercase__				:      Optional[int]=       RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size       ,  question_encoder=DPRConfig().to_dict()       ,  generator=BartConfig().to_dict()       ,  )
												lowercase__				:      List[str]=       ray.remote(snake_case__			)
												lowercase__				:      str=       [remote_cls.remote() for _ in range(1			)]
												with patch("transformers.models.rag.retrieval_rag.load_dataset"			) as mock_load_dataset:
																		lowercase__				:      Dict=       self.get_dummy_dataset()
																		lowercase__				:      List[Any]=       RagRayDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  retrieval_workers=snake_case__       ,  )
																		if init_retrieval:
																								retriever.init_retrieval()
												return retriever




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__=12345			):
												'''simple docstring'''
												lowercase__				:      Tuple=       self.get_dummy_dataset()
												lowercase__				:      List[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[int]=       os.path.join(self.tmpdirname       ,  "dataset"			)
																		lowercase__				:      Optional[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
																		lowercase__				:      int=       RagPyTorchDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  )
												else:
																		lowercase__				:      Any=       RagPyTorchDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  index=CustomHFIndex(config.retrieval_vector_size       ,  snake_case__			)       ,  )
												if init_retrieval:
																		retriever.init_retrieval(snake_case__			)
												return retriever




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												# Have to run in local mode because sys.path modifications at top of
												# file are not propogated to remote workers.
												# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
												ray.init(local_mode=snake_case__			)
												lowercase__				:      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()       ,  index_name="custom"       ,  )
												lowercase__				:      int=       ray.remote(snake_case__			)
												lowercase__				:      Optional[Any]=       [remote_cls.remote() for _ in range(1			)]
												if from_disk:
																		lowercase__				:      List[str]=       os.path.join(self.tmpdirname       ,  "dataset"			)
																		lowercase__				:      Tuple=       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=       RagRayDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  retrieval_workers=snake_case__       ,  index=CustomHFIndex.load_from_disk(
																		        vector_size=config.retrieval_vector_size       ,  dataset_path=config.passages_path       ,  index_path=config.index_path       ,  )       ,  )
												else:
																		lowercase__				:      List[Any]=       RagRayDistributedRetriever(
																		    snake_case__       ,  question_encoder_tokenizer=self.get_dpr_tokenizer()       ,  generator_tokenizer=self.get_bart_tokenizer()       ,  retrieval_workers=snake_case__       ,  index=CustomHFIndex(config.retrieval_vector_size       ,  snake_case__			)       ,  )
												if init_retrieval:
																		retriever.init_retrieval()
												return retriever




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__,	lowercase__,	lowercase__				:      Tuple=       retriever.retrieve(snake_case__       ,  n_docs=snake_case__			)
												self.assertEqual(retrieved_doc_embeds.shape       ,  (2, n_docs, self.retrieval_vector_size)			)
												self.assertEqual(len(snake_case__			)       ,  2			)
												self.assertEqual(sorted(doc_dicts[0]			)       ,  ["embeddings", "id", "text", "title"]			)
												self.assertEqual(len(doc_dicts[0]["id"]			)       ,  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			):
												'''simple docstring'''
												lowercase__				:      int=       1
												lowercase__				:      List[str]=       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=snake_case__			)       ,  snake_case__       ,  snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       1
												lowercase__				:      int=       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=snake_case__       ,  from_disk=snake_case__			)       ,  snake_case__       ,  snake_case__       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       1
												lowercase__				:      Union[str, Any]=       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=snake_case__       ,  from_disk=snake_case__			)       ,  snake_case__       ,  snake_case__       ,  )




						@require_ray
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       1
												lowercase__				:      Any=       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=snake_case__			)       ,  snake_case__       ,  snake_case__			)
												ray.shutdown()




						@require_ray
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       1
												lowercase__				:      Optional[int]=       np.array(
												    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]       ,  dtype=np.floataa			)
												with self.assertRaises(snake_case__			):
																		self.distributed_retriever_check(
																		    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__       ,  from_disk=snake_case__			)       ,  snake_case__       ,  snake_case__       ,  )
												ray.shutdown()




						@require_ray
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       1
												lowercase__				:      Dict=       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=snake_case__       ,  from_disk=snake_case__			)       ,  snake_case__       ,  snake_case__			)
												ray.shutdown()














"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






