




'''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:
				_lowerCAmelCase				     = None

if is_ray_available():
				import ray  # noqa: E402 # isort:skip
				from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
				_lowerCAmelCase				     = None
				_lowerCAmelCase				     = None
				_lowerCAmelCase				     = None

def       __lowerCAmelCase (   snake_case__						):
			if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
						__UpperCamelCase				:      List[Any]									=       unittest.skip("test requires Datasets, Faiss, psutil"						)(snake_case__						)
			return test_case




@require_distributed_retrieval
class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      Optional[int]									=       tempfile.mkdtemp()
							__UpperCamelCase				:      Optional[int]									=       8

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

							__UpperCamelCase				:      Union[str, Any]									=       os.path.join(self.tmpdirname			,  "bart_tokenizer"      )
							os.makedirs(_UpperCAmelCase			,  exist_ok=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       os.path.join(_UpperCAmelCase			,  BART_VOCAB_FILES_NAMES["vocab_file"]      )
							__UpperCamelCase				:      Dict									=       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 	a_	(self      )     ->	DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "dpr_tokenizer"      )      )
				def 	a_	(self      )     ->	BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "bart_tokenizer"      )      )
				def 	a_	(self      )     ->	Dict:
							shutil.rmtree(self.tmpdirname      )
				def 	a_	(self      )     ->	Union[str, Any]:
							__UpperCamelCase				:      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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase=1_2_3_4_5      )     ->	RagPyTorchDistributedRetriever:
							__UpperCamelCase				:      Union[str, Any]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      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:
										__UpperCamelCase				:      Optional[Any]									=       dataset
										__UpperCamelCase				:      List[str]									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
										if init_retrieval:
													retriever.init_retrieval(_UpperCAmelCase      )
							return retriever
				def 	a_	(self			,  _UpperCAmelCase      )     ->	RagRayDistributedRetriever:
							# 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=_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  )
							__UpperCamelCase				:      str									=       ray.remote(_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       [remote_cls.remote() for _ in range(1      )]
							with patch("transformers.models.rag.retrieval_rag.load_dataset"      ) as mock_load_dataset:
										__UpperCamelCase				:      str									=       self.get_dummy_dataset()
										__UpperCamelCase				:      Union[str, Any]									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  )
										if init_retrieval:
													retriever.init_retrieval()
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase=1_2_3_4_5      )     ->	List[str]:
							__UpperCamelCase				:      Union[str, Any]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      Optional[int]									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
							if from_disk:
										__UpperCamelCase				:      Optional[int]									=       os.path.join(self.tmpdirname			,  "dataset"      )
										__UpperCamelCase				:      str									=       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[str]									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
							else:
										__UpperCamelCase				:      Any									=       RagPyTorchDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  index=CustomHFIndex(config.retrieval_vector_size			,  _UpperCAmelCase      )			,  )
							if init_retrieval:
										retriever.init_retrieval(_UpperCAmelCase      )
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[int]:
							# 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=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       self.get_dummy_dataset()
							__UpperCamelCase				:      Dict									=       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
							__UpperCamelCase				:      Union[str, Any]									=       ray.remote(_UpperCAmelCase      )
							__UpperCamelCase				:      str									=       [remote_cls.remote() for _ in range(1      )]
							if from_disk:
										__UpperCamelCase				:      Tuple									=       os.path.join(self.tmpdirname			,  "dataset"      )
										__UpperCamelCase				:      List[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				:      Tuple									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  index=CustomHFIndex.load_from_disk(
										        vector_size=config.retrieval_vector_size			,  dataset_path=config.passages_path			,  index_path=config.index_path			,  )			,  )
							else:
										__UpperCamelCase				:      List[Any]									=       RagRayDistributedRetriever(
										    _UpperCAmelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=_UpperCAmelCase			,  index=CustomHFIndex(config.retrieval_vector_size			,  _UpperCAmelCase      )			,  )
							if init_retrieval:
										retriever.init_retrieval()
							return retriever
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Union[str, 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 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[Any]									=       1
							__UpperCamelCase				:      Optional[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_pytorch_distributed_retriever(init_retrieval=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
				def 	a_	(self      )     ->	str:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      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_custom_hf_index_pytorch_retriever(init_retrieval=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
				def 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      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=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
				@require_ray
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      List[str]									=       1
							__UpperCamelCase				:      Tuple									=       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=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							ray.shutdown()
				@require_ray
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[int]									=       1
							__UpperCamelCase				:      Any									=       np.array(
							    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]			,  dtype=np.floataa      )
							with self.assertRaises(_UpperCAmelCase      ):
										self.distributed_retriever_check(
										    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )
							ray.shutdown()





				@require_ray
				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      str									=       1
							__UpperCamelCase				:      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_ray_retriever(init_retrieval=_UpperCAmelCase			,  from_disk=_UpperCAmelCase      )			,  _UpperCAmelCase			,  _UpperCAmelCase      )
							ray.shutdown()









'''simple docstring'''






from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowerCAmelCase				     = logging.get_logger(__name__)

_lowerCAmelCase				     = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				     = {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

_lowerCAmelCase				     = {
    '''openai-gpt''': 512,
}



class  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    VOCAB_FILES_NAMES
				A				     =    PRETRAINED_VOCAB_FILES_MAP
				A				     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A				     =    ["input_ids", "attention_mask"]
				A				     =    OpenAIGPTTokenizer
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase="<unk>"			,  **_UpperCAmelCase      )     ->	str:
							super().__init__(_UpperCAmelCase			,  _UpperCAmelCase			,  tokenizer_file=_UpperCAmelCase			,  unk_token=_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Dict:
							return True





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None      )     ->	Tuple[str]:
							__UpperCamelCase				:      Optional[Any]									=       self._tokenizer.model.save(_UpperCAmelCase			,  name=_UpperCAmelCase      )
							return tuple(_UpperCAmelCase      )



