





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:
       UpperCamelCase__          =None

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






def 			lowerCamelCase__	(__lowerCamelCase		):
     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
          _SCREAMING_SNAKE_CASE				:		Tuple  			=							unittest.skip("test requires Datasets, Faiss, psutil"		)(__lowerCamelCase		)
     return test_case





@require_distributed_retrieval
class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE				:		str  			=							8

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

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




       def 			UpperCamelCase_	(					self							)	->   DPRQuestionEncoderTokenizer:
            return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "dpr_tokenizer"							)							)




       def 			UpperCamelCase_	(					self							)	->   BartTokenizer:
            return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,  "bart_tokenizer"							)							)




       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            shutil.rmtree(self.tmpdirname							)




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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			,  __lowerCamelCase			,  __lowerCamelCase=1_2_3_4_5							)	->   RagPyTorchDistributedRetriever:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_dummy_dataset()
            _SCREAMING_SNAKE_CASE				:		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:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							dataset
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							RagPyTorchDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
                 if init_retrieval:
                      retriever.init_retrieval(__lowerCamelCase							)
            return retriever




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase							)	->   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=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							RagConfig(
                retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  )
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							ray.remote(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[remote_cls.remote() for _ in range(1							)]
            with patch("transformers.models.rag.retrieval_rag.load_dataset"							) as mock_load_dataset:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.get_dummy_dataset()
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							RagRayDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=__lowerCamelCase			,  )
                 if init_retrieval:
                      retriever.init_retrieval()
            return retriever




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=1_2_3_4_5							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.get_dummy_dataset()
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							RagConfig(
                retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
            if from_disk:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							os.path.join(self.tmpdirname			,  "dataset"							)
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							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
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							RagPyTorchDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  )
            else:
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							RagPyTorchDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  index=CustomHFIndex(config.retrieval_vector_size			,  __lowerCamelCase							)			,  )
            if init_retrieval:
                 retriever.init_retrieval(__lowerCamelCase							)
            return retriever




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase							)	->   List[Any]:
            # 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=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.get_dummy_dataset()
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							RagConfig(
                retrieval_vector_size=self.retrieval_vector_size			,  question_encoder=DPRConfig().to_dict()			,  generator=BartConfig().to_dict()			,  index_name="custom"			,  )
            _SCREAMING_SNAKE_CASE				:		str  			=							ray.remote(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							[remote_cls.remote() for _ in range(1							)]
            if from_disk:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(self.tmpdirname			,  "dataset"							)
                 _SCREAMING_SNAKE_CASE				:		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
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							RagRayDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=__lowerCamelCase			,  index=CustomHFIndex.load_from_disk(
                         vector_size=config.retrieval_vector_size			,  dataset_path=config.passages_path			,  index_path=config.index_path			,  )			,  )
            else:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							RagRayDistributedRetriever(
                     __lowerCamelCase			,  question_encoder_tokenizer=self.get_dpr_tokenizer()			,  generator_tokenizer=self.get_bart_tokenizer()			,  retrieval_workers=__lowerCamelCase			,  index=CustomHFIndex(config.retrieval_vector_size			,  __lowerCamelCase							)			,  )
            if init_retrieval:
                 retriever.init_retrieval()
            return retriever




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   None:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							retriever.retrieve(__lowerCamelCase			,  n_docs=__lowerCamelCase							)
            self.assertEqual(retrieved_doc_embeds.shape			,  (2, n_docs, self.retrieval_vector_size)							)
            self.assertEqual(len(__lowerCamelCase							)			,  2							)
            self.assertEqual(sorted(doc_dicts[0]							)			,  ["embeddings", "id", "text", "title"]							)
            self.assertEqual(len(doc_dicts[0]["id"]							)			,  __lowerCamelCase							)
            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							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							1
            _SCREAMING_SNAKE_CASE				:		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_pytorch_distributed_retriever(init_retrieval=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1
            _SCREAMING_SNAKE_CASE				:		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_custom_hf_index_pytorch_retriever(init_retrieval=__lowerCamelCase			,  from_disk=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase			,  )




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1
            _SCREAMING_SNAKE_CASE				:		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=__lowerCamelCase			,  from_disk=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase			,  )




       @require_ray
       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							1
            _SCREAMING_SNAKE_CASE				:		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_ray_distributed_retriever(init_retrieval=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase							)
            ray.shutdown()




       @require_ray
       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							1
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							np.array(
                [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]			,  dtype=np.floataa							)
            with self.assertRaises(__lowerCamelCase							):
                 self.distributed_retriever_check(
                     self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__lowerCamelCase			,  from_disk=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase			,  )
            ray.shutdown()







       @require_ray
       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							1
            _SCREAMING_SNAKE_CASE				:		Optional[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_ray_retriever(init_retrieval=__lowerCamelCase			,  from_disk=__lowerCamelCase							)			,  __lowerCamelCase			,  __lowerCamelCase							)
            ray.shutdown()






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)