





"""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__					:     Union[str, 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__					:     Optional[int]							=				None
   a__					:     List[Any]							=				None
   a__					:     str							=				None





def 							a__			(  _UpperCAmelCase	)    ->  str:

 '''simple docstring'''






 if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  snake_case__    							=				unittest.skip("test requires Datasets, Faiss, psutil"	)(lowercase_	)
 return test_case



@require_distributed_retrieval
class      lowercase_				(     a_							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				tempfile.mkdtemp()
        snake_case__    							=				8

        # DPR tok
        snake_case__    							=				[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        snake_case__    							=				os.path.join(self.tmpdirname   ,					"dpr_tokenizer"     )
        os.makedirs(__SCREAMING_SNAKE_CASE   ,					exist_ok=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				os.path.join(__SCREAMING_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
        snake_case__    							=				[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        snake_case__    							=				dict(zip(__SCREAMING_SNAKE_CASE   ,					range(len(__SCREAMING_SNAKE_CASE     )     )     )     )
        snake_case__    							=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        snake_case__    							=				{'unk_token': '<unk>'}

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        shutil.rmtree(self.tmpdirname     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=1_2345     ):






        '''simple docstring'''

        snake_case__    							=				self.get_dummy_dataset()
        snake_case__    							=				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__    							=				dataset
         snake_case__    							=				RagPyTorchDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					)
         if init_retrieval:
          retriever.init_retrieval(__SCREAMING_SNAKE_CASE     )
        return retriever

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ray.init(local_mode=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				RagConfig(
            retrieval_vector_size=self.retrieval_vector_size   ,					question_encoder=DPRConfig().to_dict()   ,					generator=BartConfig().to_dict()   ,					)
        snake_case__    							=				ray.remote(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[remote_cls.remote() for _ in range(1     )]
        with patch("transformers.models.rag.retrieval_rag.load_dataset"     ) as mock_load_dataset:
         snake_case__    							=				self.get_dummy_dataset()
         snake_case__    							=				RagRayDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					retrieval_workers=__SCREAMING_SNAKE_CASE   ,					)
         if init_retrieval:
          retriever.init_retrieval()
        return retriever

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=1_2345     ):






        '''simple docstring'''

        snake_case__    							=				self.get_dummy_dataset()
        snake_case__    							=				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__    							=				os.path.join(self.tmpdirname   ,					"dataset"     )
         snake_case__    							=				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__    							=				RagPyTorchDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					)
        else:
         snake_case__    							=				RagPyTorchDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					index=CustomHFIndex(config.retrieval_vector_size   ,					__SCREAMING_SNAKE_CASE     )   ,					)
        if init_retrieval:
         retriever.init_retrieval(__SCREAMING_SNAKE_CASE     )
        return retriever

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ray.init(local_mode=__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				self.get_dummy_dataset()
        snake_case__    							=				RagConfig(
            retrieval_vector_size=self.retrieval_vector_size   ,					question_encoder=DPRConfig().to_dict()   ,					generator=BartConfig().to_dict()   ,					index_name="custom"   ,					)
        snake_case__    							=				ray.remote(__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[remote_cls.remote() for _ in range(1     )]
        if from_disk:
         snake_case__    							=				os.path.join(self.tmpdirname   ,					"dataset"     )
         snake_case__    							=				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__    							=				RagRayDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					retrieval_workers=__SCREAMING_SNAKE_CASE   ,					index=CustomHFIndex.load_from_disk(
                 vector_size=config.retrieval_vector_size   ,					dataset_path=config.passages_path   ,					index_path=config.index_path   ,					)   ,					)
        else:
         snake_case__    							=				RagRayDistributedRetriever(
             __SCREAMING_SNAKE_CASE   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					retrieval_workers=__SCREAMING_SNAKE_CASE   ,					index=CustomHFIndex(config.retrieval_vector_size   ,					__SCREAMING_SNAKE_CASE     )   ,					)
        if init_retrieval:
         retriever.init_retrieval()
        return retriever

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				retriever.retrieve(__SCREAMING_SNAKE_CASE   ,					n_docs=__SCREAMING_SNAKE_CASE     )
        self.assertEqual(retrieved_doc_embeds.shape   ,					(2, n_docs, self.retrieval_vector_size)     )
        self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,					2     )
        self.assertEqual(sorted(doc_dicts[0]     )   ,					["embeddings", "id", "text", "title"]     )
        self.assertEqual(len(doc_dicts[0]["id"]     )   ,					__SCREAMING_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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				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=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				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=__SCREAMING_SNAKE_CASE   ,					from_disk=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				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=__SCREAMING_SNAKE_CASE   ,					from_disk=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					)

       @require_ray
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				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=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        ray.shutdown()

       @require_ray
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				np.array(
            [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]   ,					dtype=np.floataa     )
        with self.assertRaises(__SCREAMING_SNAKE_CASE     ):
         self.distributed_retriever_check(
             self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__SCREAMING_SNAKE_CASE   ,					from_disk=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					)
        ray.shutdown()

       @require_ray
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				1
        snake_case__    							=				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=__SCREAMING_SNAKE_CASE   ,					from_disk=__SCREAMING_SNAKE_CASE     )   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        ray.shutdown()








"""simple docstring"""


def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

