





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_			(				UpperCamelCase_	)  ->		Optional[Any]:
       if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
              UpperCamelCase_ 							=	unittest.skip("test requires Datasets, Faiss, psutil"	)(UpperCamelCase_	)
       return test_case







@require_distributed_retrieval
class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def lowercase			(		self:			Tuple		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	tempfile.mkdtemp()
              UpperCamelCase_ 							=	8

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

              UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "bart_tokenizer"		)
              os.makedirs(_SCREAMING_SNAKE_CASE       , exist_ok=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	os.path.join(_SCREAMING_SNAKE_CASE       , BART_VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[str]		)							->			DPRQuestionEncoderTokenizer:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[Any]		)							->			BartTokenizer:




              """simple docstring"""


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

       def lowercase			(		self:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              shutil.rmtree(self.tmpdirname		)

       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	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 lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			str=12345		)							->			RagPyTorchDistributedRetriever:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	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_ 							=	dataset
                     UpperCamelCase_ 							=	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 lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			bool		)							->			RagRayDistributedRetriever:




              """simple docstring"""


              ray.init(local_mode=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , )
              UpperCamelCase_ 							=	ray.remote(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	[remote_cls.remote() for _ in range(1		)]
              with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
                     UpperCamelCase_ 							=	self.get_dummy_dataset()
                     UpperCamelCase_ 							=	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 lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			int=12345		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , index_name="custom"       , )
              if from_disk:
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "dataset"		)
                     UpperCamelCase_ 							=	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_ 							=	RagPyTorchDistributedRetriever(
                         _SCREAMING_SNAKE_CASE       , question_encoder_tokenizer=self.get_dpr_tokenizer()       , generator_tokenizer=self.get_bart_tokenizer()       , )
              else:
                     UpperCamelCase_ 							=	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 lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			bool       , _SCREAMING_SNAKE_CASE:			bool		)							->			List[str]:




              """simple docstring"""


              ray.init(local_mode=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.get_dummy_dataset()
              UpperCamelCase_ 							=	RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size       , question_encoder=DPRConfig().to_dict()       , generator=BartConfig().to_dict()       , index_name="custom"       , )
              UpperCamelCase_ 							=	ray.remote(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	[remote_cls.remote() for _ in range(1		)]
              if from_disk:
                     UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , "dataset"		)
                     UpperCamelCase_ 							=	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_ 							=	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:
                     UpperCamelCase_ 							=	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 lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			RagRetriever       , _SCREAMING_SNAKE_CASE:			np.array       , _SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	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 lowercase			(		self:			Optional[Any]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[str]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[str]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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 lowercase			(		self:			Optional[Any]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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 lowercase			(		self:			Optional[Any]		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	1
              UpperCamelCase_ 							=	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()








import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

