


'''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:
							__SCREAMING_SNAKE_CASE				:							int		       =		None

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







def 			a_							(  UpperCamelCase_   ):
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														A_ 						=		unittest.skip("test requires Datasets, Faiss, psutil"   )(lowerCAmelCase__   )
							return test_case







@require_distributed_retrieval
class 				__lowerCAmelCase      (      snake_case__	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		tempfile.mkdtemp()
													A_ 						=		8

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

													A_ 						=		os.path.join(self.tmpdirname   ,     "bart_tokenizer"  )
													os.makedirs(lowerCAmelCase   ,     exist_ok=lowerCAmelCase  )
													A_ 						=		os.path.join(lowerCAmelCase   ,     BART_VOCAB_FILES_NAMES["vocab_file"]  )
													A_ 						=		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       :			List[Any]  ):
													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,     "dpr_tokenizer"  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,     "bart_tokenizer"  )  )






						def 							_UpperCAmelCase					(     self       :			str  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		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       :			Any   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			Optional[Any]=1_23_45  ):
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		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:
																				A_ 						=		dataset
																				A_ 						=		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       :			Dict   ,     lowerCAmelCase       :			bool  ):
													# 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  )
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     )
													A_ 						=		ray.remote(lowerCAmelCase  )
													A_ 						=		[remote_cls.remote() for _ in range(1  )]
													with patch("transformers.models.rag.retrieval_rag.load_dataset"  ) as mock_load_dataset:
																				A_ 						=		self.get_dummy_dataset()
																				A_ 						=		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       :			List[str]   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			List[Any]=1_23_45  ):
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     index_name="custom"   ,     )
													if from_disk:
																				A_ 						=		os.path.join(self.tmpdirname   ,     "dataset"  )
																				A_ 						=		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
																				A_ 						=		RagPyTorchDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     )
													else:
																				A_ 						=		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       :			Dict   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			bool  ):
													# 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  )
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     index_name="custom"   ,     )
													A_ 						=		ray.remote(lowerCAmelCase  )
													A_ 						=		[remote_cls.remote() for _ in range(1  )]
													if from_disk:
																				A_ 						=		os.path.join(self.tmpdirname   ,     "dataset"  )
																				A_ 						=		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
																				A_ 						=		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:
																				A_ 						=		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       :			Any   ,     lowerCAmelCase       :			RagRetriever   ,     lowerCAmelCase       :			np.array   ,     lowerCAmelCase       :			int  ):
													A_ 						=		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       :			int  ):
													A_ 						=		1
													A_ 						=		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       :			Optional[int]  ):
													A_ 						=		1
													A_ 						=		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       :			List[Any]  ):
													A_ 						=		1
													A_ 						=		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       :			Optional[Any]  ):
													A_ 						=		1
													A_ 						=		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       :			List[str]  ):
													A_ 						=		1
													A_ 						=		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       :			List[Any]  ):
													A_ 						=		1
													A_ 						=		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()



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													if text is None and images is None:
																				raise ValueError("You have to specify either text or images. Both cannot be none."  )

													if text is not None:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





