
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss


@require_faiss
class 		__magic_name__ (    a_     ):





		def    _A				(				self     :						Union[str, Any]					):
							lowerCAmelCase		:       str							=  tempfile.mkdtemp()
							lowerCAmelCase		:       List[str]							=  8

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

							lowerCAmelCase		:       Optional[int]							=  os.path.join(self.tmpdirname    ,	'''bart_tokenizer'''					)
							os.makedirs(_a    ,	exist_ok=_a					)
							lowerCAmelCase		:       str							=  os.path.join(_a    ,	BART_VOCAB_FILES_NAMES['''vocab_file''']					)
							lowerCAmelCase		:       Any							=  os.path.join(_a    ,	BART_VOCAB_FILES_NAMES['''merges_file''']					)
							with open(self.vocab_file    ,	'''w'''    ,	encoding='''utf-8'''					) as fp:
												fp.write(json.dumps(_a					) + '''\n'''					)
							with open(self.merges_file    ,	'''w'''    ,	encoding='''utf-8'''					) as fp:
												fp.write('''\n'''.join(_a					)					)





		def    _A				(				self     :						str					):
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,	'''dpr_tokenizer'''					)					)





		def    _A				(				self     :						int					):
							return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,	'''dpr_tokenizer'''					)					)





		def    _A				(				self     :						Any					):
							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]					):
							lowerCAmelCase		:       Union[str, 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    _A				(				self     :						Optional[int]					):
							lowerCAmelCase		:       Union[str, Any]							=  self.get_dummy_dataset()
							lowerCAmelCase		:       str							=  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:
												lowerCAmelCase		:       List[Any]							=  dataset
												lowerCAmelCase		:       Union[str, Any]							=  RagRetriever(
												    _a    ,	question_encoder_tokenizer=self.get_dpr_tokenizer()    ,	generator_tokenizer=self.get_bart_tokenizer()    ,	)
							return retriever





		def    _A				(				self     :						List[Any]    ,	lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       Dict							=  self.get_dummy_dataset()
							lowerCAmelCase		:       Any							=  RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size    ,	question_encoder=DPRConfig().to_dict()    ,	generator=BartConfig().to_dict()    ,	index_name='''custom'''    ,	)
							if from_disk:
												lowerCAmelCase		:       Tuple							=  os.path.join(self.tmpdirname    ,	'''dataset'''					)
												lowerCAmelCase		:       Tuple							=  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
												lowerCAmelCase		:       Any							=  RagRetriever(
												    _a    ,	question_encoder_tokenizer=self.get_dpr_tokenizer()    ,	generator_tokenizer=self.get_bart_tokenizer()    ,	)
							else:
												lowerCAmelCase		:       Optional[Any]							=  RagRetriever(
												    _a    ,	question_encoder_tokenizer=self.get_dpr_tokenizer()    ,	generator_tokenizer=self.get_bart_tokenizer()    ,	index=CustomHFIndex(config.retrieval_vector_size    ,	_a					)    ,	)
							return retriever





		def    _A				(				self     :						int					):
							lowerCAmelCase		:       int							=  Dataset.from_dict(
							    {
							        '''id''': ['''0''', '''1'''],
							        '''text''': ['''foo''', '''bar'''],
							        '''title''': ['''Foo''', '''Bar'''],
							        '''embeddings''': [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
							    }					)
							dataset.add_faiss_index('''embeddings'''    ,	string_factory='''Flat'''    ,	metric_type=faiss.METRIC_INNER_PRODUCT					)

							lowerCAmelCase		:       str							=  os.path.join(self.tmpdirname    ,	'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''					)
							dataset.save_faiss_index('''embeddings'''    ,	index_file_name + '''.index.dpr'''					)
							pickle.dump(dataset['''id''']    ,	open(index_file_name + '''.index_meta.dpr'''    ,	'''wb'''					)					)

							lowerCAmelCase		:       Optional[Any]							=  os.path.join(self.tmpdirname    ,	'''psgs_w100.tsv.pkl'''					)
							lowerCAmelCase		:       Optional[Any]							=  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
							pickle.dump(_a    ,	open(_a    ,	'''wb'''					)					)

							lowerCAmelCase		:       Dict							=  RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size    ,	question_encoder=DPRConfig().to_dict()    ,	generator=BartConfig().to_dict()    ,	index_name='''legacy'''    ,	index_path=self.tmpdirname    ,	)
							lowerCAmelCase		:       List[str]							=  RagRetriever(
							    _a    ,	question_encoder_tokenizer=self.get_dpr_tokenizer()    ,	generator_tokenizer=self.get_bart_tokenizer()					)
							return retriever





		def    _A				(				self     :						int					):
							lowerCAmelCase		:       Union[str, Any]							=  1
							lowerCAmelCase		:       Union[str, Any]							=  self.get_dummy_canonical_hf_index_retriever()
							lowerCAmelCase		:       Tuple							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       Tuple							=  retriever.retrieve(_a    ,	n_docs=_a					)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(_a					)    ,	2					)
							self.assertEqual(sorted(doc_dicts[0]					)    ,	['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)    ,	_a					)
							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     :						Optional[int]					):
							lowerCAmelCase		:       Optional[Any]							=  self.get_dummy_canonical_hf_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
												with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
																	lowerCAmelCase		:       str							=  self.get_dummy_dataset()
																	retriever.save_pretrained(_a					)
																	lowerCAmelCase		:       List[str]							=  RagRetriever.from_pretrained(_a					)
																	self.assertIsInstance(_a    ,	_a					)
																	lowerCAmelCase		:       List[str]							=  np.array(
																	    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
																	lowerCAmelCase		:       Dict							=  retriever.retrieve(_a    ,	n_docs=1					)
																	self.assertTrue(out is not None					)





		def    _A				(				self     :						Optional[int]					):
							lowerCAmelCase		:       Tuple							=  1
							lowerCAmelCase		:       Any							=  self.get_dummy_custom_hf_index_retriever(from_disk=_a					)
							lowerCAmelCase		:       int							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       Tuple							=  retriever.retrieve(_a    ,	n_docs=_a					)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(_a					)    ,	2					)
							self.assertEqual(sorted(doc_dicts[0]					)    ,	['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)    ,	_a					)
							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     :						Optional[int]					):
							lowerCAmelCase		:       Optional[Any]							=  self.get_dummy_custom_hf_index_retriever(from_disk=_a					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
												retriever.save_pretrained(_a					)
												lowerCAmelCase		:       Optional[Any]							=  RagRetriever.from_pretrained(_a					)
												self.assertIsInstance(_a    ,	_a					)
												lowerCAmelCase		:       Optional[Any]							=  np.array(
												    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
												lowerCAmelCase		:       Dict							=  retriever.retrieve(_a    ,	n_docs=1					)
												self.assertTrue(out is not None					)





		def    _A				(				self     :						List[str]					):
							lowerCAmelCase		:       List[str]							=  1
							lowerCAmelCase		:       Union[str, Any]							=  self.get_dummy_custom_hf_index_retriever(from_disk=_a					)
							lowerCAmelCase		:       Optional[int]							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       Any							=  retriever.retrieve(_a    ,	n_docs=_a					)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(_a					)    ,	2					)
							self.assertEqual(sorted(doc_dicts[0]					)    ,	['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)    ,	_a					)
							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     :						str					):
							lowerCAmelCase		:       int							=  self.get_dummy_custom_hf_index_retriever(from_disk=_a					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
												retriever.save_pretrained(_a					)
												lowerCAmelCase		:       int							=  RagRetriever.from_pretrained(_a					)
												self.assertIsInstance(_a    ,	_a					)
												lowerCAmelCase		:       Optional[Any]							=  np.array(
												    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
												lowerCAmelCase		:       Union[str, Any]							=  retriever.retrieve(_a    ,	n_docs=1					)
												self.assertTrue(out is not None					)





		def    _A				(				self     :						List[str]					):
							lowerCAmelCase		:       Any							=  1
							lowerCAmelCase		:       Tuple							=  self.get_dummy_legacy_index_retriever()
							lowerCAmelCase		:       Tuple							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       List[str]							=  retriever.retrieve(_a    ,	n_docs=_a					)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(_a					)    ,	2					)
							self.assertEqual(sorted(doc_dicts[0]					)    ,	['''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''text''']					)    ,	_a					)
							self.assertEqual(doc_dicts[0]['''text'''][0]    ,	'''bar'''					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]['''text'''][0]    ,	'''foo'''					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()    ,	[[1], [0]]					)





		def    _A				(				self     :						Optional[int]					):
							lowerCAmelCase		:       Dict							=  self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
												retriever.save_pretrained(_a					)
												lowerCAmelCase		:       str							=  RagRetriever.from_pretrained(_a					)
												self.assertIsInstance(_a    ,	_a					)
												lowerCAmelCase		:       Union[str, Any]							=  np.array(
												    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
												lowerCAmelCase		:       List[str]							=  retriever.retrieve(_a    ,	n_docs=1					)
												self.assertTrue(out is not None					)





		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def    _A				(				self     :						str					):
							import torch

							lowerCAmelCase		:       Dict							=  1
							lowerCAmelCase		:       List[str]							=  self.get_dummy_canonical_hf_index_retriever()
							lowerCAmelCase		:       List[Any]							=  [[5, 7], [1_0, 1_1]]
							lowerCAmelCase		:       Optional[Any]							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       Any							=  retriever(_a    ,	_a    ,	prefix=retriever.config.generator.prefix    ,	n_docs=_a					)
							lowerCAmelCase		:       List[Any]							=  (
							    out["context_input_ids"],
							    out["context_attention_mask"],
							    out["retrieved_doc_embeds"],
							)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertIsInstance(_a    ,	_a					)
							self.assertIsInstance(_a    ,	_a					)
							self.assertIsInstance(_a    ,	np.ndarray					)

							lowerCAmelCase		:       List[str]							=  retriever(
							    _a    ,	_a    ,	prefix=retriever.config.generator.prefix    ,	n_docs=_a    ,	return_tensors='''pt'''    ,	)
							lowerCAmelCase		:       Optional[Any]							=  (  # noqa: F841
							    out["context_input_ids"],
							    out["context_attention_mask"],
							    out["retrieved_doc_embeds"],
							    out["doc_ids"],
							)
							self.assertEqual(retrieved_doc_embeds.shape    ,	(2, n_docs, self.retrieval_vector_size)					)
							self.assertIsInstance(_a    ,	torch.Tensor					)
							self.assertIsInstance(_a    ,	torch.Tensor					)
							self.assertIsInstance(_a    ,	torch.Tensor					)





		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def    _A				(				self     :						List[str]					):
							lowerCAmelCase		:       Dict							=  self.get_dpr_ctx_encoder_tokenizer()
							lowerCAmelCase		:       List[str]							=  1
							lowerCAmelCase		:       Union[str, Any]							=  self.get_dummy_custom_hf_index_retriever(from_disk=_a					)
							retriever.set_ctx_encoder_tokenizer(_a					)

							lowerCAmelCase		:       Optional[int]							=  [[5, 7], [1_0, 1_1]]
							lowerCAmelCase		:       List[str]							=  np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]    ,	dtype=np.floataa					)
							lowerCAmelCase		:       Optional[int]							=  retriever(_a    ,	_a    ,	prefix=retriever.config.generator.prefix    ,	n_docs=_a					)

							self.assertEqual(
							    len(_a					)    ,	6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
							self.assertEqual(
							    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')					)    ,	_a					)  # check for doc token related keys in dictionary.


"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


