





"""simple docstring"""





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 		SCREAMING_SNAKE_CASE_				(     __SCREAMING_SNAKE_CASE ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[Any]      ):
											__lowerCamelCase   :   Optional[int]       =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   :   List[Any]       =os.path.join(self.tmpdirname    ,     '''dpr_tokenizer'''      )
											os.makedirs(_SCREAMING_SNAKE_CASE    ,     exist_ok=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   Dict       =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
											__lowerCamelCase   :   Optional[Any]       =[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											__lowerCamelCase   :   int       =dict(zip(_SCREAMING_SNAKE_CASE    ,     range(len(_SCREAMING_SNAKE_CASE      )      )      )      )
											__lowerCamelCase   :   int       =["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											__lowerCamelCase   :   Any       ={"unk_token": "<unk>"}

											__lowerCamelCase   :   Optional[int]       =os.path.join(self.tmpdirname    ,     '''bart_tokenizer'''      )
											os.makedirs(_SCREAMING_SNAKE_CASE    ,     exist_ok=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   List[str]       =os.path.join(_SCREAMING_SNAKE_CASE    ,     BART_VOCAB_FILES_NAMES['''vocab_file''']      )
											__lowerCamelCase   :   Dict       =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]      ):
											return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,     '''dpr_tokenizer'''      )      )




				def        __lowercase	(  self	:Optional[Any]      ):
											return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,     '''dpr_tokenizer'''      )      )




				def        __lowercase	(  self	:Tuple      ):
											return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,     '''bart_tokenizer'''      )      )




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




				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   List[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        __lowercase	(  self	:Optional[Any]      ):
											__lowerCamelCase   :   Optional[Any]       =self.get_dummy_dataset()
											__lowerCamelCase   :   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:
																		__lowerCamelCase   :   List[str]       =dataset
																		__lowerCamelCase   :   Union[str, Any]       =RagRetriever(
																		    _SCREAMING_SNAKE_CASE    ,     question_encoder_tokenizer=self.get_dpr_tokenizer()    ,     generator_tokenizer=self.get_bart_tokenizer()    ,     )
											return retriever




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:bool      ):
											__lowerCamelCase   :   str       =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   :   Optional[Any]       =os.path.join(self.tmpdirname    ,     '''dataset'''      )
																		__lowerCamelCase   :   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
																		__lowerCamelCase   :   Dict       =RagRetriever(
																		    _SCREAMING_SNAKE_CASE    ,     question_encoder_tokenizer=self.get_dpr_tokenizer()    ,     generator_tokenizer=self.get_bart_tokenizer()    ,     )
											else:
																		__lowerCamelCase   :   Any       =RagRetriever(
																		    _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      )    ,     )
											return retriever




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[str]       =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   :   Any       =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   :   str       =os.path.join(self.tmpdirname    ,     '''psgs_w100.tsv.pkl'''      )
											__lowerCamelCase   :   Optional[int]       ={sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
											pickle.dump(_SCREAMING_SNAKE_CASE    ,     open(_SCREAMING_SNAKE_CASE    ,     '''wb'''      )      )

											__lowerCamelCase   :   int       =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   :   Union[str, Any]       =RagRetriever(
											    _SCREAMING_SNAKE_CASE    ,     question_encoder_tokenizer=self.get_dpr_tokenizer()    ,     generator_tokenizer=self.get_bart_tokenizer()      )
											return retriever




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Optional[Any]       =1
											__lowerCamelCase   :   str       =self.get_dummy_canonical_hf_index_retriever()
											__lowerCamelCase   :   str       =np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
											__lowerCamelCase   :   str       =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	:int      ):
											__lowerCamelCase   :   Tuple       =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   :   Union[str, Any]       =self.get_dummy_dataset()
																									retriever.save_pretrained(_SCREAMING_SNAKE_CASE      )
																									__lowerCamelCase   :   Optional[int]       =RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE      )
																									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
																									__lowerCamelCase   :   Union[str, Any]       =np.array(
																									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
																									__lowerCamelCase   :   Any       =retriever.retrieve(_SCREAMING_SNAKE_CASE    ,     n_docs=1      )
																									self.assertTrue(out is not None      )




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




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   Any       =1
											__lowerCamelCase   :   List[str]       =self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   Optional[int]       =np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
											__lowerCamelCase   :   Union[str, Any]       =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	:str      ):
											__lowerCamelCase   :   Any       =self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE      )
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(_SCREAMING_SNAKE_CASE      )
																		__lowerCamelCase   :   Any       =RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE      )
																		self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
																		__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(_SCREAMING_SNAKE_CASE    ,     n_docs=1      )
																		self.assertTrue(out is not None      )




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   str       =1
											__lowerCamelCase   :   Union[str, Any]       =self.get_dummy_legacy_index_retriever()
											__lowerCamelCase   :   List[Any]       =np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
											__lowerCamelCase   :   Optional[Any]       =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]      )    ,     ['''text''', '''title''']      )
											self.assertEqual(len(doc_dicts[0]['''text''']      )    ,     _SCREAMING_SNAKE_CASE      )
											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        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   Dict       =self.get_dummy_legacy_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(_SCREAMING_SNAKE_CASE      )
																		__lowerCamelCase   :   Any       =RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE      )
																		self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
																		__lowerCamelCase   :   Any       =np.array(
																		    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
																		__lowerCamelCase   :   List[str]       =retriever.retrieve(_SCREAMING_SNAKE_CASE    ,     n_docs=1      )
																		self.assertTrue(out is not None      )




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        __lowercase	(  self	:Tuple      ):
											import torch

											__lowerCamelCase   :   Dict       =1
											__lowerCamelCase   :   List[Any]       =self.get_dummy_canonical_hf_index_retriever()
											__lowerCamelCase   :   Tuple       =[[5, 7], [10, 11]]
											__lowerCamelCase   :   List[str]       =np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
											__lowerCamelCase   :   List[str]       =retriever(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     prefix=retriever.config.generator.prefix    ,     n_docs=_SCREAMING_SNAKE_CASE      )
											__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(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
											self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE      )
											self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     np.ndarray      )

											__lowerCamelCase   :   Dict       =retriever(
											    _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     prefix=retriever.config.generator.prefix    ,     n_docs=_SCREAMING_SNAKE_CASE    ,     return_tensors='''pt'''    ,     )
											__lowerCamelCase   :   Optional[int]       =(  # 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(_SCREAMING_SNAKE_CASE    ,     torch.Tensor      )
											self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     torch.Tensor      )
											self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,     torch.Tensor      )




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Union[str, Any]       =self.get_dpr_ctx_encoder_tokenizer()
											__lowerCamelCase   :   int       =1
											__lowerCamelCase   :   Union[str, Any]       =self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE      )
											retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE      )

											__lowerCamelCase   :   List[str]       =[[5, 7], [10, 11]]
											__lowerCamelCase   :   List[str]       =np.array(
											    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]    ,     dtype=np.floataa      )
											__lowerCamelCase   :   List[Any]       =retriever(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     prefix=retriever.config.generator.prefix    ,     n_docs=_SCREAMING_SNAKE_CASE      )

											self.assertEqual(
											    len(_SCREAMING_SNAKE_CASE      )    ,     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''')      )    ,     _SCREAMING_SNAKE_CASE      )  # check for doc token related keys in dictionary.









"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)