



"""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 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Dict   )->List[str]:
							_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(__UpperCamelCase				,   exist_ok=__UpperCamelCase   )
							_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   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(__UpperCamelCase				,   range(len(__UpperCamelCase   )   )   )   )
							_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(__UpperCamelCase				,   exist_ok=__UpperCamelCase   )
							_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   BART_VOCAB_FILES_NAMES['''vocab_file''']   )
							_UpperCAmelCase					=    os.path.join(__UpperCamelCase				,   BART_VOCAB_FILES_NAMES['''merges_file''']   )
							with open(self.vocab_file				,   '''w'''				,   encoding='''utf-8'''   ) as fp:
											fp.write(json.dumps(__UpperCamelCase   ) + '''\n'''   )
							with open(self.merges_file				,   '''w'''				,   encoding='''utf-8'''   ) as fp:
											fp.write('''\n'''.join(__UpperCamelCase   )   )



			def  lowercase__						(		self     :    Optional[int]   )->DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,   '''dpr_tokenizer'''   )   )



			def  lowercase__						(		self     :    Tuple   )->DPRContextEncoderTokenizer:
							return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,   '''dpr_tokenizer'''   )   )



			def  lowercase__						(		self     :    List[Any]   )->BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,   '''bart_tokenizer'''   )   )



			def  lowercase__						(		self     :    List[Any]   )->Tuple:
							shutil.rmtree(self.tmpdirname   )



			def  lowercase__						(		self     :    Tuple   )->Dict:
							_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     :    Optional[Any]   )->List[Any]:
							_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					=    RagRetriever(
											    __UpperCamelCase				,   question_encoder_tokenizer=self.get_dpr_tokenizer()				,   generator_tokenizer=self.get_bart_tokenizer()				,   )
							return retriever



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    bool   )->int:
							_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					=    RagRetriever(
											    __UpperCamelCase				,   question_encoder_tokenizer=self.get_dpr_tokenizer()				,   generator_tokenizer=self.get_bart_tokenizer()				,   )
							else:
											_UpperCAmelCase					=    RagRetriever(
											    __UpperCamelCase				,   question_encoder_tokenizer=self.get_dpr_tokenizer()				,   generator_tokenizer=self.get_bart_tokenizer()				,   index=CustomHFIndex(config.retrieval_vector_size				,   __UpperCamelCase   )				,   )
							return retriever



			def  lowercase__						(		self     :    List[str]   )->List[str]:
							_UpperCAmelCase					=    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   )

							_UpperCAmelCase					=    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'''   )   )

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

							_UpperCAmelCase					=    RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size				,   question_encoder=DPRConfig().to_dict()				,   generator=BartConfig().to_dict()				,   index_name='''legacy'''				,   index_path=self.tmpdirname				,   )
							_UpperCAmelCase					=    RagRetriever(
							    __UpperCamelCase				,   question_encoder_tokenizer=self.get_dpr_tokenizer()				,   generator_tokenizer=self.get_bart_tokenizer()   )
							return retriever



			def  lowercase__						(		self     :    Optional[int]   )->List[Any]:
							_UpperCAmelCase					=    1
							_UpperCAmelCase					=    self.get_dummy_canonical_hf_index_retriever()
							_UpperCAmelCase					=    np.array(
							    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    retriever.retrieve(__UpperCamelCase				,   n_docs=__UpperCamelCase   )
							self.assertEqual(retrieved_doc_embeds.shape				,   (2, n_docs, self.retrieval_vector_size)   )
							self.assertEqual(len(__UpperCamelCase   )				,   2   )
							self.assertEqual(sorted(doc_dicts[0]   )				,   ['''embeddings''', '''id''', '''text''', '''title''']   )
							self.assertEqual(len(doc_dicts[0]['''id''']   )				,   __UpperCamelCase   )
							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     :    Tuple   )->Any:
							_UpperCAmelCase					=    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:
															_UpperCAmelCase					=    self.get_dummy_dataset()
															retriever.save_pretrained(__UpperCamelCase   )
															_UpperCAmelCase					=    RagRetriever.from_pretrained(__UpperCamelCase   )
															self.assertIsInstance(__UpperCamelCase				,   __UpperCamelCase   )
															_UpperCAmelCase					=    np.array(
															    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
															_UpperCAmelCase					=    retriever.retrieve(__UpperCamelCase				,   n_docs=1   )
															self.assertTrue(out is not None   )



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



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



			def  lowercase__						(		self     :    str   )->Union[str, Any]:
							_UpperCAmelCase					=    1
							_UpperCAmelCase					=    self.get_dummy_legacy_index_retriever()
							_UpperCAmelCase					=    np.array(
							    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    retriever.retrieve(__UpperCamelCase				,   n_docs=__UpperCamelCase   )
							self.assertEqual(retrieved_doc_embeds.shape				,   (2, n_docs, self.retrieval_vector_size)   )
							self.assertEqual(len(__UpperCamelCase   )				,   2   )
							self.assertEqual(sorted(doc_dicts[0]   )				,   ['''text''', '''title''']   )
							self.assertEqual(len(doc_dicts[0]['''text''']   )				,   __UpperCamelCase   )
							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     :    str   )->List[str]:
							_UpperCAmelCase					=    self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__UpperCamelCase   )
											_UpperCAmelCase					=    RagRetriever.from_pretrained(__UpperCamelCase   )
											self.assertIsInstance(__UpperCamelCase				,   __UpperCamelCase   )
											_UpperCAmelCase					=    np.array(
											    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
											_UpperCAmelCase					=    retriever.retrieve(__UpperCamelCase				,   n_docs=1   )
											self.assertTrue(out is not None   )



			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def  lowercase__						(		self     :    Tuple   )->str:
							import torch

							_UpperCAmelCase					=    1
							_UpperCAmelCase					=    self.get_dummy_canonical_hf_index_retriever()
							_UpperCAmelCase					=    [[5, 7], [1_0, 1_1]]
							_UpperCAmelCase					=    np.array(
							    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
							_UpperCAmelCase					=    retriever(__UpperCamelCase				,   __UpperCamelCase				,   prefix=retriever.config.generator.prefix				,   n_docs=__UpperCamelCase   )
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    (
							    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(__UpperCamelCase				,   __UpperCamelCase   )
							self.assertIsInstance(__UpperCamelCase				,   __UpperCamelCase   )
							self.assertIsInstance(__UpperCamelCase				,   np.ndarray   )

							_UpperCAmelCase					=    retriever(
							    __UpperCamelCase				,   __UpperCamelCase				,   prefix=retriever.config.generator.prefix				,   n_docs=__UpperCamelCase				,   return_tensors='''pt'''				,   )
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    (  # 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(__UpperCamelCase				,   torch.Tensor   )
							self.assertIsInstance(__UpperCamelCase				,   torch.Tensor   )
							self.assertIsInstance(__UpperCamelCase				,   torch.Tensor   )



			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def  lowercase__						(		self     :    Tuple   )->Optional[int]:
							_UpperCAmelCase					=    self.get_dpr_ctx_encoder_tokenizer()
							_UpperCAmelCase					=    1
							_UpperCAmelCase					=    self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCamelCase   )
							retriever.set_ctx_encoder_tokenizer(__UpperCamelCase   )

							_UpperCAmelCase					=    [[5, 7], [1_0, 1_1]]
							_UpperCAmelCase					=    np.array(
							    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]				,   dtype=np.floataa   )
							_UpperCAmelCase					=    retriever(__UpperCamelCase				,   __UpperCamelCase				,   prefix=retriever.config.generator.prefix				,   n_docs=__UpperCamelCase   )

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








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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

__A   :							Optional[Any]										=   {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 			_a (					lowerCAmelCase			,							lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""bit"""
			UpperCamelCase__			      =	["""preactivation""", """bottleneck"""]
			UpperCamelCase__			      =	["""SAME""", """VALID"""]



			def __init__(		self     :    List[Any]				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    Optional[int]=6_4				,   __UpperCamelCase     :    List[Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,   __UpperCamelCase     :    Any=[3, 4, 6, 3]				,   __UpperCamelCase     :    str="preactivation"				,   __UpperCamelCase     :    Union[str, Any]="relu"				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Dict=3_2				,   __UpperCamelCase     :    List[str]=0.0				,   __UpperCamelCase     :    Any=False				,   __UpperCamelCase     :    Union[str, Any]=3_2				,   __UpperCamelCase     :    List[str]=1				,   __UpperCamelCase     :    List[str]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   **__UpperCamelCase     :    List[str]				,   )->List[Any]:
							super().__init__(**__UpperCamelCase   )
							if layer_type not in self.layer_types:
											raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types   )}'   )
							if global_padding is not None:
											if global_padding.upper() in self.supported_padding:
															_UpperCAmelCase					=    global_padding.upper()
											else:
															raise ValueError(F'Padding strategy {global_padding} not supported'   )
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    embedding_size
							_UpperCAmelCase					=    hidden_sizes
							_UpperCAmelCase					=    depths
							_UpperCAmelCase					=    layer_type
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    global_padding
							_UpperCAmelCase					=    num_groups
							_UpperCAmelCase					=    drop_path_rate
							_UpperCAmelCase					=    embedding_dynamic_padding
							_UpperCAmelCase					=    output_stride
							_UpperCAmelCase					=    width_factor

							_UpperCAmelCase					=    ['''stem'''] + [F'stage{idx}' for idx in range(1				,   len(__UpperCamelCase   ) + 1   )]
							_UpperCAmelCase		,						_UpperCAmelCase					=    get_aligned_output_features_output_indices(
							    out_features=__UpperCamelCase				,   out_indices=__UpperCamelCase				,   stage_names=self.stage_names   )



