



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 				snake_case__(lowerCamelCase__      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   List[Any]													=					tempfile.mkdtemp()
									lowercase__					:   Any													=					8

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

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







					def 			snake_case					(							self :     Optional[int]					):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,       "dpr_tokenizer"					)					)







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







					def 			snake_case					(							self :     int					):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,       "bart_tokenizer"					)					)







					def 			snake_case					(							self :     Optional[Any]					):
									shutil.rmtree(self.tmpdirname					)







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Tuple													=					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 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Optional[int]													=					self.get_dummy_dataset()
									lowercase__					:   int													=					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:
													lowercase__					:   Union[str, Any]													=					dataset
													lowercase__					:   Any													=					RagRetriever(
													    snake_case__				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()				,       )
									return retriever







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   List[str]													=					self.get_dummy_dataset()
									lowercase__					:   Union[str, Any]													=					RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,       question_encoder=DPRConfig().to_dict()				,       generator=BartConfig().to_dict()				,       index_name="custom"				,       )
									if from_disk:
													lowercase__					:   List[Any]													=					os.path.join(self.tmpdirname				,       "dataset"					)
													lowercase__					:   str													=					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
													lowercase__					:   Union[str, Any]													=					RagRetriever(
													    snake_case__				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()				,       )
									else:
													lowercase__					:   List[str]													=					RagRetriever(
													    snake_case__				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()				,       index=CustomHFIndex(config.retrieval_vector_size				,       snake_case__					)				,       )
									return retriever







					def 			snake_case					(							self :     int					):
									lowercase__					:   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					)

									lowercase__					:   int													=					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"					)					)

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

									lowercase__					:   Tuple													=					RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,       question_encoder=DPRConfig().to_dict()				,       generator=BartConfig().to_dict()				,       index_name="legacy"				,       index_path=self.tmpdirname				,       )
									lowercase__					:   Union[str, Any]													=					RagRetriever(
									    snake_case__				,       question_encoder_tokenizer=self.get_dpr_tokenizer()				,       generator_tokenizer=self.get_bart_tokenizer()					)
									return retriever







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







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







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







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







					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def 			snake_case					(							self :     Optional[int]					):
									import torch

									lowercase__					:   Union[str, Any]													=					1
									lowercase__					:   Optional[Any]													=					self.get_dummy_canonical_hf_index_retriever()
									lowercase__					:   Any													=					[[5, 7], [10, 11]]
									lowercase__					:   int													=					np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,       dtype=np.floataa					)
									lowercase__					:   int													=					retriever(snake_case__				,       snake_case__				,       prefix=retriever.config.generator.prefix				,       n_docs=snake_case__					)
									lowercase__					:   Tuple													=					(
									    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(snake_case__				,       snake_case__					)
									self.assertIsInstance(snake_case__				,       snake_case__					)
									self.assertIsInstance(snake_case__				,       np.ndarray					)

									lowercase__					:   Optional[int]													=					retriever(
									    snake_case__				,       snake_case__				,       prefix=retriever.config.generator.prefix				,       n_docs=snake_case__				,       return_tensors="pt"				,       )
									lowercase__					:   Dict													=					(  # 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(snake_case__				,       torch.Tensor					)
									self.assertIsInstance(snake_case__				,       torch.Tensor					)
									self.assertIsInstance(snake_case__				,       torch.Tensor					)







					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Union[str, Any]													=					self.get_dpr_ctx_encoder_tokenizer()
									lowercase__					:   str													=					1
									lowercase__					:   Any													=					self.get_dummy_custom_hf_index_retriever(from_disk=snake_case__					)
									retriever.set_ctx_encoder_tokenizer(snake_case__					)

									lowercase__					:   Tuple													=					[[5, 7], [10, 11]]
									lowercase__					:   Dict													=					np.array(
									    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]				,       dtype=np.floataa					)
									lowercase__					:   Any													=					retriever(snake_case__				,       snake_case__				,       prefix=retriever.config.generator.prefix				,       n_docs=snake_case__					)

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









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')




