





'''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    lowerCAmelCase_		(		__magic_name__		):




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      tempfile.mkdtemp()
     _lowerCAmelCase				=      8

     # DPR tok
     _lowerCAmelCase				=      [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     _lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "dpr_tokenizer"		)
     os.makedirs(_lowerCAmelCase			,     exist_ok=_lowerCAmelCase		)
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     _lowerCAmelCase				=      dict(zip(_lowerCAmelCase			,     range(len(_lowerCAmelCase		)		)		)		)
     _lowerCAmelCase				=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     _lowerCAmelCase				=      {"unk_token": "<unk>"}

     _lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "bart_tokenizer"		)
     os.makedirs(_lowerCAmelCase			,     exist_ok=_lowerCAmelCase		)
     _lowerCAmelCase				=      os.path.join(_lowerCAmelCase			,     BART_VOCAB_FILES_NAMES["vocab_file"]		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							DPRQuestionEncoderTokenizer:
     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "dpr_tokenizer"		)		)




 def 							_snake_case   (	self		)    ->							DPRContextEncoderTokenizer:
     return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "dpr_tokenizer"		)		)




 def 							_snake_case   (	self		)    ->							BartTokenizer:
     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,     "bart_tokenizer"		)		)




 def 							_snake_case   (	self		)    ->							Any:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      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		)    ->							Dict:
     _lowerCAmelCase				=      self.get_dummy_dataset()
     _lowerCAmelCase				=      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				=      dataset
         _lowerCAmelCase				=      RagRetriever(
             _lowerCAmelCase			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     )
     return retriever




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     _lowerCAmelCase				=      self.get_dummy_dataset()
     _lowerCAmelCase				=      RagConfig(
         retrieval_vector_size=self.retrieval_vector_size			,     question_encoder=DPRConfig().to_dict()			,     generator=BartConfig().to_dict()			,     index_name="custom"			,     )
     if from_disk:
         _lowerCAmelCase				=      os.path.join(self.tmpdirname			,     "dataset"		)
         _lowerCAmelCase				=      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				=      RagRetriever(
             _lowerCAmelCase			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     )
     else:
         _lowerCAmelCase				=      RagRetriever(
             _lowerCAmelCase			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()			,     index=CustomHFIndex(config.retrieval_vector_size			,     _lowerCAmelCase		)			,     )
     return retriever




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      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				=      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				=      os.path.join(self.tmpdirname			,     "psgs_w100.tsv.pkl"		)
     _lowerCAmelCase				=      {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
     pickle.dump(_lowerCAmelCase			,     open(_lowerCAmelCase			,     "wb"		)		)

     _lowerCAmelCase				=      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				=      RagRetriever(
         _lowerCAmelCase			,     question_encoder_tokenizer=self.get_dpr_tokenizer()			,     generator_tokenizer=self.get_bart_tokenizer()		)
     return retriever




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




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




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




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      1
     _lowerCAmelCase				=      self.get_dummy_legacy_index_retriever()
     _lowerCAmelCase				=      np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,     dtype=np.floataa		)
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      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]		)			,     ["text", "title"]		)
     self.assertEqual(len(doc_dicts[0]["text"]		)			,     _lowerCAmelCase		)
     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		)    ->							Tuple:
     _lowerCAmelCase				=      self.get_dummy_legacy_index_retriever()
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      RagRetriever.from_pretrained(_lowerCAmelCase		)
         self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)
         _lowerCAmelCase				=      np.array(
             [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,     dtype=np.floataa		)
         _lowerCAmelCase				=      retriever.retrieve(_lowerCAmelCase			,     n_docs=1		)
         self.assertTrue(out is not None		)




 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 							_snake_case   (	self		)    ->							List[Any]:
     import torch

     _lowerCAmelCase				=      1
     _lowerCAmelCase				=      self.get_dummy_canonical_hf_index_retriever()
     _lowerCAmelCase				=      [[5, 7], [10, 11]]
     _lowerCAmelCase				=      np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,     dtype=np.floataa		)
     _lowerCAmelCase				=      retriever(_lowerCAmelCase			,     _lowerCAmelCase			,     prefix=retriever.config.generator.prefix			,     n_docs=_lowerCAmelCase		)
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      (
         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(_lowerCAmelCase			,     _lowerCAmelCase		)
     self.assertIsInstance(_lowerCAmelCase			,     _lowerCAmelCase		)
     self.assertIsInstance(_lowerCAmelCase			,     np.ndarray		)

     _lowerCAmelCase				=      retriever(
         _lowerCAmelCase			,     _lowerCAmelCase			,     prefix=retriever.config.generator.prefix			,     n_docs=_lowerCAmelCase			,     return_tensors="pt"			,     )
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      (  # 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(_lowerCAmelCase			,     torch.Tensor		)
     self.assertIsInstance(_lowerCAmelCase			,     torch.Tensor		)
     self.assertIsInstance(_lowerCAmelCase			,     torch.Tensor		)





 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.get_dpr_ctx_encoder_tokenizer()
     _lowerCAmelCase				=      1
     _lowerCAmelCase				=      self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase		)
     retriever.set_ctx_encoder_tokenizer(_lowerCAmelCase		)

     _lowerCAmelCase				=      [[5, 7], [10, 11]]
     _lowerCAmelCase				=      np.array(
         [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,     dtype=np.floataa		)
     _lowerCAmelCase				=      retriever(_lowerCAmelCase			,     _lowerCAmelCase			,     prefix=retriever.config.generator.prefix			,     n_docs=_lowerCAmelCase		)

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











'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise TypeError("Input value must be an 'int' type"    )

    _lowerCAmelCase				=      0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
       import doctest

       doctest.testmod()


