

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					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      tempfile.mkdtemp()
										_UpperCAmelCase    :						Optional[int]							=      8

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

										_UpperCAmelCase    :						List[str]							=      os.path.join(self.tmpdirname  ,   "bart_tokenizer"		)
										os.makedirs(A_  ,   exist_ok=A_		)
										_UpperCAmelCase    :						int							=      os.path.join(A_  ,   BART_VOCAB_FILES_NAMES["vocab_file"]		)
										_UpperCAmelCase    :						Optional[int]							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,   "dpr_tokenizer"		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,   "dpr_tokenizer"		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,   "bart_tokenizer"		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.get_dummy_dataset()
										_UpperCAmelCase    :						Tuple							=      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    :						Optional[int]							=      dataset
														_UpperCAmelCase    :						List[Any]							=      RagRetriever(
														    A_  ,   question_encoder_tokenizer=self.get_dpr_tokenizer()  ,   generator_tokenizer=self.get_bart_tokenizer()  ,   )
										return retriever


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_dummy_dataset()
										_UpperCAmelCase    :						List[str]							=      RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size  ,   question_encoder=DPRConfig().to_dict()  ,   generator=BartConfig().to_dict()  ,   index_name="custom"  ,   )
										if from_disk:
														_UpperCAmelCase    :						Any							=      os.path.join(self.tmpdirname  ,   "dataset"		)
														_UpperCAmelCase    :						int							=      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    :						Union[str, Any]							=      RagRetriever(
														    A_  ,   question_encoder_tokenizer=self.get_dpr_tokenizer()  ,   generator_tokenizer=self.get_bart_tokenizer()  ,   )
										else:
														_UpperCAmelCase    :						str							=      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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      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    :						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"		)		)

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

										_UpperCAmelCase    :						Optional[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  ,   )
										_UpperCAmelCase    :						Union[str, Any]							=      RagRetriever(
										    A_  ,   question_encoder_tokenizer=self.get_dpr_tokenizer()  ,   generator_tokenizer=self.get_bart_tokenizer()		)
										return retriever


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      1
										_UpperCAmelCase    :						Any							=      self.get_dummy_canonical_hf_index_retriever()
										_UpperCAmelCase    :						Union[str, Any]							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      1
										_UpperCAmelCase    :						Union[str, Any]							=      self.get_dummy_custom_hf_index_retriever(from_disk=A_		)
										_UpperCAmelCase    :						int							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      1
										_UpperCAmelCase    :						Any							=      self.get_dummy_custom_hf_index_retriever(from_disk=A_		)
										_UpperCAmelCase    :						int							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						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]		)  ,   ["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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      1
										_UpperCAmelCase    :						List[str]							=      self.get_dummy_legacy_index_retriever()
										_UpperCAmelCase    :						Optional[int]							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[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]		)  ,   ["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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										import torch

										_UpperCAmelCase    :						int							=      1
										_UpperCAmelCase    :						Any							=      self.get_dummy_canonical_hf_index_retriever()
										_UpperCAmelCase    :						Optional[Any]							=      [[5, 7], [10, 11]]
										_UpperCAmelCase    :						Optional[Any]							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase    :						Tuple							=      retriever(A_  ,   A_  ,   prefix=retriever.config.generator.prefix  ,   n_docs=A_		)
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						int							=      (
										    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		)

										_UpperCAmelCase    :						List[str]							=      retriever(
										    A_  ,   A_  ,   prefix=retriever.config.generator.prefix  ,   n_docs=A_  ,   return_tensors="pt"  ,   )
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						str							=      (  # 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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.get_dpr_ctx_encoder_tokenizer()
										_UpperCAmelCase    :						List[Any]							=      1
										_UpperCAmelCase    :						Optional[Any]							=      self.get_dummy_custom_hf_index_retriever(from_disk=A_		)
										retriever.set_ctx_encoder_tokenizer(A_		)

										_UpperCAmelCase    :						List[str]							=      [[5, 7], [10, 11]]
										_UpperCAmelCase    :						Tuple							=      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]  ,   dtype=np.floataa		)
										_UpperCAmelCase    :						str							=      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.





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


