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    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  def       __A   (							self       :				str      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE_      =     8

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

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







  def       __A   (							self       :				List[Any]      )     ->		DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,   "dpr_tokenizer"      )      )







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







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







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







  def       __A   (							self       :				List[Any]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				List[str]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE_      =     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:
              SCREAMING_SNAKE_CASE_      =     dataset
              SCREAMING_SNAKE_CASE_      =     RagRetriever(
                  __magic_name__     ,   question_encoder_tokenizer=self.get_dpr_tokenizer()     ,   generator_tokenizer=self.get_bart_tokenizer()     ,   )
        return retriever







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				bool      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE_      =     RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     ,   question_encoder=DPRConfig().to_dict()     ,   generator=BartConfig().to_dict()     ,   index_name="custom"     ,   )
        if from_disk:
              SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   "dataset"      )
              SCREAMING_SNAKE_CASE_      =     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
              SCREAMING_SNAKE_CASE_      =     RagRetriever(
                  __magic_name__     ,   question_encoder_tokenizer=self.get_dpr_tokenizer()     ,   generator_tokenizer=self.get_bart_tokenizer()     ,   )
        else:
              SCREAMING_SNAKE_CASE_      =     RagRetriever(
                  __magic_name__     ,   question_encoder_tokenizer=self.get_dpr_tokenizer()     ,   generator_tokenizer=self.get_bart_tokenizer()     ,   index=CustomHFIndex(config.retrieval_vector_size     ,   __magic_name__      )     ,   )
        return retriever







  def       __A   (							self       :				List[Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     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      )

        SCREAMING_SNAKE_CASE_      =     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"      )      )

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

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







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







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







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







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







  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        import torch

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

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







  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def       __A   (							self       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_dpr_ctx_encoder_tokenizer()
        SCREAMING_SNAKE_CASE_      =     1
        SCREAMING_SNAKE_CASE_      =     self.get_dummy_custom_hf_index_retriever(from_disk=__magic_name__      )
        retriever.set_ctx_encoder_tokenizer(__magic_name__      )

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

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



def    a__       (						__UpperCamelCase = 1_0_0_0 ):
      SCREAMING_SNAKE_CASE_      =     -1
      SCREAMING_SNAKE_CASE_      =     0
      for a in range(1   ,					n // 3 ):
            # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
            SCREAMING_SNAKE_CASE_      =     (n * n - 2 * a * n) // (2 * n - 2 * a)
            SCREAMING_SNAKE_CASE_      =     n - a - b
            if c * c == (a * a + b * b):
                  SCREAMING_SNAKE_CASE_      =     a * b * c
                  if candidate >= product:
                        SCREAMING_SNAKE_CASE_      =     candidate
      return product


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


