


"""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  (  _a  ):







    """simple docstring"""




    def      __snake_case							( self    :   str):
      a						:		Optional[int]											=      tempfile.mkdtemp()
      a						:		Optional[int]											=      8

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

      a						:		Any											=      os.path.join(self.tmpdirname    ,			"bart_tokenizer")
      os.makedirs(__UpperCAmelCase    ,			exist_ok=__UpperCAmelCase)
      a						:		Union[str, Any]											=      os.path.join(__UpperCAmelCase    ,			BART_VOCAB_FILES_NAMES["vocab_file"])
      a						:		Optional[int]											=      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      __snake_case							( self    :   List[str]):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,			"dpr_tokenizer"))






    def      __snake_case							( self    :   str):
      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,			"dpr_tokenizer"))






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






    def      __snake_case							( self    :   Union[str, Any]):
      shutil.rmtree(self.tmpdirname)






    def      __snake_case							( self    :   int):
      a						:		List[str]											=      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    :   str):
      a						:		Any											=      self.get_dummy_dataset()
      a						:		List[str]											=      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:
        a						:		Tuple											=      dataset
        a						:		int											=      RagRetriever(
            __UpperCAmelCase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer()    ,			)
      return retriever






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   bool):
      a						:		Tuple											=      self.get_dummy_dataset()
      a						:		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:
        a						:		List[str]											=      os.path.join(self.tmpdirname    ,			"dataset")
        a						:		List[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
        a						:		List[str]											=      RagRetriever(
            __UpperCAmelCase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer()    ,			)
      else:
        a						:		Tuple											=      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      __snake_case							( self    :   List[str]):
      a						:		List[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)

      a						:		Tuple											=      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"))

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

      a						:		str											=      RagConfig(
          retrieval_vector_size=self.retrieval_vector_size    ,			question_encoder=DPRConfig().to_dict()    ,			generator=BartConfig().to_dict()    ,			index_name="legacy"    ,			index_path=self.tmpdirname    ,			)
      a						:		List[str]											=      RagRetriever(
          __UpperCAmelCase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer())
      return retriever






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






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






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






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






    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      __snake_case							( self    :   Tuple):
      import torch

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

      a						:		Optional[int]											=      retriever(
          __UpperCAmelCase    ,			__UpperCAmelCase    ,			prefix=retriever.config.generator.prefix    ,			n_docs=__UpperCAmelCase    ,			return_tensors="pt"    ,			)
      a						:		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(__UpperCAmelCase    ,			torch.Tensor)
      self.assertIsInstance(__UpperCAmelCase    ,			torch.Tensor)
      self.assertIsInstance(__UpperCAmelCase    ,			torch.Tensor)






    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      __snake_case							( self    :   Tuple):
      a						:		Tuple											=      self.get_dpr_ctx_encoder_tokenizer()
      a						:		int											=      1
      a						:		Dict											=      self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCAmelCase)
      retriever.set_ctx_encoder_tokenizer(__UpperCAmelCase)

      a						:		List[str]											=      [[5, 7], [10, 11]]
      a						:		Union[str, Any]											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		Dict											=      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 __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






