



'''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_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Optional[int]:
    __lowerCamelCase       :       List[Any]           = tempfile.mkdtemp()
    __lowerCamelCase       :       Dict           = 8

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

    __lowerCamelCase       :       Any           = os.path.join(self.tmpdirname			,	'bart_tokenizer'   )
    os.makedirs(_a			,	exist_ok=_a   )
    __lowerCamelCase       :       Dict           = os.path.join(_a			,	BART_VOCAB_FILES_NAMES['vocab_file']   )
    __lowerCamelCase       :       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 						_lowercase     ( self      :	int   )					->					DPRQuestionEncoderTokenizer:
    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,	'dpr_tokenizer'   )   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					DPRContextEncoderTokenizer:
    return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,	'dpr_tokenizer'   )   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					BartTokenizer:
    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,	'bart_tokenizer'   )   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Tuple:
    shutil.rmtree(self.tmpdirname   )




  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    __lowerCamelCase       :       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 						_lowercase     ( self      :	Optional[Any]   )					->					Tuple:
    __lowerCamelCase       :       List[Any]           = self.get_dummy_dataset()
    __lowerCamelCase       :       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:
      __lowerCamelCase       :       Union[str, Any]           = dataset
      __lowerCamelCase       :       List[str]           = RagRetriever(
          _a			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	)
    return retriever




  def 						_lowercase     ( self      :	str			,	_a      :	bool   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[Any]           = self.get_dummy_dataset()
    __lowerCamelCase       :       Dict           = RagConfig(
        retrieval_vector_size=self.retrieval_vector_size			,	question_encoder=DPRConfig().to_dict()			,	generator=BartConfig().to_dict()			,	index_name='custom'			,	)
    if from_disk:
      __lowerCamelCase       :       Dict           = os.path.join(self.tmpdirname			,	'dataset'   )
      __lowerCamelCase       :       Optional[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
      __lowerCamelCase       :       List[str]           = RagRetriever(
          _a			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	)
    else:
      __lowerCamelCase       :       Tuple           = 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 						_lowercase     ( self      :	int   )					->					Any:
    __lowerCamelCase       :       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   )

    __lowerCamelCase       :       Union[str, 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'   )   )

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

    __lowerCamelCase       :       Any           = 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       :       List[Any]           = RagRetriever(
        _a			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()   )
    return retriever




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




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




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




  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    __lowerCamelCase       :       Any           = 1
    __lowerCamelCase       :       int           = self.get_dummy_legacy_index_retriever()
    __lowerCamelCase       :       List[str]           = np.array(
        [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,	dtype=np.floataa   )
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       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]   )			,	['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 						_lowercase     ( self      :	int   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = self.get_dummy_legacy_index_retriever()
    with tempfile.TemporaryDirectory() as tmp_dirname:
      retriever.save_pretrained(_a   )
      __lowerCamelCase       :       List[str]           = RagRetriever.from_pretrained(_a   )
      self.assertIsInstance(_a			,	_a   )
      __lowerCamelCase       :       Any           = np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,	dtype=np.floataa   )
      __lowerCamelCase       :       Any           = retriever.retrieve(_a			,	n_docs=1   )
      self.assertTrue(out is not None   )




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 						_lowercase     ( self      :	Dict   )					->					str:
    import torch

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

    __lowerCamelCase       :       Union[str, Any]           = retriever(
        _a			,	_a			,	prefix=retriever.config.generator.prefix			,	n_docs=_a			,	return_tensors='pt'			,	)
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Any           = (  # 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 						_lowercase     ( self      :	Any   )					->					Optional[Any]:
    __lowerCamelCase       :       Any           = self.get_dpr_ctx_encoder_tokenizer()
    __lowerCamelCase       :       Dict           = 1
    __lowerCamelCase       :       int           = self.get_dummy_custom_hf_index_retriever(from_disk=_a   )
    retriever.set_ctx_encoder_tokenizer(_a   )

    __lowerCamelCase       :       Union[str, Any]           = [[5, 7], [10, 11]]
    __lowerCamelCase       :       Any           = np.array(
        [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,	dtype=np.floataa   )
    __lowerCamelCase       :       Any           = 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.











'''simple docstring'''





def        a_     (							_lowerCAmelCase       )      ->      list:
  if n_term == "":
    return []
  __lowerCamelCase       :       list           = []
  for temp in range(int(_lowerCAmelCase       )       ):
    series.append(F'1/{temp + 1}' if series else '1'       )
  return series


if __name__ == "__main__":
   _UpperCamelCase											=						input('Enter the last number (nth term) of the Harmonic Series')
   print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
   print(harmonic_series(nth_term))






