






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      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      tempfile.mkdtemp()
						A	 =      8

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

						A	 =      os.path.join(self.tmpdirname						,'bart_tokenizer'		)
						os.makedirs(_SCREAMING_SNAKE_CASE						,exist_ok=_SCREAMING_SNAKE_CASE		)
						A	 =      os.path.join(_SCREAMING_SNAKE_CASE						,BART_VOCAB_FILES_NAMES['vocab_file']		)
						A	 =      os.path.join(_SCREAMING_SNAKE_CASE						,BART_VOCAB_FILES_NAMES['merges_file']		)
						with open(self.vocab_file						,'w'						,encoding='utf-8'		) as fp:
										fp.write(json.dumps(_SCREAMING_SNAKE_CASE		) + '\n'		)
						with open(self.merges_file						,'w'						,encoding='utf-8'		) as fp:
										fp.write('\n'.join(_SCREAMING_SNAKE_CASE		)		)
		def       A(  self		:       Dict		)    ->   DPRQuestionEncoderTokenizer:


						'''simple docstring'''
						return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,'dpr_tokenizer'		)		)
		def       A(  self		:       int		)    ->   DPRContextEncoderTokenizer:


						'''simple docstring'''
						return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,'dpr_tokenizer'		)		)
		def       A(  self		:       List[str]		)    ->   BartTokenizer:


						'''simple docstring'''
						return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,'bart_tokenizer'		)		)
		def       A(  self		:       Dict		)    ->   Any:


						'''simple docstring'''
						shutil.rmtree(self.tmpdirname		)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      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		:       Optional[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      self.get_dummy_dataset()
						A	 =      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	 =      dataset
										A	 =      RagRetriever(
										    _SCREAMING_SNAKE_CASE						,question_encoder_tokenizer=self.get_dpr_tokenizer()						,generator_tokenizer=self.get_bart_tokenizer()						,)
						return retriever
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       bool		)    ->   Union[str, Any]:


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


						'''simple docstring'''
						A	 =      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	 =      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	 =      os.path.join(self.tmpdirname						,'psgs_w100.tsv.pkl'		)
						A	 =      {sample['id']: [sample['text'], sample['title']] for sample in dataset}
						pickle.dump(_SCREAMING_SNAKE_CASE						,open(_SCREAMING_SNAKE_CASE						,'wb'		)		)

						A	 =      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	 =      RagRetriever(
						    _SCREAMING_SNAKE_CASE						,question_encoder_tokenizer=self.get_dpr_tokenizer()						,generator_tokenizer=self.get_bart_tokenizer()		)
						return retriever
		def       A(  self		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      1
						A	 =      self.get_dummy_canonical_hf_index_retriever()
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A ,		A ,		A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=_SCREAMING_SNAKE_CASE		)
						self.assertEqual(retrieved_doc_embeds.shape						,(2, n_docs, self.retrieval_vector_size)		)
						self.assertEqual(len(_SCREAMING_SNAKE_CASE		)						,2		)
						self.assertEqual(sorted(doc_dicts[0]		)						,['embeddings', 'id', 'text', 'title']		)
						self.assertEqual(len(doc_dicts[0]['id']		)						,_SCREAMING_SNAKE_CASE		)
						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[str]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      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	 =      self.get_dummy_dataset()
														retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
														A	 =      RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
														self.assertIsInstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
														A	 =      np.array(
														    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
														A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=1		)
														self.assertTrue(out is not None		)
		def       A(  self		:       List[str]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      1
						A	 =      self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A ,		A ,		A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=_SCREAMING_SNAKE_CASE		)
						self.assertEqual(retrieved_doc_embeds.shape						,(2, n_docs, self.retrieval_vector_size)		)
						self.assertEqual(len(_SCREAMING_SNAKE_CASE		)						,2		)
						self.assertEqual(sorted(doc_dicts[0]		)						,['embeddings', 'id', 'text', 'title']		)
						self.assertEqual(len(doc_dicts[0]['id']		)						,_SCREAMING_SNAKE_CASE		)
						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		:       Optional[int]		)    ->   List[str]:


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


						'''simple docstring'''
						A	 =      1
						A	 =      self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A ,		A ,		A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=_SCREAMING_SNAKE_CASE		)
						self.assertEqual(retrieved_doc_embeds.shape						,(2, n_docs, self.retrieval_vector_size)		)
						self.assertEqual(len(_SCREAMING_SNAKE_CASE		)						,2		)
						self.assertEqual(sorted(doc_dicts[0]		)						,['embeddings', 'id', 'text', 'title']		)
						self.assertEqual(len(doc_dicts[0]['id']		)						,_SCREAMING_SNAKE_CASE		)
						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]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
						with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
										A	 =      RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A	 =      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
										A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=1		)
										self.assertTrue(out is not None		)
		def       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						A	 =      1
						A	 =      self.get_dummy_legacy_index_retriever()
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A ,		A ,		A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=_SCREAMING_SNAKE_CASE		)
						self.assertEqual(retrieved_doc_embeds.shape						,(2, n_docs, self.retrieval_vector_size)		)
						self.assertEqual(len(_SCREAMING_SNAKE_CASE		)						,2		)
						self.assertEqual(sorted(doc_dicts[0]		)						,['text', 'title']		)
						self.assertEqual(len(doc_dicts[0]['text']		)						,_SCREAMING_SNAKE_CASE		)
						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[int]:


						'''simple docstring'''
						A	 =      self.get_dummy_legacy_index_retriever()
						with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
										A	 =      RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
										self.assertIsInstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
										A	 =      np.array(
										    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
										A	 =      retriever.retrieve(_SCREAMING_SNAKE_CASE						,n_docs=1		)
										self.assertTrue(out is not None		)
		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def       A(  self		:       Tuple		)    ->   Union[str, Any]:


						'''simple docstring'''
						import torch

						A	 =      1
						A	 =      self.get_dummy_canonical_hf_index_retriever()
						A	 =      [[5, 7], [1_0, 1_1]]
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A	 =      retriever(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,prefix=retriever.config.generator.prefix						,n_docs=_SCREAMING_SNAKE_CASE		)
						A ,		A ,		A	 =      (
						    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(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						self.assertIsInstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						self.assertIsInstance(_SCREAMING_SNAKE_CASE						,np.ndarray		)

						A	 =      retriever(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,prefix=retriever.config.generator.prefix						,n_docs=_SCREAMING_SNAKE_CASE						,return_tensors='pt'						,)
						A ,		A ,		A ,		A	 =      (  # 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(_SCREAMING_SNAKE_CASE						,torch.Tensor		)
						self.assertIsInstance(_SCREAMING_SNAKE_CASE						,torch.Tensor		)
						self.assertIsInstance(_SCREAMING_SNAKE_CASE						,torch.Tensor		)







		@require_torch
		@require_tokenizers
		@require_sentencepiece
		def       A(  self		:       Tuple		)    ->   Any:


						'''simple docstring'''
						A	 =      self.get_dpr_ctx_encoder_tokenizer()
						A	 =      1
						A	 =      self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
						retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE		)

						A	 =      [[5, 7], [1_0, 1_1]]
						A	 =      np.array(
						    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]						,dtype=np.floataa		)
						A	 =      retriever(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,prefix=retriever.config.generator.prefix						,n_docs=_SCREAMING_SNAKE_CASE		)

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













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





