

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer



class       __snake_case  (    lowerCAmelCase__       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						tempfile.mkdtemp()
      SCREAMING_SNAKE_CASE_									=						5

      # Realm tok
      SCREAMING_SNAKE_CASE_									=						[
          '[UNK]',
          '[CLS]',
          '[SEP]',
          '[PAD]',
          '[MASK]',
          'test',
          'question',
          'this',
          'is',
          'the',
          'first',
          'second',
          'third',
          'fourth',
          'fifth',
          'record',
          'want',
          '##want',
          '##ed',
          'wa',
          'un',
          'runn',
          '##ing',
          ',',
          'low',
          'lowest',
      ]
      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'realm_tokenizer')
      os.makedirs(_A						,		exist_ok=_A)
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		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]))

      SCREAMING_SNAKE_CASE_									=						os.path.join(self.tmpdirname						,		'realm_block_records')
      os.makedirs(_A						,		exist_ok=_A)

  def 						lowerCAmelCase__     (					self):
      return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,		'realm_tokenizer'))

  def 						lowerCAmelCase__     (					self):
      shutil.rmtree(self.tmpdirname)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						RealmConfig(num_block_records=self.num_block_records)
      return config

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						Dataset.from_dict(
          {
              'id': ['0', '1'],
              'question': ['foo', 'bar'],
              'answers': [['Foo', 'Bar'], ['Bar']],
          })
      return dataset

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						np.array(
          [
              b'This is the first record',
              b'This is the second record',
              b'This is the third record',
              b'This is the fourth record',
              b'This is the fifth record',
              b'This is a longer longer longer record',
          ]						,		dtype=_A						,		)
      return block_records

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						RealmRetriever(
          block_records=self.get_dummy_block_records()						,		tokenizer=self.get_tokenizer()						,		)
      return retriever

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_config()
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_retriever()
      SCREAMING_SNAKE_CASE_									=						retriever.tokenizer

      SCREAMING_SNAKE_CASE_									=						np.array([0, 3]						,		dtype='long')
      SCREAMING_SNAKE_CASE_									=						tokenizer(['Test question']).input_ids
      SCREAMING_SNAKE_CASE_									=						tokenizer(
          ['the fourth']						,		add_special_tokens=_A						,		return_token_type_ids=_A						,		return_attention_mask=_A						,		).input_ids
      SCREAMING_SNAKE_CASE_									=						config.reader_seq_len

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever(
          _A						,		_A						,		answer_ids=_A						,		max_length=_A						,		return_tensors='np')

      self.assertEqual(len(_A)						,		2)
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(len(_A)						,		2)
      self.assertEqual(concat_inputs.input_ids.shape						,		(2, 10))
      self.assertEqual(concat_inputs.attention_mask.shape						,		(2, 10))
      self.assertEqual(concat_inputs.token_type_ids.shape						,		(2, 10))
      self.assertEqual(concat_inputs.special_tokens_mask.shape						,		(2, 10))
      self.assertEqual(
          tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])						,		['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']						,		)
      self.assertEqual(
          tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])						,		['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_config()
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_retriever()
      SCREAMING_SNAKE_CASE_									=						retriever.tokenizer

      SCREAMING_SNAKE_CASE_									=						np.array([0, 3, 5]						,		dtype='long')
      SCREAMING_SNAKE_CASE_									=						tokenizer(['Test question']).input_ids
      SCREAMING_SNAKE_CASE_									=						tokenizer(
          ['the fourth', 'longer longer']						,		add_special_tokens=_A						,		return_token_type_ids=_A						,		return_attention_mask=_A						,		).input_ids
      SCREAMING_SNAKE_CASE_									=						config.reader_seq_len

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						retriever(
          _A						,		_A						,		answer_ids=_A						,		max_length=_A						,		return_tensors='np')

      self.assertEqual([False, True, True]						,		_A)
      self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,		_A)
      self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,		_A)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_retriever()
      retriever.save_pretrained(os.path.join(self.tmpdirname						,		'realm_block_records'))

      # Test local path
      SCREAMING_SNAKE_CASE_									=						retriever.from_pretrained(os.path.join(self.tmpdirname						,		'realm_block_records'))
      self.assertEqual(retriever.block_records[0]						,		b'This is the first record')

      # Test mocked remote path
      with patch('transformers.models.realm.retrieval_realm.hf_hub_download') as mock_hf_hub_download:
          SCREAMING_SNAKE_CASE_									=						os.path.join(
              os.path.join(self.tmpdirname						,		'realm_block_records')						,		_REALM_BLOCK_RECORDS_FILENAME)
          SCREAMING_SNAKE_CASE_									=						RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

      self.assertEqual(retriever.block_records[0]						,		b'This is the first record')





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if index == number_of_items:
        return 0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						knapsack(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   index + 1       )
    if weights[index] <= max_weight:
        SCREAMING_SNAKE_CASE_									=						values[index] + knapsack(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   max_weight - weights[index] ,   index + 1       )
    return max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


