





'''simple docstring'''




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 					SCREAMING_SNAKE_CASE       (a__				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			tempfile.mkdtemp()
          __A		:					int						=			5

          # Realm tok
          __A		:					str						=			[
              '[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',
          ]
          __A		:					Union[str, Any]						=			os.path.join(self.tmpdirname		,      'realm_tokenizer')
          os.makedirs(_UpperCAmelCase		,      exist_ok=_UpperCAmelCase)
          __A		:					str						=			os.path.join(_UpperCAmelCase		,      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]))

          __A		:					Optional[Any]						=			os.path.join(self.tmpdirname		,      'realm_block_records')
          os.makedirs(_UpperCAmelCase		,      exist_ok=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			RealmConfig(num_block_records=self.num_block_records)
          return config


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			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=_UpperCAmelCase		,      )
          return block_records


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			RealmRetriever(
              block_records=self.get_dummy_block_records()		,      tokenizer=self.get_tokenizer()		,      )
          return retriever


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			self.get_config()
          __A		:					Optional[int]						=			self.get_dummy_retriever()
          __A		:					Dict						=			retriever.tokenizer

          __A		:					Optional[int]						=			np.array([0, 3]		,      dtype='long')
          __A		:					int						=			tokenizer(['Test question']).input_ids
          __A		:					List[str]						=			tokenizer(
              ['the fourth']		,      add_special_tokens=_UpperCAmelCase		,      return_token_type_ids=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      ).input_ids
          __A		:					List[Any]						=			config.reader_seq_len

          __A					,__A					,__A					,__A		:					Any						=			retriever(
              _UpperCAmelCase		,      _UpperCAmelCase		,      answer_ids=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      return_tensors='np')

          self.assertEqual(len(_UpperCAmelCase)		,      2)
          self.assertEqual(len(_UpperCAmelCase)		,      2)
          self.assertEqual(len(_UpperCAmelCase)		,      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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.get_config()
          __A		:					List[Any]						=			self.get_dummy_retriever()
          __A		:					Union[str, Any]						=			retriever.tokenizer

          __A		:					Any						=			np.array([0, 3, 5]		,      dtype='long')
          __A		:					Tuple						=			tokenizer(['Test question']).input_ids
          __A		:					Any						=			tokenizer(
              ['the fourth', 'longer longer']		,      add_special_tokens=_UpperCAmelCase		,      return_token_type_ids=_UpperCAmelCase		,      return_attention_mask=_UpperCAmelCase		,      ).input_ids
          __A		:					Union[str, Any]						=			config.reader_seq_len

          __A					,__A					,__A					,__A		:					Dict						=			retriever(
              _UpperCAmelCase		,      _UpperCAmelCase		,      answer_ids=_UpperCAmelCase		,      max_length=_UpperCAmelCase		,      return_tensors='np')

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.get_dummy_retriever()
          retriever.save_pretrained(os.path.join(self.tmpdirname		,      'realm_block_records'))

          # Test local path
          __A		:					str						=			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:
               __A		:					int						=			os.path.join(
                   os.path.join(self.tmpdirname		,      'realm_block_records')		,      _REALM_BLOCK_RECORDS_FILENAME)
               __A		:					int						=			RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

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






'''simple docstring'''




import mpmath  # for roots of unity
import numpy as np


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None):

          '''simple docstring'''






          __A		:					List[Any]						=			list(poly_a or [0])[:]
          __A		:					Optional[int]						=			list(poly_b or [0])[:]

          # Remove leading zero coefficients
          while self.polyA[-1] == 0:
               self.polyA.pop()
          __A		:					Union[str, Any]						=			len(self.polyA)

          while self.polyB[-1] == 0:
               self.polyB.pop()
          __A		:					Optional[int]						=			len(self.polyB)

          # Add 0 to make lengths equal a power of 2
          __A		:					Optional[Any]						=			int(
              2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

          while len(self.polyA) < self.c_max_length:
               self.polyA.append(0)
          while len(self.polyB) < self.c_max_length:
               self.polyB.append(0)
          # A complex root used for the fourier transform
          __A		:					str						=			complex(mpmath.root(x=1		,      n=self.c_max_length		,      k=1))

          # The product
          __A		:					Tuple						=			self.__multiply()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[int]						=			[[x] for x in self.polyA] if which == 'A' else [[x] for x in self.polyB]
          # Corner case
          if len(_UpperCAmelCase) <= 1:
               return dft[0]
          #
          __A		:					Dict						=			self.c_max_length // 2
          while next_ncol > 0:
               __A		:					Optional[Any]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Tuple						=			self.root**next_ncol

               # First half of next step
               __A		:					Optional[Any]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Second half of next step
               __A		:					List[str]						=			1
               for j in range(self.c_max_length // (next_ncol * 2)):
                    for i in range(_UpperCAmelCase):
                         new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                    current_root *= root
               # Update
               __A		:					Optional[int]						=			new_dft
               __A		:					Tuple						=			next_ncol // 2
          return dft[0]


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.__dft('A')
          __A		:					Optional[Any]						=			self.__dft('B')
          __A		:					str						=			[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
          del dft_a
          del dft_b

          # Corner Case
          if len(inverce_c[0]) <= 1:
               return inverce_c[0]
          # Inverse DFT
          __A		:					Dict						=			2
          while next_ncol <= self.c_max_length:
               __A		:					Optional[int]						=			[[] for i in range(_UpperCAmelCase)]
               __A		:					Any						=			self.root ** (next_ncol // 2)
               __A		:					Tuple						=			1
               # First half of next step
               for j in range(self.c_max_length // next_ncol):
                    for i in range(next_ncol // 2):
                         # Even positions
                         new_inverse_c[i].append(
                             (
                                 inverce_c[i][j]
                                 + inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / 2)
                         # Odd positions
                         new_inverse_c[i + next_ncol // 2].append(
                             (
                                 inverce_c[i][j]
                                 - inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / (2 * current_root))
                    current_root *= root
               # Update
               __A		:					int						=			new_inverse_c
               next_ncol *= 2
          # Unpack
          __A		:					Optional[int]						=			[round(x[0].real		,      8) + round(x[0].imag		,      8) * 1j for x in inverce_c]

          # Remove leading 0's
          while inverce_c[-1] == 0:
               inverce_c.pop()
          return inverce_c


     def __str__(		self):

          '''simple docstring'''






          __A		:					int						=			'A = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
          __A		:					Optional[Any]						=			'B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
          __A		:					str						=			'A*B = ' + ' + '.join(
              F'{coef}*x^{i}' for coef, i in enumerate(self.product))

          return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()