



'''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      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self							):
				A_     :						Dict     =					tempfile.mkdtemp()
				A_     :						Optional[int]     =					5

				# Realm tok
				A_     :						Optional[Any]     =					[
				    """[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_     :						Tuple     =					os.path.join(self.tmpdirname	,				"""realm_tokenizer"""							)
				os.makedirs(lowercase	,				exist_ok=lowercase							)
				A_     :						Union[str, Any]     =					os.path.join(lowercase	,				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_     :						int     =					os.path.join(self.tmpdirname	,				"""realm_block_records"""							)
				os.makedirs(lowercase	,				exist_ok=lowercase							)


	def    _a     (self							):
				return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname	,				"""realm_tokenizer"""							)							)


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


	def    _a     (self							):
				A_     :						Tuple     =					RealmConfig(num_block_records=self.num_block_records							)
				return config


	def    _a     (self							):
				A_     :						List[Any]     =					Dataset.from_dict(
				    {
				        """id""": ["""0""", """1"""],
				        """question""": ["""foo""", """bar"""],
				        """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
				    }							)
				return dataset


	def    _a     (self							):
				A_     :						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=lowercase	,				)
				return block_records


	def    _a     (self							):
				A_     :						Any     =					RealmRetriever(
				    block_records=self.get_dummy_block_records()	,				tokenizer=self.get_tokenizer()	,				)
				return retriever


	def    _a     (self							):
				A_     :						int     =					self.get_config()
				A_     :						Any     =					self.get_dummy_retriever()
				A_     :						int     =					retriever.tokenizer

				A_     :						str     =					np.array([0, 3]	,				dtype="""long"""							)
				A_     :						List[str]     =					tokenizer(["""Test question"""]							).input_ids
				A_     :						List[Any]     =					tokenizer(
				    ["""the fourth"""]	,				add_special_tokens=lowercase	,				return_token_type_ids=lowercase	,				return_attention_mask=lowercase	,				).input_ids
				A_     :						List[str]     =					config.reader_seq_len

				A_     :						List[Any]     =					retriever(
				    lowercase	,				lowercase	,				answer_ids=lowercase	,				max_length=lowercase	,				return_tensors="""np"""							)

				self.assertEqual(len(lowercase							)	,				2							)
				self.assertEqual(len(lowercase							)	,				2							)
				self.assertEqual(len(lowercase							)	,				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    _a     (self							):
				A_     :						str     =					self.get_config()
				A_     :						Optional[Any]     =					self.get_dummy_retriever()
				A_     :						Dict     =					retriever.tokenizer

				A_     :						Optional[Any]     =					np.array([0, 3, 5]	,				dtype="""long"""							)
				A_     :						List[Any]     =					tokenizer(["""Test question"""]							).input_ids
				A_     :						Any     =					tokenizer(
				    ["""the fourth""", """longer longer"""]	,				add_special_tokens=lowercase	,				return_token_type_ids=lowercase	,				return_attention_mask=lowercase	,				).input_ids
				A_     :						List[str]     =					config.reader_seq_len

				A_     :						Optional[int]     =					retriever(
				    lowercase	,				lowercase	,				answer_ids=lowercase	,				max_length=lowercase	,				return_tensors="""np"""							)

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


	def    _a     (self							):
				A_     :						Optional[int]     =					self.get_dummy_retriever()
				retriever.save_pretrained(os.path.join(self.tmpdirname	,				"""realm_block_records"""							)							)

				# Test local path
				A_     :						Dict     =					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_     :						Optional[int]     =					os.path.join(
							    os.path.join(self.tmpdirname	,				"""realm_block_records"""							)	,				_REALM_BLOCK_RECORDS_FILENAME							)
							A_     :						Dict     =					RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa"""							)

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











'''simple docstring'''




from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[Any]     =					prime_factors(lowerCamelCase__   )
			if is_square_free(lowerCamelCase__   ):
						return -1 if len(lowerCamelCase__   ) % 2 else 1
			return 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()