
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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= tempfile.mkdtemp()
					__A   			= 5

					# Realm tok
					__A   			= [
					    "[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   			= os.path.join(self.tmpdirname							,"realm_tokenizer" )
					os.makedirs(A							,exist_ok=A )
					__A   			= 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] ) )

					__A   			= os.path.join(self.tmpdirname							,"realm_block_records" )
					os.makedirs(A							,exist_ok=A )






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






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					shutil.rmtree(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= RealmConfig(num_block_records=self.num_block_records )
					return config






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= Dataset.from_dict(
					    {
					        "id": ["0", "1"],
					        "question": ["foo", "bar"],
					        "answers": [["Foo", "Bar"], ["Bar"]],
					    } )
					return dataset






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= 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 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= RealmRetriever(
					    block_records=self.get_dummy_block_records()							,tokenizer=self.get_tokenizer()							,)
					return retriever






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.get_config()
					__A   			= self.get_dummy_retriever()
					__A   			= retriever.tokenizer

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

					__A			,							__A			,							__A			,							__A   			= 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 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.get_config()
					__A   			= self.get_dummy_retriever()
					__A   			= retriever.tokenizer

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

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

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

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


SCREAMING_SNAKE_CASE							:Any							 =				256
# Modulus to hash a string
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				100_0003


def 						UpperCAmelCase			(	a_		,   a_						)		->	bool:







				"""simple docstring"""



				__A   			= len(a_						)
				__A   			= len(a_						)
				if p_len > t_len:
								return False

				__A   			= 0
				__A   			= 0
				__A   			= 1

				# Calculating the hash of pattern and substring of text
				for i in range(a_						):
								__A   			= (ord(pattern[i]						) + p_hash * alphabet_size) % modulus
								__A   			= (ord(text[i]						) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								__A   			= (modulus_power * alphabet_size) % modulus

				for i in range(0		,   t_len - p_len + 1						):
								if text_hash == p_hash and text[i : i + p_len] == pattern:
												return True
								if i == t_len - p_len:
												continue
								# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
								__A   			= (
								    (text_hash - ord(text[i]						) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]						)
								) % modulus
				return False


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= "abc1abc12"
				__A   			= "alskfjaldsabc1abc1abc12k23adsfabcabc"
				__A   			= "alskfjaldsk23adsfabcabc"
				assert rabin_karp(a_		,   a_						) and not rabin_karp(a_		,   a_						)

				# Test 2)
				__A   			= "ABABX"
				__A   			= "ABABZABABYABABX"
				assert rabin_karp(a_		,   a_						)

				# Test 3)
				__A   			= "AAAB"
				__A   			= "ABAAAAAB"
				assert rabin_karp(a_		,   a_						)

				# Test 4)
				__A   			= "abcdabcy"
				__A   			= "abcxabcdabxabcdabcdabcy"
				assert rabin_karp(a_		,   a_						)

				# Test 5)
				__A   			= "Lü"
				__A   			= "Lüsai"
				assert rabin_karp(a_		,   a_						)
				__A   			= "Lue"
				assert not rabin_karp(a_		,   a_						)
				print("Success."						)


if __name__ == "__main__":
					test_rabin_karp()
