




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__							(_UpperCamelCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  int:
							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(__lowerCamelCase      ,   exist_ok=__lowerCamelCase							)
							a					=    os.path.join(__lowerCamelCase      ,   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(__lowerCamelCase      ,   exist_ok=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  RealmTokenizer:
							return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,   "realm_tokenizer"							)							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Dict:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[str]:
							a					=    RealmConfig(num_block_records=self.num_block_records							)
							return config





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[Any]:
							a					=    Dataset.from_dict(
							    {
							        "id": ["0", "1"],
							        "question": ["foo", "bar"],
							        "answers": [["Foo", "Bar"], ["Bar"]],
							    }							)
							return dataset





				def 							__UpperCAmelCase							(   self       :  int							)      ->  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=__lowerCamelCase      ,   )
							return block_records





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Union[str, Any]:
							a					=    RealmRetriever(
							    block_records=self.get_dummy_block_records()      ,   tokenizer=self.get_tokenizer()      ,   )
							return retriever





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[Any]:
							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=__lowerCamelCase      ,   return_token_type_ids=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   ).input_ids
							a					=    config.reader_seq_len

							a					,	a					,	a					,	a					=    retriever(
							    __lowerCamelCase      ,   __lowerCamelCase      ,   answer_ids=__lowerCamelCase      ,   max_length=__lowerCamelCase      ,   return_tensors="np"							)

							self.assertEqual(len(__lowerCamelCase							)      ,   2							)
							self.assertEqual(len(__lowerCamelCase							)      ,   2							)
							self.assertEqual(len(__lowerCamelCase							)      ,   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       :  int							)      ->  List[str]:
							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=__lowerCamelCase      ,   return_token_type_ids=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   ).input_ids
							a					=    config.reader_seq_len

							a					,	a					,	a					,	a					=    retriever(
							    __lowerCamelCase      ,   __lowerCamelCase      ,   answer_ids=__lowerCamelCase      ,   max_length=__lowerCamelCase      ,   return_tensors="np"							)

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





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Dict:
							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"							)












def     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			def get_matched_characters(A	:      str,						A	:      str     ) -> str:
						a					=    []
						a					=    min(len(_stra     ),						len(_stra     )     ) // 2
						for i, l in enumerate(_stra     ):
									a					=    int(max(0,						i - limit     )     )
									a					=    int(min(i + limit + 1,						len(_stra     )     )     )
									if l in _stra[left:right]:
												matched.append(A     )
												a					=    F"""{_stra[0:_stra.index(A     )]} {_stra[_stra.index(A     ) + 1:]}"""

						return "".join(A     )

			# matching characters
			a					=    get_matched_characters(A,						A     )
			a					=    get_matched_characters(A,						A     )
			a					=    len(A     )

			# transposition
			a					=    (
			    len([(ca, ca) for ca, ca in zip(A,						A     ) if ca != ca]     ) // 2
			)

			if not match_count:
						a					=    0.0
			else:
						a					=    (
						    1
						    / 3
						    * (
						        match_count / len(A     )
						        + match_count / len(A     )
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			a					=    0
			for ca, ca in zip(stra[:4],						stra[:4]     ):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))






