


"""simple docstring"""

from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCAmelCase__  							=_symbol_database.Default()


UpperCAmelCase__  							=_descriptor_pool.Default().AddSerializedFile(
    B"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

UpperCAmelCase__  							=globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
       UpperCAmelCase__  							=None
       UpperCAmelCase__  							=B"H\003"
       # (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
       # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
       # _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
       # _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
       # _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
       UpperCAmelCase__  							=45
       UpperCAmelCase__  							=1581
       UpperCAmelCase__  							=1517
       UpperCAmelCase__  							=1570
       UpperCAmelCase__  							=1584
       UpperCAmelCase__  							=1793
       UpperCAmelCase__  							=1795
       UpperCAmelCase__  							=1916
       UpperCAmelCase__  							=1864
       UpperCAmelCase__  							=1905
       UpperCAmelCase__  							=1919
       UpperCAmelCase__  							=2429
       UpperCAmelCase__  							=2208
       UpperCAmelCase__  							=2418
       UpperCAmelCase__  							=2323
       UpperCAmelCase__  							=2407
# @@protoc_insertion_point(module_scope)




"""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__						(		_a    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	tempfile.mkdtemp()
       __lowercase									=	5

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

       __lowercase									=	os.path.join(self.tmpdirname      ,       """realm_block_records"""							)
       os.makedirs(A_      ,       exist_ok=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,       """realm_tokenizer"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       shutil.rmtree(self.tmpdirname							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


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






       '''simple docstring'''


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






       '''simple docstring'''


       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


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






       '''simple docstring'''


       __lowercase									=	self.get_config()
       __lowercase									=	self.get_dummy_retriever()
       __lowercase									=	retriever.tokenizer

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

       __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	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, 1_0)							)
       self.assertEqual(concat_inputs.attention_mask.shape      ,       (2, 1_0)							)
       self.assertEqual(concat_inputs.token_type_ids.shape      ,       (2, 1_0)							)
       self.assertEqual(concat_inputs.special_tokens_mask.shape      ,       (2, 1_0)							)
       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	:	str							):






       '''simple docstring'''


       __lowercase									=	self.get_config()
       __lowercase									=	self.get_dummy_retriever()
       __lowercase									=	retriever.tokenizer

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

       __lowercase							,		__lowercase							,		__lowercase							,		__lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname      ,       """realm_block_records"""							)							)

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

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