'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:     Optional[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Dict  ={'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE_:     int  ={
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE_:     Any  ={
    'microsoft/speecht5_asr': 10_24,
    'microsoft/speecht5_tts': 10_24,
    'microsoft/speecht5_vc': 10_24,
}




class        __A      ( UpperCamelCase__	):
    a__       :						Dict          =  VOCAB_FILES_NAMES
    a__       :						Optional[int]          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Optional[Any]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       :						str          =  ["""input_ids""", """attention_mask"""]



    def __init__(self    :    int   ,			__a    :    List[str]   ,			__a    :    Optional[Any]="<s>"   ,			__a    :    Optional[Any]="</s>"   ,			__a    :    Tuple="<unk>"   ,			__a    :    Optional[Any]="<pad>"   ,			__a    :    Optional[Dict[str, Any]] = None   ,			**__a    :    Tuple   ,			):
         UpperCAmelCase_           =   {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__a   ,			eos_token=__a   ,			unk_token=__a   ,			pad_token=__a   ,			sp_model_kwargs=self.sp_model_kwargs   ,			**__a   ,			)

         UpperCAmelCase_           =   vocab_file

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__a )



    @property
    def 					_lowercase						(self    :    Dict ):
         return self.sp_model.get_piece_size()



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab



    def __getstate__(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.__dict__.copy()
         UpperCAmelCase_           =   None
         return state



    def __setstate__(self    :    int   ,			__a    :    Any ):
         UpperCAmelCase_           =   d

         # for backward compatibility
         if not hasattr(self   ,			"sp_model_kwargs" ):
              UpperCAmelCase_           =   {}

         UpperCAmelCase_           =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )



    def 					_lowercase						(self    :    Dict   ,			__a    :    str ):
         return self.sp_model.encode(__a   ,			out_type=__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    Union[str, Any] ):
         return self.sp_model.piece_to_id(__a )



    def 					_lowercase						(self    :    int   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.sp_model.IdToPiece(__a )
         return token



    def 					_lowercase						(self    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   ""
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(__a ) + token
                   UpperCAmelCase_           =   []
              else:
                   current_sub_tokens.append(__a )
         out_string += self.sp_model.decode(__a )
         return out_string.strip()



    def 					_lowercase						(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    List[Any]=None ):
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None   ,			__a    :    bool = False ):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__a   ,			token_ids_a=__a   ,			already_has_special_tokens=__a )

         UpperCAmelCase_           =   [1]
         if token_ids_a is None:
              return ([0] * len(__a )) + suffix_ones
         return ([0] * len(__a )) + ([0] * len(__a )) + suffix_ones






    def 					_lowercase						(self    :    List[Any]   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         if not os.path.isdir(__a ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
         UpperCAmelCase_           =   os.path.join(
             __a   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file   ,			__a )
         elif not os.path.isfile(self.vocab_file ):
              with open(__a   ,			"wb" ) as fi:
                   UpperCAmelCase_           =   self.sp_model.serialized_model_proto()
                   fi.write(__a )

         return (out_vocab_file,)





'''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        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         UpperCAmelCase_           =   5

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

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_block_records" )
         os.makedirs(__a   ,			exist_ok=__a )



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



    def 					_lowercase						(self    :    Any ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   RealmConfig(num_block_records=self.num_block_records )
         return config



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



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   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 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   RealmRetriever(
             block_records=self.get_dummy_block_records()   ,			tokenizer=self.get_tokenizer()   ,			)
         return retriever



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

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

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   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 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

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

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )

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

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




