'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE_:     Any  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE_:     str  ={
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_:     int  ={
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}




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



    def __init__(self    :    List[str]   ,			__a    :    List[Any]=None   ,			__a    :    str=None   ,			__a    :    List[str]=None   ,			__a    :    Union[str, Any]="replace"   ,			__a    :    List[str]="<s>"   ,			__a    :    List[Any]="</s>"   ,			__a    :    Dict="</s>"   ,			__a    :    Optional[int]="<s>"   ,			__a    :    Any="<unk>"   ,			__a    :    List[Any]="<pad>"   ,			__a    :    List[str]="<mask>"   ,			__a    :    Tuple=False   ,			__a    :    Optional[Any]=True   ,			**__a    :    Optional[Any]   ,			):
         super().__init__(
             __a   ,			__a   ,			tokenizer_file=__a   ,			errors=__a   ,			bos_token=__a   ,			eos_token=__a   ,			sep_token=__a   ,			cls_token=__a   ,			unk_token=__a   ,			pad_token=__a   ,			mask_token=__a   ,			add_prefix_space=__a   ,			trim_offsets=__a   ,			**__a   ,			)

         UpperCAmelCase_           =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get("add_prefix_space"   ,			__a ) != add_prefix_space:
              UpperCAmelCase_           =   getattr(__a   ,			pre_tok_state.pop("type" ) )
              UpperCAmelCase_           =   add_prefix_space
              UpperCAmelCase_           =   pre_tok_class(**__a )

         UpperCAmelCase_           =   add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase_           =   "post_processor"
         UpperCAmelCase_           =   getattr(self.backend_tokenizer   ,			__a   ,			__a )
         if tokenizer_component_instance:
              UpperCAmelCase_           =   json.loads(tokenizer_component_instance.__getstate__() )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   UpperCAmelCase_           =   tuple(state["sep"] )
              if "cls" in state:
                   UpperCAmelCase_           =   tuple(state["cls"] )

              UpperCAmelCase_           =   False

              if state.get("add_prefix_space"   ,			__a ) != add_prefix_space:
                   UpperCAmelCase_           =   add_prefix_space
                   UpperCAmelCase_           =   True

              if state.get("trim_offsets"   ,			__a ) != trim_offsets:
                   UpperCAmelCase_           =   trim_offsets
                   UpperCAmelCase_           =   True

              if changes_to_apply:
                   UpperCAmelCase_           =   getattr(__a   ,			state.pop("type" ) )
                   UpperCAmelCase_           =   component_class(**__a )
                   setattr(self.backend_tokenizer   ,			__a   ,			__a )



    @property
    def 					_lowercase						(self    :    Dict ):
         if self._mask_token is None:
              if self.verbose:
                   logger.error("Using mask_token, but it is not set yet." )
              return None
         return str(self._mask_token )



    @mask_token.setter
    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   AddedToken(__a   ,			lstrip=__a   ,			rstrip=__a ) if isinstance(__a   ,			__a ) else value
         UpperCAmelCase_           =   value



    def 					_lowercase						(self    :    Any   ,			*__a    :    List[Any]   ,			**__a    :    Any ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs." )

         return super()._batch_encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    List[str]   ,			*__a    :    Optional[Any]   ,			**__a    :    Dict ):
         UpperCAmelCase_           =   kwargs.get("is_split_into_words"   ,			__a )

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                  "to use it with pretokenized inputs." )

         return super()._encode_plus(*__a   ,			**__a )



    def 					_lowercase						(self    :    str   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         UpperCAmelCase_           =   self._tokenizer.model.save(__a   ,			name=__a )
         return tuple(__a )



    def 					_lowercase						(self    :    Any   ,			__a    :    Optional[int]   ,			__a    :    str=None ):
         UpperCAmelCase_           =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    List[int]   ,			__a    :    Optional[List[int]] = None ):
         UpperCAmelCase_           =   [self.sep_token_id]
         UpperCAmelCase_           =   [self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]





'''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" )




