





"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  lowerCamelCase__				  =   None


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__				  =   {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__				  =   {
    "camembert-base": 512,
}

lowerCamelCase__				  =   "▁"



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[str]	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Tuple	     =					["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Tuple	     =					CamembertTokenizer

  def __init__(		self   :       List[Any]					,   __a   :       int=None					,   __a   :       str=None					,   __a   :       Optional[int]="<s>"					,   __a   :       Dict="</s>"					,   __a   :       List[Any]="</s>"					,   __a   :       List[str]="<s>"					,   __a   :       List[str]="<unk>"					,   __a   :       List[Any]="<pad>"					,   __a   :       List[Any]="<mask>"					,   __a   :       Tuple=["<s>NOTUSED", "</s>NOTUSED"]					,   **__a   :       Optional[Any]					,   )		->				str:
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase      :	str						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else mask_token

        super().__init__(
            __a					,   tokenizer_file=__a					,   bos_token=__a					,   eos_token=__a					,   sep_token=__a					,   cls_token=__a					,   unk_token=__a					,   pad_token=__a					,   mask_token=__a					,   additional_special_tokens=__a					,   **__a					,   )

        _UpperCamelCase      :	List[Any]						=       vocab_file
        _UpperCamelCase      :	Tuple						=       False if not self.vocab_file else True

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:

        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCamelCase      :	Optional[int]						=       [self.cls_token_id]
        _UpperCamelCase      :	List[str]						=       [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
        _UpperCamelCase      :	Union[str, Any]						=       [self.sep_token_id]
        _UpperCamelCase      :	Optional[Any]						=       [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]


  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."	)

        if not os.path.isdir(__a	):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
              return
        _UpperCamelCase      :	List[Any]						=       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	):
              copyfile(self.vocab_file					,   __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      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        _UpperCamelCase      :	Tuple						=       tempfile.mkdtemp()
        _UpperCamelCase      :	str						=       5

        # Realm tok
        _UpperCamelCase      :	Tuple						=       [
            "[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      :	Optional[int]						=       os.path.join(self.tmpdirname					,   "realm_tokenizer"	)
        os.makedirs(__a					,   exist_ok=__a	)
        _UpperCamelCase      :	Optional[Any]						=       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      :	Optional[int]						=       os.path.join(self.tmpdirname					,   "realm_block_records"	)
        os.makedirs(__a					,   exist_ok=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				RealmTokenizer:
        return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname					,   "realm_tokenizer"	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Dict:
        shutil.rmtree(self.tmpdirname	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[str]:
        _UpperCamelCase      :	Optional[Any]						=       RealmConfig(num_block_records=self.num_block_records	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				int:
        _UpperCamelCase      :	Any						=       Dataset.from_dict(
            {
                "id": ["0", "1"],
                "question": ["foo", "bar"],
                "answers": [["Foo", "Bar"], ["Bar"]],
            }	)
        return dataset

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	int						=       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   :       List[Any]	)		->				Optional[Any]:
        _UpperCamelCase      :	List[str]						=       RealmRetriever(
            block_records=self.get_dummy_block_records()					,   tokenizer=self.get_tokenizer()					,   )
        return retriever

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       self.get_config()
        _UpperCamelCase      :	int						=       self.get_dummy_retriever()
        _UpperCamelCase      :	Tuple						=       retriever.tokenizer

        _UpperCamelCase      :	List[str]						=       np.array([0, 3]					,   dtype="long"	)
        _UpperCamelCase      :	Union[str, Any]						=       tokenizer(["Test question"]	).input_ids
        _UpperCamelCase      :	List[str]						=       tokenizer(
            ["the fourth"]					,   add_special_tokens=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   ).input_ids
        _UpperCamelCase      :	str						=       config.reader_seq_len

        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       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    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[Any]:
        _UpperCamelCase      :	Any						=       self.get_config()
        _UpperCamelCase      :	Dict						=       self.get_dummy_retriever()
        _UpperCamelCase      :	Dict						=       retriever.tokenizer

        _UpperCamelCase      :	List[Any]						=       np.array([0, 3, 5]					,   dtype="long"	)
        _UpperCamelCase      :	Optional[int]						=       tokenizer(["Test question"]	).input_ids
        _UpperCamelCase      :	str						=       tokenizer(
            ["the fourth", "longer longer"]					,   add_special_tokens=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   ).input_ids
        _UpperCamelCase      :	Union[str, Any]						=       config.reader_seq_len

        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       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   :       int	)		->				List[str]:
        _UpperCamelCase      :	List[Any]						=       self.get_dummy_retriever()
        retriever.save_pretrained(os.path.join(self.tmpdirname					,   "realm_block_records"	)	)

        # Test local path
        _UpperCamelCase      :	int						=       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      :	List[Any]						=       os.path.join(
                  os.path.join(self.tmpdirname					,   "realm_block_records"	)					,   _REALM_BLOCK_RECORDS_FILENAME	)
              _UpperCamelCase      :	int						=       RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"	)

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