

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "vocab_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"
        ),
        "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt",
        "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt",
        "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "google/realm-cc-news-pretrained-embedder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"
        ),
        "google/realm-cc-news-pretrained-encoder": (
            "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-scorer": (
            "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"
        ),
        "google/realm-cc-news-pretrained-openqa": (
            "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-openqa": (
            "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-nq-reader": (
            "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-openqa": (
            "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"
        ),
        "google/realm-orqa-wq-reader": (
            "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "google/realm-cc-news-pretrained-embedder": 512,
    "google/realm-cc-news-pretrained-encoder": 512,
    "google/realm-cc-news-pretrained-scorer": 512,
    "google/realm-cc-news-pretrained-openqa": 512,
    "google/realm-orqa-nq-openqa": 512,
    "google/realm-orqa-nq-reader": 512,
    "google/realm-orqa-wq-openqa": 512,
    "google/realm-orqa-wq-reader": 512,
}

lowercase_			=  {
    "google/realm-cc-news-pretrained-embedder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-encoder": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-scorer": {"do_lower_case": True},
    "google/realm-cc-news-pretrained-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-openqa": {"do_lower_case": True},
    "google/realm-orqa-nq-reader": {"do_lower_case": True},
    "google/realm-orqa-wq-openqa": {"do_lower_case": True},
    "google/realm-orqa-wq-reader": {"do_lower_case": True},
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							RealmTokenizer


    def __init__(				self			:	Optional[int],lowercase_			:	str=None,lowercase_			:	str=None,lowercase_			:	Optional[int]=True,lowercase_			:	Optional[int]="[UNK]",lowercase_			:	Tuple="[SEP]",lowercase_			:	Dict="[PAD]",lowercase_			:	Tuple="[CLS]",lowercase_			:	Optional[Any]="[MASK]",lowercase_			:	Optional[int]=True,lowercase_			:	Union[str, Any]=None,**lowercase_			:	str,)->   str:





        '''simple docstring'''







        super().__init__(
            lowercase_,tokenizer_file=lowercase_,do_lower_case=lowercase_,unk_token=lowercase_,sep_token=lowercase_,pad_token=lowercase_,cls_token=lowercase_,mask_token=lowercase_,tokenize_chinese_chars=lowercase_,strip_accents=lowercase_,**lowercase_,)

        A__   				=       json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
        if (
            normalizer_state.get('lowercase',lowercase_		) != do_lower_case
            or normalizer_state.get('strip_accents',lowercase_		) != strip_accents
            or normalizer_state.get('handle_chinese_chars',lowercase_		) != tokenize_chinese_chars
        ):
            A__   				=       getattr(lowercase_,normalizer_state.pop('type'		)		)
            A__   				=       do_lower_case
            A__   				=       strip_accents
            A__   				=       tokenize_chinese_chars
            A__   				=       normalizer_class(**lowercase_		)

        A__   				=       do_lower_case


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Tuple,**lowercase_			:	str		)->   Any:





        '''simple docstring'''







        A__   				=       PaddingStrategy.MAX_LENGTH

        A__   				=       text
        A__   				=       kwargs.pop('text_pair',lowercase_		)
        A__   				=       kwargs.pop('return_tensors',lowercase_		)

        A__   				=       {
            'input_ids': [],
            'attention_mask': [],
            'token_type_ids': [],
        }

        for idx, candidate_text in enumerate(lowercase_		):
            if batch_text_pair is not None:
                A__   				=       batch_text_pair[idx]
            else:
                A__   				=       None

            A__   				=       super().__call__(lowercase_,lowercase_,return_tensors=lowercase_,**lowercase_		)

            A__   				=       encoded_candidates.get('input_ids'		)
            A__   				=       encoded_candidates.get('attention_mask'		)
            A__   				=       encoded_candidates.get('token_type_ids'		)

            if encoded_input_ids is not None:
                output_data["input_ids"].append(lowercase_		)
            if encoded_attention_mask is not None:
                output_data["attention_mask"].append(lowercase_		)
            if encoded_token_type_ids is not None:
                output_data["token_type_ids"].append(lowercase_		)

        A__   				=       {key: item for key, item in output_data.items() if len(lowercase_		) != 0}

        return BatchEncoding(lowercase_,tensor_type=lowercase_		)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Optional[Any],lowercase_			:	Any=None		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

        return output


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [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		) * [0] + len(token_ids_a + sep		) * [1]


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







        A__   				=       self._tokenizer.model.save(lowercase_,name=lowercase_		)
        return tuple(lowercase_		)




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase_			=  (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_			=  (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42




class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Iterable[int]		)->   None:





        '''simple docstring'''







        A__   				=       None
        for i in sorted(lowercase_,reverse=lowercase_		):
            A__   				=       Node(lowercase_,self.head		)


    def __iter__(				self			:	List[str]		)->   Iterator[int]:





        '''simple docstring'''







        A__   				=       self.head
        while node:
            yield node.data
            A__   				=       node.next_node


    def __len__(				self			:	str		)->   int:





        '''simple docstring'''







        return sum(1 for _ in self		)


    def __str__(				self			:	Optional[int]		)->   str:





        '''simple docstring'''







        return " -> ".join([str(lowercase_		) for node in self]		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     SortedLinkedList   ,      SCREAMING_SNAKE_CASE__      :     SortedLinkedList				)							->				SortedLinkedList:





    '''simple docstring'''






    return SortedLinkedList(list(SCREAMING_SNAKE_CASE__				) + list(SCREAMING_SNAKE_CASE__				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     lowercase_			=  SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

