



'''simple docstring'''
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


_UpperCamelCase							 =	logging.get_logger(__name__)

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

_UpperCamelCase							 =	{
    """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"""
        ),
    },
}

_UpperCamelCase							 =	{
    """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,
}

_UpperCamelCase							 =	{
    """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__			):
		_SCREAMING_SNAKE_CASE    :			Dict  					=	VOCAB_FILES_NAMES
		_SCREAMING_SNAKE_CASE    :			List[str]  					=	PRETRAINED_VOCAB_FILES_MAP
		_SCREAMING_SNAKE_CASE    :			Optional[Any]  					=	PRETRAINED_INIT_CONFIGURATION
		_SCREAMING_SNAKE_CASE    :			List[Any]  					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_SCREAMING_SNAKE_CASE    :			List[Any]  					=	RealmTokenizer





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

					'''simple docstring'''
					super().__init__(
					    UpperCAmelCase__       ,				tokenizer_file=UpperCAmelCase__       ,				do_lower_case=UpperCAmelCase__       ,				unk_token=UpperCAmelCase__       ,				sep_token=UpperCAmelCase__       ,				pad_token=UpperCAmelCase__       ,				cls_token=UpperCAmelCase__       ,				mask_token=UpperCAmelCase__       ,				tokenize_chinese_chars=UpperCAmelCase__       ,				strip_accents=UpperCAmelCase__       ,				**UpperCAmelCase__       ,				)

					__UpperCAmelCase      :					int   =   json.loads(self.backend_tokenizer.normalizer.__getstate__() )
					if (
					    normalizer_state.get("""lowercase"""       ,				UpperCAmelCase__ ) != do_lower_case
					    or normalizer_state.get("""strip_accents"""       ,				UpperCAmelCase__ ) != strip_accents
					    or normalizer_state.get("""handle_chinese_chars"""       ,				UpperCAmelCase__ ) != tokenize_chinese_chars
					):
								__UpperCAmelCase      :					Optional[int]   =   getattr(UpperCAmelCase__       ,				normalizer_state.pop("""type""" ) )
								__UpperCAmelCase      :					Tuple   =   do_lower_case
								__UpperCAmelCase      :					int   =   strip_accents
								__UpperCAmelCase      :					Optional[Any]   =   tokenize_chinese_chars
								__UpperCAmelCase      :					Union[str, Any]   =   normalizer_class(**UpperCAmelCase__ )

					__UpperCAmelCase      :					Optional[int]   =   do_lower_case





		def 							__A				(   self       ,				__UpperCAmelCase       ,				**__UpperCAmelCase )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   PaddingStrategy.MAX_LENGTH

					__UpperCAmelCase      :					Tuple   =   text
					__UpperCAmelCase      :					Tuple   =   kwargs.pop("""text_pair"""       ,				UpperCAmelCase__ )
					__UpperCAmelCase      :					str   =   kwargs.pop("""return_tensors"""       ,				UpperCAmelCase__ )

					__UpperCAmelCase      :					Any   =   {
					    """input_ids""": [],
					    """attention_mask""": [],
					    """token_type_ids""": [],
					}

					for idx, candidate_text in enumerate(UpperCAmelCase__ ):
								if batch_text_pair is not None:
											__UpperCAmelCase      :					Tuple   =   batch_text_pair[idx]
								else:
											__UpperCAmelCase      :					int   =   None

								__UpperCAmelCase      :					Dict   =   super().__call__(UpperCAmelCase__       ,				UpperCAmelCase__       ,				return_tensors=UpperCAmelCase__       ,				**UpperCAmelCase__ )

								__UpperCAmelCase      :					Optional[Any]   =   encoded_candidates.get("""input_ids""" )
								__UpperCAmelCase      :					Tuple   =   encoded_candidates.get("""attention_mask""" )
								__UpperCAmelCase      :					Optional[Any]   =   encoded_candidates.get("""token_type_ids""" )

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

					__UpperCAmelCase      :					str   =   {key: item for key, item in output_data.items() if len(UpperCAmelCase__ ) != 0}

					return BatchEncoding(UpperCAmelCase__       ,				tensor_type=UpperCAmelCase__ )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase=None )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   [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 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = None )				->   List[int]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   [self.sep_token_id]
					__UpperCAmelCase      :					List[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 ) * [0] + len(token_ids_a + sep ) * [1]





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = None )				->   Tuple[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   self._tokenizer.model.save(UpperCAmelCase__       ,				name=UpperCAmelCase__ )
					return tuple(UpperCAmelCase__ )










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    BeautifulSoup(requests.get(lowercase_ ,				params=lowercase_						).content ,				'''html.parser'''						)
       A__       =    soup.find('''div''' ,				attrs={'''class''': '''gs_ri'''}						)
       A__       =    div.find('''div''' ,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
       return anchors[2].get_text()


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """title""": (
          """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
          """capacitance, volumetric capacitance, and energy density"""
      ),
      """journal""": """Chem. Mater.""",
      """volume""": 30,
      """pages""": """3979-3990""",
      """year""": 2018,
      """hl""": """en""",
  }
  print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))


