
"""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


_a      :			List[str]					=							logging.get_logger(__name__)

_a      :			str					=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_a      :			Optional[int]					=							{
    '''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'''
        ),
    },
}

_a      :			Any					=							{
    '''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,
}

_a      :			Optional[int]					=							{
    '''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      _UpperCAmelCase				(					_UpperCAmelCase):
    __lowercase							: Any  =					VOCAB_FILES_NAMES
    __lowercase							: List[str]  =					PRETRAINED_VOCAB_FILES_MAP
    __lowercase							: Optional[Any]  =					PRETRAINED_INIT_CONFIGURATION
    __lowercase							: Union[str, Any]  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase							: List[Any]  =					RealmTokenizer
    def __init__(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True						,    snake_case_="[UNK]"						,    snake_case_="[SEP]"						,    snake_case_="[PAD]"						,    snake_case_="[CLS]"						,    snake_case_="[MASK]"						,    snake_case_=True						,    snake_case_=None						,    **snake_case_						,    ):
      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						,    )

      _snake_case     :						str						=   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
      ):
        _snake_case     :						Tuple						=   getattr(__UpperCamelCase						,    normalizer_state.pop("type"     )     )
        _snake_case     :						Optional[int]						=   do_lower_case
        _snake_case     :						Optional[Any]						=   strip_accents
        _snake_case     :						Optional[int]						=   tokenize_chinese_chars
        _snake_case     :						List[str]						=   normalizer_class(**__UpperCamelCase     )

      _snake_case     :						Optional[Any]						=   do_lower_case
    def 					lowerCamelCase__	(    self						,    snake_case_						,    **snake_case_     ):
      _snake_case     :						Optional[Any]						=   PaddingStrategy.MAX_LENGTH

      _snake_case     :						Optional[Any]						=   text
      _snake_case     :						Dict						=   kwargs.pop("text_pair"						,    __UpperCamelCase     )
      _snake_case     :						int						=   kwargs.pop("return_tensors"						,    __UpperCamelCase     )

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

      for idx, candidate_text in enumerate(__UpperCamelCase     ):
        if batch_text_pair is not None:
          _snake_case     :						Dict						=   batch_text_pair[idx]
        else:
          _snake_case     :						int						=   None

        _snake_case     :						Tuple						=   super().__call__(__UpperCamelCase						,    __UpperCamelCase						,    return_tensors=__UpperCamelCase						,    **__UpperCamelCase     )

        _snake_case     :						Optional[int]						=   encoded_candidates.get("input_ids"     )
        _snake_case     :						List[Any]						=   encoded_candidates.get("attention_mask"     )
        _snake_case     :						int						=   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     )

      _snake_case     :						Union[str, Any]						=   {key: item for key, item in output_data.items() if len(__UpperCamelCase     ) != 0}

      return BatchEncoding(__UpperCamelCase						,    tensor_type=__UpperCamelCase     )
    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=None     ):
      _snake_case     :						List[Any]						=   [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 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Any						=   [self.sep_token_id]
      _snake_case     :						Optional[int]						=   [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 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[Any]						=   self._tokenizer.model.save(__UpperCamelCase						,    name=__UpperCamelCase     )
      return tuple(__UpperCamelCase     )








"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
    _a      :			Dict					=							input("""Enter image url: """).strip()
    print(f'Downloading image from {url} ...')
    _a      :			str					=							BeautifulSoup(requests.get(url).content, """html.parser""")
    # The image URL is in the content field of the first meta tag with property og:image
    _a      :			str					=							soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
    _a      :			Dict					=							requests.get(image_url).content
    _a      :			str					=							f'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
    with open(file_name, """wb""") as fp:
        fp.write(image_data)
    print(f'Done. Image saved to disk as {file_name}.')

