

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


__snake_case							:	Union[str, Any]											=       logging.get_logger(__name__)

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

__snake_case							:	List[str]											=       {
    """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"""
        ),
    },
}

__snake_case							:	Optional[Any]											=       {
    """google/realm-cc-news-pretrained-embedder""": 5_12,
    """google/realm-cc-news-pretrained-encoder""": 5_12,
    """google/realm-cc-news-pretrained-scorer""": 5_12,
    """google/realm-cc-news-pretrained-openqa""": 5_12,
    """google/realm-orqa-nq-openqa""": 5_12,
    """google/realm-orqa-nq-reader""": 5_12,
    """google/realm-orqa-wq-openqa""": 5_12,
    """google/realm-orqa-wq-reader""": 5_12,
}

__snake_case							:	Optional[Any]											=       {
    """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__(a_				):
						"""simple docstring"""






						_A  :							str     =       VOCAB_FILES_NAMES
						_A  :							str     =       PRETRAINED_VOCAB_FILES_MAP
						_A  :							List[Any]     =       PRETRAINED_INIT_CONFIGURATION
						_A  :							Tuple     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A  :							List[str]     =       RealmTokenizer





						def __init__(  self   ,	_lowercase=None   ,	_lowercase=None   ,	_lowercase=True   ,	_lowercase="[UNK]"   ,	_lowercase="[SEP]"   ,	_lowercase="[PAD]"   ,	_lowercase="[CLS]"   ,	_lowercase="[MASK]"   ,	_lowercase=True   ,	_lowercase=None   ,	**_lowercase   ,	)			->      Optional[Any]:
									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_		:		Tuple      				=      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_		:		Dict      				=      getattr(_lowercase   ,	normalizer_state.pop("""type"""  )  )
												a_		:		List[Any]      				=      do_lower_case
												a_		:		List[str]      				=      strip_accents
												a_		:		Optional[int]      				=      tokenize_chinese_chars
												a_		:		Optional[Any]      				=      normalizer_class(**_lowercase  )

									a_		:		str      				=      do_lower_case





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	**_lowercase  )			->      Any:
									a_		:		Dict      				=      PaddingStrategy.MAX_LENGTH

									a_		:		List[str]      				=      text
									a_		:		List[str]      				=      kwargs.pop("""text_pair"""   ,	_lowercase  )
									a_		:		Optional[Any]      				=      kwargs.pop("""return_tensors"""   ,	_lowercase  )

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

									for idx, candidate_text in enumerate(_lowercase  ):
												if batch_text_pair is not None:
															a_		:		Optional[int]      				=      batch_text_pair[idx]
												else:
															a_		:		Union[str, Any]      				=      None

												a_		:		Any      				=      super().__call__(_lowercase   ,	_lowercase   ,	return_tensors=_lowercase   ,	**_lowercase  )

												a_		:		List[str]      				=      encoded_candidates.get("""input_ids"""  )
												a_		:		str      				=      encoded_candidates.get("""attention_mask"""  )
												a_		:		Union[str, Any]      				=      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_		:		Optional[Any]      				=      {key: item for key, item in output_data.items() if len(_lowercase  ) != 0}

									return BatchEncoding(_lowercase   ,	tensor_type=_lowercase  )





						def        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase=None  )			->      Dict:
									a_		:		Tuple      				=      [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        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase = None  )			->      List[int]:
									a_		:		Dict      				=      [self.sep_token_id]
									a_		:		List[str]      				=      [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        UpperCamelCase__ (  self   ,	_lowercase   ,	_lowercase = None  )			->      Tuple[str]:
									a_		:		int      				=      self._tokenizer.model.save(_lowercase   ,	name=_lowercase  )
									return tuple(_lowercase  )






import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

