"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =					{
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}



class 					lowerCAmelCase_      (     a__   ):



    """simple docstring"""
    _lowerCAmelCase       :      List[str]     					=  VOCAB_FILES_NAMES
    _lowerCAmelCase       :      Any     					=  PRETRAINED_VOCAB_FILES_MAP
    _lowerCAmelCase       :      List[Any]     					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCAmelCase       :      Tuple     					=  ["input_ids", "attention_mask"]
    _lowerCAmelCase       :      Dict     					=  GPTaTokenizer

    def __init__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase="<|endoftext|>"						,      lowerCAmelCase=False						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          super().__init__(
              SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      tokenizer_file=SCREAMING_SNAKE_CASE_						,      unk_token=SCREAMING_SNAKE_CASE_						,      bos_token=SCREAMING_SNAKE_CASE_						,      eos_token=SCREAMING_SNAKE_CASE_						,      add_prefix_space=SCREAMING_SNAKE_CASE_						,      **SCREAMING_SNAKE_CASE_						,      )

          snake_case        =     kwargs.pop('add_bos_token'						,      SCREAMING_SNAKE_CASE_      )

          snake_case        =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
          if pre_tok_state.get('add_prefix_space'						,      SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
                snake_case        =     getattr(SCREAMING_SNAKE_CASE_						,      pre_tok_state.pop('type'      )      )
                snake_case        =     add_prefix_space
                snake_case        =     pre_tok_class(**SCREAMING_SNAKE_CASE_      )

          snake_case        =     add_prefix_space

    def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     kwargs.get('is_split_into_words'						,      SCREAMING_SNAKE_CASE_      )
          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_						,      **SCREAMING_SNAKE_CASE_      )

    def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     kwargs.get('is_split_into_words'						,      SCREAMING_SNAKE_CASE_      )

          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*SCREAMING_SNAKE_CASE_						,      **SCREAMING_SNAKE_CASE_      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






          """simple docstring"""

          snake_case        =     self._tokenizer.model.save(SCREAMING_SNAKE_CASE_						,      name=SCREAMING_SNAKE_CASE_      )
          return tuple(SCREAMING_SNAKE_CASE_      )

    def 						snake_case	(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     []
          for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_						,      add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

          if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
                snake_case        =     input_ids[-self.model_max_length :]
          return input_ids



"""simple docstring"""
import requests
from bsa import BeautifulSoup





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "https://www.worldometers.info/coronavirus"   )   ->			dict:





						"""simple docstring"""






						snake_case        =     BeautifulSoup(requests.get(_UpperCamelCase   ).text	,     'html.parser'   )
						snake_case        =     soup.findAll('h1'   )
						snake_case        =     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(_UpperCamelCase	,     _UpperCamelCase   )}


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""")


