




"""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


_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)

_lowerCamelCase  :			List[Any]        =       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase  :			Union[str, Any]        =       {
    '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',
    },
}

_lowerCamelCase  :			List[str]        =       {
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   VOCAB_FILES_NAMES
				__lowerCAmelCase    :    List[str]         =   PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase    :    List[str]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase    :    Tuple         =   ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase    :    str         =   GPTaTokenizer







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					Optional[int]=None      ,     _lowerCamelCase   :					List[str]=None      ,     _lowerCamelCase   :					Optional[Any]="<|endoftext|>"      ,     _lowerCamelCase   :					Any="<|endoftext|>"      ,     _lowerCamelCase   :					List[str]="<|endoftext|>"      ,     _lowerCamelCase   :					List[str]=False      ,     **_lowerCamelCase   :					List[str]      ,     ):



					"""simple docstring"""

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

					A_  :				Union[str, Any]	   =		kwargs.pop('''add_bos_token'''      ,     _lowerCamelCase       )

					A_  :				List[str]	   =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
					if pre_tok_state.get('''add_prefix_space'''      ,     _lowerCamelCase       ) != add_prefix_space:
						A_  :				int	   =		getattr(_lowerCamelCase      ,     pre_tok_state.pop('''type'''       )       )
						A_  :				List[str]	   =		add_prefix_space
						A_  :				Union[str, Any]	   =		pre_tok_class(**_lowerCamelCase       )

					A_  :				Optional[Any]	   =		add_prefix_space







				def 					a_  (							self   :					str      ,     *_lowerCamelCase   :					Tuple      ,     **_lowerCamelCase   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		kwargs.get('''is_split_into_words'''      ,     _lowerCamelCase       )
					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(*_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					List[str]      ,     *_lowerCamelCase   :					int      ,     **_lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		kwargs.get('''is_split_into_words'''      ,     _lowerCamelCase       )

					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(*_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Optional[str] = None       ):



					"""simple docstring"""

					A_  :				Dict	   =		self._tokenizer.model.save(_lowerCamelCase      ,     name=_lowerCamelCase       )
					return tuple(_lowerCamelCase       )







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					"Conversation"       ):



					"""simple docstring"""

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

					if len(_lowerCamelCase       ) > self.model_max_length:
						A_  :				Optional[int]	   =		input_ids[-self.model_max_length :]
					return input_ids











"""simple docstring"""

import requests
from bsa import BeautifulSoup

def 		lowercase_      (		_UpperCAmelCase = "https://www.worldometers.info/coronavirus"	):




	"""simple docstring"""






	A_  :				str	   =		BeautifulSoup(requests.get(_UpperCAmelCase	).text  ,      '''html.parser'''	)
	A_  :				Union[str, Any]	   =		soup.findAll('''h1'''	)
	A_  :				Union[str, Any]	   =		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')





