

import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


A__						=							logging.get_logger(__name__)

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

A__						=							{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

A__						=							{
    "Salesforce/codegen-350M-mono": 2048,
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[str]       				=  VOCAB_FILES_NAMES
	_lowercase  :   Union[str, Any]       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   Tuple       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase  :   Optional[Any]       				=  ["input_ids", "attention_mask"]
	_lowercase  :   Optional[int]       				=  CodeGenTokenizer







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

						'''simple docstring'''





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

						if kwargs.pop('add_bos_token'  , __UpperCamelCase      ):
											__magic_name__							=    kwargs.pop('name_or_path'  , ''      )
											raise ValueError(
											    'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
											    'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
											    F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
											    F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
											    'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
											    ' so that the fast tokenizer works correctly.'      )

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

						__magic_name__							=    add_prefix_space







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , *__UpperCamelCase: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__							=    kwargs.get('is_split_into_words'  , __UpperCamelCase      )
						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(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , *__UpperCamelCase: Optional[Any]  , **__UpperCamelCase: Any      ):

						'''simple docstring'''





						__magic_name__							=    kwargs.get('is_split_into_words'  , __UpperCamelCase      )

						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(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[str] = None      ):

						'''simple docstring'''





						__magic_name__							=    self._tokenizer.model.save(__UpperCamelCase  , name=__UpperCamelCase      )
						return tuple(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[List[str]] = None  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





						__magic_name__							=    super().decode(
						    token_ids=__UpperCamelCase  , skip_special_tokens=__UpperCamelCase  , clean_up_tokenization_spaces=__UpperCamelCase  , **__UpperCamelCase  , )

						if truncate_before_pattern is not None and len(__UpperCamelCase      ) > 0:
											__magic_name__							=    self.truncate(__UpperCamelCase  , __UpperCamelCase      )

						return decoded_text







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						def find_re(__UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Any  , __UpperCamelCase: int      ):
											__magic_name__							=    pattern.search(__UpperCamelCase  , __UpperCamelCase      )
											return m.start() if m else -1

						__magic_name__							=    [re.compile(__UpperCamelCase  , re.MULTILINE      ) for pattern in truncate_before_pattern]

						__magic_name__							=    list(re.finditer('^print'  , __UpperCamelCase  , re.MULTILINE      )      )

						if len(__UpperCamelCase      ) > 1:
											__magic_name__							=    completion[: prints[1].start()]

						__magic_name__							=    list(re.finditer('^def'  , __UpperCamelCase  , re.MULTILINE      )      )

						if len(__UpperCamelCase      ) > 1:
											__magic_name__							=    completion[: defs[1].start()]

						__magic_name__							=    0

						__magic_name__							=    [
						    pos for pos in [find_re(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      ) for terminal in terminals] if pos != -1
						]

						if len(__UpperCamelCase      ) > 0:
											return completion[: min(__UpperCamelCase      )]
						else:
											return completion







import requests
from bsa import BeautifulSoup




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




					'''simple docstring'''







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


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''')




