


"""simple docstring"""





import requests
from bsa import BeautifulSoup


def      _snake_case						(						_snake_case				:			str = "https://www.worldometers.info/coronavirus"			):
      lowerCAmelCase					:	str       =   BeautifulSoup(requests.get(_snake_case			).text ,   '''html.parser'''			)
      lowerCAmelCase					:	Any       =   soup.findAll('''h1'''			)
      lowerCAmelCase					:	Optional[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(_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""")






"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 		snake_case_(       a__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
         '''and returns a summary of the text.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


