


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
  from ... import PreTrainedTokenizerBase, TensorType

UpperCAmelCase__   	=							logging.get_logger(__name__)







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''vision-encoder-decoder'''
     SCREAMING_SNAKE_CASE__      			=			True





     def __init__(self		,     **UpperCAmelCase       )     ->   str:
      super().__init__(**UpperCAmelCase       )
      if "encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError(
           f"A configuraton of type {self.model_type} cannot be instantiated because "
           f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"       )

      _lowercase							      =kwargs.pop('''encoder'''       )
      _lowercase							      =encoder_config.pop('''model_type'''       )
      _lowercase							      =kwargs.pop('''decoder'''       )
      _lowercase							      =decoder_config.pop('''model_type'''       )

      _lowercase							      =AutoConfig.for_model(UpperCAmelCase		,     **UpperCAmelCase       )
      _lowercase							      =AutoConfig.for_model(UpperCAmelCase		,     **UpperCAmelCase       )
      _lowercase							      =True





     @classmethod
     def      __A					(cls		,     UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   PretrainedConfig:
      logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''       )
      _lowercase							      =True
      _lowercase							      =True

      return cls(encoder=encoder_config.to_dict()		,     decoder=decoder_config.to_dict()		,     **UpperCAmelCase       )





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =copy.deepcopy(self.__dict__       )
      _lowercase							      =self.encoder.to_dict()
      _lowercase							      =self.decoder.to_dict()
      _lowercase							      =self.__class__.model_type
      return output







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			version.parse('''1.11''')





     @property
     def      __A					(self       )     ->   Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )





     @property
     def      __A					(self       )     ->   float:
      return 1e-4





     @property
     def      __A					(self       )     ->   Mapping[str, Mapping[int, str]]:
      return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}       )







class       lowerCamelCase__   (       lowerCAmelCase):





     @property
     def      __A					(self       )     ->   Mapping[str, Mapping[int, str]]:
      _lowercase							      =OrderedDict()
      _lowercase							      ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
      _lowercase							      ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
      _lowercase							      ={0: '''batch''', 1: '''encoder_sequence'''}

      return common_inputs





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = -1		,     UpperCAmelCase = -1		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     )     ->   Mapping[str, Any]:
      import torch

      _lowercase							      =OrderedDict()

      _lowercase							      =super().generate_dummy_inputs(
          UpperCAmelCase		,     batch_size=UpperCAmelCase		,     seq_length=UpperCAmelCase		,     is_pair=UpperCAmelCase		,     framework=UpperCAmelCase       )

      _lowercase      ,    _lowercase							      =dummy_input['''input_ids'''].shape
      _lowercase							      =(batch, encoder_sequence, self._config.encoder_hidden_size)
      _lowercase							      =dummy_input.pop('''input_ids'''       )
      _lowercase							      =dummy_input.pop('''attention_mask'''       )
      _lowercase							      =torch.zeros(UpperCAmelCase       )

      return common_inputs







class       lowerCamelCase__   (       lowerCAmelCase):





     @property
     def      __A					(self       )     ->   None:
      pass





     def      __A					(self		,     UpperCAmelCase       )     ->   OnnxConfig:
      return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = "default"       )     ->   OnnxConfig:
      _lowercase							      =encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase		,     UpperCAmelCase       )





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''')

