"""simple docstring"""
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			Union[str, Any]:





      """simple docstring"""






      snake_case        =     r'\w+[.]\d+'
      snake_case        =     re.findall(_UpperCamelCase	,     _UpperCamelCase   )
      for pat in pats:
            snake_case        =     key.replace(_UpperCamelCase	,     '_'.join(pat.split('.'   )   )   )
      return key





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			str:





      """simple docstring"""






      snake_case        =     pt_tuple_key[:-1] + ('scale',)
      if (
          any('norm' in str_ for str_ in pt_tuple_key   )
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            snake_case        =     pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            snake_case        =     pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            snake_case        =     pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      snake_case        =     pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            snake_case        =     pt_tensor.transpose(2	,     3	,     1	,     0   )
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      snake_case        =     pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight":
            snake_case        =     pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      snake_case        =     pt_tuple_key[:-1] + ('weight',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      snake_case        =     pt_tuple_key[:-1] + ('bias',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Dict=4_2   )   ->			Dict:





      """simple docstring"""






      snake_case        =     {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      snake_case        =     flax_model.init_weights(PRNGKey(_UpperCamelCase   )   )

      snake_case        =     flatten_dict(_UpperCamelCase   )
      snake_case        =     {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            snake_case        =     rename_key(_UpperCamelCase   )
            snake_case        =     tuple(renamed_pt_key.split('.'   )   )

            # Correctly rename weight parameters
            snake_case   ,snake_case        =     rename_key_and_reshape_tensor(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # also add unexpected weight so that warning is thrown
            snake_case        =     jnp.asarray(_UpperCamelCase   )

      return unflatten_dict(_UpperCamelCase   )



"""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""")


