





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


_snake_case           =			logging.get_logger(__name__)


def _A       (   __magic_name__	):
   lowercase__			       =  R"\w+[.]\d+"
   lowercase__			       =  re.findall(__magic_name__  , __magic_name__	)
   for pat in pats:
      lowercase__			       =  key.replace(__magic_name__  , "_".join(pat.split("."	)	)	)
   return key


def _A       (   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  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)
   ):
      lowercase__			       =  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:
      lowercase__			       =  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:
      lowercase__			       =  pt_tuple_key[:-1] + ("embedding",)
      return renamed_pt_tuple_key, pt_tensor

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

   # linear layer
   lowercase__			       =  pt_tuple_key[:-1] + ("kernel",)
   if pt_tuple_key[-1] == "weight":
      lowercase__			       =  pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

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

   # old PyTorch layer norm bias
   lowercase__			       =  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 _A       (   __magic_name__  , __magic_name__  , __magic_name__=42	):
   # Step 1: Convert pytorch tensor to numpy
   lowercase__			       =  {k: v.numpy() for k, v in pt_state_dict.items()}

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

   lowercase__			       =  flatten_dict(__magic_name__	)
   lowercase__			       =  {}

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

      # Correctly rename weight parameters
      lowercase__       ,							lowercase__			       =  rename_key_and_reshape_tensor(__magic_name__  , __magic_name__  , __magic_name__	)

      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
      lowercase__			       =  jnp.asarray(__magic_name__	)

   return unflatten_dict(__magic_name__	)













from collections import namedtuple

import requests
from lxml import html  # type: ignore

_snake_case           =			namedtuple("""covid_data""", """cases deaths recovered""")


def _A       (   __magic_name__ = "https://www.worldometers.info/coronavirus/"	):
   lowercase__			       =  "//div[@class = \"maincounter-number\"]/span/text()"
   return covid_data(*html.fromstring(requests.get(__magic_name__	).content	).xpath(__magic_name__	)	)


_snake_case           =			"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""







print(fmt.format(*covid_stats()))






