



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class 			_snake_case     (   nn.Module):
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   float       						=0.0
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   bool       						=True
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   jnp.dtype       						=jnp.floataa




				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			[]
							lowercase__   					=			[]

							for i in range(self.num_layers  ):
										lowercase__   					=			self.in_channels if i == 0 else self.out_channels

										lowercase__   					=			FlaxResnetBlockaD(
										    in_channels=__lowercase,   out_channels=self.out_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
										resnets.append(__lowercase  )

										lowercase__   					=			FlaxTransformeraDModel(
										    in_channels=self.out_channels,   n_heads=self.num_attention_heads,   d_head=self.out_channels // self.num_attention_heads,   depth=1,   use_linear_projection=self.use_linear_projection,   only_cross_attention=self.only_cross_attention,   use_memory_efficient_attention=self.use_memory_efficient_attention,   dtype=self.dtype,   )
										attentions.append(__lowercase  )

							lowercase__   					=			resnets
							lowercase__   					=			attentions

							if self.add_downsample:
										lowercase__   					=			FlaxDownsampleaD(self.out_channels,   dtype=self.dtype  )




				def __call__(  self   :  List[Any],   __lowercase   :  Tuple,   __lowercase   :  Union[str, Any],   __lowercase   :  Any,   __lowercase   :  Tuple=True  ):
							lowercase__   					=			()

							for resnet, attn in zip(self.resnets,   self.attentions  ):
										lowercase__   					=			resnet(__lowercase,   __lowercase,   deterministic=__lowercase  )
										lowercase__   					=			attn(__lowercase,   __lowercase,   deterministic=__lowercase  )
										output_states += (hidden_states,)

							if self.add_downsample:
										lowercase__   					=			self.downsamplers_a(__lowercase  )
										output_states += (hidden_states,)

							return hidden_states, output_states
class 			_snake_case     (   nn.Module):
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   float       						=0.0
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   bool       						=True
				UpperCamelCase__			:   jnp.dtype       						=jnp.floataa




				def 							A__ (  self   :  int  ):
							lowercase__   					=			[]

							for i in range(self.num_layers  ):
										lowercase__   					=			self.in_channels if i == 0 else self.out_channels

										lowercase__   					=			FlaxResnetBlockaD(
										    in_channels=__lowercase,   out_channels=self.out_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
										resnets.append(__lowercase  )
							lowercase__   					=			resnets

							if self.add_downsample:
										lowercase__   					=			FlaxDownsampleaD(self.out_channels,   dtype=self.dtype  )




				def __call__(  self   :  Optional[int],   __lowercase   :  Any,   __lowercase   :  int,   __lowercase   :  int=True  ):
							lowercase__   					=			()

							for resnet in self.resnets:
										lowercase__   					=			resnet(__lowercase,   __lowercase,   deterministic=__lowercase  )
										output_states += (hidden_states,)

							if self.add_downsample:
										lowercase__   					=			self.downsamplers_a(__lowercase  )
										output_states += (hidden_states,)

							return hidden_states, output_states
class 			_snake_case     (   nn.Module):
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   float       						=0.0
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   bool       						=True
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   jnp.dtype       						=jnp.floataa




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			[]
							lowercase__   					=			[]

							for i in range(self.num_layers  ):
										lowercase__   					=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
										lowercase__   					=			self.prev_output_channel if i == 0 else self.out_channels

										lowercase__   					=			FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels,   out_channels=self.out_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
										resnets.append(__lowercase  )

										lowercase__   					=			FlaxTransformeraDModel(
										    in_channels=self.out_channels,   n_heads=self.num_attention_heads,   d_head=self.out_channels // self.num_attention_heads,   depth=1,   use_linear_projection=self.use_linear_projection,   only_cross_attention=self.only_cross_attention,   use_memory_efficient_attention=self.use_memory_efficient_attention,   dtype=self.dtype,   )
										attentions.append(__lowercase  )

							lowercase__   					=			resnets
							lowercase__   					=			attentions

							if self.add_upsample:
										lowercase__   					=			FlaxUpsampleaD(self.out_channels,   dtype=self.dtype  )




				def __call__(  self   :  List[str],   __lowercase   :  str,   __lowercase   :  Optional[Any],   __lowercase   :  List[Any],   __lowercase   :  List[str],   __lowercase   :  Any=True  ):
							for resnet, attn in zip(self.resnets,   self.attentions  ):
										# pop res hidden states
										lowercase__   					=			res_hidden_states_tuple[-1]
										lowercase__   					=			res_hidden_states_tuple[:-1]
										lowercase__   					=			jnp.concatenate((hidden_states, res_hidden_states),   axis=-1  )

										lowercase__   					=			resnet(__lowercase,   __lowercase,   deterministic=__lowercase  )
										lowercase__   					=			attn(__lowercase,   __lowercase,   deterministic=__lowercase  )

							if self.add_upsample:
										lowercase__   					=			self.upsamplers_a(__lowercase  )

							return hidden_states
class 			_snake_case     (   nn.Module):
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   int
				UpperCamelCase__			:   float       						=0.0
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   bool       						=True
				UpperCamelCase__			:   jnp.dtype       						=jnp.floataa




				def 							A__ (  self   :  str  ):
							lowercase__   					=			[]

							for i in range(self.num_layers  ):
										lowercase__   					=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
										lowercase__   					=			self.prev_output_channel if i == 0 else self.out_channels

										lowercase__   					=			FlaxResnetBlockaD(
										    in_channels=resnet_in_channels + res_skip_channels,   out_channels=self.out_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
										resnets.append(__lowercase  )

							lowercase__   					=			resnets

							if self.add_upsample:
										lowercase__   					=			FlaxUpsampleaD(self.out_channels,   dtype=self.dtype  )




				def __call__(  self   :  Tuple,   __lowercase   :  Optional[Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[int],   __lowercase   :  Any=True  ):
							for resnet in self.resnets:
										# pop res hidden states
										lowercase__   					=			res_hidden_states_tuple[-1]
										lowercase__   					=			res_hidden_states_tuple[:-1]
										lowercase__   					=			jnp.concatenate((hidden_states, res_hidden_states),   axis=-1  )

										lowercase__   					=			resnet(__lowercase,   __lowercase,   deterministic=__lowercase  )

							if self.add_upsample:
										lowercase__   					=			self.upsamplers_a(__lowercase  )

							return hidden_states
class 			_snake_case     (   nn.Module):
				UpperCamelCase__			:   int
				UpperCamelCase__			:   float       						=0.0
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   int       						=1
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   bool       						=False
				UpperCamelCase__			:   jnp.dtype       						=jnp.floataa




				def 							A__ (  self   :  List[Any]  ):
							# there is always at least one resnet
							lowercase__   					=			[
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels,   out_channels=self.in_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
							]

							lowercase__   					=			[]

							for _ in range(self.num_layers  ):
										lowercase__   					=			FlaxTransformeraDModel(
										    in_channels=self.in_channels,   n_heads=self.num_attention_heads,   d_head=self.in_channels // self.num_attention_heads,   depth=1,   use_linear_projection=self.use_linear_projection,   use_memory_efficient_attention=self.use_memory_efficient_attention,   dtype=self.dtype,   )
										attentions.append(__lowercase  )

										lowercase__   					=			FlaxResnetBlockaD(
										    in_channels=self.in_channels,   out_channels=self.in_channels,   dropout_prob=self.dropout,   dtype=self.dtype,   )
										resnets.append(__lowercase  )

							lowercase__   					=			resnets
							lowercase__   					=			attentions




				def __call__(  self   :  Union[str, Any],   __lowercase   :  Dict,   __lowercase   :  List[str],   __lowercase   :  Optional[Any],   __lowercase   :  Union[str, Any]=True  ):
							lowercase__   					=			self.resnets[0](__lowercase,   __lowercase  )
							for attn, resnet in zip(self.attentions,   self.resnets[1:]  ):
										lowercase__   					=			attn(__lowercase,   __lowercase,   deterministic=__lowercase  )
										lowercase__   					=			resnet(__lowercase,   __lowercase,   deterministic=__lowercase  )

							return hidden_states









from collections import namedtuple

import requests
from lxml import html  # type: ignore

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



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


lowercase_						=       """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()))




