




import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def __init__(    self   :			List[str]      ,				*UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			int=None      ,				**UpperCAmelCase__   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           super().__init__(*UpperCAmelCase__      ,				**UpperCAmelCase__)
           A__   				=					eval_examples
           A__   				=					post_process_function


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[Dataset] = None      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Optional[List[str]] = None      ,				UpperCAmelCase__   :			str = "eval"      ,				**UpperCAmelCase__   :			Union[str, Any]      ,				)		->Dict[str, float]:





           '''simple docstring'''

           A__   				=					gen_kwargs.copy()
           A__   				=					(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           A__   				=					(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           A__   				=					gen_kwargs

           A__   				=					self.eval_dataset if eval_dataset is None else eval_dataset
           A__   				=					self.get_eval_dataloader(UpperCAmelCase__)
           A__   				=					self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           A__   				=					self.compute_metrics
           A__   				=					None
           A__   				=					time.time()
           A__   				=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                  A__   				=					eval_loop(
                      UpperCAmelCase__      ,				description='''Evaluation'''      ,				prediction_loss_only=True if compute_metrics is None else None      ,				ignore_keys=UpperCAmelCase__      ,				metric_key_prefix=UpperCAmelCase__      ,				)
           finally:
                  A__   				=					compute_metrics
           A__   				=					self.args.eval_batch_size * self.args.world_size
           if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
                  start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
           output.metrics.update(
               speed_metrics(
                   UpperCAmelCase__      ,				UpperCAmelCase__      ,				num_samples=output.num_samples      ,				num_steps=math.ceil(output.num_samples / total_batch_size)      ,				))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                  # Only the main node write the results by default
                  A__   				=					self.post_process_function(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					self.compute_metrics(UpperCAmelCase__)

                  # Prefix all keys with metric_key_prefix + '_'
                  for key in list(metrics.keys()):
                         if not key.startswith(f"""{metric_key_prefix}_"""):
                                A__   				=					metrics.pop(UpperCAmelCase__)

                  metrics.update(output.metrics)
           else:
                  A__   				=					output.metrics

           if self.args.should_log:
                  # Only the main node log the results by default
                  self.log(UpperCAmelCase__)

           if self.args.tpu_metrics_debug or self.args.debug:
                  # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                  xm.master_print(met.metrics_report())

           A__   				=					self.callback_handler.on_evaluate(self.args      ,				self.state      ,				self.control      ,				UpperCAmelCase__)
           return metrics





    def  SCREAMING_SNAKE_CASE							(    self   :			str      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			str = "test"      ,				**UpperCAmelCase__   :			Optional[Any])		->Dict:





           '''simple docstring'''

           A__   				=					gen_kwargs.copy()

           A__   				=					self.get_test_dataloader(UpperCAmelCase__)

           # Temporarily disable metric computation, we will do it in the loop here.
           A__   				=					self.compute_metrics
           A__   				=					None
           A__   				=					time.time()
           A__   				=					self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
                  A__   				=					eval_loop(
                      UpperCAmelCase__      ,				description='''Prediction'''      ,				prediction_loss_only=True if compute_metrics is None else None      ,				ignore_keys=UpperCAmelCase__      ,				metric_key_prefix=UpperCAmelCase__      ,				)
           finally:
                  A__   				=					compute_metrics

           A__   				=					self.args.eval_batch_size * self.args.world_size
           if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
                  start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
           output.metrics.update(
               speed_metrics(
                   UpperCAmelCase__      ,				UpperCAmelCase__      ,				num_samples=output.num_samples      ,				num_steps=math.ceil(output.num_samples / total_batch_size)      ,				))
           if self.post_process_function is None or self.compute_metrics is None:
                  return output

           A__   				=					self.post_process_function(UpperCAmelCase__      ,				UpperCAmelCase__      ,				UpperCAmelCase__      ,				'''predict''')
           A__   				=					self.compute_metrics(UpperCAmelCase__)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
                  if not key.startswith(f"""{metric_key_prefix}_"""):
                         A__   				=					metrics.pop(UpperCAmelCase__)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions      ,				label_ids=predictions.label_ids      ,				metrics=UpperCAmelCase__)








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """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()))


