





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST





class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :Tuple   ,							_lowercase  :Dict   ,							_lowercase  :Optional[int]=13   ,							_lowercase  :Union[str, Any]=7   ,							_lowercase  :List[Any]=True   ,							_lowercase  :str=True   ,							_lowercase  :Any=True   ,							_lowercase  :Dict=True   ,							_lowercase  :List[str]=99   ,							_lowercase  :str=32   ,							_lowercase  :Optional[int]=5   ,							_lowercase  :List[str]=4   ,							_lowercase  :str=37   ,							_lowercase  :Tuple="gelu"   ,							_lowercase  :List[Any]=0.1   ,							_lowercase  :Union[str, Any]=0.1   ,							_lowercase  :Optional[Any]=5_12   ,							_lowercase  :str=16   ,							_lowercase  :Union[str, Any]=2   ,							_lowercase  :Union[str, Any]=0.02   ,							_lowercase  :Tuple=False   ,							_lowercase  :Optional[Any]=True   ,							_lowercase  :str="None"   ,							_lowercase  :Dict=3   ,							_lowercase  :str=4   ,							_lowercase  :List[Any]=None   ,							):






     '''simple docstring'''





     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  seq_length
     lowercase__			       =  is_training
     lowercase__			       =  use_input_mask
     lowercase__			       =  use_token_type_ids
     lowercase__			       =  use_labels
     lowercase__			       =  vocab_size
     lowercase__			       =  hidden_size
     lowercase__			       =  num_hidden_layers
     lowercase__			       =  num_attention_heads
     lowercase__			       =  intermediate_size
     lowercase__			       =  hidden_act
     lowercase__			       =  hidden_dropout_prob
     lowercase__			       =  attention_probs_dropout_prob
     lowercase__			       =  max_position_embeddings
     lowercase__			       =  type_vocab_size
     lowercase__			       =  type_sequence_label_size
     lowercase__			       =  initializer_range
     lowercase__			       =  num_labels
     lowercase__			       =  num_choices
     lowercase__			       =  relative_attention
     lowercase__			       =  position_biased_input
     lowercase__			       =  pos_att_type
     lowercase__			       =  scope







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size )

     lowercase__			       =  None
     if self.use_input_mask:
        lowercase__			       =  ids_tensor([self.batch_size, self.seq_length]   ,							vocab_size=2 )

     lowercase__			       =  None
     if self.use_token_type_ids:
        lowercase__			       =  ids_tensor([self.batch_size, self.seq_length]   ,							self.type_vocab_size )

     lowercase__			       =  None
     lowercase__			       =  None
     lowercase__			       =  None
     if self.use_labels:
        lowercase__			       =  ids_tensor([self.batch_size]   ,							self.type_sequence_label_size )
        lowercase__			       =  ids_tensor([self.batch_size, self.seq_length]   ,							self.num_labels )
        lowercase__			       =  ids_tensor([self.batch_size]   ,							self.num_choices )

     lowercase__			       =  self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     return DebertaConfig(
         vocab_size=self.vocab_size   ,							hidden_size=self.hidden_size   ,							num_hidden_layers=self.num_hidden_layers   ,							num_attention_heads=self.num_attention_heads   ,							intermediate_size=self.intermediate_size   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							max_position_embeddings=self.max_position_embeddings   ,							type_vocab_size=self.type_vocab_size   ,							initializer_range=self.initializer_range   ,							relative_attention=self.relative_attention   ,							position_biased_input=self.position_biased_input   ,							pos_att_type=self.pos_att_type   ,							)







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.get_config()
     lowercase__			       =  3_00
     return config







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :str ):






     '''simple docstring'''





     self.parent.assertListEqual(list(result.loss.size() )   ,							[] )







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :List[str]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Dict   ,							_lowercase  :List[Any]   ,							_lowercase  :List[str]   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  DebertaModel(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase )[0]
     lowercase__			       =  model(_lowercase   ,							token_type_ids=_lowercase )[0]
     lowercase__			       =  model(_lowercase )[0]

     self.parent.assertListEqual(list(sequence_output.size() )   ,							[self.batch_size, self.seq_length, self.hidden_size] )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Any   ,							_lowercase  :int   ,							_lowercase  :Tuple   ,							_lowercase  :Dict   ,							_lowercase  :int   ,							_lowercase  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  DebertaForMaskedLM(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase   ,							labels=_lowercase )

     self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.vocab_size) )







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :str   ,							_lowercase  :Optional[int]   ,							_lowercase  :Optional[int]   ,							_lowercase  :List[Any]   ,							_lowercase  :Any   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.num_labels
     lowercase__			       =  DebertaForSequenceClassification(_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase   ,							labels=_lowercase )
     self.parent.assertListEqual(list(result.logits.size() )   ,							[self.batch_size, self.num_labels] )
     self.check_loss_output(_lowercase )







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :Optional[Any]   ,							_lowercase  :str   ,							_lowercase  :Dict   ,							_lowercase  :List[str]   ,							_lowercase  :List[str]   ,							_lowercase  :Any   ,							_lowercase  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.num_labels
     lowercase__			       =  DebertaForTokenClassification(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(_lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase   ,							labels=_lowercase )
     self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.seq_length, self.num_labels) )







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :List[Any]   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[str]   ,							_lowercase  :Tuple   ,							_lowercase  :Any   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  DebertaForQuestionAnswering(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     lowercase__			       =  model(
         _lowercase   ,							attention_mask=_lowercase   ,							token_type_ids=_lowercase   ,							start_positions=_lowercase   ,							end_positions=_lowercase   ,							)
     self.parent.assertEqual(result.start_logits.shape   ,							(self.batch_size, self.seq_length) )
     self.parent.assertEqual(result.end_logits.shape   ,							(self.batch_size, self.seq_length) )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.prepare_config_and_inputs()
     (
         (
         lowercase__
     )       ,							(
         lowercase__
     )       ,							(
         lowercase__
     )       ,							(
         lowercase__
     )       ,							(
         lowercase__
     )       ,							(
         lowercase__
     )       ,							(
         lowercase__
     )       ,							
     )			       =  config_and_inputs
     lowercase__			       =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict





@require_torch
class  lowerCAmelCase							(       lowercase_							,							lowercase_							,							unittest.TestCase ):
  __lowerCamelCase		=      (
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  __lowerCamelCase		=      (
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  __lowerCamelCase		=      True
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  DebertaModelTester(self )
     lowercase__			       =  ConfigTester(self   ,							config_class=_lowercase   ,							hidden_size=37 )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     self.config_tester.run_common_tests()







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*_lowercase )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowercase )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*_lowercase )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*_lowercase )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*_lowercase )







  @slow
  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase__			       =  DebertaModel.from_pretrained(_lowercase )
        self.assertIsNotNone(_lowercase )





@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase							(       unittest.TestCase ):







  @unittest.skip(reason="Model not available yet" )
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     pass







  @slow
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  DebertaModel.from_pretrained("microsoft/deberta-base" )

     lowercase__			       =  torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
     lowercase__			       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
     with torch.no_grad():
        lowercase__			       =  model(_lowercase   ,							attention_mask=_lowercase )[0]
     # compare the actual values for a slice.
     lowercase__			       =  torch.tensor(
         [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,							_lowercase   ,							atol=1e-4 )   ,							f'''{output[:, 1:4, 1:4]}''' )













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()))






