


import unittest

from transformers import DebertaVaConfig, 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 (
       DebertaVaForMaskedLM,
       DebertaVaForMultipleChoice,
       DebertaVaForQuestionAnswering,
       DebertaVaForSequenceClassification,
       DebertaVaForTokenClassification,
       DebertaVaModel,
   )
   from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST


class      __lowerCAmelCase  (			_UpperCAmelCase       ):






  """simple docstring"""





  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any=1_3     ,					_lowerCAmelCase							:							Dict=7     ,					_lowerCAmelCase							:							int=True     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							str=True     ,					_lowerCAmelCase							:							int=True     ,					_lowerCAmelCase							:							Optional[int]=9_9     ,					_lowerCAmelCase							:							Dict=3_2     ,					_lowerCAmelCase							:							Optional[Any]=5     ,					_lowerCAmelCase							:							List[str]=4     ,					_lowerCAmelCase							:							List[str]=3_7     ,					_lowerCAmelCase							:							Dict="gelu"     ,					_lowerCAmelCase							:							int=0.1     ,					_lowerCAmelCase							:							Optional[Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							List[Any]=1_6     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							str=0.02     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					_lowerCAmelCase							:							str="None"     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							Tuple=4     ,					_lowerCAmelCase							:							Optional[Any]=None     ,					) ->			Optional[int]:




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



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size	)

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

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

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

     snake_case_            =       self.get_config()

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



  def        lowerCAmelCase__	(  self							:							Any	) ->			Optional[int]:




     """simple docstring"""
     return DebertaVaConfig(
         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        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							Optional[int]	) ->			Any:




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



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Dict	) ->			Any:




     """simple docstring"""
     snake_case_            =       DebertaVaModel(config=SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_	)[0]
     snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_	)[0]
     snake_case_            =       model(SCREAMING_SNAKE_CASE_	)[0]

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



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       DebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_     ,					labels=SCREAMING_SNAKE_CASE_	)

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



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.num_labels
     snake_case_            =       DebertaVaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_     ,					labels=SCREAMING_SNAKE_CASE_	)
     self.parent.assertListEqual(list(result.logits.size()	)     ,					[self.batch_size, self.num_labels]	)
     self.check_loss_output(SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.num_labels
     snake_case_            =       DebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_     ,					labels=SCREAMING_SNAKE_CASE_	)
     self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.seq_length, self.num_labels)	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Optional[int]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       DebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       model(
         SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_     ,					start_positions=SCREAMING_SNAKE_CASE_     ,					end_positions=SCREAMING_SNAKE_CASE_     ,					)
     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        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       DebertaVaForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
     model.to(SCREAMING_SNAKE_CASE_	)
     model.eval()
     snake_case_            =       input_ids.unsqueeze(1	).expand(-1     ,					self.num_choices     ,					-1	).contiguous()
     snake_case_            =       token_type_ids.unsqueeze(1	).expand(-1     ,					self.num_choices     ,					-1	).contiguous()
     snake_case_            =       input_mask.unsqueeze(1	).expand(-1     ,					self.num_choices     ,					-1	).contiguous()
     snake_case_            =       model(
         SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_     ,					token_type_ids=SCREAMING_SNAKE_CASE_     ,					labels=SCREAMING_SNAKE_CASE_     ,					)
     self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_choices)	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.prepare_config_and_inputs()
     (
         snake_case_			
     )            =       config_and_inputs
     snake_case_            =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict






@require_torch
class      __lowerCAmelCase  (			_UpperCAmelCase			,	_UpperCAmelCase			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE		     =							(
      {
          'feature-extraction': DebertaVaModel,
          'fill-mask': DebertaVaForMaskedLM,
          'question-answering': DebertaVaForQuestionAnswering,
          'text-classification': DebertaVaForSequenceClassification,
          'token-classification': DebertaVaForTokenClassification,
          'zero-shot': DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE		     =							True
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False
  _SCREAMING_SNAKE_CASE		     =							False



  def        lowerCAmelCase__	(  self							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       DebertaVaModelTester(self	)
     snake_case_            =       ConfigTester(self     ,					config_class=SCREAMING_SNAKE_CASE_     ,					hidden_size=3_7	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Optional[Any]:




     """simple docstring"""
     self.config_tester.run_common_tests()



  def        lowerCAmelCase__	(  self							:							Any	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)



  @slow
  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Dict:




     """simple docstring"""
     for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case_            =       DebertaVaModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






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






  """simple docstring"""





  @unittest.skip(reason="Model not available yet"	)
  def        lowerCAmelCase__	(  self							:							Dict	) ->			int:




     """simple docstring"""
     pass



  @slow
  def        lowerCAmelCase__	(  self							:							int	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"	)

     snake_case_            =       torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
     snake_case_            =       torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
     with torch.no_grad():
        snake_case_            =       model(SCREAMING_SNAKE_CASE_     ,					attention_mask=SCREAMING_SNAKE_CASE_	)[0]
     # compare the actual values for a slice.
     snake_case_            =       torch.tensor(
         [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]	)
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,					SCREAMING_SNAKE_CASE_     ,					atol=1e-4	)     ,					F'''{output[:, 1:4, 1:4]}'''	)








'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "https://www.worldometers.info/coronavirus/"				)	->						covid_data:
     __lowerCamelCase  :      Union[str, Any]														=						'//div[@class = "maincounter-number"]/span/text()'
     return covid_data(*html.fromstring(requests.get(UpperCAmelCase_				).content				).xpath(UpperCAmelCase_				)				)


A__						:   str				=    """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()))






