


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=2_4		,     UpperCAmelCase=2		,     UpperCAmelCase=6		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     UpperCAmelCase=1_0_0_0		,     )     ->   List[str]:
      _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							      =scope
      _lowercase							      =range_bbox





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =ids_tensor([self.batch_size, self.seq_length, 4]		,     self.range_bbox       )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]       ):
       for j in range(bbox.shape[1]       ):
        if bbox[i, j, 3] < bbox[i, j, 1]:
         _lowercase							      =bbox[i, j, 3]
         _lowercase							      =bbox[i, j, 1]
         _lowercase							      =t
        if bbox[i, j, 2] < bbox[i, j, 0]:
         _lowercase							      =bbox[i, j, 2]
         _lowercase							      =bbox[i, j, 0]
         _lowercase							      =t

      _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
      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							      =self.get_config()

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





     def      __A					(self       )     ->   str:
      return LiltConfig(
          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		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   str:
      _lowercase							      =LiltModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     bbox=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase		,     bbox=UpperCAmelCase		,     token_type_ids=UpperCAmelCase       )
      _lowercase							      =model(UpperCAmelCase		,     bbox=UpperCAmelCase       )

      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )
      self.parent.assertEqual(result.pooler_output.shape		,     (self.batch_size, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Tuple:
      _lowercase							      =self.num_labels
      _lowercase							      =LiltForTokenClassification(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Tuple:
      _lowercase							      =LiltForQuestionAnswering(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     start_positions=UpperCAmelCase		,     end_positions=UpperCAmelCase		,     )
      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      __A					(self       )     ->   int:
      _lowercase							      =self.prepare_config_and_inputs()
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =config_and_inputs
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''bbox''': bbox,
          '''token_type_ids''': token_type_ids,
          '''attention_mask''': input_mask,
      }
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''feature-extraction''': LiltModel,
             '''question-answering''': LiltForQuestionAnswering,
             '''text-classification''': LiltForSequenceClassification,
             '''token-classification''': LiltForTokenClassification,
             '''zero-shot''': LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      return True





     def      __A					(self       )     ->   int:
      _lowercase							      =LiltModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Union[str, Any]:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Any:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       _lowercase							      =type
       self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   List[Any]:
      for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =LiltModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )







@require_torch
@slow
class       lowerCamelCase__   (       unittest.TestCase):





     def      __A					(self       )     ->   str:
      _lowercase							      =LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(UpperCAmelCase       )

      _lowercase							      =torch.tensor([[1, 2]]		,     device=UpperCAmelCase       )
      _lowercase							      =torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,     device=UpperCAmelCase       )

      # forward pass
      with torch.no_grad():
       _lowercase							      =model(input_ids=UpperCAmelCase		,     bbox=UpperCAmelCase       )

      _lowercase							      =torch.Size([1, 2, 7_6_8]       )
      _lowercase							      =torch.tensor(
          [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]		,     device=UpperCAmelCase		,     )

      self.assertTrue(outputs.last_hidden_state.shape		,     UpperCAmelCase       )
      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]		,     UpperCAmelCase		,     atol=1e-3       )       )





import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

