



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,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=99,     SCREAMING_SNAKE_CASE_=24,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=6,     SCREAMING_SNAKE_CASE_=37,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=512,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=0.02,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=1000,     )	->						List[str]:
          UpperCamelCase       :    Optional[int]		      =      parent
          UpperCamelCase       :    Union[str, Any]		      =      batch_size
          UpperCamelCase       :    Union[str, Any]		      =      seq_length
          UpperCamelCase       :    Union[str, Any]		      =      is_training
          UpperCamelCase       :    Optional[Any]		      =      use_input_mask
          UpperCamelCase       :    Optional[int]		      =      use_token_type_ids
          UpperCamelCase       :    Tuple		      =      use_labels
          UpperCamelCase       :    Any		      =      vocab_size
          UpperCamelCase       :    Tuple		      =      hidden_size
          UpperCamelCase       :    Any		      =      num_hidden_layers
          UpperCamelCase       :    str		      =      num_attention_heads
          UpperCamelCase       :    List[str]		      =      intermediate_size
          UpperCamelCase       :    str		      =      hidden_act
          UpperCamelCase       :    Optional[Any]		      =      hidden_dropout_prob
          UpperCamelCase       :    List[Any]		      =      attention_probs_dropout_prob
          UpperCamelCase       :    List[Any]		      =      max_position_embeddings
          UpperCamelCase       :    List[str]		      =      type_vocab_size
          UpperCamelCase       :    Optional[int]		      =      type_sequence_label_size
          UpperCamelCase       :    Tuple		      =      initializer_range
          UpperCamelCase       :    Optional[int]		      =      num_labels
          UpperCamelCase       :    Tuple		      =      scope
          UpperCamelCase       :    Any		      =      range_bbox
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    int		      =      ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)

          UpperCamelCase       :    Dict		      =      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]:
                   UpperCamelCase       :    Union[str, Any]		      =      bbox[i, j, 3]
                   UpperCamelCase       :    Optional[int]		      =      bbox[i, j, 1]
                   UpperCamelCase       :    Union[str, Any]		      =      t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                   UpperCamelCase       :    str		      =      bbox[i, j, 2]
                   UpperCamelCase       :    List[Any]		      =      bbox[i, j, 0]
                   UpperCamelCase       :    int		      =      t

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

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

          UpperCamelCase       :    List[Any]		      =      None
          UpperCamelCase       :    int		      =      None
          if self.use_labels:
             UpperCamelCase       :    Optional[int]		      =      ids_tensor([self.batch_size],     self.type_sequence_label_size			)
             UpperCamelCase       :    Any		      =      ids_tensor([self.batch_size, self.seq_length],     self.num_labels			)

          UpperCamelCase       :    Tuple		      =      self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
       def    snake_case_			(     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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						Optional[int]:
          UpperCamelCase       :    Optional[Any]		      =      LiltModel(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Tuple		      =      model(__lowerCamelCase,     bbox=__lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase			)
          UpperCamelCase       :    str		      =      model(__lowerCamelCase,     bbox=__lowerCamelCase,     token_type_ids=__lowerCamelCase			)
          UpperCamelCase       :    Any		      =      model(__lowerCamelCase,     bbox=__lowerCamelCase			)

          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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						Dict:
          UpperCamelCase       :    str		      =      self.num_labels
          UpperCamelCase       :    List[Any]		      =      LiltForTokenClassification(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    Tuple		      =      model(
              __lowerCamelCase,     bbox=__lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     labels=__lowerCamelCase			)
          self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels)			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     )	->						str:
          UpperCamelCase       :    Optional[Any]		      =      LiltForQuestionAnswering(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase       :    List[Any]		      =      model(
              __lowerCamelCase,     bbox=__lowerCamelCase,     attention_mask=__lowerCamelCase,     token_type_ids=__lowerCamelCase,     start_positions=__lowerCamelCase,     end_positions=__lowerCamelCase,     )
          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    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    List[str]		      =      self.prepare_config_and_inputs()
          (
              UpperCamelCase							
          )       :    Any		      =      config_and_inputs
          UpperCamelCase       :    Union[str, Any]		      =      {
              '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				):
       UpperCAmelCase__							:   Optional[int]  	=    (
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__							:   Optional[int]  	=    (
           {
               "feature-extraction": LiltModel,
               "question-answering": LiltForQuestionAnswering,
               "text-classification": LiltForSequenceClassification,
               "token-classification": LiltForTokenClassification,
               "zero-shot": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCAmelCase__							:   Any  	=    False
       UpperCAmelCase__							:   List[str]  	=    False
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int:
          return True
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    List[Any]		      =      LiltModelTester(self			)
          UpperCamelCase       :    Tuple		      =      ConfigTester(self,     config_class=__lowerCamelCase,     hidden_size=37			)
       def    snake_case_			(     self			)	->						Optional[int]:
          self.config_tester.run_common_tests()
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCamelCase       :    Tuple		      =      type
             self.model_tester.create_and_check_model(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase       :    Optional[int]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Union[str, Any]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)






       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    str		      =      LiltModel.from_pretrained(__lowerCamelCase			)
             self.assertIsNotNone(__lowerCamelCase			)






@require_torch
@slow
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Union[str, Any]		      =      LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'			).to(__lowerCamelCase			)

          UpperCamelCase       :    List[str]		      =      torch.tensor([[1, 2]],     device=__lowerCamelCase			)
          UpperCamelCase       :    Dict		      =      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]],     device=__lowerCamelCase			)

          # forward pass
          with torch.no_grad():
             UpperCamelCase       :    Any		      =      model(input_ids=__lowerCamelCase,     bbox=__lowerCamelCase			)

          UpperCamelCase       :    int		      =      torch.Size([1, 2, 768]			)
          UpperCamelCase       :    Dict		      =      torch.tensor(
              [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]],     device=__lowerCamelCase,     )

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









'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    div.find('div'				,      attrs={'class': 'gs_fl'}		).find_all('a'		)
      return anchors[2].get_text()


if __name__ == "__main__":
      A_			      =		{
          "title": (
              "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
              "capacitance, volumetric capacitance, and energy density"
          ),
          "journal": "Chem. Mater.",
          "volume": 30,
          "pages": "3979-3990",
          "year": 2018,
          "hl": "en",
      }
      print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
