


'''simple docstring'''



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.layoutlm.modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMModel,
      )



class 			a       :


     """simple docstring"""






     def __init__(       self  :	List[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[str]=13							,				snake_case  :	str=7							,				snake_case  :	int=True							,				snake_case  :	int=True							,				snake_case  :	Any=True							,				snake_case  :	int=True							,				snake_case  :	List[str]=99							,				snake_case  :	str=32							,				snake_case  :	List[str]=2							,				snake_case  :	Dict=4							,				snake_case  :	str=37							,				snake_case  :	List[str]="gelu"							,				snake_case  :	List[Any]=0.1							,				snake_case  :	Tuple=0.1							,				snake_case  :	List[Any]=512							,				snake_case  :	int=16							,				snake_case  :	List[str]=2							,				snake_case  :	List[Any]=0.02							,				snake_case  :	Optional[Any]=3							,				snake_case  :	Optional[int]=4							,				snake_case  :	Optional[int]=None							,				snake_case  :	Optional[Any]=1000							,				)		->							str:
          __UpperCAmelCase			:				int													=    parent
          __UpperCAmelCase			:				Dict													=    batch_size
          __UpperCAmelCase			:				str													=    seq_length
          __UpperCAmelCase			:				int													=    is_training
          __UpperCAmelCase			:				Tuple													=    use_input_mask
          __UpperCAmelCase			:				Optional[Any]													=    use_token_type_ids
          __UpperCAmelCase			:				List[Any]													=    use_labels
          __UpperCAmelCase			:				Tuple													=    vocab_size
          __UpperCAmelCase			:				Dict													=    hidden_size
          __UpperCAmelCase			:				List[Any]													=    num_hidden_layers
          __UpperCAmelCase			:				Dict													=    num_attention_heads
          __UpperCAmelCase			:				List[str]													=    intermediate_size
          __UpperCAmelCase			:				Optional[int]													=    hidden_act
          __UpperCAmelCase			:				int													=    hidden_dropout_prob
          __UpperCAmelCase			:				List[Any]													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				Union[str, Any]													=    max_position_embeddings
          __UpperCAmelCase			:				Tuple													=    type_vocab_size
          __UpperCAmelCase			:				Union[str, Any]													=    type_sequence_label_size
          __UpperCAmelCase			:				Optional[Any]													=    initializer_range
          __UpperCAmelCase			:				Optional[int]													=    num_labels
          __UpperCAmelCase			:				Dict													=    num_choices
          __UpperCAmelCase			:				List[str]													=    scope
          __UpperCAmelCase			:				Optional[Any]													=    range_bbox

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          __UpperCAmelCase			:				Union[str, Any]													=    ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size    )

          # convert bbox to numpy since TF does not support item assignment
          __UpperCAmelCase			:				Optional[int]													=    ids_tensor([self.batch_size, self.seq_length, 4]							,				self.range_bbox    ).numpy()
          # 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			:				Dict													=    bbox[i, j, 3]
                         __UpperCAmelCase			:				Tuple													=    bbox[i, j, 1]
                         __UpperCAmelCase			:				List[str]													=    t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         __UpperCAmelCase			:				List[str]													=    bbox[i, j, 2]
                         __UpperCAmelCase			:				Tuple													=    bbox[i, j, 0]
                         __UpperCAmelCase			:				Optional[Any]													=    t
          __UpperCAmelCase			:				Tuple													=    tf.convert_to_tensor(snake_case    )

          __UpperCAmelCase			:				List[str]													=    None
          if self.use_input_mask:
               __UpperCAmelCase			:				Optional[int]													=    random_attention_mask([self.batch_size, self.seq_length]    )

          __UpperCAmelCase			:				Optional[int]													=    None
          if self.use_token_type_ids:
               __UpperCAmelCase			:				List[Any]													=    ids_tensor([self.batch_size, self.seq_length]							,				self.type_vocab_size    )

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

          __UpperCAmelCase			:				Optional[Any]													=    LayoutLMConfig(
              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							,				)

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

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	List[str]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]							,				snake_case  :	List[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Dict							,				snake_case  :	Optional[Any]							,				snake_case  :	Any    )		->							List[str]:
          __UpperCAmelCase			:				Optional[Any]													=    TFLayoutLMModel(config=snake_case    )

          __UpperCAmelCase			:				Tuple													=    model(snake_case							,				snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				Any													=    model(snake_case							,				snake_case							,				token_type_ids=snake_case    )
          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				snake_case    )
          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 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	int							,				snake_case  :	Dict							,				snake_case  :	str							,				snake_case  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Dict							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]    )		->							List[str]:
          __UpperCAmelCase			:				Tuple													=    TFLayoutLMForMaskedLM(config=snake_case    )

          __UpperCAmelCase			:				Dict													=    model(snake_case							,				snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	Tuple							,				snake_case  :	Dict							,				snake_case  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int    )		->							int:
          __UpperCAmelCase			:				int													=    self.num_labels
          __UpperCAmelCase			:				Any													=    TFLayoutLMForSequenceClassification(config=snake_case    )

          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	int							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Dict							,				snake_case  :	Optional[Any]							,				snake_case  :	List[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	List[Any]    )		->							int:
          __UpperCAmelCase			:				Dict													=    self.num_labels
          __UpperCAmelCase			:				List[Any]													=    TFLayoutLMForTokenClassification(config=snake_case    )

          __UpperCAmelCase			:				Tuple													=    model(snake_case							,				snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )
          self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)    )

     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	int							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]							,				snake_case  :	Dict							,				snake_case  :	Optional[Any]							,				snake_case  :	str							,				snake_case  :	Tuple							,				snake_case  :	Dict    )		->							str:
          __UpperCAmelCase			:				Any													=    TFLayoutLMForQuestionAnswering(config=snake_case    )

          __UpperCAmelCase			:				List[str]													=    model(snake_case							,				snake_case							,				attention_mask=snake_case							,				token_type_ids=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  :	List[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Tuple													=    self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     (
              __UpperCAmelCase
          )							,     
          )			:				Union[str, Any]													=    config_and_inputs
          __UpperCAmelCase			:				str													=    {
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict



@require_tf
class 			a       (	_a			,						_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     (
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     (
         {
             "feature-extraction": TFLayoutLMModel,
             "fill-mask": TFLayoutLMForMaskedLM,
             "text-classification": TFLayoutLMForSequenceClassification,
             "token-classification": TFLayoutLMForTokenClassification,
             "zero-shot": TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE :				Dict							     =     False
     SCREAMING_SNAKE_CASE :				str							     =     True
     SCREAMING_SNAKE_CASE :				Tuple							     =     1_0

     def 				lowerCamelCase__				(       self  :	str    )		->							Optional[Any]:
          __UpperCAmelCase			:				List[str]													=    TFLayoutLMModelTester(self    )
          __UpperCAmelCase			:				Optional[int]													=    ConfigTester(self							,				config_class=snake_case							,				hidden_size=37    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
          self.config_tester.run_common_tests()

     def 				lowerCamelCase__				(       self  :	int    )		->							int:
          __UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							str:
          __UpperCAmelCase			:				Tuple													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Tuple													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							str:
          __UpperCAmelCase			:				List[str]													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							int:
          __UpperCAmelCase			:				Any													=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Dict:
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				Optional[Any]													=    TFLayoutLMModel.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )






     @unittest.skip('''Onnx compliancy broke with TF 2.10'''    )
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Union[str, Any]:
          pass




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				List[Any]													=    tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]						)  # noqa: E231
     __UpperCAmelCase			:				Any													=    tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
     __UpperCAmelCase			:				List[str]													=    tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						)  # noqa: E231
     __UpperCAmelCase			:				List[Any]													=    tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     __UpperCAmelCase			:				Any													=    tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @slow
     def 				lowerCamelCase__				(       self  :	int    )		->							Any:
          __UpperCAmelCase			:				Optional[Any]													=    TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase			:				Optional[Any]													=    model(input_ids=snake_case							,				bbox=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )

          # test the sequence output on [0, :3, :3]
          __UpperCAmelCase			:				Optional[int]													=    tf.convert_to_tensor(
              [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]							,				)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]							,				snake_case							,				atol=1E-3    )    )

          # test the pooled output on [1, :3]
          __UpperCAmelCase			:				Optional[int]													=    tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]    )

          self.assertTrue(np.allclose(outputs.pooler_output[1, :3]							,				snake_case							,				atol=1E-3    )    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							List[Any]:
          # initialize model with randomly initialized sequence classification head
          __UpperCAmelCase			:				Dict													=    TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,				num_labels=2    )

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				List[Any]													=    prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase			:				Dict													=    model(
              input_ids=snake_case							,				bbox=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=tf.convert_to_tensor([1, 1]    )							,				)

          # test whether we get a loss as a scalar
          __UpperCAmelCase			:				Optional[Any]													=    outputs.loss
          __UpperCAmelCase			:				Optional[int]													=    (2,)
          self.assertEqual(loss.shape							,				snake_case    )

          # test the shape of the logits
          __UpperCAmelCase			:				Dict													=    outputs.logits
          __UpperCAmelCase			:				str													=    (2, 2)
          self.assertEqual(logits.shape							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							List[Any]:
          # initialize model with randomly initialized token classification head
          __UpperCAmelCase			:				Union[str, Any]													=    TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,				num_labels=13    )

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase			:				Optional[Any]													=    model(
              input_ids=snake_case							,				bbox=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case							,				labels=snake_case    )

          # test the shape of the logits
          __UpperCAmelCase			:				List[Any]													=    outputs.logits
          __UpperCAmelCase			:				Dict													=    tf.convert_to_tensor((2, 25, 13)    )
          self.assertEqual(logits.shape							,				snake_case    )






     @slow
     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          # initialize model with randomly initialized token classification head
          __UpperCAmelCase			:				List[str]													=    TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

          __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase			:				Optional[Any]													=    model(input_ids=snake_case							,				bbox=snake_case							,				attention_mask=snake_case							,				token_type_ids=snake_case    )

          # test the shape of the logits
          __UpperCAmelCase			:				int													=    tf.convert_to_tensor((2, 25)    )
          self.assertEqual(outputs.start_logits.shape							,				snake_case    )
          self.assertEqual(outputs.end_logits.shape							,				snake_case    )



'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	int    )		->							List[Any]:
          if dst_width < 0 or dst_height < 0:
               raise ValueError('''Destination width/height should be > 0'''    )

          __UpperCAmelCase			:				str													=    img
          __UpperCAmelCase			:				List[Any]													=    img.shape[1]
          __UpperCAmelCase			:				Optional[Any]													=    img.shape[0]
          __UpperCAmelCase			:				Dict													=    dst_width
          __UpperCAmelCase			:				List[str]													=    dst_height

          __UpperCAmelCase			:				Union[str, Any]													=    self.src_w / self.dst_w
          __UpperCAmelCase			:				List[str]													=    self.src_h / self.dst_h

          __UpperCAmelCase			:				Optional[int]													=    (
              np.ones((self.dst_h, self.dst_w, 3)							,				np.uinta    ) * 255
          )

     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          for i in range(self.dst_h    ):
               for j in range(self.dst_w    ):
                    __UpperCAmelCase			:				Any													=    self.img[self.get_y(snake_case    )][self.get_x(snake_case    )]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	int    )		->							int:
          return int(self.ratio_x * x    )






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	int    )		->							int:
          return int(self.ratio_y * y    )


if __name__ == "__main__":
      __UpperCAmelCase       ,					__UpperCAmelCase    :int				       =     8_0_0, 6_0_0
      __UpperCAmelCase    :Dict				       =     imread("image_data/lena.jpg", 1)
      __UpperCAmelCase    :int				       =     NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()