





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  :      int			,     UpperCAmelCase  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]=13			,     UpperCAmelCase  :      Optional[int]=7			,     UpperCAmelCase  :      int=True			,     UpperCAmelCase  :      Any=True			,     UpperCAmelCase  :      Union[str, Any]=True			,     UpperCAmelCase  :      List[Any]=True			,     UpperCAmelCase  :      int=99			,     UpperCAmelCase  :      Tuple=24			,     UpperCAmelCase  :      Any=2			,     UpperCAmelCase  :      Tuple=6			,     UpperCAmelCase  :      int=37			,     UpperCAmelCase  :      Optional[Any]="gelu"			,     UpperCAmelCase  :      Optional[Any]=0.1			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      str=512			,     UpperCAmelCase  :      List[str]=16			,     UpperCAmelCase  :      int=2			,     UpperCAmelCase  :      List[str]=0.0_2			,     UpperCAmelCase  :      int=3			,     UpperCAmelCase  :      Optional[Any]=None			,     UpperCAmelCase  :      str=1000			,     )			->	int:
     lowerCamelCase__    :      List[str]					 =		parent
     lowerCamelCase__    :      Union[str, Any]					 =		batch_size
     lowerCamelCase__    :      Tuple					 =		seq_length
     lowerCamelCase__    :      Any					 =		is_training
     lowerCamelCase__    :      Optional[int]					 =		use_input_mask
     lowerCamelCase__    :      Any					 =		use_token_type_ids
     lowerCamelCase__    :      int					 =		use_labels
     lowerCamelCase__    :      str					 =		vocab_size
     lowerCamelCase__    :      int					 =		hidden_size
     lowerCamelCase__    :      Dict					 =		num_hidden_layers
     lowerCamelCase__    :      List[Any]					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		intermediate_size
     lowerCamelCase__    :      int					 =		hidden_act
     lowerCamelCase__    :      str					 =		hidden_dropout_prob
     lowerCamelCase__    :      List[Any]					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Union[str, Any]					 =		max_position_embeddings
     lowerCamelCase__    :      Optional[Any]					 =		type_vocab_size
     lowerCamelCase__    :      int					 =		type_sequence_label_size
     lowerCamelCase__    :      str					 =		initializer_range
     lowerCamelCase__    :      List[Any]					 =		num_labels
     lowerCamelCase__    :      List[str]					 =		scope
     lowerCamelCase__    :      str					 =		range_bbox
 def      A_	(      self  :      Tuple	)			->	Tuple:
     lowerCamelCase__    :      List[Any]					 =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)

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

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

     lowerCamelCase__    :      List[Any]					 =		None
     if self.use_token_type_ids:
         lowerCamelCase__    :      Tuple					 =		ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size	)

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

     lowerCamelCase__    :      Tuple					 =		self.get_config()

     return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
 def      A_	(      self  :      List[str]	)			->	Tuple:
     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  :      Optional[Any]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Any			,     )			->	List[str]:
     lowerCamelCase__    :      str					 =		LiltModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      str					 =		model(UpperCAmelCase			,     bbox=UpperCAmelCase			,     attention_mask=UpperCAmelCase			,     token_type_ids=UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		model(UpperCAmelCase			,     bbox=UpperCAmelCase			,     token_type_ids=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		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  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Tuple			,     )			->	int:
     lowerCamelCase__    :      Tuple					 =		self.num_labels
     lowerCamelCase__    :      List[str]					 =		LiltForTokenClassification(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Dict					 =		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  :      List[str]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]			,     )			->	Any:
     lowerCamelCase__    :      Optional[int]					 =		LiltForQuestionAnswering(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Optional[int]					 =		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  :      Tuple	)			->	Tuple:
     lowerCamelCase__    :      Tuple					 =		self.prepare_config_and_inputs()
     (
         (
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							(
         lowerCamelCase__
     ) ,							
     )    :      Optional[int]					 =		config_and_inputs
     lowerCamelCase__    :      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							(					__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(
     (
         LiltModel,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__													=		(
     {
         """feature-extraction""": LiltModel,
         """question-answering""": LiltForQuestionAnswering,
         """text-classification""": LiltForSequenceClassification,
         """token-classification""": LiltForTokenClassification,
         """zero-shot""": LiltForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[str]	)			->	Tuple:
     return True
 def      A_	(      self  :      Union[str, Any]	)			->	int:
     lowerCamelCase__    :      int					 =		LiltModelTester(self	)
     lowerCamelCase__    :      Tuple					 =		ConfigTester(self			,     config_class=UpperCAmelCase			,     hidden_size=37	)
 def      A_	(      self  :      int	)			->	Tuple:
     self.config_tester.run_common_tests()
 def      A_	(      self  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[int]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Dict	)			->	List[Any]:
     lowerCamelCase__    :      int					 =		self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         lowerCamelCase__    :      Union[str, Any]					 =		type
         self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Optional[int]	)			->	Tuple:
     lowerCamelCase__    :      Optional[int]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase	)







 @slow
 def      A_	(      self  :      List[str]	)			->	Tuple:
     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      Optional[int]					 =		LiltModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)


@require_torch
@slow
class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      str	)			->	Dict:
     lowerCamelCase__    :      List[str]					 =		LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'	).to(UpperCAmelCase	)

     lowerCamelCase__    :      List[Any]					 =		torch.tensor([[1, 2]]			,     device=UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,     device=UpperCAmelCase	)

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

     lowerCamelCase__    :      List[str]					 =		torch.Size([1, 2, 768]	)
     lowerCamelCase__    :      Any					 =		torch.tensor(
         [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]			,     device=UpperCAmelCase			,     )

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






from __future__ import annotations





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]	):  # noqa: E741
 while r - l > 1:
  UpperCamelCase_					:						Union[str, Any] =					(l + r) // 2
  if v[m] >= key:
   UpperCamelCase_					:						str =					m
  else:
   UpperCamelCase_					:						List[Any] =					m  # noqa: E741
 return r





def    __lowercase     (						lowerCamelCase					:     list[int]	):
 if len(lowerCamelCase	) == 0:
  return 0

 UpperCamelCase_					:						Tuple =					[0] * len(lowerCamelCase	)
 UpperCamelCase_					:						int =					1

 UpperCamelCase_					:						Dict =					v[0]

 for i in range(1  ,  len(lowerCamelCase	)	):
  if v[i] < tail[0]:
   UpperCamelCase_					:						Any =					v[i]
  elif v[i] > tail[length - 1]:
   UpperCamelCase_					:						Dict =					v[i]
   length += 1
  else:
   UpperCamelCase_					:						List[str] =					v[i]

 return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()



