



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,      )					->		str:
          UpperCamelCase   :  List[str]						     =     parent
          UpperCamelCase   :  str						     =     batch_size
          UpperCamelCase   :  List[str]						     =     seq_length
          UpperCamelCase   :  str						     =     is_training
          UpperCamelCase   :  Optional[Any]						     =     use_input_mask
          UpperCamelCase   :  str						     =     use_token_type_ids
          UpperCamelCase   :  Union[str, Any]						     =     use_labels
          UpperCamelCase   :  Union[str, Any]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  Any						     =     num_hidden_layers
          UpperCamelCase   :  List[Any]						     =     num_attention_heads
          UpperCamelCase   :  List[Any]						     =     intermediate_size
          UpperCamelCase   :  List[Any]						     =     hidden_act
          UpperCamelCase   :  int						     =     hidden_dropout_prob
          UpperCamelCase   :  Optional[Any]						     =     attention_probs_dropout_prob
          UpperCamelCase   :  Dict						     =     max_position_embeddings
          UpperCamelCase   :  Tuple						     =     type_vocab_size
          UpperCamelCase   :  Union[str, Any]						     =     type_sequence_label_size
          UpperCamelCase   :  Union[str, Any]						     =     initializer_range
          UpperCamelCase   :  List[Any]						     =     num_labels
          UpperCamelCase   :  List[Any]						     =     scope
          UpperCamelCase   :  Dict						     =     range_bbox
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

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

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

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

          UpperCamelCase   :  int						     =     None
          UpperCamelCase   :  str						     =     None
          if self.use_labels:
             UpperCamelCase   :  Dict						     =     ids_tensor([self.batch_size],      self.type_sequence_label_size	)
             UpperCamelCase   :  Optional[int]						     =     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	)					->		Any:
          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_,      )					->		List[str]:
          UpperCamelCase   :  Any						     =     LiltModel(config=SCREAMING_SNAKE_CASE_	)
          model.to(SCREAMING_SNAKE_CASE_	)
          model.eval()
          UpperCamelCase   :  int						     =     model(SCREAMING_SNAKE_CASE_,      bbox=SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_,      token_type_ids=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     model(SCREAMING_SNAKE_CASE_,      bbox=SCREAMING_SNAKE_CASE_,      token_type_ids=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     model(SCREAMING_SNAKE_CASE_,      bbox=SCREAMING_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  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   :  Optional[int]						     =     self.num_labels
          UpperCamelCase   :  List[str]						     =     LiltForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
          model.to(SCREAMING_SNAKE_CASE_	)
          model.eval()
          UpperCamelCase   :  Dict						     =     model(
              SCREAMING_SNAKE_CASE_,      bbox=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  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      )					->		int:
          UpperCamelCase   :  Dict						     =     LiltForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
          model.to(SCREAMING_SNAKE_CASE_	)
          model.eval()
          UpperCamelCase   :  Union[str, Any]						     =     model(
              SCREAMING_SNAKE_CASE_,      bbox=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  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Dict						     =     self.prepare_config_and_inputs()
          (
              UpperCamelCase					
          )   :  Optional[Any]						     =     config_and_inputs
          UpperCamelCase   :  Tuple						     =     {
              'input_ids': input_ids,
              'bbox': bbox,
              'token_type_ids': token_type_ids,
              'attention_mask': input_mask,
          }
          return config, inputs_dict






@require_torch
class     lowerCAmelCase_		(  a__	,   a__	,   a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  (
           (
               LiltModel,
               LiltForSequenceClassification,
               LiltForTokenClassification,
               LiltForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__    : Optional[Any]       					=  (
           {
               "feature-extraction": LiltModel,
               "question-answering": LiltForQuestionAnswering,
               "text-classification": LiltForSequenceClassification,
               "token-classification": LiltForTokenClassification,
               "zero-shot": LiltForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCAmelCase__    : Any       					=  False
       UpperCAmelCase__    : Tuple       					=  False
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          return True
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     LiltModelTester(self	)
          UpperCamelCase   :  Optional[int]						     =     ConfigTester(self,      config_class=SCREAMING_SNAKE_CASE_,      hidden_size=37	)
       def  snake_case_	(      self	)					->		Tuple:
          self.config_tester.run_common_tests()
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  List[Any]						     =     self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCamelCase   :  str						     =     type
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Dict						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)






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






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

          UpperCamelCase   :  Optional[int]						     =     torch.tensor([[1, 2]],      device=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]],      device=SCREAMING_SNAKE_CASE_	)

          # forward pass
          with torch.no_grad():
             UpperCamelCase   :  Union[str, Any]						     =     model(input_ids=SCREAMING_SNAKE_CASE_,      bbox=SCREAMING_SNAKE_CASE_	)

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

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








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



