
"""simple docstring"""




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 						a__		:
   def __init__(     self      ,       _a      ,       _a=13      ,       _a=7      ,       _a=True      ,       _a=True      ,       _a=True      ,       _a=True      ,       _a=99      ,       _a=24      ,       _a=2      ,       _a=6      ,       _a=37      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=512      ,       _a=16      ,       _a=2      ,       _a=0.0_2      ,       _a=3      ,       _a=None      ,       _a=1_000      ,       ):
          lowercase       :							str									=				parent
          lowercase       :							List[str]									=				batch_size
          lowercase       :							List[str]									=				seq_length
          lowercase       :							Dict									=				is_training
          lowercase       :							Tuple									=				use_input_mask
          lowercase       :							str									=				use_token_type_ids
          lowercase       :							Optional[Any]									=				use_labels
          lowercase       :							Tuple									=				vocab_size
          lowercase       :							Optional[Any]									=				hidden_size
          lowercase       :							Optional[int]									=				num_hidden_layers
          lowercase       :							str									=				num_attention_heads
          lowercase       :							Tuple									=				intermediate_size
          lowercase       :							List[Any]									=				hidden_act
          lowercase       :							Any									=				hidden_dropout_prob
          lowercase       :							Any									=				attention_probs_dropout_prob
          lowercase       :							List[str]									=				max_position_embeddings
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							Dict									=				type_sequence_label_size
          lowercase       :							Union[str, Any]									=				initializer_range
          lowercase       :							int									=				num_labels
          lowercase       :							List[Any]									=				scope
          lowercase       :							Dict									=				range_bbox
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

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

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

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

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

          lowercase       :							Union[str, Any]									=				self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
   def      __magic_name__     (     self							):
          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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							int									=				LiltModel(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							int									=				model(_a      ,       bbox=_a      ,       attention_mask=_a      ,       token_type_ids=_a							)
          lowercase       :							Any									=				model(_a      ,       bbox=_a      ,       token_type_ids=_a							)
          lowercase       :							Optional[int]									=				model(_a      ,       bbox=_a							)

          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      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							Union[str, Any]									=				self.num_labels
          lowercase       :							Tuple									=				LiltForTokenClassification(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							List[Any]									=				model(
              _a      ,       bbox=_a      ,       attention_mask=_a      ,       token_type_ids=_a      ,       labels=_a							)
          self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       _a      ,       ):
          lowercase       :							Optional[int]									=				LiltForQuestionAnswering(config=_a							)
          model.to(_a							)
          model.eval()
          lowercase       :							int									=				model(
              _a      ,       bbox=_a      ,       attention_mask=_a      ,       token_type_ids=_a      ,       start_positions=_a      ,       end_positions=_a      ,       )
          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      __magic_name__     (     self							):
          lowercase       :							List[str]									=				self.prepare_config_and_inputs()
          (
              (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       (
              lowercase
          )						,       
          )       :							List[str]									=				config_and_inputs
          lowercase       :							List[Any]									=				{
              "input_ids": input_ids,
              "bbox": bbox,
              "token_type_ids": token_type_ids,
              "attention_mask": input_mask,
          }
          return config, inputs_dict


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

   @slow
   def      __magic_name__     (     self							):
          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase       :							Optional[int]									=				LiltModel.from_pretrained(_a							)
                 self.assertIsNotNone(_a							)


@require_torch
@slow
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							str									=				LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"							).to(_a							)

          lowercase       :							Any									=				torch.tensor([[1, 2]]      ,       device=_a							)
          lowercase       :							str									=				torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]      ,       device=_a							)

          # forward pass
          with torch.no_grad():
                 lowercase       :							Optional[int]									=				model(input_ids=_a      ,       bbox=_a							)

          lowercase       :							List[str]									=				torch.Size([1, 2, 768]							)
          lowercase       :							int									=				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=_a      ,       )

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




"""simple docstring"""




def     __magic_name__      ( __snake_case							:			Any	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Union[str, Any]=False    )					->							Optional[Any]:

       if isinstance(__snake_case	,					__snake_case    ) and isinstance(__snake_case	,					__snake_case    ):
              lowercase       :							Tuple									=				len(set_a.intersection(__snake_case    )    )

              if alternative_union:
                     lowercase       :							Union[str, Any]									=				len(__snake_case    ) + len(__snake_case    )
              else:
                     lowercase       :							Optional[Any]									=				len(set_a.union(__snake_case    )    )

              return intersection / union

       if isinstance(__snake_case	,					(list, tuple)    ) and isinstance(__snake_case	,					(list, tuple)    ):
              lowercase       :							Tuple									=				[element for element in set_a if element in set_b]

              if alternative_union:
                     lowercase       :							List[str]									=				len(__snake_case    ) + len(__snake_case    )
                     return len(__snake_case    ) / union
              else:
                     lowercase       :							Dict									=				set_a + [element for element in set_b if element not in set_a]
                     return len(__snake_case    ) / len(__snake_case    )

              return len(__snake_case    ) / len(__snake_case    )
       return None


if __name__ == "__main__":
   _A     :			List[str]  						=			{"""a""", """b""", """c""", """d""", """e"""}
   _A     :			Dict  						=			{"""c""", """d""", """e""", """f""", """h""", """i"""}
   print(jaccard_similarity(set_a, set_b))


