




'''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        lowercase_      :





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





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   int	      =    ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size			)

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

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

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

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

           snake_case__       :   Tuple	      =    self.get_config()

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





     def 			_lowerCAmelCase      (     self     :					Union[str, 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 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					List[str]     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					List[str]     ,							):
           snake_case__       :   Optional[Any]	      =    LiltModel(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   Tuple	      =    model(__lowerCamelCase     ,							bbox=__lowerCamelCase     ,							attention_mask=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase			)
           snake_case__       :   str	      =    model(__lowerCamelCase     ,							bbox=__lowerCamelCase     ,							token_type_ids=__lowerCamelCase			)
           snake_case__       :   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 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					List[Any]     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Tuple     ,							):
           snake_case__       :   str	      =    self.num_labels
           snake_case__       :   List[Any]	      =    LiltForTokenClassification(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   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 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Union[str, Any]     ,							):
           snake_case__       :   Optional[Any]	      =    LiltForQuestionAnswering(config=__lowerCamelCase			)
           model.to(__lowerCamelCase			)
           model.eval()
           snake_case__       :   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 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   List[str]	      =    self.prepare_config_and_inputs()
           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   Any	      =    config_and_inputs
           snake_case__       :   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        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			(
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     A_											=			(
         {
             "feature-extraction": LiltModel,
             "question-answering": LiltForQuestionAnswering,
             "text-classification": LiltForSequenceClassification,
             "token-classification": LiltForTokenClassification,
             "zero-shot": LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A_											=			False
     A_											=			False





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					str			):
           return True





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   List[Any]	      =    LiltModelTester(self			)
           snake_case__       :   Tuple	      =    ConfigTester(self     ,							config_class=__lowerCamelCase     ,							hidden_size=37			)





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]			):
           self.config_tester.run_common_tests()





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   str	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Dict	      =    self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 snake_case__       :   Tuple	      =    type
                 self.model_tester.create_and_check_model(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Optional[int]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Union[str, Any]	      =    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase			)





     @slow
     def 			_lowerCAmelCase      (     self     :					int			):
           for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case__       :   str	      =    LiltModel.from_pretrained(__lowerCamelCase			)
                 self.assertIsNotNone(__lowerCamelCase			)






@require_torch
@slow
class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Any			):
           snake_case__       :   Union[str, Any]	      =    LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'			).to(__lowerCamelCase			)

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

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

           snake_case__       :   int	      =    torch.Size([1, 2, 768]			)
           snake_case__       :   Dict	      =    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=__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 functools






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Any	      =    len(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   List[str]	      =    len(__SCREAMING_SNAKE_CASE		)

      @functools.cache
      def min_distance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		) -> int:
            # if first word index is overflow - delete all from the second word
            if indexa >= len_worda:
                  return len_worda - indexa
            # if second word index is overflow - delete all from the first word
            if indexa >= len_worda:
                  return len_worda - indexa
            snake_case__       :   Union[str, Any]	      =    int(worda[indexa] != worda[indexa]		)  # current letters not identical
            return min(
                1 + min_distance(indexa + 1				,      __SCREAMING_SNAKE_CASE		)				,      1 + min_distance(__SCREAMING_SNAKE_CASE				,      indexa + 1		)				,      diff + min_distance(indexa + 1				,      indexa + 1		)				,      )

      return min_distance(0				,      0		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
