



'''simple docstring'''





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__a          =		TypeVar("KT")
__a          =		TypeVar("VT")







class     UpperCAmelCase_       (       Generic[KT, VT]	):
    """simple docstring"""







    def __init__(    self					:     Optional[Any] ,     snake_case_					:     KT | str = "root" ,     snake_case_					:     VT | None = None						):
          snake_case__  :    Union[str, Any]    						=      key
          snake_case__  :    str    						=      value
          snake_case__  :    list[Node[KT, VT]]    						=      []




    def __repr__(    self					:     Tuple						):
          return f"Node({self.key}: {self.value})"




    @property
    def lowerCamelCase       (    self					:     Optional[Any]						):
          return len(self.forward						)







class     UpperCAmelCase_       (       Generic[KT, VT]	):
    """simple docstring"""







    def __init__(    self					:     List[Any] ,     snake_case_					:     float = 0.5 ,     snake_case_					:     int = 16						):
          snake_case__  :    Node[KT, VT]    						=      Node[KT, VT]()
          snake_case__  :    Optional[Any]    						=      0
          snake_case__  :    Union[str, Any]    						=      p
          snake_case__  :    int    						=      max_level




    def __str__(    self					:     Optional[Any]						):
          snake_case__  :    str    						=      list(self						)

          if len(snake_case_						) == 0:
                return f"SkipList(level={self.level})"

          snake_case__  :    Optional[Any]    						=      max((len(str(snake_case_						)						) for item in items) ,     default=4						)
          snake_case__  :    Optional[Any]    						=      max(snake_case_ ,     4						) + 4

          snake_case__  :    Optional[Any]    						=      self.head
          snake_case__  :    Dict    						=      []

          snake_case__  :    Tuple    						=      node.forward.copy()
          lines.append(f"[{node.key}]".ljust(snake_case_ ,     """-"""						) + """* """ * len(snake_case_						)						)
          lines.append(""" """ * label_size + """| """ * len(snake_case_						)						)

          while len(node.forward						) != 0:
                snake_case__  :    Tuple    						=      node.forward[0]

                lines.append(
                    f"[{node.key}]".ljust(snake_case_ ,     """-"""						)
                    + """ """.join(str(n.key						) if n.key == node.key else """|""" for n in forwards						)						)
                lines.append(""" """ * label_size + """| """ * len(snake_case_						)						)
                snake_case__  :    List[str]    						=      node.forward

          lines.append("""None""".ljust(snake_case_						) + """* """ * len(snake_case_						)						)
          return f"SkipList(level={self.level})\n" + "\n".join(snake_case_						)




    def __iter__(    self					:     Tuple						):
          snake_case__  :    int    						=      self.head

          while len(node.forward						) != 0:
                yield node.forward[0].key
                snake_case__  :    Dict    						=      node.forward[0]




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Union[str, Any]    						=      1
          while random() < self.p and level < self.max_level:
                level += 1

          return level




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     List[str]						):
          snake_case__  :    Optional[Any]    						=      []

          snake_case__  :    Tuple    						=      self.head

          for i in reversed(range(self.level						)						):
                # i < node.level - When node level is lesser than `i` decrement `i`.
                # node.forward[i].key < key - Jumping to node with key value higher
                #                             or equal to searched key would result
                #                             in skipping searched key.
                while i < node.level and node.forward[i].key < key:
                      snake_case__  :    Dict    						=      node.forward[i]
                # Each leftmost node (relative to searched node) will potentially have to
                # be updated.
                update_vector.append(snake_case_						)

          update_vector.reverse()  # Note that we were inserting values in reverse order.

          # len(node.forward) != 0 - If current node doesn't contain any further
          #                          references then searched key is not present.
          # node.forward[0].key == key - Next node key should be equal to search key
          #                              if key is present.
          if len(node.forward						) != 0 and node.forward[0].key == key:
                return node.forward[0], update_vector
          else:
                return None, update_vector




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     KT						):
          snake_case__						,				snake_case__  :    List[str]    						=      self._locate_node(snake_case_						)

          if node is not None:
                for i, update_node in enumerate(snake_case_						):
                      # Remove or replace all references to removed node.
                      if update_node.level > i and update_node.forward[i].key == key:
                            if node.level > i:
                                  snake_case__  :    Union[str, Any]    						=      node.forward[i]
                            else:
                                  snake_case__  :    Any    						=      update_node.forward[:i]




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     KT ,     snake_case_					:     VT						):
          snake_case__						,				snake_case__  :    Optional[int]    						=      self._locate_node(snake_case_						)
          if node is not None:
                snake_case__  :    str    						=      value
          else:
                snake_case__  :    Tuple    						=      self.random_level()

                if level > self.level:
                      # After level increase we have to add additional nodes to head.
                      for _ in range(self.level - 1 ,     snake_case_						):
                            update_vector.append(self.head						)
                      snake_case__  :    int    						=      level

                snake_case__  :    str    						=      Node(snake_case_ ,     snake_case_						)

                for i, update_node in enumerate(update_vector[:level]						):
                      # Change references to pass through new node.
                      if update_node.level > i:
                            new_node.forward.append(update_node.forward[i]						)

                      if update_node.level < i + 1:
                            update_node.forward.append(snake_case_						)
                      else:
                            snake_case__  :    Union[str, Any]    						=      new_node




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     VT						):
          snake_case__						,				snake_case__  :    Optional[Any]    						=      self._locate_node(snake_case_						)

          if node is not None:
                return node.value

          return None





def 						__snake_case(				)		-> str:
      snake_case__  :    str    						=      SkipList()
      skip_list.insert("""Key1"""     ,   3		)
      skip_list.insert("""Key2"""     ,   12		)
      skip_list.insert("""Key3"""     ,   41		)
      skip_list.insert("""Key4"""     ,   -19		)

      snake_case__  :    Dict    						=      skip_list.head
      snake_case__  :    Dict    						=      {}
      while node.level != 0:
            snake_case__  :    Dict    						=      node.forward[0]
            snake_case__  :    List[Any]    						=      node.value

      assert len(_lowerCAmelCase		) == 4
      assert all_values["Key1"] == 3
      assert all_values["Key2"] == 12
      assert all_values["Key3"] == 41
      assert all_values["Key4"] == -19





def 						__snake_case(				)		-> str:
      snake_case__  :    Union[str, Any]    						=      SkipList()
      skip_list.insert("""Key1"""     ,   10		)
      skip_list.insert("""Key1"""     ,   12		)

      skip_list.insert("""Key5"""     ,   7		)
      skip_list.insert("""Key7"""     ,   10		)
      skip_list.insert("""Key10"""     ,   5		)

      skip_list.insert("""Key7"""     ,   7		)
      skip_list.insert("""Key5"""     ,   5		)
      skip_list.insert("""Key10"""     ,   10		)

      snake_case__  :    Tuple    						=      skip_list.head
      snake_case__  :    int    						=      {}
      while node.level != 0:
            snake_case__  :    Any    						=      node.forward[0]
            snake_case__  :    Optional[int]    						=      node.value

      if len(_lowerCAmelCase		) != 4:
            print()
      assert len(_lowerCAmelCase		) == 4
      assert all_values["Key1"] == 12
      assert all_values["Key7"] == 7
      assert all_values["Key5"] == 5
      assert all_values["Key10"] == 10





def 						__snake_case(				)		-> Any:
      snake_case__  :    str    						=      SkipList()
      assert skip_list.find("""Some key"""		) is None





def 						__snake_case(				)		-> Optional[Any]:
      snake_case__  :    Dict    						=      SkipList()

      skip_list.insert("""Key2"""     ,   20		)
      assert skip_list.find("""Key2"""		) == 20

      skip_list.insert("""Some Key"""     ,   10		)
      skip_list.insert("""Key2"""     ,   8		)
      skip_list.insert("""V"""     ,   13		)

      assert skip_list.find("""Y"""		) is None
      assert skip_list.find("""Key2"""		) == 8
      assert skip_list.find("""Some Key"""		) == 10
      assert skip_list.find("""V"""		) == 13





def 						__snake_case(				)		-> Union[str, Any]:
      snake_case__  :    int    						=      SkipList()
      skip_list.delete("""Some key"""		)

      assert len(skip_list.head.forward		) == 0





def 						__snake_case(				)		-> Any:
      snake_case__  :    Tuple    						=      SkipList()

      skip_list.insert("""Key1"""     ,   12		)
      skip_list.insert("""V"""     ,   13		)
      skip_list.insert("""X"""     ,   14		)
      skip_list.insert("""Key2"""     ,   15		)

      skip_list.delete("""V"""		)
      skip_list.delete("""Key2"""		)

      assert skip_list.find("""V"""		) is None
      assert skip_list.find("""Key2"""		) is None





def 						__snake_case(				)		-> int:
      snake_case__  :    Dict    						=      SkipList()

      skip_list.insert("""Key1"""     ,   12		)
      skip_list.insert("""V"""     ,   13		)
      skip_list.insert("""X"""     ,   14		)
      skip_list.insert("""Key2"""     ,   15		)

      skip_list.delete("""V"""		)
      assert skip_list.find("""V"""		) is None
      assert skip_list.find("""X"""		) == 14
      assert skip_list.find("""Key1"""		) == 12
      assert skip_list.find("""Key2"""		) == 15

      skip_list.delete("""X"""		)
      assert skip_list.find("""V"""		) is None
      assert skip_list.find("""X"""		) is None
      assert skip_list.find("""Key1"""		) == 12
      assert skip_list.find("""Key2"""		) == 15

      skip_list.delete("""Key1"""		)
      assert skip_list.find("""V"""		) is None
      assert skip_list.find("""X"""		) is None
      assert skip_list.find("""Key1"""		) is None
      assert skip_list.find("""Key2"""		) == 15

      skip_list.delete("""Key2"""		)
      assert skip_list.find("""V"""		) is None
      assert skip_list.find("""X"""		) is None
      assert skip_list.find("""Key1"""		) is None
      assert skip_list.find("""Key2"""		) is None





def 						__snake_case(				)		-> Optional[int]:
      snake_case__  :    Dict    						=      SkipList()

      skip_list.insert("""Key1"""     ,   12		)
      skip_list.insert("""V"""     ,   13		)
      skip_list.insert("""X"""     ,   142		)
      skip_list.insert("""Key2"""     ,   15		)

      skip_list.delete("""X"""		)

      def traverse_keys(_lowerCAmelCase		):
            yield node.key
            for forward_node in node.forward:
                  yield from traverse_keys(_lowerCAmelCase		)

      assert len(set(traverse_keys(skip_list.head		)		)		) == 4





def 						__snake_case(				)		-> Tuple:
      def is_sorted(_lowerCAmelCase		):
            return all(next_item >= item for item, next_item in zip(_lowerCAmelCase     ,   lst[1:]		)		)

      snake_case__  :    Optional[Any]    						=      SkipList()
      for i in range(10		):
            skip_list.insert(_lowerCAmelCase     ,   _lowerCAmelCase		)
      assert is_sorted(list(_lowerCAmelCase		)		)
      skip_list.delete(5		)
      skip_list.delete(8		)
      skip_list.delete(2		)
      assert is_sorted(list(_lowerCAmelCase		)		)
      skip_list.insert(-12     ,   -12		)
      skip_list.insert(77     ,   77		)
      assert is_sorted(list(_lowerCAmelCase		)		)





def 						__snake_case(				)		-> List[str]:
      for _ in range(100		):
            # Repeat test 100 times due to the probabilistic nature of skip list
            # random values == random bugs
            test_insert()
            test_insert_overrides_existing_value()

            test_searching_empty_list_returns_none()
            test_search()

            test_deleting_item_from_empty_list_do_nothing()
            test_deleted_items_are_not_founded_by_find_method()
            test_delete_removes_only_given_key()
            test_delete_doesnt_leave_dead_nodes()

            test_iter_always_yields_sorted_values()





def 						__snake_case(				)		-> Dict:
      snake_case__  :    Any    						=      SkipList()
      skip_list.insert(2     ,   """2"""		)
      skip_list.insert(4     ,   """4"""		)
      skip_list.insert(6     ,   """4"""		)
      skip_list.insert(4     ,   """5"""		)
      skip_list.insert(8     ,   """4"""		)
      skip_list.insert(9     ,   """4"""		)

      skip_list.delete(4		)

      print(_lowerCAmelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()





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   UpperCAmelCase_    :







							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=0.02,			__a=3,			__a=None,			__a=1000,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    parent
														_lowerCAmelCase     :							List[str]    =    batch_size
														_lowerCAmelCase     :							int    =    seq_length
														_lowerCAmelCase     :							Optional[int]    =    is_training
														_lowerCAmelCase     :							Dict    =    use_input_mask
														_lowerCAmelCase     :							List[str]    =    use_token_type_ids
														_lowerCAmelCase     :							str    =    use_labels
														_lowerCAmelCase     :							Optional[Any]    =    vocab_size
														_lowerCAmelCase     :							Tuple    =    hidden_size
														_lowerCAmelCase     :							List[Any]    =    num_hidden_layers
														_lowerCAmelCase     :							Optional[Any]    =    num_attention_heads
														_lowerCAmelCase     :							Any    =    intermediate_size
														_lowerCAmelCase     :							List[str]    =    hidden_act
														_lowerCAmelCase     :							Union[str, Any]    =    hidden_dropout_prob
														_lowerCAmelCase     :							Any    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							int    =    max_position_embeddings
														_lowerCAmelCase     :							Optional[int]    =    type_vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    type_sequence_label_size
														_lowerCAmelCase     :							List[str]    =    initializer_range
														_lowerCAmelCase     :							List[Any]    =    num_labels
														_lowerCAmelCase     :							Tuple    =    scope
														_lowerCAmelCase     :							str    =    range_bbox







							def    snake_case__      (   self):

														'''simple docstring'''

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

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

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

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

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

														_lowerCAmelCase     :							Optional[int]    =    self.get_config()

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







							def    snake_case__      (   self):

														'''simple docstring'''

														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,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    LiltModel(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Dict    =    model(__a,			bbox=__a,			attention_mask=__a,			token_type_ids=__a)
														_lowerCAmelCase     :							str    =    model(__a,			bbox=__a,			token_type_ids=__a)
														_lowerCAmelCase     :							List[Any]    =    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    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.num_labels
														_lowerCAmelCase     :							Optional[Any]    =    LiltForTokenClassification(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Dict    =    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    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    LiltForQuestionAnswering(config=__a)
														model.to(__a)
														model.eval()
														_lowerCAmelCase     :							Tuple    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.prepare_config_and_inputs()
														(
														    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    (
														    _lowerCAmelCase
														)   ,    
														)     :							Dict    =    config_and_inputs
														_lowerCAmelCase     :							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   UpperCAmelCase_    (       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    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														return True







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    LiltModelTester(self)
														_lowerCAmelCase     :							Union[str, Any]    =    ConfigTester(self,			config_class=__a,			hidden_size=37)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					_lowerCAmelCase     :							Any    =    type
																					self.model_tester.create_and_check_model(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_lowerCAmelCase     :							str    =    LiltModel.from_pretrained(__a)
																					self.assertIsNotNone(__a)



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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)

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

														# forward pass
														with torch.no_grad():
																					_lowerCAmelCase     :							Optional[Any]    =    model(input_ids=__a,			bbox=__a)

														_lowerCAmelCase     :							Optional[int]    =    torch.Size([1, 2, 768])
														_lowerCAmelCase     :							List[str]    =    torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]],			device=__a,			)

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