



from __future__ import annotations

from random import random
from typing import Generic, TypeVar

lowercase_								=     TypeVar("""KT""")
lowercase_								=     TypeVar("""VT""")
class 				_snake_case			(					Generic[KT, VT]):




    def __init__(						self					:						List[Any],					__lowercase					:						KT | str = "root",					__lowercase					:						VT | None = None						):
       lowercase__					  =				key
       lowercase__					  =				value
       lowercase__					  =				[]




    def __repr__(						self					:						Dict						):
       return F'''Node({self.key}: {self.value})'''




    @property
    def 							A__      (						self					:						Optional[Any]						):
       return len(self.forward						)
class 				_snake_case			(					Generic[KT, VT]):




    def __init__(						self					:						List[str],					__lowercase					:						float = 0.5,					__lowercase					:						int = 16						):
       lowercase__					  =				Node[KT, VT]()
       lowercase__					  =				0
       lowercase__					  =				p
       lowercase__					  =				max_level




    def __str__(						self					:						str						):
       lowercase__					  =				list(self						)

       if len(__lowercase						) == 0:
          return F'''SkipList(level={self.level})'''

       lowercase__					  =				max((len(str(__lowercase						)						) for item in items),					default=4						)
       lowercase__					  =				max(__lowercase,					4						) + 4

       lowercase__					  =				self.head
       lowercase__					  =				[]

       lowercase__					  =				node.forward.copy()
       lines.append(F'''[{node.key}]'''.ljust(__lowercase,					"-"						) + "* " * len(__lowercase						)						)
       lines.append(" " * label_size + "| " * len(__lowercase						)						)

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

          lines.append(
              F'''[{node.key}]'''.ljust(__lowercase,					"-"						)
              + " ".join(str(n.key						) if n.key == node.key else "|" for n in forwards						)						)
          lines.append(" " * label_size + "| " * len(__lowercase						)						)
          lowercase__					  =				node.forward

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




    def __iter__(						self					:						Optional[int]						):
       lowercase__					  =				self.head

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




    def 							A__      (						self					:						int						):
       lowercase__					  =				1
       while random() < self.p and level < self.max_level:
          level += 1

       return level




    def 							A__      (						self					:						Dict,					__lowercase					:						List[str]						):
       lowercase__					  =				[]

       lowercase__					  =				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:
             lowercase__					  =				node.forward[i]
          # Each leftmost node (relative to searched node) will potentially have to
          # be updated.
          update_vector.append(__lowercase						)

       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 							A__      (						self					:						List[str],					__lowercase					:						KT						):
       lowercase__					  =				self._locate_node(__lowercase						)

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




    def 							A__      (						self					:						List[str],					__lowercase					:						KT,					__lowercase					:						VT						):
       lowercase__					  =				self._locate_node(__lowercase						)
       if node is not None:
          lowercase__					  =				value
       else:
          lowercase__					  =				self.random_level()

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

          lowercase__					  =				Node(__lowercase,					__lowercase						)

          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(__lowercase						)
             else:
                lowercase__					  =				new_node




    def 							A__      (						self					:						List[str],					__lowercase					:						VT						):
       lowercase__					  =				self._locate_node(__lowercase						)

       if node is not None:
          return node.value

       return None



def __lowerCAmelCase (     ):
   lowercase__					  =				SkipList()
   skip_list.insert("Key1"						,  3  )
   skip_list.insert("Key2"						,  12  )
   skip_list.insert("Key3"						,  41  )
   skip_list.insert("Key4"						,  -19  )

   lowercase__					  =				skip_list.head
   lowercase__					  =				{}
   while node.level != 0:
      lowercase__					  =				node.forward[0]
      lowercase__					  =				node.value

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



def __lowerCAmelCase (     ):
   lowercase__					  =				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  )

   lowercase__					  =				skip_list.head
   lowercase__					  =				{}
   while node.level != 0:
      lowercase__					  =				node.forward[0]
      lowercase__					  =				node.value

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



def __lowerCAmelCase (     ):
   lowercase__					  =				SkipList()
   assert skip_list.find("Some key"  ) is None



def __lowerCAmelCase (     ):
   lowercase__					  =				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 __lowerCAmelCase (     ):
   lowercase__					  =				SkipList()
   skip_list.delete("Some key"  )

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



def __lowerCAmelCase (     ):
   lowercase__					  =				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 __lowerCAmelCase (     ):
   lowercase__					  =				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 __lowerCAmelCase (     ):
   lowercase__					  =				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(SCREAMING_SNAKE_CASE_  ):
      yield node.key
      for forward_node in node.forward:
         yield from traverse_keys(SCREAMING_SNAKE_CASE_  )

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



def __lowerCAmelCase (     ):
   def is_sorted(SCREAMING_SNAKE_CASE_  ):
      return all(next_item >= item for item, next_item in zip(SCREAMING_SNAKE_CASE_						,  lst[1:]  )  )

   lowercase__					  =				SkipList()
   for i in range(10  ):
      skip_list.insert(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
   assert is_sorted(list(SCREAMING_SNAKE_CASE_  )  )
   skip_list.delete(5  )
   skip_list.delete(8  )
   skip_list.delete(2  )
   assert is_sorted(list(SCREAMING_SNAKE_CASE_  )  )
   skip_list.insert(-12						,  -12  )
   skip_list.insert(77						,  77  )
   assert is_sorted(list(SCREAMING_SNAKE_CASE_  )  )



def __lowerCAmelCase (     ):
   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 __lowerCAmelCase (     ):
   lowercase__					  =				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(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




