





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

UpperCAmelCase     :     Optional[int]							=	TypeVar("""KT""")
UpperCAmelCase     :     str							=	TypeVar("""VT""")







class     __lowerCAmelCase	(   Generic[KT, VT]):







      def __init__(   self		,				lowerCAmelCase__ = "root"		,				lowerCAmelCase__ = None							)       ->						Dict:



        '''simple docstring'''




        a__     : Union[str, Any]  =key
        a__     : str  =value
        a__     : list[Node[KT, VT]]  =[]







      def __repr__(   self							)       ->						str:



        '''simple docstring'''




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







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.forward							)







class     __lowerCAmelCase	(   Generic[KT, VT]):







      def __init__(   self		,				lowerCAmelCase__ = 0.5		,				lowerCAmelCase__ = 1_6							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Node[KT, VT]  =Node[KT, VT]()
        a__     : int  =0
        a__     : List[str]  =p
        a__     : List[Any]  =max_level







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        a__     : List[Any]  =list(self							)

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

        a__     : Tuple  =max((len(str(SCREAMING_SNAKE_CASE_							)							) for item in items)		,				default=4							)
        a__     : List[Any]  =max(SCREAMING_SNAKE_CASE_		,				4							) + 4

        a__     : List[str]  =self.head
        a__     : Union[str, Any]  =[]

        a__     : Optional[Any]  =node.forward.copy()
        lines.append(F'''[{node.key}]'''.ljust(SCREAMING_SNAKE_CASE_		,				"-"							) + "* " * len(SCREAMING_SNAKE_CASE_							)							)
        lines.append(" " * label_size + "| " * len(SCREAMING_SNAKE_CASE_							)							)

        while len(node.forward							) != 0:
          a__     : List[Any]  =node.forward[0]

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

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







      def __iter__(   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : List[str]  =self.head

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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




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

        return level







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:



        '''simple docstring'''




        a__     : Any  =[]

        a__     : Optional[int]  =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:
            a__     : str  =node.forward[i]
          # Each leftmost node (relative to searched node) will potentially have to
          # be updated.
          update_vector.append(SCREAMING_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        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




        a__     : List[Any]  =self._locate_node(SCREAMING_SNAKE_CASE_							)

        if node is not None:
          for i, update_node in enumerate(SCREAMING_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:
                a__     : List[str]  =node.forward[i]
              else:
                a__     : Any  =update_node.forward[:i]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[Any]  =self._locate_node(SCREAMING_SNAKE_CASE_							)
        if node is not None:
          a__     : str  =value
        else:
          a__     : Dict  =self.random_level()

          if level > self.level:
            # After level increase we have to add additional nodes to head.
            for _ in range(self.level - 1		,				SCREAMING_SNAKE_CASE_							):
              update_vector.append(self.head							)
            a__     : List[str]  =level

          a__     : Optional[int]  =Node(SCREAMING_SNAKE_CASE_		,				SCREAMING_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(SCREAMING_SNAKE_CASE_							)
            else:
              a__     : int  =new_node







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						VT | None:



        '''simple docstring'''




        a__     : Union[str, Any]  =self._locate_node(SCREAMING_SNAKE_CASE_							)

        if node is not None:
          return node.value

        return None
def 					_A	(		):





  """simple docstring"""



  a__     : Union[str, Any]  =SkipList()
  skip_list.insert("Key1"	,	3						)
  skip_list.insert("Key2"	,	12						)
  skip_list.insert("Key3"	,	41						)
  skip_list.insert("Key4"	,	-19						)

  a__     : int  =skip_list.head
  a__     : Tuple  ={}
  while node.level != 0:
    a__     : int  =node.forward[0]
    a__     : Union[str, 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 					_A	(		):





  """simple docstring"""



  a__     : Tuple  =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						)

  a__     : List[Any]  =skip_list.head
  a__     : Union[str, Any]  ={}
  while node.level != 0:
    a__     : Union[str, Any]  =node.forward[0]
    a__     : List[Any]  =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 					_A	(		):





  """simple docstring"""



  a__     : List[str]  =SkipList()
  assert skip_list.find("Some key"						) is None
def 					_A	(		):





  """simple docstring"""



  a__     : List[str]  =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 					_A	(		):





  """simple docstring"""



  a__     : Any  =SkipList()
  skip_list.delete("Some key"						)

  assert len(skip_list.head.forward						) == 0
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[Any]  =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 					_A	(		):





  """simple docstring"""



  a__     : 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"						)
  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 					_A	(		):





  """simple docstring"""



  a__     : Any  =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					:       List[str]						):
    yield node.key
    for forward_node in node.forward:
      yield from traverse_keys(lowerCAmelCase__						)

  assert len(set(traverse_keys(skip_list.head						)						)						) == 4
def 					_A	(		):





  """simple docstring"""



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

  a__     : Optional[int]  =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 					_A	(		):





  """simple docstring"""



  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 					_A	(		):





  """simple docstring"""



  a__     : List[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 (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     :     List[Any]							=	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : str            =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase    : Tuple            =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase    : Dict            =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase    : int            =      {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"							)

        a__     : Any  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : Any  =text_classifier("This is great !"		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__     : Tuple  =text_classifier(["This is great !", "This is bad"]		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : List[Any]  =text_classifier("This is great !"		,				top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__     : Any  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : List[str]  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__     : Optional[int]  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : int  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,				)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        import torch

        a__     : Dict  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"		,				device=torch.device("cpu"							)		,				)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[int]  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="tf"							)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =pipeline("text-classification"							)

        a__     : Union[str, Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : Optional[Any]  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =pipeline("text-classification"		,				framework="tf"							)

        a__     : str  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : str  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[Any]  =TextClassificationPipeline(model=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__     : List[Any]  ="HuggingFace is in"
        a__     : int  =text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__     : Optional[int]  =["HuggingFace is in ", "Paris is in France"]
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__     : Union[str, Any]  =text_classifier(lowerCAmelCase__		,				top_k=lowerCAmelCase__							)
        a__     : Optional[Any]  =len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,				)

        a__     : List[str]  ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,				)
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__     : Any  =[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__     : Optional[int]  =text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)






