



from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_UpperCamelCase							       =		TypeVar('T', bound=bool)






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			Optional[int]    ,			_a   :			T     )      ->      None:
    __lowerCamelCase : T					 =				value
    __lowerCamelCase : SkewNode[T] | None					 =				None
    __lowerCamelCase : SkewNode[T] | None					 =				None




  @property
  def 		_lowercase  (				self   :			Tuple     )      ->      T:
    return self._value




  @staticmethod
  def 		_lowercase  (				_a   :			SkewNode[T] | None    ,			_a   :			SkewNode[T] | None     )      ->      SkewNode[T] | None:







    if not roota:
      return roota

    if not roota:
      return roota

    if roota.value > roota.value:
      __lowerCamelCase : str					 =				roota, roota

    __lowerCamelCase : Optional[Any]					 =				roota
    __lowerCamelCase : Optional[Any]					 =				roota.right
    __lowerCamelCase : Optional[Any]					 =				roota.left
    __lowerCamelCase : List[str]					 =				SkewNode.merge(UpperCamelCase__    ,			UpperCamelCase__     )

    return result






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			Union[str, Any]    ,			_a   :			Iterable[T] | None = ()     )      ->      None:
    __lowerCamelCase : SkewNode[T] | None					 =				None
    if data:
      for item in data:
        self.insert(UpperCamelCase__     )




  def __bool__(				self   :			Dict     )      ->      bool:
    return self._root is not None




  def __iter__(				self   :			Dict     )      ->      Iterator[T]:
    __lowerCamelCase : list[Any]					 =				[]
    while self:
      result.append(self.pop()     )

    # Pushing items back to the heap not to clear it.
    for item in result:
      self.insert(UpperCamelCase__     )

    return iter(UpperCamelCase__     )




  def 		_lowercase  (				self   :			Dict    ,			_a   :			T     )      ->      None:
    __lowerCamelCase : Union[str, Any]					 =				SkewNode.merge(self._root    ,			SkewNode(UpperCamelCase__     )     )




  def 		_lowercase  (				self   :			str     )      ->      T | None:
    __lowerCamelCase : Tuple					 =				self.top()
    __lowerCamelCase : Optional[Any]					 =				(
        SkewNode.merge(self._root.left    ,			self._root.right     ) if self._root else None
    )

    return result




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







    if not self._root:
      raise IndexError('Can\'t get top element for the empty heap.'     )
    return self._root.value




  def 		_lowercase  (				self   :			Dict     )      ->      None:
    __lowerCamelCase : Dict					 =				None


if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  @slow
  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : str					 =				CamembertModel.from_pretrained('camembert-base'     )
    model.to(_a     )

    __lowerCamelCase : List[Any]					 =				torch.tensor(
        [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]    ,			device=_a    ,			dtype=torch.long    ,			)  # J'aime le camembert !
    with torch.no_grad():
      __lowerCamelCase : int					 =				model(_a     )['last_hidden_state']
    __lowerCamelCase : Optional[int]					 =				torch.Size((1, 10, 768)     )
    self.assertEqual(output.shape    ,			_a     )
    # compare the actual values for a slice.
    __lowerCamelCase : Tuple					 =				torch.tensor(
        [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,			device=_a    ,			dtype=torch.float    ,			)
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(torch.allclose(output[:, :3, :3]    ,			_a    ,			atol=1e-4     )     )






