
"""simple docstring"""
from __future__ import annotations

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

lowercase		:						List[str] =					TypeVar('T', bound=bool)




class    _snake_case  (						Generic[T]       ):






							def __init__(					self						:			int						, snake_case__						:			Tuple    ):






												"""simple docstring"""



												__a:						str		    =					value
												__a:						List[Any]		    =					None
												__a:						str		    =					None






							@property
							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												return self._value






							@staticmethod
							def 							__lowercase  (					snake_case__						:			List[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												if not roota:
																	return roota

												if not roota:
																	return roota

												if roota.value > roota.value:
																	__a    ,				__a:						Tuple		    =					roota, roota

												__a:						List[str]		    =					roota
												__a:						Tuple		    =					roota.right
												__a:						List[Any]		    =					roota.left
												__a:						List[str]		    =					SkewNode.merge(lowerCamelCase_						, lowerCamelCase_    )

												return result






class    _snake_case  (						Generic[T]       ):






							def __init__(					self						:			Optional[int]						, snake_case__						:			Tuple = ()    ):






												"""simple docstring"""



												__a:						Tuple		    =					None
												if data:
																	for item in data:
																						self.insert(lowerCamelCase_    )






							def __bool__(					self						:			List[Any]    ):






												"""simple docstring"""



												return self._root is not None






							def __iter__(					self						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					[]
												while self:
																	result.append(self.pop()    )

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

												return iter(lowerCamelCase_    )






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					SkewNode.merge(self._root						, SkewNode(lowerCamelCase_    )    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					self.top()
												__a:						Union[str, Any]		    =					(
												    SkewNode.merge(self._root.left						, self._root.right    ) if self._root else None
												)

												return result






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						List[str]		    =					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    _snake_case  (						unittest.TestCase       ):






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Any		    =					CamembertModel.from_pretrained("""camembert-base"""    )
												model.to(snake_case__    )

												__a:						str		    =					torch.tensor(
												    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]						, device=snake_case__						, dtype=torch.long						, )  # J'aime le camembert !
												with torch.no_grad():
																	__a:						str		    =					model(snake_case__    )["""last_hidden_state"""]
												__a:						Optional[Any]		    =					torch.Size((1, 1_0, 7_6_8)    )
												self.assertEqual(output.shape						, snake_case__    )
												# compare the actual values for a slice.
												__a:						Union[str, Any]		    =					torch.tensor(
												    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]						, device=snake_case__						, 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]						, snake_case__						, atol=1e-4    )    )
