




from __future__ import annotations

from typing import Generic, TypeVar

_lowerCamelCase			 =						TypeVar('T')



class 			a     (   _A			):

     '''simple docstring'''





     pass



class 			a     (   _A			):

     '''simple docstring'''





     pass



class 			a     (   Generic[T]			):

     '''simple docstring'''





     def __init__(	self   :			Union[str, Any]					,    __snake_case   :			int = 10				):
         UpperCAmelCase_							 =     []
         UpperCAmelCase_							 =     limit
     def __bool__(	self   :			Tuple				):
         return bool(self.stack				)
     def __str__(	self   :			Tuple				):
         return str(self.stack				)
     def   lowerCamelCase_   (	self   :			Optional[int]					,    __snake_case   :			T				):
         if len(self.stack				) >= self.limit:
             raise StackOverflowError
         self.stack.append(__snake_case				)
     def   lowerCamelCase_   (	self   :			Tuple				):
         if not self.stack:
             raise StackUnderflowError
         return self.stack.pop()
     def   lowerCamelCase_   (	self   :			Optional[int]				):
         if not self.stack:
             raise StackUnderflowError
         return self.stack[-1]
     def   lowerCamelCase_   (	self   :			Optional[int]				):
         return not bool(self.stack				)
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         return self.size() == self.limit
     def   lowerCamelCase_   (	self   :			int				):
         return len(self.stack				)




     def __contains__(	self   :			Optional[int]					,    __snake_case   :			T				):
         return item in self.stack




def 					SCREAMING_SNAKE_CASE	(       )  ->							None:
    UpperCAmelCase_							 =     Stack(10      )
    assert bool(__UpperCamelCase      ) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(__UpperCamelCase      ) == "[]"

    try:
        UpperCAmelCase_							 =     stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        UpperCAmelCase_							 =     stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10      ):
        assert stack.size() == i
        stack.push(__UpperCamelCase      )

    assert bool(__UpperCamelCase      )
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(__UpperCamelCase      ) == str(list(range(10      )      )      )
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100      )
    assert str(__UpperCamelCase      ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]      )

    try:
        stack.push(200      )
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()









"""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        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      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						)						)