




from __future__ import annotations

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

_snake_case										=       TypeVar("T")







class 						lowercase							(   Generic[T]		):

     def __init__(  self		,     _a				)						->							str:
           _A							:     Union[str, Any]			=						data
           _A							:     Node[T] | None			=						None



     def __str__(  self				)						->							str:
           return F'''{self.data}'''







class 						lowercase							(   Generic[T]		):

     def __init__(  self				)						->							None:
           _A							:     Node[T] | None			=						None

     def __iter__(  self				)						->							Iterator[T]:
           _A							:     Union[str, Any]			=						self.top
           while node:
                 yield node.data
                 _A							:     Any			=						node.next

     def __str__(  self				)						->							str:
           return "->".join([str(_a				) for item in self]				)

     def __len__(  self				)						->							int:
           return len(tuple(iter(self				)				)				)

     def 					a__	(  self				)						->							bool:
           return self.top is None

     def 					a__	(  self		,     _a				)						->							None:
           _A							:     Any			=						Node(_a				)
           if not self.is_empty():
                 _A							:     int			=						self.top
           _A							:     List[str]			=						node

     def 					a__	(  self				)						->							T:
           if self.is_empty():
                 raise IndexError("""pop from empty stack"""				)
           assert isinstance(self.top		,     _a				)
           _A							:     int			=						self.top
           _A							:     Any			=						self.top.next
           return pop_node.data

     def 					a__	(  self				)						->							T:
           if self.is_empty():
                 raise IndexError("""peek from empty stack"""				)

           assert self.top is not None
           return self.top.data



     def 					a__	(  self				)						->							None:
           _A							:     Dict			=						None


if __name__ == "__main__":
  from doctest import testmod

  testmod()







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

