


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 	__lowerCamelCase			(unittest.TestCase						):







       @slow
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			TFCamembertModel.from_pretrained('jplu/tf-camembert-base'					)

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

            __UpperCamelCase =			model(A_					)['last_hidden_state']
            __UpperCamelCase =			tf.TensorShape((1, 10, 768)					)
            self.assertEqual(output.shape,A_					)
            # compare the actual values for a slice.
            __UpperCamelCase =			tf.convert_to_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]]],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					)					)




'''simple docstring'''



_lowercase          =						"""
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowercase          =						[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase          =						{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
