
"""simple docstring"""





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      ):




			"""simple docstring"""







			@slow
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"	)

								A_				 =						tf.convert_to_tensor(
								    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,   dtype=tf.intaa       ,   )  # J'aime le camembert !"

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





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

		UpperCamelCase_					:						List[Any]				= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




