

a		={
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602_176_634E-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__lowerCamelCase    :    Tuple 	=			(
		    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
		    F"Valid values are: {', '.join(lowerCamelCase__      )}"
		)
		raise ValueError(lowerCamelCase__      )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
					import doctest

					doctest.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 	A_		(	unittest.TestCase    ):
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Tuple 	=			TFCamembertModel.from_pretrained('jplu/tf-camembert-base')

								__lowerCamelCase    :    str 	=			tf.convert_to_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]]       ,dtype=tf.intaa       ,)  # J'aime le camembert !"

								__lowerCamelCase    :    int 	=			model(SCREAMING_SNAKE_CASE__)['last_hidden_state']
								__lowerCamelCase    :    Dict 	=			tf.TensorShape((1, 1_0, 7_6_8))
								self.assertEqual(output.shape       ,SCREAMING_SNAKE_CASE__)
								# compare the actual values for a slice.
								__lowerCamelCase    :    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))




