





_UpperCAmelCase							:			Union[str, Any]        =							[
    (10_00, """M"""),
    (9_00, """CM"""),
    (5_00, """D"""),
    (4_00, """CD"""),
    (1_00, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	int:
    lowerCamelCase__       : int   					=						{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
    lowerCamelCase__       : Optional[Any]   					=						0
    lowerCamelCase__       : Any   					=						0
    while place < len(_UpperCAmelCase					):
        if (place + 1 < len(_UpperCAmelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
            total += vals[roman[place + 1]] - vals[roman[place]]
            place += 2
        else:
            total += vals[roman[place]]
            place += 1
    return total


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Tuple   					=						[]
    for arabic, roman in ROMAN:
        ((lowerCamelCase__)					,				(lowerCamelCase__))       : int   					=						divmod(_UpperCAmelCase		,      _UpperCAmelCase					)
        result.append(roman * factor					)
        if number == 0:
            break
    return "".join(_UpperCAmelCase					)


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   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : Union[str, Any]      )     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

     lowerCamelCase__       : Any   					=						tf.convert_to_tensor(
         [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]     ,	dtype=tf.intaa     ,	)  # J'aime le camembert !"

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )['last_hidden_state']
     lowerCamelCase__       : Tuple   					=						tf.TensorShape((1, 10, 768)      )
     self.assertEqual(output.shape     ,	UpperCAmelCase      )
     # compare the actual values for a slice.
     lowerCamelCase__       : Optional[Any]   					=						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      )      )





