





def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	float:

    def get_matched_characters(_UpperCAmelCase		,      _UpperCAmelCase					) -> str:
        lowerCamelCase__       : Tuple   					=						[]
        lowerCamelCase__       : Optional[int]   					=						min(len(_stra					)		,      len(_stra					)					) // 2
        for i, l in enumerate(_stra					):
            lowerCamelCase__       : str   					=						int(max(0		,      i - limit					)					)
            lowerCamelCase__       : Optional[Any]   					=						int(min(i + limit + 1		,      len(_stra					)					)					)
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase					)
                lowerCamelCase__       : List[Any]   					=						F"""{_stra[0:_stra.index(_UpperCAmelCase					)]} {_stra[_stra.index(_UpperCAmelCase					) + 1:]}"""

        return "".join(_UpperCAmelCase					)

    # matching characters
    lowerCamelCase__       : Optional[int]   					=						get_matched_characters(_UpperCAmelCase		,      _UpperCAmelCase					)
    lowerCamelCase__       : Optional[Any]   					=						get_matched_characters(_UpperCAmelCase		,      _UpperCAmelCase					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)

    # transposition
    lowerCamelCase__       : Optional[Any]   					=						(
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase		,      _UpperCAmelCase					) if ca != ca]					) // 2
    )

    if not match_count:
        lowerCamelCase__       : Any   					=						0.0
    else:
        lowerCamelCase__       : str   					=						(
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase					)
                + match_count / len(_UpperCAmelCase					)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    lowerCamelCase__       : Optional[int]   					=						0
    for ca, ca in zip(stra[:4]		,      stra[:4]					):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(jaro_winkler("""hello""", """world"""))












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      )      )





