




"""simple docstring"""




from collections import deque

from .hash_table import HashTable

class   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						*UpperCamelCase_				,						**UpperCamelCase_       )						->       Any:
					super().__init__(*UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Optional[int]:
					__lowercase			:  Tuple								=       deque([]       ) if self.values[key] is None else self.values[key]
					self.values[key].appendleft(UpperCamelCase_       )
					__lowercase			:  Tuple								=       self.values[key]
		def 	_lowerCamelCase							(						self       )						->       List[Any]:
					return (
					    sum(self.charge_factor - len(UpperCamelCase_       ) for slot in self.values       )
					    / self.size_table
					    * self.charge_factor
					)
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_=None       )						->       List[str]:
					if not (
					    len(self.values[key]       ) == self.charge_factor and self.values.count(UpperCamelCase_       ) == 0
					):
								return key
					return super()._collision_resolution(UpperCamelCase_				,						UpperCamelCase_       )










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      )      )





