from __future__ import annotations

from collections import Counter
from random import random
class     lowerCAmelCase_     :







							def __init__(   self      :     List[str]		):
												_UpperCamelCase			     =     {}







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												_UpperCamelCase			     =     {}







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     str ,			_A      :     str ,			_A      :     float		):
												if nodea not in self.connections:
																	self.add_node(_A		)
												if nodea not in self.connections:
																	self.add_node(_A		)
												_UpperCamelCase			     =     probability







							def  UpperCamelCase_						(   self      :     str		):
												return list(self.connections		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     str		):
												_UpperCamelCase			     =     0
												_UpperCamelCase			     =     random()

												for dest in self.connections[node]:
																	current_probability += self.connections[node][dest]
																	if current_probability > random_value:
																						return dest
												return ""




def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__snake_case  ,     __snake_case  ,     __snake_case						)

					_UpperCamelCase			     =     Counter(graph.get_nodes()						)
					_UpperCamelCase			     =     start

					for _ in range(__snake_case						):
										_UpperCamelCase			     =     graph.transition(__snake_case						)
										visited[node] += 1

					return visited


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      )      )





