
"""simple docstring"""







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				):
   __lowerCAmelCase    :		int		    =   TFCamembertModel.from_pretrained('jplu/tf-camembert-base'				)

   __lowerCAmelCase    :		Any		    =   tf.convert_to_tensor(
       [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,	dtype=tf.intaa ,	)  # J'aime le camembert !"

   __lowerCAmelCase    :		List[Any]		    =   model(lowerCAmelCase__				)["last_hidden_state"]
   __lowerCAmelCase    :		Optional[Any]		    =   tf.TensorShape((1, 10, 7_68)				)
   self.assertEqual(output.shape ,	lowerCAmelCase__				)
   # compare the actual values for a slice.
   __lowerCAmelCase    :		str		    =   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				)				)

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

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



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

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

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

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

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()