



'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase					) ->			Any:  # noqa: E741
	_lowercase					:       str       =		len(_lowercase					)
	_lowercase					:       Union[str, Any]       =		0
	_lowercase					:       Any       =		[0] * n
	_lowercase					:       Dict       =		[False] * n
	_lowercase					:       Optional[Any]       =		[False] * n

	def dfs(_lowercase,				_lowercase,				_lowercase,				_lowercase					):
		if parent == root:
			out_edge_count += 1
		_lowercase					:       int       =		True
		_lowercase					:       Optional[Any]       =		at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				_lowercase					:       Optional[int]       =		dfs(_lowercase,				_lowercase,				_lowercase,				_lowercase					)
				_lowercase					:       Dict       =		min(low[at],				low[to]					)

				# AP found via bridge
				if at < low[to]:
					_lowercase					:       str       =		True
				# AP found via cycle
				if at == low[to]:
					_lowercase					:       int       =		True
			else:
				_lowercase					:       List[str]       =		min(low[at],				_lowercase					)
		return out_edge_count

	for i in range(_lowercase					):
		if not visited[i]:
			_lowercase					:       str       =		0
			_lowercase					:       Dict       =		dfs(_lowercase,				_lowercase,				-1,				_lowercase					)
			_lowercase					:       str       =		out_edge_count > 1

	for x in range(len(_lowercase					)					):
		if is_art[x] is True:
			print(_lowercase					)


# Adjacency list of graph
_A				:    str     						=				{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)





'''simple docstring'''

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       str       =		AutoTokenizer.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       List[Any]       =		'The dog is cute and lives in the garden house'
						_lowercase					:       Optional[int]       =		jnp.array([tokenizer.encode(UpperCamelCase_					)]					)

						_lowercase					:       int       =		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
						_lowercase					:       Tuple       =		jnp.array(
						    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]					)

						_lowercase					:       List[str]       =		model(UpperCamelCase_					)['last_hidden_state']
						self.assertEqual(output.shape     ,  UpperCamelCase_					)
						# compare the actual values for a slice of last dim
						self.assertTrue(jnp.allclose(output[:, :, -1]     ,  UpperCamelCase_     ,  atol=1E-3					)					)
