

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							A__        =			set_counts
							A__        =			max(lowercase       )
							A__        =			len(lowercase       )
							A__        =			[1] * num_sets
							A__        =			list(range(lowercase       )       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						bool:
							'''simple docstring'''






							A__        =			self.get_parent(lowercase       )
							A__        =			self.get_parent(lowercase       )

							if src_parent == dst_parent:
											return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											A__        =			0
											A__        =			dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
															self.ranks[dst_parent] += 1
											A__        =			self.set_counts[dst_parent]
							else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											A__        =			0
											A__        =			src_parent
											A__        =			self.set_counts[src_parent]

							A__        =			max(self.max_set      ,      lowercase       )
							return True


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							if self.parents[disj_set] == disj_set:
											return disj_set
							A__        =			self.get_parent(self.parents[disj_set]       )
							return self.parents[disj_set]




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			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			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       )       )

