
"""simple docstring"""




class 			snake_case  (				__lowercase ):
							pass







class 			snake_case  (				__lowercase ):
							pass







class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[
									    [],
									    [],
									    [],
									]

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									try:
											if len(self.queues[priority]     ) >= 1_00:
													raise OverflowError('''Maximum queue size is 100'''     )
											self.queues[priority].append(SCREAMING_SNAKE_CASE_     )
									except IndexError:
											raise ValueError('''Valid priorities are 0, 1, and 2'''     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for queue in self.queues:
											if queue:
													return queue.pop(0     )
									raise UnderFlowError('''All queues are empty'''     )







							def __str__(self     ):

									"""simple docstring"""







									return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues     )     )







class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[]

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									if len(self.queue     ) == 1_00:
											raise OverFlowError('''Maximum queue size is 100'''     )
									self.queue.append(SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									if not self.queue:
											raise UnderFlowError('''The queue is empty'''     )
									else:
											SCREAMING_SNAKE_CASE_    			=			min(self.queue     )
											self.queue.remove(SCREAMING_SNAKE_CASE_     )
											return data







							def __str__(self     ):

									"""simple docstring"""







									return str(self.queue     )







def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			FixedPriorityQueue()
		fpq.enqueue(0,				10     )
		fpq.enqueue(1,				70     )
		fpq.enqueue(0,				100     )
		fpq.enqueue(2,				1     )
		fpq.enqueue(2,				5     )
		fpq.enqueue(1,				7     )
		fpq.enqueue(2,				4     )
		fpq.enqueue(1,				64     )
		fpq.enqueue(0,				128     )
		print(__a     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(__a     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )
		print(fpq.dequeue()     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			ElementPriorityQueue()
		epq.enqueue(10     )
		epq.enqueue(70     )
		epq.enqueue(100     )
		epq.enqueue(1     )
		epq.enqueue(5     )
		epq.enqueue(7     )
		epq.enqueue(4     )
		epq.enqueue(64     )
		epq.enqueue(128     )
		print(__a     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(__a     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )
		print(epq.dequeue()     )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()

"""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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			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 !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							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     )     )