from __future__ import annotations





def 		__lowerCamelCase							(UpperCAmelCase__       :							list[int]					):  # This function is recursive
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# If the array contains only one element, we return it (it's the stop condition of
					# recursion)
					if array_length <= 1:
										return array
										# Else
					SCREAMING_SNAKE_CASE           =    array[0]
					SCREAMING_SNAKE_CASE           =    False
					SCREAMING_SNAKE_CASE           =    1
					SCREAMING_SNAKE_CASE           =    []
					while not is_found and i < array_length:
										if array[i] < pivot:
															SCREAMING_SNAKE_CASE           =    True
															SCREAMING_SNAKE_CASE           =    [element for element in array[i:] if element >= array[i]]
															SCREAMING_SNAKE_CASE           =    longest_subsequence(UpperCAmelCase__					)
															if len(UpperCAmelCase__					) > len(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    temp_array
										else:
															i += 1

					SCREAMING_SNAKE_CASE           =    [element for element in array[1:] if element >= pivot]
					SCREAMING_SNAKE_CASE           =    [pivot, *longest_subsequence(UpperCAmelCase__					)]
					if len(UpperCAmelCase__					) > len(UpperCAmelCase__					):
										return temp_array
					else:
										return longest_subseq


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    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 ) )




