from __future__ import annotations






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:  # This function is recursive



			"""simple docstring"""







			lowercase							=		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
			lowercase							=		array[0]
			lowercase							=		False
			lowercase							=		1
			lowercase							=		[]
			while not is_found and i < array_length:
						if array[i] < pivot:
									lowercase							=		True
									lowercase							=		[element for element in array[i:] if element >= array[i]]
									lowercase							=		longest_subsequence(UpperCAmelCase       )
									if len(UpperCAmelCase       ) > len(UpperCAmelCase       ):
												lowercase							=		temp_array
						else:
									i += 1

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_      =      {
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTBigCodeForSequenceClassification",
						    "GPTBigCodeForTokenClassification",
						    "GPTBigCodeForCausalLM",
						    "GPTBigCodeModel",
						    "GPTBigCodePreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_bigcode import (
												    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTBigCodeForCausalLM,
												    GPTBigCodeForSequenceClassification,
												    GPTBigCodeForTokenClassification,
												    GPTBigCodeModel,
												    GPTBigCodePreTrainedModel,
												)


else:
						import sys

						A_      =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





