




from __future__ import annotations



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[int]	)     ->   Tuple:  # This function is recursive




 """simple docstring"""







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

 UpperCAmelCase__							 =						[element for element in array[1:] if element >= pivot]
 UpperCAmelCase__							 =						[pivot, *longest_subsequence(_lowerCAmelCase	)]
 if len(_lowerCAmelCase	) > len(_lowerCAmelCase	):
  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_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





