





from __future__ import annotations


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):  # This function is recursive
		lowerCamelCase_	:     Dict 						=      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
		lowerCamelCase_	:     Dict 						=      array[0]
		lowerCamelCase_	:     Optional[Any] 						=      False
		lowerCamelCase_	:     str 						=      1
		lowerCamelCase_	:     list[int] 						=      []
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowerCamelCase_	:     List[Any] 						=      True
						lowerCamelCase_	:     List[Any] 						=      [element for element in array[i:] if element >= array[i]]
						lowerCamelCase_	:     Optional[int] 						=      longest_subsequence(lowerCAmelCase__						)
						if len(lowerCAmelCase__						) > len(lowerCAmelCase__						):
								lowerCamelCase_	:     int 						=      temp_array
				else:
						i += 1

		lowerCamelCase_	:     Optional[int] 						=      [element for element in array[1:] if element >= pivot]
		lowerCamelCase_	:     Dict 						=      [pivot, *longest_subsequence(lowerCAmelCase__						)]
		if len(lowerCAmelCase__						) > len(lowerCAmelCase__						):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
		import doctest

		doctest.testmod()







import os
import sys

_lowercase		:  List[str]			       =os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_lowercase		:  Optional[int]			       =[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]


@add_start_docstrings(AutoConfig.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoConfig.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoTokenizer.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoTokenizer.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModel.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModel.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForCausalLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForMaskedLM.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__						)
def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__							,**lowerCAmelCase__						):
		return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__							,**lowerCAmelCase__						)
