


from __future__ import annotations

from random import randrange





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):






							"""simple docstring"""



							if len(SCREAMING_SNAKE_CASE_      ) < 2:
														return collection
							a_       :   List[str]			=	randrange(len(SCREAMING_SNAKE_CASE_      )      )  # Use random element as pivot
							a_       :   int			=	collection[pivot_index]
							a_       :   list[int]			=	[]  # All elements greater than pivot
							a_       :   list[int]			=	[]  # All elements less than or equal to pivot

							for element in collection[:pivot_index]:
														(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE_      )

							for element in collection[pivot_index + 1 :]:
														(greater if element > pivot else lesser).append(SCREAMING_SNAKE_CASE_      )

							return [*quick_sort(SCREAMING_SNAKE_CASE_      ), pivot, *quick_sort(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE : Union[str, Any]          =    input("Enter numbers separated by a comma:\n").strip()
	SCREAMING_SNAKE_CASE : List[Any]          =    [int(item) for item in user_input.split(",")]
	print(quick_sort(unsorted))






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[Any]          =    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Tuple         = """oneformer"""
	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}





	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
								"""simple docstring"""






								if backbone_config is None:
															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
															a_       :   str			=	backbone_config.get("""model_type"""							)
															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)

								a_       :   str			=	backbone_config

								a_       :   List[str]			=	ignore_value
								a_       :   Optional[int]			=	num_queries
								a_       :   Optional[Any]			=	no_object_weight
								a_       :   List[Any]			=	class_weight
								a_       :   Union[str, Any]			=	mask_weight
								a_       :   Union[str, Any]			=	dice_weight
								a_       :   Optional[Any]			=	contrastive_weight
								a_       :   Any			=	contrastive_temperature
								a_       :   Optional[int]			=	train_num_points
								a_       :   Tuple			=	oversample_ratio
								a_       :   str			=	importance_sample_ratio
								a_       :   List[Any]			=	init_std
								a_       :   int			=	init_xavier_std
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   Optional[int]			=	is_training
								a_       :   List[Any]			=	use_auxiliary_loss
								a_       :   str			=	output_auxiliary_logits
								a_       :   Dict			=	strides
								a_       :   List[Any]			=	task_seq_len
								a_       :   int			=	text_encoder_width
								a_       :   List[Any]			=	text_encoder_context_length
								a_       :   List[str]			=	text_encoder_num_layers
								a_       :   Union[str, Any]			=	text_encoder_vocab_size
								a_       :   Optional[Any]			=	text_encoder_proj_layers
								a_       :   Optional[int]			=	text_encoder_n_ctx
								a_       :   Optional[Any]			=	conv_dim
								a_       :   str			=	mask_dim
								a_       :   Optional[Any]			=	hidden_dim
								a_       :   Any			=	encoder_feedforward_dim
								a_       :   Dict			=	norm
								a_       :   List[Any]			=	encoder_layers
								a_       :   int			=	decoder_layers
								a_       :   Any			=	use_task_norm
								a_       :   Union[str, Any]			=	num_attention_heads
								a_       :   List[str]			=	dropout
								a_       :   List[Any]			=	dim_feedforward
								a_       :   Union[str, Any]			=	pre_norm
								a_       :   Dict			=	enforce_input_proj
								a_       :   Tuple			=	query_dec_layers
								a_       :   Any			=	common_stride
								a_       :   List[str]			=	decoder_layers

								super().__init__(**UpperCamelCase_							)





	def     A				(  self							)		->			Dict[str, any]:
								"""simple docstring"""






								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
								a_       :   Any			=	self.backbone_config.to_dict()
								a_       :   Union[str, Any]			=	self.__class__.model_type
								return output


