






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
				pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :    int   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Dict   		=   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align_text_model"



					def __init__(      self  :						Dict							,	__A  :						Optional[Any]=3_0_5_2_2							,	__A  :						Optional[int]=7_6_8							,	__A  :						Dict=1_2							,	__A  :						Any=1_2							,	__A  :						List[str]=3_0_7_2							,	__A  :						Tuple="gelu"							,	__A  :						List[Any]=0.1							,	__A  :						List[str]=0.1							,	__A  :						Optional[Any]=5_1_2							,	__A  :						Optional[int]=2							,	__A  :						int=0.0_2							,	__A  :						Dict=1e-1_2							,	__A  :						Optional[int]=0							,	__A  :						List[Any]="absolute"							,	__A  :						str=True							,	**__A  :						Optional[Any]							,	):
						super().__init__(**__A )

						snake_case__				:   Any     =						vocab_size
						snake_case__				:   str     =						hidden_size
						snake_case__				:   str     =						num_hidden_layers
						snake_case__				:   List[str]     =						num_attention_heads
						snake_case__				:   Optional[int]     =						hidden_act
						snake_case__				:   Tuple     =						intermediate_size
						snake_case__				:   Optional[Any]     =						hidden_dropout_prob
						snake_case__				:   Dict     =						attention_probs_dropout_prob
						snake_case__				:   Optional[Any]     =						max_position_embeddings
						snake_case__				:   List[str]     =						type_vocab_size
						snake_case__				:   Union[str, Any]     =						initializer_range
						snake_case__				:   Dict     =						layer_norm_eps
						snake_case__				:   Optional[int]     =						position_embedding_type
						snake_case__				:   Tuple     =						use_cache
						snake_case__				:   List[str]     =						pad_token_id






					@classmethod
					def  _lowercase  (      cls  :						Tuple							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__				:   Dict     =						cls.get_config_dict(__A							,	**__A )

						# get the text config dict if we are loading from AlignConfig
						if config_dict.get("model_type" ) == "align":
							snake_case__				:   Any     =						config_dict["text_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align_vision_model"



					def __init__(      self  :						Union[str, Any]							,	__A  :						int = 3							,	__A  :						int = 6_0_0							,	__A  :						float = 2.0							,	__A  :						float = 3.1							,	__A  :						int = 8							,	__A  :						List[int] = [3, 3, 5, 3, 5, 5, 3]							,	__A  :						List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]							,	__A  :						List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]							,	__A  :						List[int] = []							,	__A  :						List[int] = [1, 2, 2, 2, 1, 2, 1]							,	__A  :						List[int] = [1, 2, 2, 3, 3, 4, 1]							,	__A  :						List[int] = [1, 6, 6, 6, 6, 6, 6]							,	__A  :						float = 0.2_5							,	__A  :						str = "swish"							,	__A  :						int = 2_5_6_0							,	__A  :						str = "mean"							,	__A  :						float = 0.0_2							,	__A  :						float = 0.0_0_1							,	__A  :						float = 0.9_9							,	__A  :						float = 0.2							,	**__A  :						Optional[int]							,	):
						super().__init__(**__A )

						snake_case__				:   Optional[Any]     =						num_channels
						snake_case__				:   str     =						image_size
						snake_case__				:   Optional[Any]     =						width_coefficient
						snake_case__				:   int     =						depth_coefficient
						snake_case__				:   List[str]     =						depth_divisor
						snake_case__				:   int     =						kernel_sizes
						snake_case__				:   str     =						in_channels
						snake_case__				:   Optional[Any]     =						out_channels
						snake_case__				:   Union[str, Any]     =						depthwise_padding
						snake_case__				:   Dict     =						strides
						snake_case__				:   Optional[int]     =						num_block_repeats
						snake_case__				:   Any     =						expand_ratios
						snake_case__				:   List[Any]     =						squeeze_expansion_ratio
						snake_case__				:   List[Any]     =						hidden_act
						snake_case__				:   Dict     =						hidden_dim
						snake_case__				:   str     =						pooling_type
						snake_case__				:   Any     =						initializer_range
						snake_case__				:   List[str]     =						batch_norm_eps
						snake_case__				:   Optional[Any]     =						batch_norm_momentum
						snake_case__				:   Any     =						drop_connect_rate
						snake_case__				:   Optional[int]     =						sum(__A ) * 4






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Dict ):
						cls._set_token_in_kwargs(__A )

						snake_case__				:   Any     =						cls.get_config_dict(__A							,	**__A )

						# get the vision config dict if we are loading from AlignConfig
						if config_dict.get("model_type" ) == "align":
							snake_case__				:   str     =						config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align"
					a_			=	True



					def __init__(      self  :						int							,	__A  :						List[str]=None							,	__A  :						Any=None							,	__A  :						Tuple=6_4_0							,	__A  :						int=1.0							,	__A  :						Tuple=0.0_2							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

						if text_config is None:
							snake_case__				:   List[Any]     =						{}
							logger.info("text_config is None. Initializing the AlignTextConfig with default values." )

						if vision_config is None:
							snake_case__				:   Union[str, Any]     =						{}
							logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )

						snake_case__				:   Any     =						AlignTextConfig(**__A )
						snake_case__				:   List[str]     =						AlignVisionConfig(**__A )

						snake_case__				:   List[str]     =						projection_dim
						snake_case__				:   Union[str, Any]     =						temperature_init_value
						snake_case__				:   int     =						initializer_range



					@classmethod
					def  _lowercase  (      cls  :						Optional[Any]							,	__A  :						AlignTextConfig							,	__A  :						AlignVisionConfig							,	**__A  :						Optional[int] ):
						return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A )






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Dict     =						copy.deepcopy(self.__dict__ )
						snake_case__				:   List[Any]     =						self.text_config.to_dict()
						snake_case__				:   Dict     =						self.vision_config.to_dict()
						snake_case__				:   Dict     =						self.__class__.model_type
						return output











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							list     ):
	if len(snake_case_     ) <= 1:
		return lst

	snake_case__				:   List[Any]     =						1

	while i < len(snake_case_     ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			snake_case__,		snake_case__				:   Tuple     =						lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				snake_case__				:   Union[str, Any]     =						1

	return lst


if __name__ == "__main__":
	__lowerCamelCase      :    Dict   		=   input("""Enter numbers separated by a comma:\n""").strip()
	__lowerCamelCase      :    Tuple   		=   [int(item) for item in user_input.split(""",""")]
	print(gnome_sort(unsorted))



