






import copy
import os
from typing import Union

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


__lowerCamelCase      :    Optional[int]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Dict   		=   {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"bridgetower_vision_model"



					def __init__(      self  :						Dict							,	__A  :						Optional[int]=7_6_8							,	__A  :						Optional[int]=1_2							,	__A  :						Dict=3							,	__A  :						Optional[Any]=1_6							,	__A  :						Any=2_8_8							,	__A  :						str=1							,	__A  :						Any=1e-0_5							,	__A  :						Optional[int]=False							,	__A  :						Optional[Any]=True							,	__A  :						List[str]=False							,	**__A  :						Union[str, Any]							,	):
						super().__init__(**__A )
						snake_case__				:   Any     =						hidden_size
						snake_case__				:   List[Any]     =						num_hidden_layers
						snake_case__				:   Any     =						num_channels
						snake_case__				:   str     =						patch_size
						snake_case__				:   Dict     =						image_size
						snake_case__				:   Union[str, Any]     =						initializer_factor
						snake_case__				:   Dict     =						layer_norm_eps
						snake_case__				:   Tuple     =						stop_gradient
						snake_case__				:   Any     =						share_layernorm
						snake_case__				:   Tuple     =						remove_last_layer






					@classmethod
					def  _lowercase  (      cls  :						Dict							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Tuple ):
						snake_case__				:   Tuple     =						cls.get_config_dict(__A							,	**__A )

						if config_dict.get("model_type" ) == "bridgetower":
							snake_case__				:   List[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_			=	"bridgetower_text_model"



					def __init__(      self  :						Optional[int]							,	__A  :						str=5_0_2_6_5							,	__A  :						List[Any]=7_6_8							,	__A  :						int=1_2							,	__A  :						Optional[Any]=1_2							,	__A  :						str=1							,	__A  :						Dict=3_0_7_2							,	__A  :						Tuple="gelu"							,	__A  :						Optional[Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=5_1_4							,	__A  :						List[str]=1							,	__A  :						List[Any]=1e-0_5							,	__A  :						int=1							,	__A  :						str=0							,	__A  :						str=2							,	__A  :						Union[str, Any]="absolute"							,	__A  :						Optional[Any]=True							,	**__A  :						Optional[Any]							,	):
						super().__init__(**__A )

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






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

						if config_dict.get("model_type" ) == "bridgetower":
							snake_case__				:   Optional[int]     =						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_			=	"bridgetower"



					def __init__(      self  :						Optional[int]							,	__A  :						Dict=True							,	__A  :						Union[str, Any]="gelu"							,	__A  :						Tuple=7_6_8							,	__A  :						Dict=1							,	__A  :						Optional[Any]=1e-0_5							,	__A  :						Optional[int]=False							,	__A  :						int="add"							,	__A  :						List[Any]=1_2							,	__A  :						Any=6							,	__A  :						List[str]=False							,	__A  :						int=False							,	__A  :						List[Any]=None							,	__A  :						Union[str, Any]=None							,	**__A  :						str							,	):
						# TODO: remove this once the Hub files are updated.
						snake_case__				:   Optional[int]     =						kwargs.pop("text_config_dict"							,	__A )
						snake_case__				:   Tuple     =						kwargs.pop("vision_config_dict"							,	__A )

						super().__init__(**__A )
						snake_case__				:   Dict     =						share_cross_modal_transformer_layers
						snake_case__				:   Optional[int]     =						hidden_act
						snake_case__				:   Optional[Any]     =						hidden_size
						snake_case__				:   Any     =						initializer_factor
						snake_case__				:   Any     =						layer_norm_eps
						snake_case__				:   Optional[Any]     =						share_link_tower_layers
						snake_case__				:   Any     =						link_tower_type
						snake_case__				:   Optional[Any]     =						num_attention_heads
						snake_case__				:   Dict     =						num_hidden_layers
						snake_case__				:   Tuple     =						tie_word_embeddings
						snake_case__				:   Any     =						init_layernorm_from_vision_encoder

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

						if vision_config is None:
							snake_case__				:   int     =						{}
							logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values." )

						snake_case__				:   Optional[int]     =						BridgeTowerTextConfig(**__A )
						snake_case__				:   Optional[Any]     =						BridgeTowerVisionConfig(**__A )



					@classmethod
					def  _lowercase  (      cls  :						Any							,	__A  :						BridgeTowerTextConfig							,	__A  :						BridgeTowerVisionConfig							,	**__A  :						List[str] ):
						return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A )






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Tuple     =						copy.deepcopy(self.__dict__ )
						snake_case__				:   str     =						self.text_config.to_dict()
						snake_case__				:   Dict     =						self.vision_config.to_dict()
						snake_case__				:   str     =						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))



