
"""simple docstring"""





import copy
import os
from typing import Union

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower_vision_model'



		def __init__( self    ,		__a=7_68    ,		__a=12    ,		__a=3    ,		__a=16    ,		__a=2_88    ,		__a=1    ,		__a=1e-05    ,		__a=False    ,		__a=True    ,		__a=False    ,		**__a    ,		)	->	List[Any]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  stop_gradient
							_UpperCamelCase										=  share_layernorm
							_UpperCamelCase										=  remove_last_layer




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase										=  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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower_text_model'



		def __init__( self    ,		__a=5_02_65    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=1    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_14    ,		__a=1    ,		__a=1e-05    ,		__a=1    ,		__a=0    ,		__a=2    ,		__a="absolute"    ,		__a=True    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							super().__init__(**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  pad_token_id
							_UpperCamelCase										=  bos_token_id
							_UpperCamelCase										=  eos_token_id




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							if config_dict.get('''model_type''') == "bridgetower":
												_UpperCamelCase										=  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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bridgetower'



		def __init__( self    ,		__a=True    ,		__a="gelu"    ,		__a=7_68    ,		__a=1    ,		__a=1e-05    ,		__a=False    ,		__a="add"    ,		__a=12    ,		__a=6    ,		__a=False    ,		__a=False    ,		__a=None    ,		__a=None    ,		**__a    ,		)	->	int:






							'''simple docstring'''
							# TODO: remove this once the Hub files are updated.
							_UpperCamelCase										=  kwargs.pop('''text_config_dict'''    ,		__a)
							_UpperCamelCase										=  kwargs.pop('''vision_config_dict'''    ,		__a)

							super().__init__(**__a)
							_UpperCamelCase										=  share_cross_modal_transformer_layers
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  initializer_factor
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  share_link_tower_layers
							_UpperCamelCase										=  link_tower_type
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  tie_word_embeddings
							_UpperCamelCase										=  init_layernorm_from_vision_encoder

							if text_config is None:
												_UpperCamelCase										=  {}
												logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''')

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

							_UpperCamelCase										=  BridgeTowerTextConfig(**__a)
							_UpperCamelCase										=  BridgeTowerVisionConfig(**__a)



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		__a    ,		**__a)	->	Union[str, Any]:






							'''simple docstring'''
							return cls(text_config=text_config.to_dict()    ,		vision_config=vision_config.to_dict()    ,		**__a)




		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							_UpperCamelCase										=  self.text_config.to_dict()
							_UpperCamelCase										=  self.vision_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output








"""simple docstring"""





from __future__ import annotations



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					if len(__snake_case     ) <= 1 or n <= 1:
										return

					insert_next(__snake_case,					n - 1     )
					rec_insertion_sort(__snake_case,					n - 1     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Dict:


					"""simple docstring"""





					if index >= len(__snake_case     ) or collection[index - 1] <= collection[index]:
										return

					# Swaps adjacent elements since they are not in ascending order
					_UpperCamelCase			,  _UpperCamelCase										=  (
					    collection[index],
					    collection[index - 1],
					)

					insert_next(__snake_case,					index + 1     )


if __name__ == "__main__":
				_a														=		input("""Enter integers separated by spaces: """)
				_a														=		[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)






