



import copy
import os
from typing import Union

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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "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_					( a):
							lowerCamelCase__         =	'bridgetower_vision_model'







							def __init__(     self,    __a=768,    __a=12,    __a=3,    __a=16,    __a=288,    __a=1,    __a=1E-05,    __a=False,    __a=True,    __a=False,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase  :							int				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_channels
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Any				     =					image_size
														_lowerCAmelCase  :							Optional[Any]				     =					initializer_factor
														_lowerCAmelCase  :							Any				     =					layer_norm_eps
														_lowerCAmelCase  :							Dict				     =					stop_gradient
														_lowerCAmelCase  :							List[Any]				     =					share_layernorm
														_lowerCAmelCase  :							Tuple				     =					remove_last_layer







							@classmethod
							def      snake_case__   (     cls,    __a,    **__a):

														'''simple docstring'''

														_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					cls.get_config_dict(__a,    **__a)

														if config_dict.get("model_type") == "bridgetower":
																					_lowerCAmelCase  :							Dict				     =					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_					( a):
							lowerCamelCase__         =	'bridgetower_text_model'







							def __init__(     self,    __a=5_0265,    __a=768,    __a=12,    __a=12,    __a=1,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=514,    __a=1,    __a=1E-05,    __a=1,    __a=0,    __a=2,    __a="absolute",    __a=True,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

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







							@classmethod
							def      snake_case__   (     cls,    __a,    **__a):

														'''simple docstring'''

														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					cls.get_config_dict(__a,    **__a)

														if config_dict.get("model_type") == "bridgetower":
																					_lowerCAmelCase  :							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 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'bridgetower'







							def __init__(     self,    __a=True,    __a="gelu",    __a=768,    __a=1,    __a=1E-05,    __a=False,    __a="add",    __a=12,    __a=6,    __a=False,    __a=False,    __a=None,    __a=None,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					kwargs.pop("text_config_dict",    __a)
														_lowerCAmelCase  :							Any				     =					kwargs.pop("vision_config_dict",    __a)

														super().__init__(**__a)
														_lowerCAmelCase  :							Optional[Any]				     =					share_cross_modal_transformer_layers
														_lowerCAmelCase  :							List[str]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_size
														_lowerCAmelCase  :							Tuple				     =					initializer_factor
														_lowerCAmelCase  :							Tuple				     =					layer_norm_eps
														_lowerCAmelCase  :							Optional[int]				     =					share_link_tower_layers
														_lowerCAmelCase  :							Any				     =					link_tower_type
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							Optional[int]				     =					num_hidden_layers
														_lowerCAmelCase  :							str				     =					tie_word_embeddings
														_lowerCAmelCase  :							Tuple				     =					init_layernorm_from_vision_encoder

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

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

														_lowerCAmelCase  :							Any				     =					BridgeTowerTextConfig(**__a)
														_lowerCAmelCase  :							List[str]				     =					BridgeTowerVisionConfig(**__a)







							@classmethod
							def      snake_case__   (     cls,    __a,    __a,    **__a):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							Tuple				     =					self.text_config.to_dict()
														_lowerCAmelCase  :							Optional[int]				     =					self.vision_config.to_dict()
														_lowerCAmelCase  :							int				     =					self.__class__.model_type
														return output





def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					len(_lowerCamelCase						)
							for i in range(1       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							List[Any]				     =					collection[i]
														_lowerCAmelCase  :							str				     =					0
														_lowerCAmelCase  :							Union[str, Any]				     =					i - 1

														while low <= high:
																					_lowerCAmelCase  :							List[str]				     =					(low + high) // 2
																					if val < collection[mid]:
																												_lowerCAmelCase  :							Optional[int]				     =					mid - 1
																					else:
																												_lowerCAmelCase  :							List[str]				     =					mid + 1
														for j in range(_lowerCamelCase       ,				_lowerCamelCase       ,				-1						):
																					_lowerCAmelCase  :							int				     =					collection[j - 1]
														_lowerCAmelCase  :							Optional[int]				     =					val
							return collection


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