






"""simple docstring"""



import copy
import os
from typing import Union

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


__A		:       int  				=		logging.get_logger(__name__)

__A		:       List[str]  				=		{
    '''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							):
					SCREAMING_SNAKE_CASE_		:				int							    =   '''bridgetower_vision_model'''




					def __init__(				self     :						Optional[Any]    ,						A     :						Optional[Any]=7_68    ,						A     :						int=12    ,						A     :						Optional[int]=3    ,						A     :						Any=16    ,						A     :						str=2_88    ,						A     :						Dict=1    ,						A     :						Union[str, Any]=1e-05    ,						A     :						List[Any]=False    ,						A     :						Optional[Any]=True    ,						A     :						Dict=False    ,						**A     :						Dict    ,						)  ->  List[str]:
							super().__init__(**lowerCAmelCase__   )
							lowercase_ : Union[str, Any]							       =   hidden_size
							lowercase_ : Dict							       =   num_hidden_layers
							lowercase_ : str							       =   num_channels
							lowercase_ : Optional[Any]							       =   patch_size
							lowercase_ : Dict							       =   image_size
							lowercase_ : Optional[int]							       =   initializer_factor
							lowercase_ : List[str]							       =   layer_norm_eps
							lowercase_ : int							       =   stop_gradient
							lowercase_ : Optional[Any]							       =   share_layernorm
							lowercase_ : Optional[int]							       =   remove_last_layer






					@classmethod
					def   A  (				cls     :						str    ,						A     :						Union[str, Any]    ,						**A     :						Tuple   )  ->  List[str]:
							lowercase_       ,					lowercase_ : int							       =   cls.get_config_dict(lowerCAmelCase__    ,						**lowerCAmelCase__   )

							if config_dict.get('''model_type'''   ) == "bridgetower":
									lowercase_ : 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(lowerCAmelCase__    ,						**lowerCAmelCase__   )





class 					_UpperCAmelCase       ( __a							):
					SCREAMING_SNAKE_CASE_		:				int							    =   '''bridgetower_text_model'''




					def __init__(				self     :						int    ,						A     :						List[Any]=5_02_65    ,						A     :						Optional[Any]=7_68    ,						A     :						List[Any]=12    ,						A     :						Any=12    ,						A     :						Optional[int]=1    ,						A     :						Optional[int]=30_72    ,						A     :						str="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						Optional[Any]=0.1    ,						A     :						List[Any]=5_14    ,						A     :						List[Any]=1    ,						A     :						int=1e-05    ,						A     :						List[Any]=1    ,						A     :						Dict=0    ,						A     :						int=2    ,						A     :						Dict="absolute"    ,						A     :						List[str]=True    ,						**A     :						Optional[int]    ,						)  ->  Union[str, Any]:
							super().__init__(**lowerCAmelCase__   )

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






					@classmethod
					def   A  (				cls     :						int    ,						A     :						List[str]    ,						**A     :						List[Any]   )  ->  str:
							lowercase_       ,					lowercase_ : Optional[Any]							       =   cls.get_config_dict(lowerCAmelCase__    ,						**lowerCAmelCase__   )

							if config_dict.get('''model_type'''   ) == "bridgetower":
									lowercase_ : Optional[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(lowerCAmelCase__    ,						**lowerCAmelCase__   )





class 					_UpperCAmelCase       ( __a							):
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   '''bridgetower'''




					def __init__(				self     :						Union[str, Any]    ,						A     :						Optional[int]=True    ,						A     :						List[Any]="gelu"    ,						A     :						Union[str, Any]=7_68    ,						A     :						Optional[int]=1    ,						A     :						str=1e-05    ,						A     :						str=False    ,						A     :						int="add"    ,						A     :						int=12    ,						A     :						Union[str, Any]=6    ,						A     :						Dict=False    ,						A     :						int=False    ,						A     :						Tuple=None    ,						A     :						str=None    ,						**A     :						Optional[int]    ,						)  ->  str:
							# TODO: remove this once the Hub files are updated.
							lowercase_ : Union[str, Any]							       =   kwargs.pop('''text_config_dict'''    ,						lowerCAmelCase__   )
							lowercase_ : Optional[int]							       =   kwargs.pop('''vision_config_dict'''    ,						lowerCAmelCase__   )

							super().__init__(**lowerCAmelCase__   )
							lowercase_ : Union[str, Any]							       =   share_cross_modal_transformer_layers
							lowercase_ : Dict							       =   hidden_act
							lowercase_ : str							       =   hidden_size
							lowercase_ : str							       =   initializer_factor
							lowercase_ : str							       =   layer_norm_eps
							lowercase_ : Union[str, Any]							       =   share_link_tower_layers
							lowercase_ : Optional[Any]							       =   link_tower_type
							lowercase_ : Optional[Any]							       =   num_attention_heads
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : List[str]							       =   tie_word_embeddings
							lowercase_ : int							       =   init_layernorm_from_vision_encoder

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

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

							lowercase_ : Optional[Any]							       =   BridgeTowerTextConfig(**lowerCAmelCase__   )
							lowercase_ : Optional[int]							       =   BridgeTowerVisionConfig(**lowerCAmelCase__   )




					@classmethod
					def   A  (				cls     :						Any    ,						A     :						int    ,						A     :						Tuple    ,						**A     :						str   )  ->  int:
							return cls(text_config=text_config.to_dict()    ,						vision_config=vision_config.to_dict()    ,						**lowerCAmelCase__   )






					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Tuple							       =   copy.deepcopy(self.__dict__   )
							lowercase_ : int							       =   self.text_config.to_dict()
							lowercase_ : int							       =   self.vision_config.to_dict()
							lowercase_ : str							       =   self.__class__.model_type
							return output





"""simple docstring"""





from collections import defaultdict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = first_str.lower().strip()
		__SCREAMING_SNAKE_CASE            = second_str.lower().strip()

		# Remove whitespace
		__SCREAMING_SNAKE_CASE            = first_str.replace(""" """  ,				""""""				)
		__SCREAMING_SNAKE_CASE            = second_str.replace(""" """  ,				""""""				)

		# Strings of different lengths are not anagrams
		if len(UpperCamelCase_				) != len(UpperCamelCase_				):
				return False

		# Default values for count should be 0
		__SCREAMING_SNAKE_CASE            = defaultdict(UpperCamelCase_				)

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(UpperCamelCase_				)				):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							__magic_name__       =			input("Enter the first string ").strip()
							__magic_name__       =			input("Enter the second string ").strip()

							__magic_name__       =			check_anagrams(input_a, input_b)
							print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")




