




import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__     :     List[str]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       {
    "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       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''bridgetower_vision_model'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=768      ,     SCREAMING_SNAKE_CASE=12      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=16      ,     SCREAMING_SNAKE_CASE=288      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=1e-05      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=False      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Any:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						hidden_size
				SCREAMING_SNAKE_CASE_								=						num_hidden_layers
				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						patch_size
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						initializer_factor
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						stop_gradient
				SCREAMING_SNAKE_CASE_								=						share_layernorm
				SCREAMING_SNAKE_CASE_								=						remove_last_layer





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					"PretrainedConfig":




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						cls.get_config_dict(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )

				if config_dict.get('model_type'      ) == "bridgetower":
							SCREAMING_SNAKE_CASE_								=						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(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''bridgetower_text_model'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=5_0265      ,     SCREAMING_SNAKE_CASE=768      ,     SCREAMING_SNAKE_CASE=12      ,     SCREAMING_SNAKE_CASE=12      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=3072      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=0.1      ,     SCREAMING_SNAKE_CASE=514      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=1e-05      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=0      ,     SCREAMING_SNAKE_CASE=2      ,     SCREAMING_SNAKE_CASE="absolute"      ,     SCREAMING_SNAKE_CASE=True      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Optional[Any]:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

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





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					"PretrainedConfig":




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						cls.get_config_dict(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )

				if config_dict.get('model_type'      ) == "bridgetower":
							SCREAMING_SNAKE_CASE_								=						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(SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''bridgetower'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="gelu"      ,     SCREAMING_SNAKE_CASE=768      ,     SCREAMING_SNAKE_CASE=1      ,     SCREAMING_SNAKE_CASE=1e-05      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE="add"      ,     SCREAMING_SNAKE_CASE=12      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE=None      ,     SCREAMING_SNAKE_CASE=None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						kwargs.pop('text_config_dict'      ,     SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('vision_config_dict'      ,     SCREAMING_SNAKE_CASE      )

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

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

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

				SCREAMING_SNAKE_CASE_								=						BridgeTowerTextConfig(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						BridgeTowerVisionConfig(**SCREAMING_SNAKE_CASE      )





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Optional[int]:




				"""simple docstring"""



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





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						copy.deepcopy(self.__dict__      )
				SCREAMING_SNAKE_CASE_								=						self.text_config.to_dict()
				SCREAMING_SNAKE_CASE_								=						self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE_								=						self.__class__.model_type
				return output






def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if index == r:
						for j in range(SCREAMING_SNAKE_CASE ):
									print(data[j]       ,    end=' ' )
						print(' ' )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			SCREAMING_SNAKE_CASE_								=						arr[i]
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    0       ,    SCREAMING_SNAKE_CASE       ,    0 )


if __name__ == "__main__":
				# Driver code to check the function above
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [10, 20, 30, 40, 50]
				print_combination(arr, len(arr), 3)
				# This code is contributed by Ambuj sahu
