


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase       			=						logging.get_logger(__name__)

lowerCAmelCase       			=						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}





class  A       (  A_					):
							UpperCamelCase_  :				Optional[int]			='''instructblip_vision_model'''


							def __init__(self ,							lowerCAmelCase=1_4_0_8 ,							lowerCAmelCase=6_1_4_4 ,							lowerCAmelCase=3_9 ,							lowerCAmelCase=1_6 ,							lowerCAmelCase=2_2_4 ,							lowerCAmelCase=1_4 ,							lowerCAmelCase="gelu" ,							lowerCAmelCase=1E-6 ,							lowerCAmelCase=0.0 ,							lowerCAmelCase=1E-10 ,							lowerCAmelCase=True ,							**lowerCAmelCase ,							):
									super().__init__(**A__   )

									__lowercase=     hidden_size
									__lowercase=     intermediate_size
									__lowercase=     num_hidden_layers
									__lowercase=     num_attention_heads
									__lowercase=     patch_size
									__lowercase=     image_size
									__lowercase=     initializer_range
									__lowercase=     attention_dropout
									__lowercase=     layer_norm_eps
									__lowercase=     hidden_act
									__lowercase=     qkv_bias







							@classmethod
							def      _A			(cls ,							lowerCAmelCase ,							**lowerCAmelCase   ):
									cls._set_token_in_kwargs(A__   )

									__lowercase=     cls.get_config_dict(A__ ,							**A__   )

									# get the vision config dict if we are loading from InstructBlipConfig
									if config_dict.get('model_type'   ) == "instructblip":
											__lowercase=     config_dict["""vision_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  A       (  A_					):
							UpperCamelCase_  :				Dict			='''instructblip_qformer'''


							def __init__(self ,							lowerCAmelCase=3_0_5_2_2 ,							lowerCAmelCase=7_6_8 ,							lowerCAmelCase=1_2 ,							lowerCAmelCase=1_2 ,							lowerCAmelCase=3_0_7_2 ,							lowerCAmelCase="gelu" ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=5_1_2 ,							lowerCAmelCase=0.02 ,							lowerCAmelCase=1E-12 ,							lowerCAmelCase=0 ,							lowerCAmelCase="absolute" ,							lowerCAmelCase=2 ,							lowerCAmelCase=1_4_0_8 ,							**lowerCAmelCase ,							):
									super().__init__(pad_token_id=A__ ,							**A__   )

									__lowercase=     vocab_size
									__lowercase=     hidden_size
									__lowercase=     num_hidden_layers
									__lowercase=     num_attention_heads
									__lowercase=     hidden_act
									__lowercase=     intermediate_size
									__lowercase=     hidden_dropout_prob
									__lowercase=     attention_probs_dropout_prob
									__lowercase=     max_position_embeddings
									__lowercase=     initializer_range
									__lowercase=     layer_norm_eps
									__lowercase=     position_embedding_type
									__lowercase=     cross_attention_frequency
									__lowercase=     encoder_hidden_size







							@classmethod
							def      _A			(cls ,							lowerCAmelCase ,							**lowerCAmelCase   ):
									cls._set_token_in_kwargs(A__   )

									__lowercase=     cls.get_config_dict(A__ ,							**A__   )

									# get the qformer config dict if we are loading from InstructBlipConfig
									if config_dict.get('model_type'   ) == "instructblip":
											__lowercase=     config_dict["""qformer_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  A       (  A_					):
							UpperCamelCase_  :				Optional[Any]			='''instructblip'''
							UpperCamelCase_  :				Tuple			=True


							def __init__(self ,							lowerCAmelCase=None ,							lowerCAmelCase=None ,							lowerCAmelCase=None ,							lowerCAmelCase=3_2 ,							**lowerCAmelCase   ):
									super().__init__(**A__   )

									if vision_config is None:
											__lowercase=     {}
											logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

									if qformer_config is None:
											__lowercase=     {}
											logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

									if text_config is None:
											__lowercase=     {}
											logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'   )

									__lowercase=     InstructBlipVisionConfig(**A__   )
									__lowercase=     InstructBlipQFormerConfig(**A__   )
									__lowercase=     text_config["""model_type"""] if """model_type""" in text_config else """opt"""
									__lowercase=     CONFIG_MAPPING[text_model_type](**A__   )

									__lowercase=     self.text_config.tie_word_embeddings
									__lowercase=     self.text_config.is_encoder_decoder

									__lowercase=     num_query_tokens
									__lowercase=     self.vision_config.hidden_size
									__lowercase=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									__lowercase=     1.0
									__lowercase=     0.02


							@classmethod
							def      _A			(cls ,							lowerCAmelCase ,							lowerCAmelCase ,							lowerCAmelCase ,							**lowerCAmelCase ,							):
									return cls(
									    vision_config=vision_config.to_dict() ,							qformer_config=qformer_config.to_dict() ,							text_config=text_config.to_dict() ,							**A__ ,							)







							def      _A			(self   ):
									__lowercase=     copy.deepcopy(self.__dict__   )
									__lowercase=     self.vision_config.to_dict()
									__lowercase=     self.qformer_config.to_dict()
									__lowercase=     self.text_config.to_dict()
									__lowercase=     self.__class__.model_type
									return output









def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



