
import copy
import os
from typing import Union

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


A     :	Tuple    = logging.get_logger(__name__)

A     :	Dict    = {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}







class 			__A( a			):
				snake_case_						   =    '''blip_text_model'''




				def __init__(     self		,     _snake_case=30_524		,     _snake_case=768		,     _snake_case=768		,     _snake_case=3_072		,     _snake_case=768		,     _snake_case=12		,     _snake_case=8		,     _snake_case=512		,     _snake_case="gelu"		,     _snake_case=1E-12		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=30_522		,     _snake_case=2		,     _snake_case=0		,     _snake_case=102		,     _snake_case=True		,     _snake_case=True		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								super().__init__(
								    pad_token_id=_snake_case		,     bos_token_id=_snake_case		,     eos_token_id=_snake_case		,     sep_token_id=_snake_case		,     **_snake_case		,     )

								__a										=    vocab_size
								__a										=    hidden_size
								__a										=    encoder_hidden_size
								__a										=    intermediate_size
								__a										=    projection_dim
								__a										=    hidden_dropout_prob
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    max_position_embeddings
								__a										=    layer_norm_eps
								__a										=    hidden_act
								__a										=    initializer_range
								__a										=    attention_probs_dropout_prob
								__a										=    is_decoder
								__a										=    use_cache




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     **_snake_case				)    ->      "PretrainedConfig":


								'''simple docstring'''




								cls._set_token_in_kwargs(_snake_case				)

								__a							,     __a										=    cls.get_config_dict(_snake_case		,     **_snake_case				)

								# get the text config dict if we are loading from BlipConfig
								if config_dict.get('''model_type'''				) == "blip":
												__a										=    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(_snake_case		,     **_snake_case				)







class 			__A( a			):
				snake_case_						   =    '''blip_vision_model'''




				def __init__(     self		,     _snake_case=768		,     _snake_case=3_072		,     _snake_case=512		,     _snake_case=12		,     _snake_case=12		,     _snake_case=384		,     _snake_case=16		,     _snake_case="gelu"		,     _snake_case=1E-5		,     _snake_case=0.0		,     _snake_case=1E-10		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

								__a										=    hidden_size
								__a										=    intermediate_size
								__a										=    projection_dim
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    patch_size
								__a										=    image_size
								__a										=    initializer_range
								__a										=    attention_dropout
								__a										=    layer_norm_eps
								__a										=    hidden_act




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     **_snake_case				)    ->      "PretrainedConfig":


								'''simple docstring'''




								cls._set_token_in_kwargs(_snake_case				)

								__a							,     __a										=    cls.get_config_dict(_snake_case		,     **_snake_case				)

								# get the vision config dict if we are loading from BlipConfig
								if config_dict.get('''model_type'''				) == "blip":
												__a										=    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(_snake_case		,     **_snake_case				)







class 			__A( a			):
				snake_case_						   =    '''blip'''
				snake_case_						   =    True




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=512		,     _snake_case=2.6592		,     _snake_case=256		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

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

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

								__a										=    BlipTextConfig(**_snake_case				)
								__a										=    BlipVisionConfig(**_snake_case				)

								__a										=    self.vision_config.hidden_size

								__a										=    projection_dim
								__a										=    logit_scale_init_value
								__a										=    1.0
								__a										=    0.02
								__a										=    image_text_hidden_size




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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

def     __lowerCAmelCase			(			a__  )       ->  str:
				__a										=    []
				__a										=    set({'''(''', '''[''', '''{'''}  )
				__a										=    set({''')''', ''']''', '''}'''}  )
				__a										=    {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

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

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

				return len(a__  ) == 0


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    input('''Enter sequence of brackets: '''  )
				if is_balanced(a__  ):
								print(a__						,    '''is balanced'''  )
				else:
								print(a__						,    '''is not balanced'''  )


if __name__ == "__main__":
					main()