'''simple docstring'''

def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=set()
						# To detect a back edge, keep track of vertices currently in the recursion stack
						_lowerCAmelCase										=set()
						return any(
						    node not in visited and depth_first_search(a__			,      a__			,      a__			,      a__      )
						    for node in graph      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						visited.add(a__      )
						rec_stk.add(a__      )

						for node in graph[vertex]:
												if node not in visited:
																		if depth_first_search(a__			,      a__			,      a__			,      a__      ):
																								return True
												elif node in rec_stk:
																		return True

    # The node needs to be removed from recursion stack before function ends
						rec_stk.remove(a__      )
						return False


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

							testmod()






'''simple docstring'''

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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							'blip_2_vision_model'






		def __init__(   self		,						__A=1408		,						__A=6144		,						__A=39		,						__A=16		,						__A=224		,						__A=14		,						__A="gelu"		,						__A=0.00_001		,						__A=0.0		,						__A=1E-10		,						__A=True		,						**__A		,						) ->     int:
								super().__init__(**__A				)

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






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						**__A				) ->     "PretrainedConfig":
								cls._set_token_in_kwargs(__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

								# get the vision config dict if we are loading from Blip2Config
								if config_dict.get('model_type'				) == "blip-2":
														_lowerCAmelCase										=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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'blip_2_qformer'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=0.02		,						__A=1E-12		,						__A=0		,						__A="absolute"		,						__A=2		,						__A=1408		,						**__A		,						) ->     List[str]:
								super().__init__(pad_token_id=__A		,						**__A				)

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






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						**__A				) ->     "PretrainedConfig":
								cls._set_token_in_kwargs(__A				)

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

								# get the qformer config dict if we are loading from Blip2Config
								if config_dict.get('model_type'				) == "blip-2":
														_lowerCAmelCase										=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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							'blip-2'
		lowercase   :      Any       =							True






		def __init__(   self		,						__A=None		,						__A=None		,						__A=None		,						__A=32		,						**__A				) ->     int:
								super().__init__(**__A				)

								if vision_config is None:
														_lowerCAmelCase										={}
														logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'				)

								if qformer_config is None:
														_lowerCAmelCase										={}
														logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'				)

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

								_lowerCAmelCase										=BlipaVisionConfig(**__A				)
								_lowerCAmelCase										=BlipaQFormerConfig(**__A				)
								_lowerCAmelCase										=text_config['model_type'] if 'model_type' in text_config else 'opt'
								_lowerCAmelCase										=CONFIG_MAPPING[text_model_type](**__A				)

								_lowerCAmelCase										=self.text_config.tie_word_embeddings
								_lowerCAmelCase										=self.text_config.is_encoder_decoder

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






		@classmethod
		def 		UpperCamelCase__       (   cls		,						__A		,						__A		,						__A		,						**__A		,						) ->     Any:
								return cls(
								    vision_config=vision_config.to_dict()		,						qformer_config=qformer_config.to_dict()		,						text_config=text_config.to_dict()		,						**__A		,						)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=copy.deepcopy(self.__dict__				)
								_lowerCAmelCase										=self.vision_config.to_dict()
								_lowerCAmelCase										=self.qformer_config.to_dict()
								_lowerCAmelCase										=self.text_config.to_dict()
								_lowerCAmelCase										=self.__class__.model_type
								return output





