





'''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__   :				List[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''blip_2_vision_model'''


     def __init__(						self						,     _UpperCAmelCase=1408						,     _UpperCAmelCase=6144						,     _UpperCAmelCase=39						,     _UpperCAmelCase=16						,     _UpperCAmelCase=224						,     _UpperCAmelCase=14						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.00001						,     _UpperCAmelCase=0.0						,     _UpperCAmelCase=1e-1_0						,     _UpperCAmelCase=True						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)

          __A						:			str								=	hidden_size
          __A						:			Optional[int]								=	intermediate_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			Union[str, Any]								=	num_attention_heads
          __A						:			List[str]								=	patch_size
          __A						:			List[Any]								=	image_size
          __A						:			Union[str, Any]								=	initializer_range
          __A						:			Optional[Any]								=	attention_dropout
          __A						:			Dict								=	layer_norm_eps
          __A						:			List[str]								=	hidden_act
          __A						:			Optional[int]								=	qkv_bias


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          cls._set_token_in_kwargs(_UpperCAmelCase)

          __A						:			Optional[Any]								=	cls.get_config_dict(_UpperCAmelCase						,     **_UpperCAmelCase)

          # get the vision config dict if we are loading from Blip2Config
          if config_dict.get('model_type') == "blip-2":
               __A						:			Optional[Any]								=	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(_UpperCAmelCase						,     **_UpperCAmelCase)




class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''blip_2_qformer'''


     def __init__(						self						,     _UpperCAmelCase=3_0522						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=0						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=2						,     _UpperCAmelCase=1408						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,     **_UpperCAmelCase)

          __A						:			Union[str, Any]								=	vocab_size
          __A						:			str								=	hidden_size
          __A						:			List[Any]								=	num_hidden_layers
          __A						:			Any								=	num_attention_heads
          __A						:			Union[str, Any]								=	hidden_act
          __A						:			Any								=	intermediate_size
          __A						:			Tuple								=	hidden_dropout_prob
          __A						:			Dict								=	attention_probs_dropout_prob
          __A						:			Tuple								=	max_position_embeddings
          __A						:			str								=	initializer_range
          __A						:			Union[str, Any]								=	layer_norm_eps
          __A						:			Union[str, Any]								=	position_embedding_type
          __A						:			Optional[Any]								=	cross_attention_frequency
          __A						:			List[str]								=	encoder_hidden_size


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          cls._set_token_in_kwargs(_UpperCAmelCase)

          __A						:			int								=	cls.get_config_dict(_UpperCAmelCase						,     **_UpperCAmelCase)

          # get the qformer config dict if we are loading from Blip2Config
          if config_dict.get('model_type') == "blip-2":
               __A						:			List[str]								=	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(_UpperCAmelCase						,     **_UpperCAmelCase)




class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''blip-2'''
     lowerCAmelCase		      =	True


     def __init__(						self						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=32						,     **_UpperCAmelCase):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)

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

          if qformer_config is None:
               __A						:			Optional[Any]								=	{}
               logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.')

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

          __A						:			Optional[Any]								=	BlipaVisionConfig(**_UpperCAmelCase)
          __A						:			Optional[Any]								=	BlipaQFormerConfig(**_UpperCAmelCase)
          __A						:			int								=	text_config['model_type'] if 'model_type' in text_config else 'opt'
          __A						:			Any								=	CONFIG_MAPPING[text_model_type](**_UpperCAmelCase)

          __A						:			int								=	self.text_config.tie_word_embeddings
          __A						:			Union[str, Any]								=	self.text_config.is_encoder_decoder

          __A						:			int								=	num_query_tokens
          __A						:			str								=	self.vision_config.hidden_size
          __A						:			Tuple								=	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          __A						:			Any								=	1.0
          __A						:			Optional[int]								=	0.02


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          return cls(
              vision_config=vision_config.to_dict()						,     qformer_config=qformer_config.to_dict()						,     text_config=text_config.to_dict()						,     **_UpperCAmelCase						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	copy.deepcopy(self.__dict__)
          __A						:			int								=	self.vision_config.to_dict()
          __A						:			Optional[int]								=	self.qformer_config.to_dict()
          __A						:			Dict								=	self.text_config.to_dict()
          __A						:			Union[str, Any]								=	self.__class__.model_type
          return output






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					str:
     __A						:			Optional[Any]								=	[0] * len(__snake_case      )
     __A						:			Dict								=	[]
     __A						:			Optional[int]								=	[1] * len(__snake_case      )

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(__snake_case      )      ):
          if indegree[i] == 0:
               queue.append(__snake_case      )

     while queue:
          __A						:			int								=	queue.pop(0      )
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    __A						:			str								=	long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(__snake_case      )

     print(max(__snake_case      )      )


# Adjacency list of Graph
lowercase__   :				Dict      =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}







longest_distance(graph)