
"""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


A_    							=					logging.get_logger(__name__)

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




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Optional[int]          =    'blip_2_vision_model'




				def __init__(				self							,						UpperCAmelCase=1408							,						UpperCAmelCase=6144							,						UpperCAmelCase=39							,						UpperCAmelCase=16							,						UpperCAmelCase=224							,						UpperCAmelCase=14							,						UpperCAmelCase="gelu"							,						UpperCAmelCase=0.0_0_0_0_1							,						UpperCAmelCase=0.0							,						UpperCAmelCase=1e-1_0							,						UpperCAmelCase=True							,						**UpperCAmelCase							,						):
							super().__init__(**UpperCAmelCase   )

							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							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							cls._set_token_in_kwargs(UpperCAmelCase   )

							lowerCamelCase_		, lowerCamelCase_					=  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":
										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(UpperCAmelCase							,						**UpperCAmelCase   )




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Optional[int]          =    '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=0.0_2							,						UpperCAmelCase=1e-1_2							,						UpperCAmelCase=0							,						UpperCAmelCase="absolute"							,						UpperCAmelCase=2							,						UpperCAmelCase=1408							,						**UpperCAmelCase							,						):
							super().__init__(pad_token_id=UpperCAmelCase							,						**UpperCAmelCase   )

							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							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							cls._set_token_in_kwargs(UpperCAmelCase   )

							lowerCamelCase_		, lowerCamelCase_					=  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":
										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(UpperCAmelCase							,						**UpperCAmelCase   )




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Union[str, Any]          =    'blip-2'
				a__:      str          =    True




				def __init__(				self							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=None							,						UpperCAmelCase=32							,						**UpperCAmelCase   ):
							super().__init__(**UpperCAmelCase   )

							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(**UpperCAmelCase   )
							lowerCamelCase_					=  BlipaQFormerConfig(**UpperCAmelCase   )
							lowerCamelCase_					=  text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
							lowerCamelCase_					=  CONFIG_MAPPING[text_model_type](**UpperCAmelCase   )

							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_					=  0.0_2




				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						**UpperCAmelCase							,						):
							return cls(
							    vision_config=vision_config.to_dict()							,						qformer_config=qformer_config.to_dict()							,						text_config=text_config.to_dict()							,						**UpperCAmelCase							,						)





				def        UpperCAmelCase__						(				self   ):
							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









'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






