



'''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


lowerCamelCase							:int				    =					logging.get_logger(__name__)

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







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					'blip_2_vision_model'


	def __init__(self       ,      lowercase=1408       ,      lowercase=6144       ,      lowercase=39       ,      lowercase=16       ,      lowercase=224       ,      lowercase=14       ,      lowercase="gelu"       ,      lowercase=0.0_00_01       ,      lowercase=0.0       ,      lowercase=1E-10       ,      lowercase=True       ,      **lowercase       ,      ):
				super().__init__(**lowercase							)

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


	@classmethod
	def     _a	(cls       ,      lowercase       ,      **lowercase							):
				cls._set_token_in_kwargs(lowercase							)

				A_,    A_	:						str	=     cls.get_config_dict(lowercase       ,      **lowercase							)

				# get the vision config dict if we are loading from Blip2Config
				if config_dict.get("""model_type"""							) == "blip-2":
							A_	:						List[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(lowercase       ,      **lowercase							)







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Tuple					=					'blip_2_qformer'


	def __init__(self       ,      lowercase=30522       ,      lowercase=768       ,      lowercase=12       ,      lowercase=12       ,      lowercase=3072       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=512       ,      lowercase=0.02       ,      lowercase=1E-12       ,      lowercase=0       ,      lowercase="absolute"       ,      lowercase=2       ,      lowercase=1408       ,      **lowercase       ,      ):
				super().__init__(pad_token_id=lowercase       ,      **lowercase							)

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


	@classmethod
	def     _a	(cls       ,      lowercase       ,      **lowercase							):
				cls._set_token_in_kwargs(lowercase							)

				A_,    A_	:						str	=     cls.get_config_dict(lowercase       ,      **lowercase							)

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







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'blip-2'
	__SCREAMING_SNAKE_CASE   :		Dict					=					True


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

				if vision_config is None:
							A_	:						Dict	=     {}
							logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""							)

				if qformer_config is None:
							A_	:						Union[str, Any]	=     {}
							logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""							)

				if text_config is None:
							A_	:						Optional[Any]	=     {}
							logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""							)

				A_	:						int	=     BlipaVisionConfig(**lowercase							)
				A_	:						str	=     BlipaQFormerConfig(**lowercase							)
				A_	:						Optional[Any]	=     text_config["""model_type"""] if """model_type""" in text_config else """opt"""
				A_	:						List[str]	=     CONFIG_MAPPING[text_model_type](**lowercase							)

				A_	:						Tuple	=     self.text_config.tie_word_embeddings
				A_	:						Tuple	=     self.text_config.is_encoder_decoder

				A_	:						Optional[int]	=     num_query_tokens
				A_	:						List[Any]	=     self.vision_config.hidden_size
				A_	:						Dict	=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				A_	:						List[Any]	=     1.0
				A_	:						Optional[int]	=     0.02


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


	def     _a	(self							):
				A_	:						List[str]	=     copy.deepcopy(self.__dict__							)
				A_	:						Any	=     self.vision_config.to_dict()
				A_	:						Tuple	=     self.qformer_config.to_dict()
				A_	:						str	=     self.text_config.to_dict()
				A_	:						List[str]	=     self.__class__.model_type
				return output




'''simple docstring'''




# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCamelCase							:str				    =					TypeVar('''T''')







class 	_lowerCAmelCase							(			Generic[T] ):


	def __init__(self       ,      lowercase = True							):
				A_	:						dict[T, list[T]]	=     {}  # dictionary of lists
				A_	:						Any	=     directed


	def     _a	(self       ,      lowercase       ,      lowercase							):

				if not self.directed:  # For undirected graphs
							# if both source vertex and destination vertex are both present in the
							# adjacency list, add destination vertex to source vertex list of adjacent
							# vertices and add source vertex to destination vertex list of adjacent
							# vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										self.adj_list[destination_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination vertex
							# to source vertex list of adjacent vertices, then create a new vertex with
							# destination vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Dict	=     [source_vertex]
							# if only destination vertex is present in adjacency list, add source vertex
							# to destination vertex list of adjacent vertices, then create a new vertex
							# with source vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif destination_vertex in self.adj_list:
										self.adj_list[destination_vertex].append(lowercase							)
										A_	:						int	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and assign a list
							# containing the destination vertex as it's first adjacent vertex also
							# create a new vertex with destination vertex as key and assign a list
							# containing the source vertex as it's first adjacent vertex.
							else:
										A_	:						Optional[Any]	=     [destination_vertex]
										A_	:						Tuple	=     [source_vertex]
				else:  # For directed graphs
							# if both source vertex and destination vertex are present in adjacency
							# list, add destination vertex to source vertex list of adjacent vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination
							# vertex to source vertex list of adjacent vertices and create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Tuple	=     []
							# if only destination vertex is present in adjacency list, create a new
							# vertex with source vertex as key and assign a list containing destination
							# vertex as first adjacent vertex
							elif destination_vertex in self.adj_list:
										A_	:						Tuple	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and a list containing
							# destination vertex as it's first adjacent vertex. Then create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							else:
										A_	:						int	=     [destination_vertex]
										A_	:						List[str]	=     []

				return self


	def __repr__(self							):
				return pformat(self.adj_list							)