





'''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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				str											=       "instructblip_vision_model"


	def __init__(self     ,_lowerCamelCase=1408     ,_lowerCamelCase=6144     ,_lowerCamelCase=39     ,_lowerCamelCase=16     ,_lowerCamelCase=224     ,_lowerCamelCase=14     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=1E-6     ,_lowerCamelCase=0.0     ,_lowerCamelCase=1E-1_0     ,_lowerCamelCase=True     ,**_lowerCamelCase     ,)							->							Tuple:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )

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


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,**_lowerCamelCase )							->							"PretrainedConfig":







				'''simple docstring'''





				cls._set_token_in_kwargs(_lowerCamelCase )

				__lowercase						,		__lowercase										=							cls.get_config_dict(_lowerCamelCase     ,**_lowerCamelCase )

				# get the vision config dict if we are loading from InstructBlipConfig
				if config_dict.get('''model_type''' ) == "instructblip":
							__lowercase										=							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(_lowerCamelCase     ,**_lowerCamelCase )






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Dict											=       "instructblip_qformer"


	def __init__(self     ,_lowerCamelCase=30522     ,_lowerCamelCase=768     ,_lowerCamelCase=12     ,_lowerCamelCase=12     ,_lowerCamelCase=3072     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=0.1     ,_lowerCamelCase=0.1     ,_lowerCamelCase=512     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=1E-1_2     ,_lowerCamelCase=0     ,_lowerCamelCase="absolute"     ,_lowerCamelCase=2     ,_lowerCamelCase=1408     ,**_lowerCamelCase     ,)							->							Union[str, Any]:







				'''simple docstring'''





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

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


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,**_lowerCamelCase )							->							"PretrainedConfig":







				'''simple docstring'''





				cls._set_token_in_kwargs(_lowerCamelCase )

				__lowercase						,		__lowercase										=							cls.get_config_dict(_lowerCamelCase     ,**_lowerCamelCase )

				# get the qformer config dict if we are loading from InstructBlipConfig
				if config_dict.get('''model_type''' ) == "instructblip":
							__lowercase										=							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(_lowerCamelCase     ,**_lowerCamelCase )






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[str]											=       "instructblip"
	a    :				str											=       True


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=32     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )

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

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

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

				__lowercase										=							InstructBlipVisionConfig(**_lowerCamelCase )
				__lowercase										=							InstructBlipQFormerConfig(**_lowerCamelCase )
				__lowercase										=							text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
				__lowercase										=							CONFIG_MAPPING[text_model_type](**_lowerCamelCase )

				__lowercase										=							self.text_config.tie_word_embeddings
				__lowercase										=							self.text_config.is_encoder_decoder

				__lowercase										=							num_query_tokens
				__lowercase										=							self.vision_config.hidden_size
				__lowercase										=							self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
				__lowercase										=							1.0
				__lowercase										=							0.0_2


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,**_lowerCamelCase     ,)							->							Optional[int]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





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












'''simple docstring'''



from __future__ import annotations

from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							num_of_nodes
				__lowercase										=							[]
				__lowercase										=							{}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				self.m_edges.append([u_node, v_node, weight] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				if self.m_component[u_node] == u_node:
							return u_node
				return self.find_component(self.m_component[u_node] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if self.m_component[u_node] != u_node:
							for k in self.m_component:
										__lowercase										=							self.find_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if component_size[u_node] <= component_size[v_node]:
							__lowercase										=							v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(_lowerCamelCase )

				elif component_size[u_node] >= component_size[v_node]:
							__lowercase										=							self.find_component(_lowerCamelCase )
							component_size[u_node] += component_size[v_node]
							self.set_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							None:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							0

				__lowercase										=							[-1] * self.m_num_of_nodes

				# A list of components (initialized to all of the nodes)
				for node in range(self.m_num_of_nodes ):
							self.m_component.update({node: node} )
							component_size.append(1 )

				__lowercase										=							self.m_num_of_nodes

				while num_of_components > 1:
							for edge in self.m_edges:
										__lowercase						,		__lowercase						,		__lowercase										=							edge

										__lowercase										=							self.m_component[u]
										__lowercase										=							self.m_component[v]

										if u_component != v_component:

													for component in (u_component, v_component):
																if (
																    minimum_weight_edge[component] == -1
																    or minimum_weight_edge[component][2] > w
																):
																			__lowercase										=							[u, v, w]

							for edge in minimum_weight_edge:
										if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
													__lowercase						,		__lowercase						,		__lowercase										=							edge

													__lowercase										=							self.m_component[u]
													__lowercase										=							self.m_component[v]

													if u_component != v_component:
																mst_weight += w
																self.union(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
																print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
																num_of_components -= 1

							__lowercase										=							[-1] * self.m_num_of_nodes
				print(f"The total weight of the minimal spanning tree is: {mst_weight}" )





def    _lowerCAmelCase						(				):
			pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





