



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bridgetower_vision_model"""



       def __init__(     self     ,lowercase=768     ,lowercase=12     ,lowercase=3     ,lowercase=16     ,lowercase=288     ,lowercase=1     ,lowercase=1E-05     ,lowercase=False     ,lowercase=True     ,lowercase=False     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_hidden_layers
             UpperCAmelCase_  :	Dict				      =		num_channels
             UpperCAmelCase_  :	List[str]				      =		patch_size
             UpperCAmelCase_  :	str				      =		image_size
             UpperCAmelCase_  :	Tuple				      =		initializer_factor
             UpperCAmelCase_  :	str				      =		layer_norm_eps
             UpperCAmelCase_  :	Optional[int]				      =		stop_gradient
             UpperCAmelCase_  :	Dict				      =		share_layernorm
             UpperCAmelCase_  :	Tuple				      =		remove_last_layer



       @classmethod
       def  A_   (     cls     ,lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		cls.get_config_dict(lowercase     ,**lowercase)

             if config_dict.get("model_type") == "bridgetower":
                   UpperCAmelCase_  :	Dict				      =		config_dict["text_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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bridgetower_text_model"""



       def __init__(     self     ,lowercase=50265     ,lowercase=768     ,lowercase=12     ,lowercase=12     ,lowercase=1     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=514     ,lowercase=1     ,lowercase=1E-05     ,lowercase=1     ,lowercase=0     ,lowercase=2     ,lowercase="absolute"     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	Dict				      =		vocab_size
             UpperCAmelCase_  :	Union[str, Any]				      =		hidden_size
             UpperCAmelCase_  :	List[Any]				      =		num_hidden_layers
             UpperCAmelCase_  :	int				      =		num_attention_heads
             UpperCAmelCase_  :	Tuple				      =		hidden_act
             UpperCAmelCase_  :	Tuple				      =		initializer_factor
             UpperCAmelCase_  :	Tuple				      =		intermediate_size
             UpperCAmelCase_  :	Any				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[str]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		max_position_embeddings
             UpperCAmelCase_  :	int				      =		type_vocab_size
             UpperCAmelCase_  :	Optional[int]				      =		layer_norm_eps
             UpperCAmelCase_  :	Dict				      =		position_embedding_type
             UpperCAmelCase_  :	List[Any]				      =		use_cache
             UpperCAmelCase_  :	int				      =		pad_token_id
             UpperCAmelCase_  :	int				      =		bos_token_id
             UpperCAmelCase_  :	Union[str, Any]				      =		eos_token_id



       @classmethod
       def  A_   (     cls     ,lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		cls.get_config_dict(lowercase     ,**lowercase)

             if config_dict.get("model_type") == "bridgetower":
                   UpperCAmelCase_  :	Tuple				      =		config_dict["text_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   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bridgetower"""



       def __init__(     self     ,lowercase=True     ,lowercase="gelu"     ,lowercase=768     ,lowercase=1     ,lowercase=1E-05     ,lowercase=False     ,lowercase="add"     ,lowercase=12     ,lowercase=6     ,lowercase=False     ,lowercase=False     ,lowercase=None     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("text_config_dict"     ,lowercase)
             UpperCAmelCase_  :	str				      =		kwargs.pop("vision_config_dict"     ,lowercase)

             super().__init__(**lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		share_cross_modal_transformer_layers
             UpperCAmelCase_  :	List[str]				      =		hidden_act
             UpperCAmelCase_  :	Union[str, Any]				      =		hidden_size
             UpperCAmelCase_  :	Any				      =		initializer_factor
             UpperCAmelCase_  :	int				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		share_link_tower_layers
             UpperCAmelCase_  :	List[Any]				      =		link_tower_type
             UpperCAmelCase_  :	Optional[int]				      =		num_attention_heads
             UpperCAmelCase_  :	int				      =		num_hidden_layers
             UpperCAmelCase_  :	List[Any]				      =		tie_word_embeddings
             UpperCAmelCase_  :	Optional[int]				      =		init_layernorm_from_vision_encoder

             if text_config is None:
                   UpperCAmelCase_  :	Union[str, Any]				      =		{}
                   logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.")

             if vision_config is None:
                   UpperCAmelCase_  :	Any				      =		{}
                   logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.")

             UpperCAmelCase_  :	List[str]				      =		BridgeTowerTextConfig(**lowercase)
             UpperCAmelCase_  :	List[str]				      =		BridgeTowerVisionConfig(**lowercase)



       @classmethod
       def  A_   (     cls     ,lowercase     ,lowercase     ,**lowercase):

             """simple docstring"""





             return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		copy.deepcopy(self.__dict__)
             UpperCAmelCase_  :	List[Any]				      =		self.text_config.to_dict()
             UpperCAmelCase_  :	Union[str, Any]				      =		self.vision_config.to_dict()
             UpperCAmelCase_  :	List[str]				      =		self.__class__.model_type
             return output





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	list[list[Edge]]				      =		[[] for _ in range(lowercase)]
             UpperCAmelCase_  :	str				      =		size



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             return iter(self._graph[vertex])



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._size



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1.")

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size).")

             self._graph[from_vertex].append(Edge(lowercase     ,lowercase))



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		deque([start_vertex])
             UpperCAmelCase_  :	list[int | None]				      =		[None] * self.size
             UpperCAmelCase_  :	Dict				      =		0

             while queue:
                   UpperCAmelCase_  :	List[Any]				      =		queue.popleft()
                   UpperCAmelCase_  :	Any				      =		distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         UpperCAmelCase_  :	Tuple				      =		current_distance + edge.weight
                         UpperCAmelCase_  :	Optional[Any]				      =		distances[edge.destination_vertex]
                         if (
                             isinstance(lowercase     ,lowercase)
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         UpperCAmelCase_  :	Tuple				      =		new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex)
                         else:
                               queue.append(edge.destination_vertex)

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex.")

             return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
