




'''simple docstring'''



import copy
import os
from typing import Union

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


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				{
    "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 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Any        =						'''bridgetower_vision_model'''







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







             '''simple docstring'''







             super().__init__(**A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						hidden_size
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						num_hidden_layers
             SCREAMING_SNAKE_CASE   :      Tuple    					=						num_channels
             SCREAMING_SNAKE_CASE   :      str    					=						patch_size
             SCREAMING_SNAKE_CASE   :      int    					=						image_size
             SCREAMING_SNAKE_CASE   :      int    					=						initializer_factor
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						layer_norm_eps
             SCREAMING_SNAKE_CASE   :      Any    					=						stop_gradient
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						share_layernorm
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						remove_last_layer







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						cls.get_config_dict(A,							**A  )

             if config_dict.get('model_type'  ) == "bridgetower":
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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(A,							**A  )






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						'''bridgetower_text_model'''







       def __init__(      self,							A=50_265,							A=768,							A=12,							A=12,							A=1,							A=3_072,							A="gelu",							A=0.1,							A=0.1,							A=514,							A=1,							A=1E-05,							A=1,							A=0,							A=2,							A="absolute",							A=True,							**A,							):







             '''simple docstring'''







             super().__init__(**A  )

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







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						cls.get_config_dict(A,							**A  )

             if config_dict.get('model_type'  ) == "bridgetower":
                   SCREAMING_SNAKE_CASE   :      int    					=						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(A,							**A  )






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						'''bridgetower'''







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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('text_config_dict',							A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						kwargs.pop('vision_config_dict',							A  )

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

             if text_config is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						{}
                   logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'  )

             if vision_config is None:
                   SCREAMING_SNAKE_CASE   :      int    					=						{}
                   logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						BridgeTowerTextConfig(**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						BridgeTowerVisionConfig(**A  )







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							A,							**A  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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








'''simple docstring'''



from __future__ import annotations

import queue






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						data
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None
             SCREAMING_SNAKE_CASE   :      List[str]    					=						None




def   lowercase__(			):

      """simple docstring"""






      print('\n********Press N to stop entering at any point of time********\n'    )
      SCREAMING_SNAKE_CASE   :      str    					=						input('Enter the value of the root node: '    ).strip().lower()
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      SCREAMING_SNAKE_CASE   :      Dict    					=						TreeNode(int(__UpperCamelCase    )    )
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						q.get()
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"Enter the left node of {node_found.data}: "
            SCREAMING_SNAKE_CASE   :      Any    					=						input(__UpperCamelCase    ).strip().lower() or 'n'
            if check == "n":
                  return tree_node
            SCREAMING_SNAKE_CASE   :      str    					=						TreeNode(int(__UpperCamelCase    )    )
            SCREAMING_SNAKE_CASE   :      Any    					=						left_node
            q.put(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						f"Enter the right node of {node_found.data}: "
            SCREAMING_SNAKE_CASE   :      Dict    					=						input(__UpperCamelCase    ).strip().lower() or 'n'
            if check == "n":
                  return tree_node
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						TreeNode(int(__UpperCamelCase    )    )
            SCREAMING_SNAKE_CASE   :      Any    					=						right_node
            q.put(__UpperCamelCase    )
      raise




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      print(node.data      ,end=','    )
      pre_order(node.left    )
      pre_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      in_order(node.left    )
      print(node.data      ,end=','    )
      in_order(node.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      post_order(node.left    )
      post_order(node.right    )
      print(node.data      ,end=','    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						q.get()
            print(node_dequeued.data      ,end=','    )
            if node_dequeued.left:
                  q.put(node_dequeued.left    )
            if node_dequeued.right:
                  q.put(node_dequeued.right    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      queue.Queue    					=						queue.Queue()
      q.put(__UpperCamelCase    )
      while not q.empty():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
            while not q.empty():
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						q.get()
                  print(node_dequeued.data      ,end=','    )
                  if node_dequeued.left:
                        list_.append(node_dequeued.left    )
                  if node_dequeued.right:
                        list_.append(node_dequeued.right    )
            print()
            for node in list_:
                  q.put(__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						node
      while n or stack:
            while n:  # start from root node, find its left child
                  print(n.data      ,end=','    )
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Any    					=						n.left
            # end of while means current node doesn't have left child
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						stack.pop()
            # start to traverse its right child
            SCREAMING_SNAKE_CASE   :      Any    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE   :      list[TreeNode]    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						node
      while n or stack:
            while n:
                  stack.append(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						n.left
            SCREAMING_SNAKE_CASE   :      Tuple    					=						stack.pop()
            print(n.data      ,end=','    )
            SCREAMING_SNAKE_CASE   :      str    					=						n.right




def   lowercase__(			__UpperCamelCase:					TreeNode    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,__UpperCamelCase    ) or not node:
            return
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						[], []
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						node
      stacka.append(__UpperCamelCase    )
      while stacka:  # to find the reversed order of post order, store it in stack2
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						stacka.pop()
            if n.left:
                  stacka.append(n.left    )
            if n.right:
                  stacka.append(n.right    )
            stacka.append(__UpperCamelCase    )
      while stacka:  # pop up from stack2 will be the post order
            print(stacka.pop().data      ,end=','    )




def   lowercase__(			__UpperCamelCase:					str = ""      ,__UpperCamelCase:					Dict=50      ,__UpperCamelCase:					Optional[int]="*"    ):

      """simple docstring"""






      if not s:
            return "\n" + width * char
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						divmod(width - len(__UpperCamelCase    ) - 2      ,2    )
      return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(prompt("Binary Tree Traversals"))

      UpperCamelCase_    	=				build_tree()
      print(prompt("Pre Order Traversal"))
      pre_order(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal"))
      in_order(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal"))
      post_order(node)
      print(prompt() + "\n")

      print(prompt("Level Order Traversal"))
      level_order(node)
      print(prompt() + "\n")

      print(prompt("Actual Level Order Traversal"))
      level_order_actual(node)
      print("*" * 5_0 + "\n")

      print(prompt("Pre Order Traversal - Iteration Version"))
      pre_order_iter(node)
      print(prompt() + "\n")

      print(prompt("In Order Traversal - Iteration Version"))
      in_order_iter(node)
      print(prompt() + "\n")

      print(prompt("Post Order Traversal - Iteration Version"))
      post_order_iter(node)
      print(prompt())


