


"""simple docstring"""







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__lowercase    =				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__( self    :   Optional[int]    ,			__UpperCAmelCase    :   Optional[int]):
      super().__init__()
      a						:		List[str]											=      torchvision.models.resnetaaa(pretrained=__UpperCAmelCase)
      a						:		str											=      list(model.children())[:-2]
      a						:		Tuple											=      nn.Sequential(*__UpperCAmelCase)
      a						:		Optional[int]											=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      a						:		str											=      self.pool(self.model(__UpperCAmelCase))
      a						:		Any											=      torch.flatten(__UpperCAmelCase    ,			start_dim=2)
      a						:		Union[str, Any]											=      out.transpose(1    ,			2).contiguous()
      return out  # BxNx2048






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		Dict											=      [json.loads(__UpperCAmelCase) for l in open(__UpperCAmelCase)]
      a						:		Union[str, Any]											=      os.path.dirname(__UpperCAmelCase)
      a						:		Dict											=      tokenizer
      a						:		Dict											=      labels
      a						:		Any											=      len(__UpperCAmelCase)
      a						:		Tuple											=      max_seq_length

      a						:		Union[str, Any]											=      transforms






    def __len__( self    :   str):
      return len(self.data)






    def __getitem__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		Optional[int]											=      torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]    ,			add_special_tokens=__UpperCAmelCase))
      a							,		a							,		a						:		str											=      sentence[0], sentence[1:-1], sentence[-1]
      a						:		Tuple											=      sentence[: self.max_seq_length]

      a						:		Any											=      torch.zeros(self.n_classes)
      a						:		int											=      1

      a						:		Any											=      Image.open(os.path.join(self.data_dir    ,			self.data[index]["img"])).convert("RGB")
      a						:		Optional[int]											=      self.transforms(__UpperCAmelCase)

      return {
          "image_start_token": start_token,
          "image_end_token": end_token,
          "sentence": sentence,
          "image": image,
          "label": label,
      }






    def      __snake_case							( self    :   Optional[Any]):
      a						:		Union[str, Any]											=      Counter()
      for row in self.data:
        label_freqs.update(row["label"])
      return label_freqs






def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		int											=      [len(row["sentence"]	) for row in batch]
  a							,		a						:		Tuple											=      len(A_	), max(A_	)

  a						:		Union[str, Any]											=      torch.zeros(A_  ,       A_  ,       dtype=torch.long	)
  a						:		Any											=      torch.zeros(A_  ,       A_  ,       dtype=torch.long	)

  for i_batch, (input_row, length) in enumerate(zip(A_  ,       A_	)	):
    a						:		int											=      input_row["sentence"]
    a						:		Union[str, Any]											=      1

  a						:		str											=      torch.stack([row["image"] for row in batch]	)
  a						:		List[str]											=      torch.stack([row["label"] for row in batch]	)
  a						:		Union[str, Any]											=      torch.stack([row["image_start_token"] for row in batch]	)
  a						:		Any											=      torch.stack([row["image_end_token"] for row in batch]	)

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor


def 					lowercase      (    )->				Optional[Any]:
  '''simple docstring'''







  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]







def 					lowercase      (    )->				List[str]:
  '''simple docstring'''







  return transforms.Compose(
      [
          transforms.Resize(256	),
          transforms.CenterCrop(224	),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]  ,       std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]  ,       ),
      ]	)










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      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      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		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(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







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


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

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

 __lowercase    =				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("""*""" * 50 + """\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())






