





from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class     SCREAMING_SNAKE_CASE       :
   _UpperCamelCase   :		int
   _UpperCamelCase   :		Node | None			 =	None
   _UpperCamelCase   :		Node | None			 =	None






def       __UpperCamelCase  ()    ->       Node | None:
  lowercase__           =  Node(1		)
  lowercase__           =  Node(2		)
  lowercase__           =  Node(3		)
  lowercase__           =  Node(4		)
  lowercase__           =  Node(5		)
  return tree




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       list[int]:
  return [root.data, *preorder(root.left		), *preorder(root.right		)] if root else []




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       list[int]:
  return postorder(root.left		) + postorder(root.right		) + [root.data] if root else []




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       list[int]:
  return [*inorder(root.left		), root.data, *inorder(root.right		)] if root else []




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       int:
  return (max(height(root.left		)		,						height(root.right		)		) + 1) if root else 0




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Sequence[Node | None]:
  lowercase__           =  []

  if root is None:
    return output

  lowercase__           =  deque([root]		)

  while process_queue:
    lowercase__           =  process_queue.popleft()
    output.append(node.data		)

    if node.left:
      process_queue.append(node.left		)
    if node.right:
      process_queue.append(node.right		)
  return output




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Sequence[Node | None]:
  lowercase__           =  []

  def populate_output(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data		)
    elif level > 1:
      populate_output(root.left		,						level - 1		)
      populate_output(root.right		,						level - 1		)

  populate_output(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  return output




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Sequence[Node | None]:
  lowercase__           =  []

  def populate_output(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data		)
    elif level > 1:
      populate_output(root.right		,						level - 1		)
      populate_output(root.left		,						level - 1		)

  populate_output(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
  return output




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  lowercase__           =  []

  lowercase__           =  0
  lowercase__           =  height(_SCREAMING_SNAKE_CASE		)

  for h in range(1		,						height_tree + 1		):
    if not flag:
      output.append(get_nodes_from_left_to_right(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)		)
      lowercase__           =  1
    else:
      output.append(get_nodes_from_right_to_left(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)		)
      lowercase__           =  0

  return output




def       __UpperCamelCase  ()    ->       None:  # Main function for testing.
  lowercase__           =  make_tree()
  print(F"""In-order Traversal: {inorder(_SCREAMING_SNAKE_CASE		)}"""		)
  print(F"""Pre-order Traversal: {preorder(_SCREAMING_SNAKE_CASE		)}"""		)
  print(F"""Post-order Traversal: {postorder(_SCREAMING_SNAKE_CASE		)}"""		,						'\n'		)

  print(F"""Height of Tree: {height(_SCREAMING_SNAKE_CASE		)}"""		,						'\n'		)

  print('Complete Level Order Traversal: '		)
  print(level_order(_SCREAMING_SNAKE_CASE		)		,						'\n'		)

  print('Level-wise order Traversal: '		)

  for level in range(1		,						height(_SCREAMING_SNAKE_CASE		) + 1		):
    print(F"""Level {level}:"""		,						get_nodes_from_left_to_right(_SCREAMING_SNAKE_CASE		,						level=_SCREAMING_SNAKE_CASE		)		)

  print('\nZigZag order Traversal: '		)
  print(zigzag(_SCREAMING_SNAKE_CASE		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()









import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

  if "_quant" in model_name:
    raise ValueError('Quantized models are not supported.'		)

  lowercase__           =  re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'		,						_SCREAMING_SNAKE_CASE		)
  if matches:
    lowercase__           =  float(matches[1]		)
    lowercase__           =  int(matches[2]		)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  lowercase__           =  1001
  lowercase__           =  'imagenet-1k-id2label.json'
  lowercase__           =  'huggingface/label-files'
  lowercase__           =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		)		,						'r'		)		)
  lowercase__           =  {int(_SCREAMING_SNAKE_CASE		) + 1: v for k, v in idalabel.items()}
  lowercase__           =  'background'
  lowercase__           =  idalabel
  lowercase__           =  {v: k for k, v in idalabel.items()}

  return config




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)
  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

  # Load 🤗 model
  lowercase__           =  MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowercase__           =  MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}		,						size={'shortest_edge': config.image_size + 32}		,						)
  lowercase__           =  image_processor(images=prepare_img()		,						return_tensors='pt'		)
  lowercase__           =  model(**_SCREAMING_SNAKE_CASE		)
  lowercase__           =  outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowercase__           =  torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		)
  elif model_name == "mobilenet_v1_0.75_192":
    lowercase__           =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowercase__           =  None

  if expected_logits is not None:
    assert torch.allclose(logits[0, :3]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-4		)

  Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(_SCREAMING_SNAKE_CASE		)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

  if push_to_hub:
    print('Pushing to the hub...'		)
    lowercase__           =  'google/' + model_name
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
    model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v1_1.0_224""",
          type=str,
          help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      lowercase_          =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


