





from __future__ import annotations

import queue

class 		snake_case_   :







	'''simple docstring'''




	def __init__(						self							:     int					,      __lowerCamelCase							:     List[str]   )       -> str:

			'''simple docstring'''




			__lowercase											=     data
			__lowercase											=     None
			__lowercase											=     None





def  SCREAMING_SNAKE_CASE				(      )		->    TreeNode:
		print('\n********Press N to stop entering at any point of time********\n'							)
		__lowercase											=     input('Enter the value of the root node: '							).strip().lower()
		__lowercase											=     queue.Queue()
		__lowercase											=     TreeNode(int(snake_case							)							)
		q.put(snake_case							)
		while not q.empty():
				__lowercase											=     q.get()
				__lowercase											=     F"Enter the left node of {node_found.data}: "
				__lowercase											=     input(snake_case							).strip().lower() or 'n'
				if check == "n":
						return tree_node
				__lowercase											=     TreeNode(int(snake_case							)							)
				__lowercase											=     left_node
				q.put(snake_case							)
				__lowercase											=     F"Enter the right node of {node_found.data}: "
				__lowercase											=     input(snake_case							).strip().lower() or 'n'
				if check == "n":
						return tree_node
				__lowercase											=     TreeNode(int(snake_case							)							)
				__lowercase											=     right_node
				q.put(snake_case							)
		raise



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		print(node.data						,   end=','							)
		pre_order(node.left							)
		pre_order(node.right							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		in_order(node.left							)
		print(node.data						,   end=','							)
		in_order(node.right							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		post_order(node.left							)
		post_order(node.right							)
		print(node.data						,   end=','							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		__lowercase											=     queue.Queue()
		q.put(snake_case							)
		while not q.empty():
				__lowercase											=     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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		__lowercase											=     queue.Queue()
		q.put(snake_case							)
		while not q.empty():
				__lowercase											=     []
				while not q.empty():
						__lowercase											=     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(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		__lowercase											=     []
		__lowercase											=     node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data						,   end=','							)
						stack.append(snake_case							)
						__lowercase											=     n.left
				# end of while means current node doesn't have left child
				__lowercase											=     stack.pop()
				# start to traverse its right child
				__lowercase											=     n.right



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		__lowercase											=     []
		__lowercase											=     node
		while n or stack:
				while n:
						stack.append(snake_case							)
						__lowercase											=     n.left
				__lowercase											=     stack.pop()
				print(n.data						,   end=','							)
				__lowercase											=     n.right



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    None:
		if not isinstance(snake_case						,   snake_case							) or not node:
				return
		__lowercase						,      __lowercase											=     [], []
		__lowercase											=     node
		stacka.append(snake_case							)
		while stacka:  # to find the reversed order of post order, store it in stack2
				__lowercase											=     stacka.pop()
				if n.left:
						stacka.append(n.left							)
				if n.right:
						stacka.append(n.right							)
				stacka.append(snake_case							)
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data						,   end=','							)



def  SCREAMING_SNAKE_CASE				(      snake_case = ""						,   snake_case=50						,   snake_case="*"							)		->    str:
		if not s:
				return "\n" + width * char
		__lowercase						,      __lowercase											=     divmod(width - len(snake_case							) - 2						,   2							)
		return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
		import doctest

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

		SCREAMING_SNAKE_CASE_   :      TreeNode			=   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())











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()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   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											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(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  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   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(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

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

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

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




