'''simple docstring'''





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 	lowerCAmelCase_				(					snake_case_       : int			)    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   MobileNetVaConfig(layer_norm_eps=0.001			)

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

     UpperCAmelCase_           =   re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"					,    snake_case_			)
     if matches:
          UpperCAmelCase_           =   float(matches[1]			)
          UpperCAmelCase_           =   int(matches[2]			)

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     UpperCAmelCase_           =   10_01
     UpperCAmelCase_           =   "imagenet-1k-id2label.json"
     UpperCAmelCase_           =   "huggingface/label-files"
     UpperCAmelCase_           =   json.load(open(hf_hub_download(snake_case_					,    snake_case_					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCAmelCase_           =   {int(snake_case_			) + 1: v for k, v in idalabel.items()}
     UpperCAmelCase_           =   "background"
     UpperCAmelCase_           =   idalabel
     UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     return config





def 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_           =   Image.open(requests.get(snake_case_					,    stream=snake_case_			).raw			)
     return im





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : Tuple					,    snake_case_       : str					,    snake_case_       : Dict=False			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   get_mobilenet_va_config(snake_case_			)

     # Load 🤗 model
     UpperCAmelCase_           =   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
     UpperCAmelCase_           =   MobileNetVaImageProcessor(
         crop_size={"width": config.image_size, "height": config.image_size}					,    size={"shortest_edge": config.image_size + 32}					,    )
     UpperCAmelCase_           =   image_processor(images=prepare_img()					,    return_tensors="pt"			)
     UpperCAmelCase_           =   model(**snake_case_			)
     UpperCAmelCase_           =   outputs.logits

     assert logits.shape == (1, 10_01)

     if model_name == "mobilenet_v1_1.0_224":
          UpperCAmelCase_           =   torch.tensor([-4.1739, -1.1233, 3.1205]			)
     elif model_name == "mobilenet_v1_0.75_192":
          UpperCAmelCase_           =   torch.tensor([-3.9440, -2.3141, -0.3333]			)
     else:
          UpperCAmelCase_           =   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..."			)
          UpperCAmelCase_           =   "google/" + model_name
          image_processor.push_to_hub(snake_case_			)
          model.push_to_hub(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     List[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_:     Tuple  =parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )





'''simple docstring'''





from __future__ import annotations

import queue




class        __A      :



    def __init__(self    :    Optional[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   data
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None





def 	lowerCAmelCase_				(					)    ->      TreeNode:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   []
          while not q.empty():
               UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   node
     while n or stack:
          while n:
               stack.append(snake_case_			)
               UpperCAmelCase_           =   n.left
          UpperCAmelCase_           =   stack.pop()
          print(n.data					,    end=","			)
          UpperCAmelCase_           =   n.right





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_    , UpperCAmelCase_           =   [], []
     UpperCAmelCase_           =   node
     stacka.append(snake_case_			)
     while stacka:  # to find the reversed order of post order, store it in stack2
          UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : str = ""					,    snake_case_       : Any=50					,    snake_case_       : Union[str, Any]="*"			)    ->      str:


     '''simple docstring'''




     if not s:
          return "\n" + width * char
     UpperCAmelCase_    , UpperCAmelCase_           =   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())




