




"""simple docstring"""

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
a							      =     logging.get_logger()





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	List[nn.Module]     =      field(default_factory=__lowerCAmelCase				)
 UpperCAmelCase       :	list     =      field(default_factory=__lowerCAmelCase				)

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Tensor       ,				_UpperCAmelCase			:			Tensor						):
       _A     =  len(list(m.modules()						)						) == 1 or isinstance(_UpperCAmelCase       ,				nn.Convad						) or isinstance(_UpperCAmelCase       ,				nn.BatchNormad						)
       if has_not_submodules:
             self.traced.append(_UpperCAmelCase						)

 def __call__(					self			:			List[str]       ,				_UpperCAmelCase			:			Tensor						):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook						)						)
       self.module(_UpperCAmelCase						)
       [x.remove() for x in self.handles]
       return self



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _UpperCAmelCase						: len(list(x.state_dict().keys()						)						) > 0       ,				self.traced						)						)





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	nn.Module
 UpperCAmelCase       :	int     =      0
 UpperCAmelCase       :	List     =      field(default_factory=__lowerCAmelCase				)
 UpperCAmelCase       :	List     =      field(default_factory=__lowerCAmelCase				)

 def __call__(					self			:			Dict       ,				_UpperCAmelCase			:			Tensor						):
       _A     =  Tracker(self.dest						)(_UpperCAmelCase						).parametrized
       _A     =  Tracker(self.src						)(_UpperCAmelCase						).parametrized

       _A     =  list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.src_skip       ,				_UpperCAmelCase						)						)
       _A     =  list(filter(lambda _UpperCAmelCase						: type(_UpperCAmelCase						) not in self.dest_skip       ,				_UpperCAmelCase						)						)

       if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
             raise Exception(
                 F'''Numbers of operations are different. Source module has {len(_UpperCAmelCase						)} operations while'''
                 F''' destination module has {len(_UpperCAmelCase						)}.'''						)

       for dest_m, src_m in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             dest_m.load_state_dict(src_m.state_dict()						)
             if self.verbose == 1:
                   print(F'''Transfered from={src_m} to={dest_m}'''						)






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   ResNetConfig							,    _snake_case				:   Path							,    _snake_case				:   bool = True				)       ->   Union[str, Any]:






      '''simple docstring'''


      print(F'''Converting {name}...'''				)
      with torch.no_grad():
            _A     =  timm.create_model(_snake_case							,    pretrained=_snake_case				).eval()
            _A     =  ResNetForImageClassification(_snake_case				).eval()
            _A     =  ModuleTransfer(src=_snake_case							,    dest=_snake_case				)
            _A     =  torch.randn((1, 3, 2_24, 2_24)				)
            module_transfer(_snake_case				)

      assert torch.allclose(from_model(_snake_case				)							,    our_model(_snake_case				).logits				), "The model logits don't match the original one."

      _A     =  F'''resnet{"-".join(name.split("resnet"				)				)}'''
      print(_snake_case				)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name							,    commit_message='Add model'							,    use_temp_dir=_snake_case							,    )

            # we can use the convnext one
            _A     =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name							,    commit_message='Add image processor'							,    use_temp_dir=_snake_case							,    )

            print(F'''Pushed {checkpoint_name}'''				)






def 		_snake_case	(   _snake_case				:   Path							,    _snake_case				:   str = None							,    _snake_case				:   bool = True				)       ->   Tuple:






      '''simple docstring'''


      _A     =  'imagenet-1k-id2label.json'
      _A     =  10_00
      _A     =  (1, num_labels)

      _A     =  'huggingface/label-files'
      _A     =  num_labels
      _A     =  json.load(open(hf_hub_download(_snake_case							,    _snake_case							,    repo_type='dataset'				)							,    'r'				)				)
      _A     =  {int(_snake_case				): v for k, v in idalabel.items()}

      _A     =  idalabel
      _A     =  {v: k for k, v in idalabel.items()}

      _A     =  partial(_snake_case							,    num_labels=_snake_case							,    idalabel=_snake_case							,    labelaid=_snake_case				)

      _A     =  {
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]							,    hidden_sizes=[64, 1_28, 2_56, 5_12]							,    layer_type='basic'				),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]							,    hidden_sizes=[64, 1_28, 2_56, 5_12]							,    layer_type='basic'				),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]							,    hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,    layer_type='bottleneck'				),
      }

      if model_name:
            convert_weight_and_push(_snake_case							,    names_to_config[model_name]							,    _snake_case							,    _snake_case				)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(_snake_case							,    _snake_case							,    _snake_case							,    _snake_case				)
      return config, expected_shape


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default=None,
           type=str,
           help=(
               '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
               ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=Path,
           required=True,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''',
           default=True,
           type=bool,
           required=False,
           help='''If True, push model and image processor to the hub.''',
       )

       a							      =     parser.parse_args()
       a							      =     args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''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())


