class  UpperCamelCase				:

					'''simple docstring'''

					def __init__(					self				,				A_ = ""				,				A_ = False						) ->				None:






											"""simple docstring"""
											_lowerCamelCase			    =				{}

											# A node will be a leaf if the tree contains its word
											_lowerCamelCase			    =				is_leaf

											_lowerCamelCase			    =				prefix



					def 		UpperCamelCase_						(					self				,				A_						) ->				tuple[str, str, str]:






											"""simple docstring"""
											_lowerCamelCase			    =				0
											for q, w in zip(self.prefix				,				A_						):
																	if q != w:
																							break

																	x += 1

											return self.prefix[:x], self.prefix[x:], word[x:]



					def 		UpperCamelCase_						(					self				,				A_						) ->				None:






											"""simple docstring"""
											for word in words:
																	self.insert(A_						)



					def 		UpperCamelCase_						(					self				,				A_						) ->				None:






											"""simple docstring"""
											# Case 1: If the word is the prefix of the node
											# Solution: We set the current node as leaf
											if self.prefix == word:
																	_lowerCamelCase			    =				True

											# Case 2: The node has no edges that have a prefix to the word
											# Solution: We create an edge from the current node to a new one
											# containing the word
											elif word[0] not in self.nodes:
																	_lowerCamelCase			    =				RadixNode(prefix=A_				,				is_leaf=A_						)

											else:
																	_lowerCamelCase			    =				self.nodes[word[0]]
																	_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				incoming_node.match(
																	    A_						)

																	# Case 3: The node prefix is equal to the matching
																	# Solution: We insert remaining word on the next node
																	if remaining_prefix == "":
																							self.nodes[matching_string[0]].insert(A_						)

																	# Case 4: The word is greater equal to the matching
																	# Solution: Create a node in between both nodes, change
																	# prefixes and add the new node for the remaining word
																	else:
																							_lowerCamelCase			    =				remaining_prefix

																							_lowerCamelCase			    =				self.nodes[matching_string[0]]
																							_lowerCamelCase			    =				RadixNode(A_				,				A_						)
																							_lowerCamelCase			    =				aux_node

																							if remaining_word == "":
																													_lowerCamelCase			    =				True
																							else:
																													self.nodes[matching_string[0]].insert(A_						)



					def 		UpperCamelCase_						(					self				,				A_						) ->				bool:






											"""simple docstring"""
											_lowerCamelCase			    =				self.nodes.get(word[0]				,				A_						)
											if not incoming_node:
																	return False
											else:
																	_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				incoming_node.match(
																	    A_						)
																	# If there is remaining prefix, the word can't be on the tree
																	if remaining_prefix != "":
																							return False
																	# This applies when the word and the prefix are equal
																	elif remaining_word == "":
																							return incoming_node.is_leaf
																	# We have word remaining so we check the next node
																	else:
																							return incoming_node.find(A_						)



					def 		UpperCamelCase_						(					self				,				A_						) ->				bool:






											"""simple docstring"""
											_lowerCamelCase			    =				self.nodes.get(word[0]				,				A_						)
											if not incoming_node:
																	return False
											else:
																	_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				incoming_node.match(
																	    A_						)
																	# If there is remaining prefix, the word can't be on the tree
																	if remaining_prefix != "":
																							return False
																	# We have word remaining so we check the next node
																	elif remaining_word != "":
																							return incoming_node.delete(A_						)
																	else:
																							# If it is not a leaf, we don't have to delete
																							if not incoming_node.is_leaf:
																													return False
																							else:
																													# We delete the nodes if no edges go from it
																													if len(incoming_node.nodes						) == 0:
																																			del self.nodes[word[0]]
																																			# We merge the current node with its only child
																																			if len(self.nodes						) == 1 and not self.is_leaf:
																																									_lowerCamelCase			    =				list(self.nodes.values()						)[0]
																																									_lowerCamelCase			    =				merging_node.is_leaf
																																									self.prefix += merging_node.prefix
																																									_lowerCamelCase			    =				merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																													elif len(incoming_node.nodes						) > 1:
																																			_lowerCamelCase			    =				False
																													# If there is 1 edge, we merge it with its child
																													else:
																																			_lowerCamelCase			    =				list(incoming_node.nodes.values()						)[0]
																																			_lowerCamelCase			    =				merging_node.is_leaf
																																			incoming_node.prefix += merging_node.prefix
																																			_lowerCamelCase			    =				merging_node.nodes

																													return True





					def 		UpperCamelCase_						(					self				,				A_ = 0						) ->				None:






											"""simple docstring"""
											if self.prefix != "":
																	print('''-''' * height				,				self.prefix				,				'''  (leaf)''' if self.is_leaf else ''''''						)

											for value in self.nodes.values():
																	value.print_tree(height + 1						)





def       __magic_name__(  )						->    bool:



						'''simple docstring'''

						_lowerCamelCase			    =				'''banana bananas bandana band apple all beast'''.split()
						_lowerCamelCase			    =				RadixNode()
						root.insert_many(__UpperCAmelCase		)

						assert all(root.find(__UpperCAmelCase		) for word in words		)
						assert not root.find('''bandanas'''		)
						assert not root.find('''apps'''		)
						root.delete('''all'''		)
						assert not root.find('''all'''		)
						root.delete('''banana'''		)
						assert not root.find('''banana'''		)
						assert root.find('''bananas'''		)

						return True
def       __magic_name__(  )						->    None:



						'''simple docstring'''

						assert test_trie()
def       __magic_name__(  )						->    None:



						'''simple docstring'''

						_lowerCamelCase			    =				RadixNode()
						_lowerCamelCase			    =				'''banana bananas bandanas bandana band apple all beast'''.split()
						root.insert_many(__UpperCAmelCase		)

						print('''Words:''' ,						__UpperCAmelCase		)
						print('''Tree:'''		)
						root.print_tree()


if __name__ == "__main__":
					main()
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()
snake_case__				= logging.get_logger()



@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				len(list(m.modules()						)						) == 1 or isinstance(A_				,				nn.Convad						) or isinstance(A_				,				nn.BatchNormad						)
           if has_not_submodules:
                 self.traced.append(A_						)



     def __call__(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook						)						)
           self.module(A_						)
           [x.remove() for x in self.handles]
           return self





     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # check the len of the state_dict keys to see if we have learnable params
           return list(filter(lambda A_						: len(list(x.state_dict().keys()						)						) > 0				,				self.traced						)						)





@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  42
     A_										=  0
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def __call__(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				Tracker(self.dest						)(A_						).parametrized
           _lowerCamelCase			    =				Tracker(self.src						)(A_						).parametrized

           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.src_skip				,				A_						)						)
           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.dest_skip				,				A_						)						)

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

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





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      print(F'Converting {name}...'		)
      with torch.no_grad():
            _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ResNetForImageClassification(__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ModuleTransfer(src=__UpperCAmelCase ,						dest=__UpperCAmelCase		)
            _lowerCamelCase			    =				torch.randn((1, 3, 224, 224)		)
            module_transfer(__UpperCAmelCase		)

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

      _lowerCamelCase			    =				F'resnet{"-".join(name.split("resnet"		)		)}'
      print(__UpperCAmelCase		)

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

            # we can use the convnext one
            _lowerCamelCase			    =				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=__UpperCAmelCase ,						)

            print(F'Pushed {checkpoint_name}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase = None ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
      _lowerCamelCase			    =				1000
      _lowerCamelCase			    =				(1, num_labels)

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

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

      _lowerCamelCase			    =				partial(__UpperCAmelCase ,						num_labels=__UpperCAmelCase ,						idalabel=__UpperCAmelCase ,						labelaid=__UpperCAmelCase		)

      _lowerCamelCase			    =				{
          '''resnet18''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet26''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet34''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet50''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet101''': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet152''': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
      }

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


if __name__ == "__main__":
     snake_case__				= 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.',
     )

     snake_case__				= parser.parse_args()
     snake_case__				= 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)