"""simple docstring"""







import functools
from typing import Any
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    list[str]  )							->      bool:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ) or len(UpperCAmelCase_  ) == 0:
		raise ValueError('the string should be not empty string'  )

	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ) or not all(
	    isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ) and len(UpperCAmelCase_  ) > 0 for item in words  ):
		raise ValueError('the words should be a list of non-empty strings'  )

	# Build trie
	__snake_case      :							dict[str, Any]						=			{}
	__snake_case      :							Tuple						=			'WORD_KEEPER'

	for word in words:
		__snake_case      :							List[str]						=			trie
		for c in word:
			if c not in trie_node:
				__snake_case      :							Optional[Any]						=			{}

			__snake_case      :							Union[str, Any]						=			trie_node[c]

		__snake_case      :							str						=			True

	__snake_case      :							Optional[Any]						=			len(UpperCAmelCase_  )

	# Dynamic programming method
	@functools.cache
	def is_breakable(UpperCAmelCase_   :    int  ) -> bool:
		if index == len_string:
			return True

		__snake_case      :							str						=			trie
		for i in range(UpperCAmelCase_      ,							UpperCAmelCase_  ):
			__snake_case      :							List[Any]						=			trie_node.get(string[i]      ,							UpperCAmelCase_  )

			if trie_node is None:
				return False

			if trie_node.get(UpperCAmelCase_      ,							UpperCAmelCase_  ) and is_breakable(i + 1  ):
				return True

		return False

	return is_breakable(0  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






"""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					:					Optional[int]=			logging.get_logger()

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			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    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					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  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# 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      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

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

	__snake_case      :							str						=			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
		__snake_case      :							int						=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

	__snake_case      :							List[Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			num_labels
	__snake_case      :							Any						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Any						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    '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(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__":
	_a					:					Optional[Any]=			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					:					Union[str, Any]=			parser.parse_args()
	_a					:					Path=			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)





