






from __future__ import annotations

from random import choice







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[int]:
     return choice(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   List[str]       				=  random_pivot(__lowerCAmelCase			)

     # partition based on pivot
     # linear time
     UpperCamelCase__			:   Tuple       				=  [e for e in lst if e < pivot]
     UpperCamelCase__			:   Union[str, Any]       				=  [e for e in lst if e > pivot]

     # if we get lucky, pivot might be the element we want.
     # we can easily see this:
     # small (elements smaller than k)
     # + pivot (kth element)
     # + big (elements larger than k)
     if len(__lowerCAmelCase			) == k - 1:
          return pivot
     # pivot is in elements bigger than k
     elif len(__lowerCAmelCase			) < k - 1:
          return kth_number(__lowerCAmelCase					,    k - len(__lowerCAmelCase			) - 1			)
     # pivot is in elements smaller than k
     else:
          return kth_number(__lowerCAmelCase					,    __lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







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





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  len(list(m.modules()						)						) == 1 or isinstance(SCREAMING_SNAKE_CASE							,   nn.Convad						) or isinstance(SCREAMING_SNAKE_CASE							,   nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(SCREAMING_SNAKE_CASE						)







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
      self.module(SCREAMING_SNAKE_CASE						)
      [x.remove() for x in self.handles]
      return self







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return list(filter(lambda SCREAMING_SNAKE_CASE						: len(list(x.state_dict().keys()						)						) > 0							,   self.traced						)						)







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.dest_skip							,   SCREAMING_SNAKE_CASE						)						)

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

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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

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

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

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

          # we can use the convnext one
          UpperCamelCase__			:   List[Any]       				=  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=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

     UpperCamelCase__			:   Union[str, Any]       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  num_labels
     UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   Optional[int]       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "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(__lowerCAmelCase					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, 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.''',
 )

 lowerCamelCase	:							int				=parser.parse_args()
 lowerCamelCase	:							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)