


'''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()
__snake_case       		=logging.get_logger()

@dataclass
class 						UpperCAmelCase_ :
     lowerCamelCase							:      nn.Module
     lowerCamelCase							:      List[nn.Module]            =	field(default_factory=_lowercase						)
     lowerCamelCase							:      list            =	field(default_factory=_lowercase						)





     def 				__UpperCAmelCase (					self			: Any       , UpperCAmelCase__			: str       , UpperCAmelCase__			: str       , UpperCAmelCase__			: Optional[Any]       )							->    Union[str, Any]:
        lowerCAmelCase	=     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			: Optional[Any]       , UpperCAmelCase__			: Dict       )							->    str:



        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 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    int:
        return list(filter(lambda UpperCAmelCase__       : len(list(x.state_dict().keys()       )       ) > 0       , self.traced       )       )




@dataclass
class 						UpperCAmelCase_ :
     lowerCamelCase							:      nn.Module
     lowerCamelCase							:      nn.Module
     lowerCamelCase							:      int            =	0
     lowerCamelCase							:      List            =	field(default_factory=_lowercase						)
     lowerCamelCase							:      List            =	field(default_factory=_lowercase						)





     def __call__(					self			: int       , UpperCAmelCase__			: Tuple       )							->    int:
        lowerCAmelCase	=     Tracker(self.dest       )(__SCREAMING_SNAKE_CASE       ).parametrized
        lowerCAmelCase	=     Tracker(self.src       )(__SCREAMING_SNAKE_CASE       ).parametrized

        lowerCAmelCase	=     list(filter(lambda UpperCAmelCase__       : type(__SCREAMING_SNAKE_CASE       ) not in self.src_skip       , __SCREAMING_SNAKE_CASE       )       )
        lowerCAmelCase	=     list(filter(lambda UpperCAmelCase__       : 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       a_   (					lowerCamelCase      :	str      ,					lowerCamelCase      :	ResNetConfig      ,					lowerCamelCase      :	Path      ,					lowerCamelCase      :	bool = True							):
   print(f'''Converting {name}...'''							)
   with torch.no_grad():
      lowerCAmelCase	=     timm.create_model(lowerCamelCase      ,					pretrained=lowerCamelCase							).eval()
      lowerCAmelCase	=     ResNetForImageClassification(lowerCamelCase							).eval()
      lowerCAmelCase	=     ModuleTransfer(src=lowerCamelCase      ,					dest=lowerCamelCase							)
      lowerCAmelCase	=     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."

   lowerCAmelCase	=     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
      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=lowerCamelCase      ,					)

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





def       a_   (					lowerCamelCase      :	Path      ,					lowerCamelCase      :	str = None      ,					lowerCamelCase      :	bool = True							):
   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(lowerCamelCase      ,					lowerCamelCase      ,					repo_type='dataset'							)      ,					'r'							)							)
   lowerCAmelCase	=     {int(lowerCamelCase							): v for k, v in idalabel.items()}

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

   lowerCAmelCase	=     partial(lowerCamelCase      ,					num_labels=lowerCamelCase      ,					idalabel=lowerCamelCase      ,					labelaid=lowerCamelCase							)

   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(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__":
       __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)







"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )


