


"""simple docstring"""




import math


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						str:
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					0
						while num > 0:
												SCREAMING_SNAKE_CASE													=					num % 8
												SCREAMING_SNAKE_CASE													=					octal + (remainder * math.floor(math.pow(10 ,			__lowerCamelCase  )  ))
												counter += 1
												SCREAMING_SNAKE_CASE													=					math.floor(num / 8  )  # basically /= 8 without remainder if any
												# This formatting removes trailing '.0' from `octal`.
						return F'0o{int(__lowerCamelCase  )}'


def     lowercase			()   ->						None:
						print('\n2 in octal is:'  )
						print(decimal_to_octal(2  )  )  # = 2
						print('\n8 in octal is:'  )
						print(decimal_to_octal(8  )  )  # = 10
						print('\n65 in octal is:'  )
						print(decimal_to_octal(65  )  )  # = 101
						print('\n216 in octal is:'  )
						print(decimal_to_octal(2_16  )  )  # = 330
						print('\n512 in octal is:'  )
						print(decimal_to_octal(5_12  )  )  # = 1000
						print('\n'  )


if __name__ == "__main__":
	main()









'''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()
lowercase_					= logging.get_logger()


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			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       )		->   str:
        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 			snake_case_(		self       )		->   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 					a_     :

    '''simple docstring'''
    UpperCamelCase     = 42
    UpperCamelCase     = 42
    UpperCamelCase     = 0
    UpperCamelCase     = field(default_factory=snake_case_       )
    UpperCamelCase     = field(default_factory=snake_case_       )


    def __call__(		self ,		A       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			Tracker(self.dest       )(A       ).parametrized
        _SCREAMING_SNAKE_CASE									=			Tracker(self.src       )(A       ).parametrized

        _SCREAMING_SNAKE_CASE									=			list(filter(lambda A       : type(A       ) not in self.src_skip ,		A       )       )
        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    ResNetConfig					,       __lowerCamelCase :    Path					,       __lowerCamelCase :    bool = True    )						->int:
    print(F'Converting {name}...'    )
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ResNetForImageClassification(__lowerCamelCase    ).eval()
        _SCREAMING_SNAKE_CASE									=			ModuleTransfer(src=__lowerCamelCase					,       dest=__lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			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."

    _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    Path					,       __lowerCamelCase :    str = None					,       __lowerCamelCase :    bool = True    )						->Any:
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			(1, num_labels)

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

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

    _SCREAMING_SNAKE_CASE									=			partial(__lowerCamelCase					,       num_labels=__lowerCamelCase					,       idalabel=__lowerCamelCase					,       labelaid=__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			{
        """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__":
      lowercase_					= 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.""",
      )

      lowercase_					= parser.parse_args()
      lowercase_					= 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)



