'''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()
__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger()

@dataclass
class 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  List[nn.Module]						=						field(default_factory=snake_case__    )
      __UpperCamelCase:  list						=						field(default_factory=snake_case__    )




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				Tensor						,					A						:				Tensor       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    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						:				Any						,					A						:				Tensor       ):
             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      _A	(       self						:				List[Any]       ):
             # 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 	lowerCamelCase_	:
      '''simple docstring'''

      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  nn.Module
      __UpperCamelCase:  int						=						0
      __UpperCamelCase:  List						=						field(default_factory=snake_case__    )
      __UpperCamelCase:  List						=						field(default_factory=snake_case__    )




      def __call__(       self						:				Optional[Any]						,					A						:				Tensor       ):
             _UpperCAmelCase     :  Optional[Any]							      =    Tracker(self.dest       )(A       ).parametrized
             _UpperCAmelCase     :  List[Any]							      =    Tracker(self.src       )(A       ).parametrized

             _UpperCAmelCase     :  str							      =    list(filter(lambda A       : type(A       ) not in self.src_skip						,					A       )       )
             _UpperCAmelCase     :  int							      =    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    )    ->    str:


       """simple docstring"""







       print(F"""Converting {name}..."""    )
       with torch.no_grad():
              _UpperCAmelCase     :  Any							      =    timm.create_model(_UpperCAmelCase			,	pretrained=_UpperCAmelCase    ).eval()
              _UpperCAmelCase     :  Union[str, Any]							      =    ResNetForImageClassification(_UpperCAmelCase    ).eval()
              _UpperCAmelCase     :  Optional[int]							      =    ModuleTransfer(src=_UpperCAmelCase			,	dest=_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    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."

       _UpperCAmelCase     :  Tuple							      =    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
              _UpperCAmelCase     :  Tuple							      =    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    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    "imagenet-1k-id2label.json"
       _UpperCAmelCase     :  Optional[int]							      =    1_000
       _UpperCAmelCase     :  Optional[int]							      =    (1, num_labels)

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

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

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

       _UpperCAmelCase     :  Optional[int]							      =    {
           "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, 1_024, 2_048]			,	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, 1_024, 2_048]			,	layer_type="bottleneck"    ),
           "resnet101": ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3]			,	hidden_sizes=[256, 512, 1_024, 2_048]			,	layer_type="bottleneck"    ),
           "resnet152": ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3]			,	hidden_sizes=[256, 512, 1_024, 2_048]			,	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__":
  __SCREAMING_SNAKE_CASE       :     Dict     =  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.""",
  )

  __SCREAMING_SNAKE_CASE       :     Tuple     =  parser.parse_args()
  __SCREAMING_SNAKE_CASE       :     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)





"""simple docstring"""







def      lowercase			(				A_       )->      str:
  '''simple docstring'''







  if isinstance(A_  ,   A_       ):
    raise TypeError("'float' object cannot be interpreted as an integer"       )
  if isinstance(A_  ,   A_       ):
    raise TypeError("'str' object cannot be interpreted as an integer"       )

  if num == 0:
    return "0b0"

  a						:		Optional[Any]      							=			False

  if num < 0:
    a						:		Tuple      							=			True
    a						:		str      							=			-num

  a						:		list[int]      							=			[]
  while num > 0:
    binary.insert(0  ,   num % 2       )
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_       ) for e in binary       )

  return "0b" + "".join(str(A_       ) for e in binary       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






