




"""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_      =    logging.get_logger()






@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[str, Any],				a_:		Tensor,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Optional[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:		List[Any],				a_:		Tensor		):




   '''simple docstring'''



   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 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



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







@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = 0
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def __call__(    self:		Dict,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  Tracker(self.dest		)(a_		).parametrized
   _snake_case							:		int								=  Tracker(self.src		)(a_		).parametrized

   _snake_case							:		Tuple								=  list(filter(lambda a_		: type(a_		) not in self.src_skip,				a_		)		)
   _snake_case							:		Union[str, 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__	(snake_case__							: str		,       snake_case__							: ResNetConfig		,       snake_case__							: Path		,       snake_case__							: bool = True   ):
  """simple docstring"""

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

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

  _snake_case							:		Optional[int]								=  F"resnet{'-'.join(name.split('resnet'   )   )}"
  print(snake_case__   )

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

    # we can use the convnext one
    _snake_case							:		Union[str, 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=snake_case__		,       )

    print(F"Pushed {checkpoint_name}"   )


def  UpperCAmelCase__	(snake_case__							: Path		,       snake_case__							: str = None		,       snake_case__							: bool = True   ):
  """simple docstring"""

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

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

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

  _snake_case							:		Tuple								=  partial(snake_case__		,       num_labels=snake_case__		,       idalabel=snake_case__		,       labelaid=snake_case__   )

  _snake_case							:		Optional[int]								=  {
      """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(snake_case__		,       names_to_config[model_name]		,       snake_case__		,       snake_case__   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
  return config, expected_shape


if __name__ == "__main__":
     A_      =    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_      =    parser.parse_args()
     A_      =    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  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


if __name__ == "__main__":
     print(solution())


