




import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A_      =    {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}


def  UpperCAmelCase__	(snake_case__							: Dict		,       snake_case__							: str		,       snake_case__							: Union[str, Any]		,       snake_case__							: Dict		,       snake_case__							: Optional[Any]		,       snake_case__							: Tuple   ):
  """simple docstring"""

  if got_ver is None or want_ver is None:
    raise ValueError(
        F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
        F" reinstalling {pkg}."   )
  if not ops[op](version.parse(snake_case__   )		,       version.parse(snake_case__   )   ):
    raise ImportError(
        F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"   )


def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: Optional[str] = None   ):
  """simple docstring"""

  _snake_case							:		str								=  F"\n{hint}" if hint is not None else """"""

  # non-versioned check
  if re.match(R"""^[\w_\-\d]+$"""		,       snake_case__   ):
    _snake_case							:		Union[str, Any]								=  requirement, None, None
  else:
    _snake_case							:		Optional[int]								=  re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""		,       snake_case__   )
    if not match:
      raise ValueError(
          """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
          F" got {requirement}"   )
    _snake_case							:		List[Any]								=  match[0]
    _snake_case							:		int								=  want_full.split(""","""   )  # there could be multiple requirements
    _snake_case							:		Dict								=  {}
    for w in want_range:
      _snake_case							:		List[Any]								=  re.findall(R"""^([\s!=<>]{1,2})(.+)"""		,       snake_case__   )
      if not match:
        raise ValueError(
            """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
            F" but got {requirement}"   )
      _snake_case							:		List[Any]								=  match[0]
      _snake_case							:		List[str]								=  want_ver
      if op not in ops:
        raise ValueError(F"{requirement}: need one of {list(ops.keys()   )}, but got {op}"   )

    # special case
  if pkg == "python":
    _snake_case							:		List[Any]								=  """.""".join([str(snake_case__   ) for x in sys.version_info[:3]]   )
    for op, want_ver in wanted.items():
      _compare_versions(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
    return

  # check if any version is installed
  try:
    _snake_case							:		int								=  importlib.metadata.version(snake_case__   )
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"The '{requirement}' distribution was not found and is required by this application. {hint}"   )

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )






def  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

  _snake_case							:		int								=  """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
  return require_version(snake_case__		,       snake_case__   )








"""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)


