






'''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()
UpperCamelCase      					=logging.get_logger()




@dataclass
class      A     :






 """simple docstring"""





 __a      :    nn.Module
 __a      :    List[nn.Module]			  =    field(default_factory=SCREAMING_SNAKE_CASE__    )
 __a      :    list			  =    field(default_factory=SCREAMING_SNAKE_CASE__    )
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				len(list(m.modules()			)			) == 1 or isinstance(__lowerCAmelCase      ,  nn.Convad			) or isinstance(__lowerCAmelCase      ,  nn.BatchNormad			)
       if has_not_submodules:
             self.traced.append(__lowerCAmelCase			)
 def __call__(				self      ,  __lowerCAmelCase			):
       for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook			)			)
       self.module(__lowerCAmelCase			)
       [x.remove() for x in self.handles]
       return self





 @property
 def 	_UpperCAmelCase							(				self			):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda __lowerCAmelCase			: len(list(x.state_dict().keys()			)			) > 0      ,  self.traced			)			)






@dataclass
class      A     :






 """simple docstring"""





 __a      :    nn.Module
 __a      :    nn.Module
 __a      :    int			  =    0
 __a      :    List			  =    field(default_factory=SCREAMING_SNAKE_CASE__    )
 __a      :    List			  =    field(default_factory=SCREAMING_SNAKE_CASE__    )
 def __call__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Tuple            =				Tracker(self.dest			)(__lowerCAmelCase			).parametrized
       UpperCamelCase_					: Tuple            =				Tracker(self.src			)(__lowerCAmelCase			).parametrized

       UpperCamelCase_					: Optional[Any]            =				list(filter(lambda __lowerCAmelCase			: type(__lowerCAmelCase			) not in self.src_skip      ,  __lowerCAmelCase			)			)
       UpperCamelCase_					: List[str]            =				list(filter(lambda __lowerCAmelCase			: type(__lowerCAmelCase			) not in self.dest_skip      ,  __lowerCAmelCase			)			)

       if len(__lowerCAmelCase			) != len(__lowerCAmelCase			):
             raise Exception(
                 F"Numbers of operations are different. Source module has {len(__lowerCAmelCase			)} operations while"
                 F" destination module has {len(__lowerCAmelCase			)}."			)

       for dest_m, src_m in zip(__lowerCAmelCase      ,  __lowerCAmelCase			):
             dest_m.load_state_dict(src_m.state_dict()			)
             if self.verbose == 1:
                   print(F"Transfered from={src_m} to={dest_m}"			)






def 				snake_case       (  a_				:			str     , a_				:			ResNetConfig     , a_				:			Path     , a_				:			bool = True						)							->					Tuple:





      """simple docstring"""

      print(f"Converting {name}..."						)
      with torch.no_grad():
            UpperCamelCase_					: Tuple            =				timm.create_model(a_     , pretrained=a_						).eval()
            UpperCamelCase_					: Optional[int]            =				ResNetForImageClassification(a_						).eval()
            UpperCamelCase_					: Dict            =				ModuleTransfer(src=a_     , dest=a_						)
            UpperCamelCase_					: List[str]            =				torch.randn((1, 3, 224, 224)						)
            module_transfer(a_						)

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

      UpperCamelCase_					: str            =				f"resnet{'-'.join(name.split('resnet'						)						)}"
      print(a_						)

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

            # we can use the convnext one
            UpperCamelCase_					: Optional[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=a_     , )

            print(f"Pushed {checkpoint_name}"						)



def 				snake_case       (  a_				:			Path     , a_				:			str = None     , a_				:			bool = True						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				"""imagenet-1k-id2label.json"""
      UpperCamelCase_					: Tuple            =				1_000
      UpperCamelCase_					: Optional[Any]            =				(1, num_labels)

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

      UpperCamelCase_					: Any            =				idalabel
      UpperCamelCase_					: int            =				{v: k for k, v in idalabel.items()}

      UpperCamelCase_					: Tuple            =				partial(a_     , num_labels=a_     , idalabel=a_     , labelaid=a_						)

      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(a_     , names_to_config[model_name]     , a_     , a_						)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(a_     , a_     , a_     , a_						)
      return config, expected_shape


if __name__ == "__main__":
      UpperCamelCase      					=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.",
      )

      UpperCamelCase      					=parser.parse_args()
      UpperCamelCase      					=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'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase      					=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase      					=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase      					=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase      					=re.compile(R"(_{2,})")

UpperCamelCase      					=R"^\w+(\.\w+)*$"

UpperCamelCase      					=R"<>:/\|?*"



def 				snake_case       (  a_				:			Union[str, Any]						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				_uppercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      UpperCamelCase_					: Any            =				_lowercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      return name.lower()



def 				snake_case       (  a_				:			str						)							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				_single_underscore_re.split(a_						)
      UpperCamelCase_					: Any            =				[_multiple_underscores_re.split(a_						) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_						) if n != """"""						)



def 				snake_case       (  a_				:			str						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      return camelcase_to_snakecase(a_						)



def 				snake_case       (  a_				:			int     , a_				:			List[Any]						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      if not re.match(_split_re     , a_						):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
      return f"{filename_prefix_for_name(a_						)}-{split}"



def 				snake_case       (  a_				:			str     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			Optional[int]=None						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				filename_prefix_for_split(a_     , a_						)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_					: List[str]            =				os.path.join(a_     , a_						)
      return f"{filepath}*"



def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			int=None     , a_				:			Dict=None						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				filename_prefix_for_split(a_     , a_						)
      UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , a_						)

      if shard_lengths:
            UpperCamelCase_					: List[Any]            =				len(a_						)
            UpperCamelCase_					: Optional[int]            =				[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_						)]
            if filetype_suffix:
                  UpperCamelCase_					: Tuple            =				[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_					: List[Any]            =				prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






