





'''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__							:  Any    =logging.get_logger()




@dataclass
class 					UpperCAmelCase	:
      _lowercase:     nn.Module
      _lowercase:     List[nn.Module]			=  field(default_factory=snake_case_  )
      _lowercase:     list			=  field(default_factory=snake_case_  )




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					Dict     ,     __snake_case     :					Tensor     ,     __snake_case     :					Tensor    )							-> Dict:
            _lowerCAmelCase					=				len(list(m.modules()    )    ) == 1 or isinstance(__snake_case     ,     nn.Convad    ) or isinstance(__snake_case     ,     nn.BatchNormad    )
            if has_not_submodules:
                  self.traced.append(__snake_case    )




      def __call__(     self     :					Union[str, Any]     ,     __snake_case     :					Tensor    )							-> Optional[Any]:
            for m in self.module.modules():
                  self.handles.append(m.register_forward_hook(self._forward_hook    )    )
            self.module(__snake_case    )
            [x.remove() for x in self.handles]
            return self




      @property
      def 							lowercase__       (     self     :					Dict    )							-> Optional[Any]:
            # check the len of the state_dict keys to see if we have learnable params
            return list(filter(lambda __snake_case    : len(list(x.state_dict().keys()    )    ) > 0     ,     self.traced    )    )




@dataclass
class 					UpperCAmelCase	:
      _lowercase:     nn.Module
      _lowercase:     nn.Module
      _lowercase:     int			=  0
      _lowercase:     List			=  field(default_factory=snake_case_  )
      _lowercase:     List			=  field(default_factory=snake_case_  )




      def __call__(     self     :					Dict     ,     __snake_case     :					Tensor    )							-> Any:
            _lowerCAmelCase					=				Tracker(self.dest    )(__snake_case    ).parametrized
            _lowerCAmelCase					=				Tracker(self.src    )(__snake_case    ).parametrized

            _lowerCAmelCase					=				list(filter(lambda __snake_case    : type(__snake_case    ) not in self.src_skip     ,     __snake_case    )    )
            _lowerCAmelCase					=				list(filter(lambda __snake_case    : type(__snake_case    ) not in self.dest_skip     ,     __snake_case    )    )

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

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




def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase = True    ):





      """simple docstring"""


      print(f"Converting {name}..."    )
      with torch.no_grad():
            _lowerCAmelCase					=				timm.create_model(lowerCAmelCase				,      pretrained=lowerCAmelCase    ).eval()
            _lowerCAmelCase					=				ResNetForImageClassification(lowerCAmelCase    ).eval()
            _lowerCAmelCase					=				ModuleTransfer(src=lowerCAmelCase				,      dest=lowerCAmelCase    )
            _lowerCAmelCase					=				torch.randn((1, 3, 2_24, 2_24)    )
            module_transfer(lowerCAmelCase    )

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

      _lowerCAmelCase					=				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
            _lowerCAmelCase					=				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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase = None				,      lowerCAmelCase = True    ):





      """simple docstring"""


      _lowerCAmelCase					=				"""imagenet-1k-id2label.json"""
      _lowerCAmelCase					=				10_00
      _lowerCAmelCase					=				(1, num_labels)

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

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

      _lowerCAmelCase					=				partial(lowerCAmelCase				,      num_labels=lowerCAmelCase				,      idalabel=lowerCAmelCase				,      labelaid=lowerCAmelCase    )

      _lowerCAmelCase					=				{
          """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(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__":
      A__							:  Union[str, Any]    =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__							:  int    =parser.parse_args()
      A__							:  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'''






import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__							:  Optional[int]    ='''.'''


if __name__ == "__main__":
      A__							:  int    =os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
      A__							:  Optional[int]    =[]
      A__							:  Tuple    =[]
      with open(doctest_file_path) as fp:
            for line in fp:
                  A__							:  Optional[Any]    =line.strip()
                  A__							:  Union[str, Any]    =os.path.join(REPO_PATH, line)
                  if not (os.path.isfile(path) or os.path.isdir(path)):
                        non_existent_paths.append(line)
                  all_paths.append(path)
      if len(non_existent_paths) > 0:
            A__							:  List[Any]    ='''\n'''.join(non_existent_paths)
            raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
      if all_paths != sorted(all_paths):
            raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






