





"""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()
lowerCamelCase_								=		logging.get_logger()






@dataclass
class 					_SCREAMING_SNAKE_CASE:
       SCREAMING_SNAKE_CASE_   :      nn.Module
       SCREAMING_SNAKE_CASE_   :      List[nn.Module]							=  field(default_factory=A  )
       SCREAMING_SNAKE_CASE_   :      list							=  field(default_factory=A  )




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  len(list(m.modules()							)							) == 1 or isinstance(SCREAMING_SNAKE_CASE__		,nn.Convad							) or isinstance(SCREAMING_SNAKE_CASE__		,nn.BatchNormad							)
             if has_not_submodules:
                   self.traced.append(SCREAMING_SNAKE_CASE__							)




       def __call__(						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[int]:





             """simple docstring"""






             for m in self.module.modules():
                   self.handles.append(m.register_forward_hook(self._forward_hook							)							)
             self.module(SCREAMING_SNAKE_CASE__							)
             [x.remove() for x in self.handles]
             return self




       @property
       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






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






@dataclass
class 					_SCREAMING_SNAKE_CASE:
       SCREAMING_SNAKE_CASE_   :      nn.Module
       SCREAMING_SNAKE_CASE_   :      nn.Module
       SCREAMING_SNAKE_CASE_   :      int							=  0
       SCREAMING_SNAKE_CASE_   :      List							=  field(default_factory=A  )
       SCREAMING_SNAKE_CASE_   :      List							=  field(default_factory=A  )




       def __call__(						self		,SCREAMING_SNAKE_CASE__							)			->      Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  Tracker(self.dest							)(SCREAMING_SNAKE_CASE__							).parametrized
             __SCREAMING_SNAKE_CASE   :int						=  Tracker(self.src							)(SCREAMING_SNAKE_CASE__							).parametrized

             __SCREAMING_SNAKE_CASE   :Any						=  list(filter(lambda SCREAMING_SNAKE_CASE__							: type(SCREAMING_SNAKE_CASE__							) not in self.src_skip		,SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  list(filter(lambda SCREAMING_SNAKE_CASE__							: type(SCREAMING_SNAKE_CASE__							) not in self.dest_skip		,SCREAMING_SNAKE_CASE__							)							)

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

             for dest_m, src_m in zip(SCREAMING_SNAKE_CASE__		,SCREAMING_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      __lowerCamelCase   (							a_     :		str ,   a_     :		ResNetConfig ,   a_     :		Path ,   a_     :		bool = True							)			->      List[str]:
      print(f'''Converting {name}...'''							)
      with torch.no_grad():
            __SCREAMING_SNAKE_CASE   :List[str]						=  timm.create_model(a_ ,   pretrained=a_							).eval()
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  ResNetForImageClassification(a_							).eval()
            __SCREAMING_SNAKE_CASE   :List[str]						=  ModuleTransfer(src=a_ ,   dest=a_							)
            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.randn((1, 3, 2_24, 2_24)							)
            module_transfer(a_							)

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

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  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
            __SCREAMING_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=a_ ,   )

            print(f'''Pushed {checkpoint_name}'''							)



def      __lowerCamelCase   (							a_     :		Path ,   a_     :		str = None ,   a_     :		bool = True							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  '''imagenet-1k-id2label.json'''
      __SCREAMING_SNAKE_CASE   :Dict						=  10_00
      __SCREAMING_SNAKE_CASE   :List[str]						=  (1, num_labels)

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

      __SCREAMING_SNAKE_CASE   :List[Any]						=  idalabel
      __SCREAMING_SNAKE_CASE   :List[Any]						=  {v: k for k, v in idalabel.items()}

      __SCREAMING_SNAKE_CASE   :List[Any]						=  partial(a_ ,   num_labels=a_ ,   idalabel=a_ ,   labelaid=a_							)

      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  {
          '''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(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__":
  lowerCamelCase_								=		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.",
  )

  lowerCamelCase_								=		parser.parse_args()
  lowerCamelCase_								=		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"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		["DPTFeatureExtractor"]
  lowerCamelCase_								=		["DPTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_								=		[
      "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DPTForDepthEstimation",
      "DPTForSemanticSegmentation",
      "DPTModel",
      "DPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase_								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)