


"""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__				:
       """simple docstring"""


       a  :				nn.Module
       a  :				List[nn.Module]   =field(default_factory=__snake_case				)
       a  :				list   =field(default_factory=__snake_case				)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							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		,     snake_case__						):






         """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        lowercase__							(						self						):






         """simple docstring"""





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


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				nn.Module
       a  :				nn.Module
       a  :				int   =0
       a  :				List   =field(default_factory=__snake_case				)
       a  :				List   =field(default_factory=__snake_case				)

       def __call__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							Tracker(self.dest						)(__SCREAMING_SNAKE_CASE						).parametrized
         lowerCAmelCase     :     Optional[Any]								=							Tracker(self.src						)(__SCREAMING_SNAKE_CASE						).parametrized

         lowerCAmelCase     :     List[Any]								=							list(filter(lambda snake_case__						: type(__SCREAMING_SNAKE_CASE						) not in self.src_skip		,     __SCREAMING_SNAKE_CASE						)						)
         lowerCAmelCase     :     Optional[Any]								=							list(filter(lambda 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 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    ResNetConfig				,      SCREAMING_SNAKE_CASE    :    Path				,      SCREAMING_SNAKE_CASE    :    bool = True				):


  '''simple docstring'''







  print(f"""Converting {name}..."""				)
  with torch.no_grad():
    lowerCAmelCase     :     str								=							timm.create_model(_lowerCamelCase				,      pretrained=_lowerCamelCase				).eval()
    lowerCAmelCase     :     List[Any]								=							ResNetForImageClassification(_lowerCamelCase				).eval()
    lowerCAmelCase     :     Union[str, Any]								=							ModuleTransfer(src=_lowerCamelCase				,      dest=_lowerCamelCase				)
    lowerCAmelCase     :     int								=							torch.randn((1, 3, 2_2_4, 2_2_4)				)
    module_transfer(_lowerCamelCase				)

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

  lowerCAmelCase     :     List[Any]								=							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     :     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=_lowerCamelCase				,      )

    print(f"""Pushed {checkpoint_name}"""				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Path				,      SCREAMING_SNAKE_CASE    :    str = None				,      SCREAMING_SNAKE_CASE    :    bool = True				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							"imagenet-1k-id2label.json"
  lowerCAmelCase     :     Optional[Any]								=							1_0_0_0
  lowerCAmelCase     :     Any								=							(1, num_labels)

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

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

  lowerCAmelCase     :     Any								=							partial(_lowerCamelCase				,      num_labels=_lowerCamelCase				,      idalabel=_lowerCamelCase				,      labelaid=_lowerCamelCase				)

  lowerCAmelCase     :     Dict								=							{
      "resnet18": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]				,      hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,      layer_type="basic"				),
      "resnet26": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]				,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,      layer_type="bottleneck"				),
      "resnet34": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]				,      hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]				,      layer_type="basic"				),
      "resnet50": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]				,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,      layer_type="bottleneck"				),
      "resnet101": ImageNetPreTrainedConfig(
          depths=[3, 4, 2_3, 3]				,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,      layer_type="bottleneck"				),
      "resnet152": ImageNetPreTrainedConfig(
          depths=[3, 8, 3_6, 3]				,      hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,      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__":
     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 math import pow, sqrt







def      lowerCamelCase__       (							*_lowerCamelCase       :		float )      ->				bool:
  lowerCamelCase_         =						len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase )
      else ValueError('Input Error: Molar mass values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,				2 )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,				2 ) / molar_mass		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )




