


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()
SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger()


@dataclass
class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							field(default_factory=a       )
  _SCREAMING_SNAKE_CASE		     =							field(default_factory=a       )



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Tensor     ,					_lowerCAmelCase							:							Tensor	) ->			Any:




     """simple docstring"""
     snake_case_            =       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							:							int     ,					_lowerCAmelCase							:							Tensor	) ->			List[Any]:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     # 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      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							0
  _SCREAMING_SNAKE_CASE		     =							field(default_factory=a       )
  _SCREAMING_SNAKE_CASE		     =							field(default_factory=a       )



  def __call__(  self							:							int     ,					_lowerCAmelCase							:							Tensor	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       Tracker(self.dest	)(_lowerCAmelCase	).parametrized
     snake_case_            =       Tracker(self.src	)(_lowerCAmelCase	).parametrized

     snake_case_            =       list(filter(lambda _lowerCAmelCase	: type(_lowerCAmelCase	) not in self.src_skip     ,					_lowerCAmelCase	)	)
     snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:ResNetConfig		,    lowerCAmelCase_				:Path		,    lowerCAmelCase_				:bool = True				)->Tuple:
   '''simple docstring'''






   print(F'''Converting {name}...'''				)
   with torch.no_grad():
      snake_case_            =       timm.create_model(lowerCAmelCase_		,    pretrained=lowerCAmelCase_				).eval()
      snake_case_            =       ResNetForImageClassification(lowerCAmelCase_				).eval()
      snake_case_            =       ModuleTransfer(src=lowerCAmelCase_		,    dest=lowerCAmelCase_				)
      snake_case_            =       torch.randn((1, 3, 224, 224)				)
      module_transfer(lowerCAmelCase_				)

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

   snake_case_            =       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
      snake_case_            =       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       _lowerCAmelCase    (				lowerCAmelCase_				:Path		,    lowerCAmelCase_				:str = None		,    lowerCAmelCase_				:bool = True				)->str:
   '''simple docstring'''






   snake_case_            =       "imagenet-1k-id2label.json"
   snake_case_            =       1_000
   snake_case_            =       (1, num_labels)

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

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

   snake_case_            =       partial(lowerCAmelCase_		,    num_labels=lowerCAmelCase_		,    idalabel=lowerCAmelCase_		,    labelaid=lowerCAmelCase_				)

   snake_case_            =       {
       "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(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__":
   SCREAMING_SNAKE_CASE						:Dict			=  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.''',
   )

   SCREAMING_SNAKE_CASE						:Optional[Any]			=  parser.parse_args()
   SCREAMING_SNAKE_CASE						: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)








from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							None



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Any:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     if not self.is_available():
        raise RuntimeError(
            F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''	)



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict	) ->			Tuple:




     """simple docstring"""
     return F'''`pip install {cls.pip_package or cls.name}`'''






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'optuna'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_optuna_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""
     return run_hp_search_optuna(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     return default_hp_space_optuna(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'ray'
  _SCREAMING_SNAKE_CASE		     =							'\'ray[tune]\''



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_ray_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     return run_hp_search_ray(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Any:




     """simple docstring"""
     return default_hp_space_ray(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'sigopt'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[str]:




     """simple docstring"""
     return is_sigopt_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Tuple	) ->			str:




     """simple docstring"""
     return run_hp_search_sigopt(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Tuple	) ->			Union[str, Any]:




     """simple docstring"""
     return default_hp_space_sigopt(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wandb'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[Any]:




     """simple docstring"""
     return is_wandb_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			Dict:




     """simple docstring"""
     return run_hp_search_wandb(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Any:




     """simple docstring"""
     return default_hp_space_wandb(_lowerCAmelCase	)






SCREAMING_SNAKE_CASE						:Union[str, Any]			=  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def       _lowerCAmelCase    (				)->str:
   '''simple docstring'''






   snake_case_            =       [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
   if len(lowerCAmelCase_				) > 0:
      snake_case_            =       available_backends[0].name
      if len(lowerCAmelCase_				) > 1:
         logger.info(
             F'''{len(lowerCAmelCase_				)} hyperparameter search backends available. Using {name} as the default.'''				)
      return name
   raise RuntimeError(
       "No hyperparameter search backend available.\n"
       + "\n".join(
           F''' - To install {backend.name} run {backend.pip_install()}'''
           for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)




