






import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_    					=					logging.get_logger()


@dataclass
class  snake_case		:


   '''simple docstring'''

   A_						:     nn.Module
   A_						:     List[nn.Module]									=			field(default_factory=_lowerCAmelCase						)
   A_						:     list									=			field(default_factory=_lowerCAmelCase						)
   A_						:     Dict[str, nn.Module]									=			field(default_factory=_lowerCAmelCase						)


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int],	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Tensor,	_lowerCamelCase			:					Tensor,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     __A						      =			len(list(m.modules()  )  ) == 1 or isinstance(_lowerCamelCase,	nn.Convad  ) or isinstance(_lowerCamelCase,	nn.BatchNormad  )
     if has_not_submodules:
       self.traced.append(_lowerCamelCase  )
       __A						      =			m


   def __call__(     self			:					List[str],	_lowerCamelCase			:					Tensor  ):

     '''simple docstring'''
     for name, m in self.module.named_modules():
       self.handles.append(m.register_forward_hook(partial(self._forward_hook,	name=_lowerCamelCase  )  )  )
     self.module(_lowerCamelCase  )
     [x.remove() for x in self.handles]
     return self






   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()  )  ) > 0}





class  snake_case		(							nn.Module						):


   '''simple docstring'''

   def __init__(     self			:					Union[str, Any],	_lowerCamelCase			:					nn.Module  ):

     '''simple docstring'''
     super().__init__()

     __A						      =			[]
     # - get the stem
     feature_blocks.append(('''conv1''', model.stem)  )
     # - get all the feature blocks
     for k, v in model.trunk_output.named_children():
       assert k.startswith('''block'''  ), f'Unexpected layer name {k}'
       __A						      =			len(_lowerCamelCase  ) + 1
       feature_blocks.append((f'res{block_index}', v)  )

     __A						      =			nn.ModuleDict(_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					Tensor  ):

     '''simple docstring'''
     return get_trunk_forward_outputs(
         _lowerCamelCase,	out_feat_keys=_lowerCamelCase,	feature_blocks=self._feature_blocks,	)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			RegNetConfig(depths=[2, 7, 1_7, 1]		,	hidden_sizes=[8, 8, 8, 8]		,	groups_width=8			)
  if "in1k" in model_name:
    __A						      =			RegNetForImageClassification(__UpperCamelCase			)
  else:
    __A						      =			RegNetModel(__UpperCamelCase			)
  # create from model (with small weights)
  __A						      =			FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=2_7		,	group_width=1_0_1_0		,	w_a=1_7_4_4		,	w_a=620.83		,	w_m=2.52			)			)			)

  with torch.no_grad():
    __A						      =			from_model.eval()
    __A						      =			our_model.eval()

    __A						      =			torch.randn((1, 3, 3_2, 3_2)			)
    # trace both
    __A						      =			Tracker(__UpperCamelCase			)
    __A						      =			dest_tracker(__UpperCamelCase			).parametrized

    pprint(dest_tracker.nameamodule			)
    __A						      =			Tracker(__UpperCamelCase			)
    __A						      =			src_tracker(__UpperCamelCase			).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(__UpperCamelCase			):
    __A						      =			OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        __A						      =			param
    return params_dict

  __A						      =			{}

  __A						      =			to_params_dict(__UpperCamelCase			)
  __A						      =			to_params_dict(__UpperCamelCase			)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()		,	dst_state_dict.items()			):
    __A						      =			dest_key
    logger.info(f'{src_key} -> {dest_key}'			)
  # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  if "in1k" in model_name:
    __A						      =			'''classifier.1.weight'''
    __A						      =			'''classifier.1.bias'''

  return from_to_ours_keys



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase = None		,	__UpperCamelCase = True			):




  """simple docstring"""







  __A						      =			'''imagenet-1k-id2label.json'''
  __A						      =			1_0_0_0

  __A						      =			'''huggingface/label-files'''
  __A						      =			num_labels
  __A						      =			json.load(open(cached_download(hf_hub_url(__UpperCamelCase		,	__UpperCamelCase		,	repo_type='''dataset'''			)			)		,	'''r'''			)			)
  __A						      =			{int(__UpperCamelCase			): v for k, v in idalabel.items()}

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

  __A						      =			partial(__UpperCamelCase		,	num_labels=__UpperCamelCase		,	idalabel=__UpperCamelCase		,	labelaid=__UpperCamelCase			)

  __A						      =			{
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]		,	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,	groups_width=1_0_1_0			),
      # finetuned on imagenet
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1]		,	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,	groups_width=1_0_1_0			),
  }

  # add seer weights logic
  def load_using_classy_vision(__UpperCamelCase			) -> Tuple[Dict, Dict]:
    __A						      =			torch.hub.load_state_dict_from_url(__UpperCamelCase		,	model_dir=str(__UpperCamelCase			)		,	map_location='''cpu'''			)
    # check if we have a head, if yes add it
    __A						      =			files['''classy_state_dict''']['''base_model''']['''model''']
    return model_state_dict["trunk"], model_state_dict["heads"]

  __A						      =			{
      '''regnet-y-10b-seer''': partial(
          __UpperCamelCase		,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''		,	),
      '''regnet-y-10b-seer-in1k''': partial(
          __UpperCamelCase		,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''		,	),
  }

  __A						      =			get_from_to_our_keys(__UpperCamelCase			)

  if not (save_directory / f'{model_name}.pth').exists():
    logger.info('''Loading original state_dict.'''			)
    __A      ,   __A						      =			names_to_from_model[model_name]()
    __A						      =			from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      __A						      =			{**from_state_dict_trunk, **from_state_dict_head}
    logger.info('''Done!'''			)

    __A						      =			{}

    __A						      =			list(from_state_dict.keys()			)
    __A						      =			r'''\.block.-part.'''
    # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
    for key in from_state_dict.keys():
      # remove the weird "block[0,1]-part" from the key
      __A						      =			re.sub(__UpperCamelCase		,	''''''		,	__UpperCamelCase			)
      # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
      __A						      =			from_to_ours_keys[src_key]
      # store the parameter with our key
      __A						      =			from_state_dict[key]
      not_used_keys.remove(__UpperCamelCase			)
    # check that all keys have been updated
    assert len(__UpperCamelCase			) == 0, f'Some keys where not used {",".join(__UpperCamelCase			)}'

    logger.info(f'The following keys were not used: {",".join(__UpperCamelCase			)}'			)

    # save our state dict to disk
    torch.save(__UpperCamelCase		,	save_directory / f'{model_name}.pth'			)

    del converted_state_dict
  else:
    logger.info('''The state_dict was already stored on disk.'''			)
  if push_to_hub:
    logger.info(f'Token is {os.environ["HF_TOKEN"]}'			)
    logger.info('''Loading our model.'''			)
    # create our model
    __A						      =			names_to_config[model_name]
    __A						      =			RegNetModel
    if "in1k" in model_name:
      __A						      =			RegNetForImageClassification
    __A						      =			our_model_func(__UpperCamelCase			)
    # place our model to the meta device (so remove all the weights)
    our_model.to(torch.device('''meta'''			)			)
    logger.info('''Loading state_dict in our model.'''			)
    # load state dict
    __A						      =			our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        __UpperCamelCase		,	__UpperCamelCase		,	[save_directory / f'{model_name}.pth']			)
    logger.info('''Finally, pushing!'''			)
    # push it to hub
    our_model.push_to_hub(
        repo_path_or_name=save_directory / model_name		,	commit_message='''Add model'''		,	output_dir=save_directory / model_name		,	)
    __A						      =			3_8_4
    # we can use the convnext one
    __A						      =			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		,	size=__UpperCamelCase			)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / model_name		,	commit_message='''Add image processor'''		,	output_dir=save_directory / model_name		,	)


if __name__ == "__main__":
       lowercase_    					=					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 regnet* architecture,'
               ' currently: regnetx-*, regnety-*. 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.',
       )

       lowercase_    					=					parser.parse_args()

       lowercase_    					=					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 collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if is_torch_available():
    import torch

    if isinstance(__UpperCamelCase		,	torch.Tensor			):
      return True

  return isinstance(__UpperCamelCase		,	np.ndarray			)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			fields(self  )

     # Safety and consistency checks
     if not len(_lowerCamelCase  ):
       raise ValueError(f'{self.__class__.__name__} has no fields.'  )

     __A						      =			getattr(self,	class_fields[0].name  )
     __A						      =			all(getattr(self,	field.name  ) is None for field in class_fields[1:]  )

     if other_fields_are_none and isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       for key, value in first_field.items():
         __A						      =			value
     else:
       for field in class_fields:
         __A						      =			getattr(self,	field.name  )
         if v is not None:
           __A						      =			v


   def __delitem__(     self			:					Dict,	*_lowerCamelCase			:					int,	**_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	*_lowerCamelCase			:					List[str],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	*_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	*_lowerCamelCase			:					Any,	**_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.'  )


   def __getitem__(     self			:					int,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			dict(self.items()  )
       return inner_dict[k]
     else:
       return self.to_tuple()[k]


   def __setattr__(     self			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )


   def __setitem__(     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     # Will raise a KeyException if needed
     super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return tuple(self[k] for k in self.keys()  )




