
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()
__SCREAMING_SNAKE_CASE			:   List[Any]         =     logging.get_logger()


@dataclass
class 		__magic_name__   :
  _lowerCAmelCase		=	42
  _lowerCAmelCase		=	field(default_factory=snake_case    )
  _lowerCAmelCase		=	field(default_factory=snake_case    )
  _lowerCAmelCase		=	field(default_factory=snake_case    )





  def       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Tensor						,  lowerCamelCase__    :	Tensor						,  lowerCamelCase__    :	str				):
       lowerCAmelCase		:					List[str]  	=     len(list(m.modules()				)				) == 1 or isinstance(lowerCamelCase__						,  nn.Convad				) or isinstance(lowerCamelCase__						,  nn.BatchNormad				)
       if has_not_submodules:
            self.traced.append(lowerCamelCase__				)
            lowerCAmelCase		:					Optional[int]  	=     m





  def __call__(							self    :	List[Any]						,  lowerCamelCase__    :	Tensor				):
       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       _A							(							self    :	Optional[int]				):
       # check the len of the state_dict keys to see if we have learnable params
       return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}



class 		__magic_name__   (						nn.Module    ):





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

       lowerCAmelCase		:					List[Tuple[str, nn.Module]]  	=     []
       # - 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}'''
            lowerCAmelCase		:					List[str]  	=     len(lowerCamelCase__				) + 1
            feature_blocks.append((f'''res{block_index}''', v)				)

       lowerCAmelCase		:					List[str]  	=     nn.ModuleDict(lowerCamelCase__				)





  def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	Tensor				):
       return get_trunk_forward_outputs(
           lowerCamelCase__						,  out_feat_keys=lowerCamelCase__						,  feature_blocks=self._feature_blocks						,  )



class 		__magic_name__   (						snake_case    ):





  def       _A							(							self    :	Dict				):
       return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]



def     UpperCAmelCase__							(       __magic_name__						:     str  ):


     '''simple docstring'''

     lowerCAmelCase		:					Dict  	=     RegNetConfig(depths=[2, 7, 17, 1]							,  hidden_sizes=[8, 8, 8, 8]							,  groups_width=8  )
     if "in1k" in model_name:
          lowerCAmelCase		:					Dict  	=     RegNetForImageClassification(__magic_name__  )
     else:
          lowerCAmelCase		:					str  	=     RegNetModel(__magic_name__  )
     # create from model (with small weights)
     lowerCAmelCase		:					int  	=     FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27							,  group_width=10_10							,  w_a=17_44							,  w_a=620.83							,  w_m=2.52  )  )  )

     with torch.no_grad():
          lowerCAmelCase		:					Optional[int]  	=     from_model.eval()
          lowerCAmelCase		:					str  	=     our_model.eval()

          lowerCAmelCase		:					Tuple  	=     torch.randn((1, 3, 32, 32)  )
          # trace both
          lowerCAmelCase		:					Union[str, Any]  	=     Tracker(__magic_name__  )
          lowerCAmelCase		:					Optional[Any]  	=     dest_tracker(__magic_name__  ).parametrized

          pprint(dest_tracker.nameamodule  )
          lowerCAmelCase		:					List[Any]  	=     Tracker(__magic_name__  )
          lowerCAmelCase		:					Optional[int]  	=     src_tracker(__magic_name__  ).parametrized

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

     lowerCAmelCase		:					Dict  	=     {}

     lowerCAmelCase		:					int  	=     to_params_dict(__magic_name__  )
     lowerCAmelCase		:					str  	=     to_params_dict(__magic_name__  )

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,  dst_state_dict.items()  ):
          lowerCAmelCase		:					Any  	=     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:
          lowerCAmelCase		:					Tuple  	=     '''classifier.1.weight'''
          lowerCAmelCase		:					Tuple  	=     '''classifier.1.bias'''

     return from_to_ours_keys
def     UpperCAmelCase__							(       __magic_name__						:     Path							,  __magic_name__						:     str = None							,  __magic_name__						:     bool = True  ):


     '''simple docstring'''

     lowerCAmelCase		:					Union[str, Any]  	=     '''imagenet-1k-id2label.json'''
     lowerCAmelCase		:					Optional[Any]  	=     10_00

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

     lowerCAmelCase		:					Any  	=     idalabel
     lowerCAmelCase		:					str  	=     {v: k for k, v in idalabel.items()}

     lowerCAmelCase		:					Union[str, Any]  	=     partial(__magic_name__							,  num_labels=__magic_name__							,  idalabel=__magic_name__							,  labelaid=__magic_name__  )

     lowerCAmelCase		:					str  	=     {
         '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]							,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,  groups_width=10_10  ),
         # finetuned on imagenet
         '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]							,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,  groups_width=10_10  ),
     }

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

     lowerCAmelCase		:					Optional[Any]  	=     {
         '''regnet-y-10b-seer''': partial(
             __magic_name__							,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''							,  ),
         '''regnet-y-10b-seer-in1k''': partial(
             __magic_name__							,  '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''							,  ),
     }

     lowerCAmelCase		:					Any  	=     get_from_to_our_keys(__magic_name__  )

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

          lowerCAmelCase		:					Union[str, Any]  	=     {}

          lowerCAmelCase		:					Optional[int]  	=     list(from_state_dict.keys()  )
          lowerCAmelCase		:					Any  	=     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
               lowerCAmelCase		:					Tuple  	=     re.sub(__magic_name__							,  ''''''							,  __magic_name__  )
               # 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
               lowerCAmelCase		:					Optional[int]  	=     from_to_ours_keys[src_key]
               # store the parameter with our key
               lowerCAmelCase		:					Optional[Any]  	=     from_state_dict[key]
               not_used_keys.remove(__magic_name__  )
          # check that all keys have been updated
          assert len(__magic_name__  ) == 0, f'''Some keys where not used {",".join(__magic_name__  )}'''

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

          # save our state dict to disk
          torch.save(__magic_name__							,  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
          lowerCAmelCase		:					int  	=     names_to_config[model_name]
          lowerCAmelCase		:					str  	=     RegNetModel
          if "in1k" in model_name:
               lowerCAmelCase		:					List[str]  	=     RegNetForImageClassification
          lowerCAmelCase		:					str  	=     our_model_func(__magic_name__  )
          # 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
          lowerCAmelCase		:					Optional[Any]  	=     our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              __magic_name__							,  __magic_name__							,  [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							,  )
          lowerCAmelCase		:					Union[str, Any]  	=     3_84
          # we can use the convnext one
          lowerCAmelCase		:					List[str]  	=     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''							,  size=__magic_name__  )
          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__":
  __SCREAMING_SNAKE_CASE			:   Optional[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 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.',
  )

  __SCREAMING_SNAKE_CASE			:   str         =     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 ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
