




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()
UpperCAmelCase__						:       str							=    logging.get_logger()

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							nn.Module
   __SCREAMING_SNAKE_CASE		:							List[nn.Module]							=					field(default_factory=A   )
   __SCREAMING_SNAKE_CASE		:							list							=					field(default_factory=A   )
   __SCREAMING_SNAKE_CASE		:							Dict[str, nn.Module]							=					field(default_factory=A   )






   def  _a						(	self   :       Any						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Tensor						,					UpperCamelCase_   :       Tensor						,					UpperCamelCase_   :       str     )->			Union[str, Any]:
     _lowercase: List[Any]						     =    len(list(m.modules()     )     ) == 1 or isinstance(UpperCamelCase_						,					nn.Convad     ) or isinstance(UpperCamelCase_						,					nn.BatchNormad     )
     if has_not_submodules:
       self.traced.append(UpperCamelCase_     )
       _lowercase: int						     =    m






   def __call__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Tensor     )->			int:
     for name, m in self.module.named_modules():
       self.handles.append(m.register_forward_hook(partial(self._forward_hook						,					name=UpperCamelCase_     )     )     )
     self.module(UpperCamelCase_     )
     [x.remove() for x in self.handles]
     return self






   @property
   def  _a						(	self   :       Tuple     )->			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      UpperCAmelCase						(			nn.Module   ):






   def __init__(	self   :       int						,					UpperCamelCase_   :       nn.Module     )->			int:
     super().__init__()

     _lowercase: 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}'
       _lowercase: Tuple						     =    len(UpperCamelCase_     ) + 1
       feature_blocks.append((f'res{block_index}', v)     )

     _lowercase: List[Any]						     =    nn.ModuleDict(UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       Tensor     )->			Dict:
     return get_trunk_forward_outputs(
         UpperCamelCase_						,					out_feat_keys=UpperCamelCase_						,					feature_blocks=self._feature_blocks						,					)

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Optional[int]     )->			Any:
     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							)			->		Dict[str, str]:





  """simple docstring"""





  _lowercase: List[str]						     =    RegNetConfig(depths=[2, 7, 17, 1]					,     hidden_sizes=[8, 8, 8, 8]					,     groups_width=8							)
  if "in1k" in model_name:
    _lowercase: str						     =    RegNetForImageClassification(UpperCamelCase							)
  else:
    _lowercase: str						     =    RegNetModel(UpperCamelCase							)
  # create from model (with small weights)
  _lowercase: int						     =    FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27					,     group_width=1_010					,     w_a=1_744					,     w_a=620.83					,     w_m=2.52							)							)							)

  with torch.no_grad():
    _lowercase: List[Any]						     =    from_model.eval()
    _lowercase: str						     =    our_model.eval()

    _lowercase: Union[str, Any]						     =    torch.randn((1, 3, 32, 32)							)
    # trace both
    _lowercase: Optional[Any]						     =    Tracker(UpperCamelCase							)
    _lowercase: Dict						     =    dest_tracker(UpperCamelCase							).parametrized

    pprint(dest_tracker.nameamodule							)
    _lowercase: Tuple						     =    Tracker(UpperCamelCase							)
    _lowercase: str						     =    src_tracker(UpperCamelCase							).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(UpperCamelCase							):
    _lowercase: List[str]						     =    OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        _lowercase: List[str]						     =    param
    return params_dict

  _lowercase: Optional[Any]						     =    {}

  _lowercase: str						     =    to_params_dict(UpperCamelCase							)
  _lowercase: Any						     =    to_params_dict(UpperCamelCase							)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,     dst_state_dict.items()							):
    _lowercase: Tuple						     =    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:
    _lowercase: Any						     =    'classifier.1.weight'
    _lowercase: List[str]						     =    'classifier.1.bias'

  return from_to_ours_keys


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase = None					,     UpperCamelCase = True							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    'imagenet-1k-id2label.json'
  _lowercase: Tuple						     =    1_000

  _lowercase: Union[str, Any]						     =    'huggingface/label-files'
  _lowercase: Any						     =    num_labels
  _lowercase: int						     =    json.load(open(cached_download(hf_hub_url(UpperCamelCase					,     UpperCamelCase					,     repo_type='dataset'							)							)					,     'r'							)							)
  _lowercase: str						     =    {int(UpperCamelCase							): v for k, v in idalabel.items()}

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

  _lowercase: Any						     =    partial(UpperCamelCase					,     num_labels=UpperCamelCase					,     idalabel=UpperCamelCase					,     labelaid=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    {
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]					,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]					,     groups_width=1_010							),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]					,     hidden_sizes=[2_020, 4_040, 11_110, 28_280]					,     groups_width=1_010							),
  }

  # add seer weights logic
  def load_using_classy_vision(UpperCamelCase							) -> Tuple[Dict, Dict]:
    _lowercase: Optional[int]						     =    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
    _lowercase: Optional[Any]						     =    files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  _lowercase: Optional[Any]						     =    {
      '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'					,     ),
  }

  _lowercase: int						     =    get_from_to_our_keys(UpperCamelCase							)

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

    _lowercase: List[Any]						     =    {}

    _lowercase: Union[str, Any]						     =    list(from_state_dict.keys()							)
    _lowercase: List[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
      _lowercase: Union[str, Any]						     =    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
      _lowercase: Optional[int]						     =    from_to_ours_keys[src_key]
      # store the parameter with our key
      _lowercase: int						     =    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
    _lowercase: Optional[int]						     =    names_to_config[model_name]
    _lowercase: Optional[Any]						     =    RegNetModel
    if "in1k" in model_name:
      _lowercase: Union[str, Any]						     =    RegNetForImageClassification
    _lowercase: Optional[Any]						     =    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
    _lowercase: Optional[Any]						     =    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					,     )
    _lowercase: Optional[Any]						     =    384
    # we can use the convnext one
    _lowercase: List[Any]						     =    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__":
   UpperCAmelCase__						:       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.',
   )

   UpperCAmelCase__						:       List[str]							=    parser.parse_args()

   UpperCAmelCase__						:       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)






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
