





'''simple docstring'''





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()
_A				:							Any							= logging.get_logger()



@dataclass
class 				__snake_case  :


    '''simple docstring'''







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



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= len(list(m.modules()    )    ) == 1 or isinstance(A_      ,							nn.Convad    ) or isinstance(A_      ,							nn.BatchNormad    )
        if has_not_submodules:
            self.traced.append(A_    )
            SCREAMING_SNAKE_CASE__       							= m



    def __call__(					self      ,							A_    ):





        '''simple docstring'''

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




    @property
    def    lowercase_				(					self    ):





        '''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      ,							A_    ):





        '''simple docstring'''

        super().__init__()

        SCREAMING_SNAKE_CASE__       							= []
        # - 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}'''
            SCREAMING_SNAKE_CASE__       							= len(A_    ) + 1
            feature_blocks.append((f'''res{block_index}''', v)    )

        SCREAMING_SNAKE_CASE__       							= nn.ModuleDict(A_    )




    def    lowercase_				(					self      ,							A_    ):





        '''simple docstring'''

        return get_trunk_forward_outputs(
            A_      ,							out_feat_keys=A_      ,							feature_blocks=self._feature_blocks      ,							)





class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    def    lowercase_				(					self    ):





        '''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        __snake_case       (						lowerCAmelCase_  )							->	Dict[str, str]:
    SCREAMING_SNAKE_CASE__       							= RegNetConfig(depths=[2, 7, 1_7, 1]   ,       hidden_sizes=[8, 8, 8, 8]   ,       groups_width=8  )
    if "in1k" in model_name:
        SCREAMING_SNAKE_CASE__       							= RegNetForImageClassification(lowerCAmelCase_  )
    else:
        SCREAMING_SNAKE_CASE__       							= RegNetModel(lowerCAmelCase_  )
    # create from model (with small weights)
    SCREAMING_SNAKE_CASE__       							= FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=2_7   ,       group_width=1_0_1_0   ,       w_a=1_7_4_4   ,       w_a=6_2_0.8_3   ,       w_m=2.52  )  )  )

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

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

        pprint(dest_tracker.nameamodule  )
        SCREAMING_SNAKE_CASE__       							= Tracker(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= src_tracker(lowerCAmelCase_  ).parametrized

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

    SCREAMING_SNAKE_CASE__       							= {}

    SCREAMING_SNAKE_CASE__       							= to_params_dict(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= to_params_dict(lowerCAmelCase_  )

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

    return from_to_ours_keys


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = None   ,       lowerCAmelCase_ = True  )							->	int:
    SCREAMING_SNAKE_CASE__       							= '''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__       							= 1_0_0_0

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

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

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

    SCREAMING_SNAKE_CASE__       							= {
        '''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(lowerCAmelCase_  ) -> Tuple[Dict, Dict]:
        SCREAMING_SNAKE_CASE__       							= torch.hub.load_state_dict_from_url(lowerCAmelCase_   ,       model_dir=str(lowerCAmelCase_  )   ,       map_location='''cpu'''  )
        # check if we have a head, if yes add it
        SCREAMING_SNAKE_CASE__       							= files['''classy_state_dict''']['''base_model''']['''model''']
        return model_state_dict["trunk"], model_state_dict["heads"]

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

    SCREAMING_SNAKE_CASE__       							= get_from_to_our_keys(lowerCAmelCase_  )

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

        SCREAMING_SNAKE_CASE__       							= {}

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

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

        # save our state dict to disk
        torch.save(lowerCAmelCase_   ,       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
        SCREAMING_SNAKE_CASE__       							= names_to_config[model_name]
        SCREAMING_SNAKE_CASE__       							= RegNetModel
        if "in1k" in model_name:
            SCREAMING_SNAKE_CASE__       							= RegNetForImageClassification
        SCREAMING_SNAKE_CASE__       							= our_model_func(lowerCAmelCase_  )
        # 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
        SCREAMING_SNAKE_CASE__       							= our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            lowerCAmelCase_   ,       lowerCAmelCase_   ,       [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   ,       )
        SCREAMING_SNAKE_CASE__       							= 3_8_4
        # we can use the convnext one
        SCREAMING_SNAKE_CASE__       							= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   ,       size=lowerCAmelCase_  )
        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__":
  _A				:							List[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.""",
  )

  _A				:							str							= parser.parse_args()

  _A				:							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 os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
