





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       : Optional[Any]						    = logging.get_logger()







@dataclass
class     lowerCamelCase:
     '''simple docstring'''


     __magic_name__        = 42
     __magic_name__        = field(default_factory=__snake_case  )
     __magic_name__        = field(default_factory=__snake_case  )
     __magic_name__        = field(default_factory=__snake_case  )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       len(list(m.modules()       )       ) == 1 or isinstance(snake_case_							,    nn.Convad       ) or isinstance(snake_case_							,    nn.BatchNormad       )
         if has_not_submodules:
             self.traced.append(snake_case_       )
             _A    =       m




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




     @property
     def 					lowerCAmelCase__		(						self       ):
         # 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     lowerCamelCase(	nn.Module  ):
     '''simple docstring'''


     def __init__(						self							,    snake_case_       ):
         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(snake_case_       ) + 1
             feature_blocks.append((F"res{block_index}", v)       )

         _A    =       nn.ModuleDict(snake_case_       )




     def 					lowerCAmelCase__		(						self							,    snake_case_       ):
         return get_trunk_forward_outputs(
             snake_case_							,    out_feat_keys=snake_case_							,    feature_blocks=self._feature_blocks							,    )







class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         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(					_SCREAMING_SNAKE_CASE							)       ->   Dict[str, str]:




    """simple docstring"""




    _A    =       RegNetConfig(depths=[2, 7, 17, 1]       ,    hidden_sizes=[8, 8, 8, 8]       ,    groups_width=8							)
    if "in1k" in model_name:
        _A    =       RegNetForImageClassification(_SCREAMING_SNAKE_CASE							)
    else:
        _A    =       RegNetModel(_SCREAMING_SNAKE_CASE							)
    # create from model (with small weights)
    _A    =       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():
        _A    =       from_model.eval()
        _A    =       our_model.eval()

        _A    =       torch.randn((1, 3, 32, 32)							)
        # trace both
        _A    =       Tracker(_SCREAMING_SNAKE_CASE							)
        _A    =       dest_tracker(_SCREAMING_SNAKE_CASE							).parametrized

        pprint(dest_tracker.nameamodule							)
        _A    =       Tracker(_SCREAMING_SNAKE_CASE							)
        _A    =       src_tracker(_SCREAMING_SNAKE_CASE							).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(_SCREAMING_SNAKE_CASE							):
        _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(_SCREAMING_SNAKE_CASE							)
    _A    =       to_params_dict(_SCREAMING_SNAKE_CASE							)

    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(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = None       ,    _SCREAMING_SNAKE_CASE = True							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       'imagenet-1k-id2label.json'
    _A    =       1_000

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

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

    _A    =       partial(_SCREAMING_SNAKE_CASE       ,    num_labels=_SCREAMING_SNAKE_CASE       ,    idalabel=_SCREAMING_SNAKE_CASE       ,    labelaid=_SCREAMING_SNAKE_CASE							)

    _A    =       {
        '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(_SCREAMING_SNAKE_CASE							) -> Tuple[Dict, Dict]:
        _A    =       torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE       ,    model_dir=str(_SCREAMING_SNAKE_CASE							)       ,    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(
            _SCREAMING_SNAKE_CASE       ,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,    ),
        'regnet-y-10b-seer-in1k': partial(
            _SCREAMING_SNAKE_CASE       ,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,    ),
    }

    _A    =       get_from_to_our_keys(_SCREAMING_SNAKE_CASE							)

    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(_SCREAMING_SNAKE_CASE       ,    ''       ,    _SCREAMING_SNAKE_CASE							)
            # 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(_SCREAMING_SNAKE_CASE							)
        # check that all keys have been updated
        assert len(_SCREAMING_SNAKE_CASE							) == 0, F"Some keys where not used {','.join(_SCREAMING_SNAKE_CASE							)}"

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

        # save our state dict to disk
        torch.save(_SCREAMING_SNAKE_CASE       ,    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(_SCREAMING_SNAKE_CASE							)
        # 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(
            _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    [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    =       384
        # we can use the convnext one
        _A    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,    size=_SCREAMING_SNAKE_CASE							)
        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       : Tuple						    = 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)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : List[str]						    = [
     "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "HubertForCTC",
     "HubertForSequenceClassification",
     "HubertModel",
     "HubertPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFHubertForCTC",
     "TFHubertModel",
     "TFHubertPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





