



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_									=   logging.get_logger()







@dataclass
class       _snake_case				:






   '''simple docstring'''






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


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Tensor  ,lowerCamelCase_:     Tensor  ,lowerCamelCase_:     str							)	->      Dict:
       UpperCAmelCase_     :       Tuple  		=					len(list(m.modules()							)							) == 1 or isinstance(lowerCamelCase_  ,nn.Convad							) or isinstance(lowerCamelCase_  ,nn.BatchNormad							)
       if has_not_submodules:
           self.traced.append(lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					m


   def __call__(    self:     Tuple  ,lowerCamelCase_:     Tensor							)	->      List[str]:
       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:     str							)	->      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       _snake_case				(     nn.Module ):






   '''simple docstring'''






   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     nn.Module							)	->      Any:
       super().__init__()

       UpperCAmelCase_     :       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}'''
           UpperCAmelCase_     :       Dict  		=					len(lowerCamelCase_							) + 1
           feature_blocks.append((F'''res{block_index}''', v)							)

       UpperCAmelCase_     :       Tuple  		=					nn.ModuleDict(lowerCamelCase_							)






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Tensor							)	->      Dict:
       return get_trunk_forward_outputs(
           lowerCamelCase_  ,out_feat_keys=lowerCamelCase_  ,feature_blocks=self._feature_blocks  ,)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Dict							)	->      Dict:
       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_  (    _a       :						str    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					RegNetConfig(depths=[2, 7, 17, 1]   ,		hidden_sizes=[8, 8, 8, 8]   ,		groups_width=8    )
    if "in1k" in model_name:
        UpperCAmelCase_     :       Optional[int]  		=					RegNetForImageClassification(_a    )
    else:
        UpperCAmelCase_     :       Optional[Any]  		=					RegNetModel(_a    )
    # create from model (with small weights)
    UpperCAmelCase_     :       Tuple  		=					FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27   ,		group_width=1010   ,		w_a=1744   ,		w_a=6_2_0.8_3   ,		w_m=2.5_2    )    )    )

    with torch.no_grad():
        UpperCAmelCase_     :       int  		=					from_model.eval()
        UpperCAmelCase_     :       List[Any]  		=					our_model.eval()

        UpperCAmelCase_     :       Tuple  		=					torch.randn((1, 3, 32, 32)    )
        # trace both
        UpperCAmelCase_     :       int  		=					Tracker(_a    )
        UpperCAmelCase_     :       Optional[Any]  		=					dest_tracker(_a    ).parametrized

        pprint(dest_tracker.nameamodule    )
        UpperCAmelCase_     :       Optional[Any]  		=					Tracker(_a    )
        UpperCAmelCase_     :       List[Any]  		=					src_tracker(_a    ).parametrized

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

    UpperCAmelCase_     :       int  		=					{}

    UpperCAmelCase_     :       str  		=					to_params_dict(_a    )
    UpperCAmelCase_     :       Dict  		=					to_params_dict(_a    )

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,		dst_state_dict.items()    ):
        UpperCAmelCase_     :       Union[str, 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:
        UpperCAmelCase_     :       int  		=					"""classifier.1.weight"""
        UpperCAmelCase_     :       Union[str, Any]  		=					"""classifier.1.bias"""

    return from_to_ours_keys







def       lowerCamelCase_  (    _a       :						Path   ,		_a       :						str = None   ,		_a       :						bool = True    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					"""imagenet-1k-id2label.json"""
    UpperCAmelCase_     :       List[Any]  		=					1000

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

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

    UpperCAmelCase_     :       Dict  		=					partial(_a   ,		num_labels=_a   ,		idalabel=_a   ,		labelaid=_a    )

    UpperCAmelCase_     :       int  		=					{
        """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]   ,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]   ,		groups_width=1010    ),
        # finetuned on imagenet
        """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]   ,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]   ,		groups_width=1010    ),
    }

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

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

    UpperCAmelCase_     :       Union[str, Any]  		=					get_from_to_our_keys(_a    )

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

        UpperCAmelCase_     :       Any  		=					{}

        UpperCAmelCase_     :       List[Any]  		=					list(from_state_dict.keys()    )
        UpperCAmelCase_     :       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
            UpperCAmelCase_     :       Dict  		=					re.sub(_a   ,		""""""   ,		_a    )
            # 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
            UpperCAmelCase_     :       List[str]  		=					from_to_ours_keys[src_key]
            # store the parameter with our key
            UpperCAmelCase_     :       str  		=					from_state_dict[key]
            not_used_keys.remove(_a    )
        # check that all keys have been updated
        assert len(_a    ) == 0, F'''Some keys where not used {','.join(_a    )}'''

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

        # save our state dict to disk
        torch.save(_a   ,		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
        UpperCAmelCase_     :       int  		=					names_to_config[model_name]
        UpperCAmelCase_     :       Dict  		=					RegNetModel
        if "in1k" in model_name:
            UpperCAmelCase_     :       Dict  		=					RegNetForImageClassification
        UpperCAmelCase_     :       List[str]  		=					our_model_func(_a    )
        # 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
        UpperCAmelCase_     :       Any  		=					our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            _a   ,		_a   ,		[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   ,		)
        UpperCAmelCase_     :       int  		=					384
        # we can use the convnext one
        UpperCAmelCase_     :       int  		=					AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   ,		size=_a    )
        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_									=   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_									=   parser.parse_args()

      UpperCamelCase_									=   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 typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

