





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class        __lowerCAmelCase (					UpperCamelCase__):
      _lowercase       :      Any        =    {
          """sld_warmup_steps""": 15,
          """sld_guidance_scale""": 20,
          """sld_threshold""": 0.0,
          """sld_momentum_scale""": 0.0,
          """sld_mom_beta""": 0.0,
      }
      _lowercase       :      Dict        =    {
          """sld_warmup_steps""": 10,
          """sld_guidance_scale""": 1000,
          """sld_threshold""": 0.0_1,
          """sld_momentum_scale""": 0.3,
          """sld_mom_beta""": 0.4,
      }
      _lowercase       :      Dict        =    {
          """sld_warmup_steps""": 7,
          """sld_guidance_scale""": 2000,
          """sld_threshold""": 0.0_2_5,
          """sld_momentum_scale""": 0.5,
          """sld_mom_beta""": 0.7,
      }
      _lowercase       :      Union[str, Any]        =    {
          """sld_warmup_steps""": 0,
          """sld_guidance_scale""": 5000,
          """sld_threshold""": 1.0,
          """sld_momentum_scale""": 0.5,
          """sld_mom_beta""": 0.7,
      }







@dataclass
class        __lowerCAmelCase (					UpperCamelCase__):
      _lowercase       :      Union[List[PIL.Image.Image], np.ndarray]
      _lowercase       :      Optional[List[bool]]
      _lowercase       :      Optional[Union[List[PIL.Image.Image], np.ndarray]]
      _lowercase       :      Optional[str]


if is_transformers_available() and is_torch_available():
     from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
     from .safety_checker import SafeStableDiffusionSafetyChecker













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			:			Optional[Any]	  = logging.get_logger()







@dataclass
class        __lowerCAmelCase :
      _lowercase       :      nn.Module
      _lowercase       :      List[nn.Module]        =    field(default_factory=UpperCamelCase__)
      _lowercase       :      list        =    field(default_factory=UpperCamelCase__)
      _lowercase       :      Dict[str, nn.Module]        =    field(default_factory=UpperCamelCase__)







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




        a__			:      Dict            =len(list(m.modules()							)							) == 1 or isinstance(lowerCAmelCase__	,   nn.Convad							) or isinstance(lowerCAmelCase__	,   nn.BatchNormad							)
        if has_not_submodules:
          self.traced.append(lowerCAmelCase__							)
          a__			:      List[Any]            =m







      def __call__(					self	,   lowerCAmelCase__							)     ->		Optional[Any]:



        '''simple docstring'''




        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      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}







class        __lowerCAmelCase (					nn.Module):







      def __init__(					self	,   lowerCAmelCase__							)     ->		List[Any]:



        '''simple docstring'''




        super().__init__()

        a__			:      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}'''
          a__			:      int            =len(lowerCAmelCase__							) + 1
          feature_blocks.append((F'''res{block_index}''', v)							)

        a__			:      List[str]            =nn.ModuleDict(lowerCAmelCase__							)







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:



        '''simple docstring'''




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







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self							)     ->		Tuple:



        '''simple docstring'''




        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   _A (	SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Union[str, Any]            =RegNetConfig(depths=[2, 7, 17, 1] , hidden_sizes=[8, 8, 8, 8] , groups_width=8		)
  if "in1k" in model_name:
    a__			:      Dict            =RegNetForImageClassification(SCREAMING_SNAKE_CASE		)
  else:
    a__			:      List[Any]            =RegNetModel(SCREAMING_SNAKE_CASE		)
  # create from model (with small weights)
  a__			:      Tuple            =FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=6_2_0.8_3 , w_m=2.5_2		)		)		)

  with torch.no_grad():
    a__			:      int            =from_model.eval()
    a__			:      int            =our_model.eval()

    a__			:      Dict            =torch.randn((1, 3, 32, 32)		)
    # trace both
    a__			:      Tuple            =Tracker(SCREAMING_SNAKE_CASE		)
    a__			:      List[Any]            =dest_tracker(SCREAMING_SNAKE_CASE		).parametrized

    pprint(dest_tracker.nameamodule		)
    a__			:      Optional[Any]            =Tracker(SCREAMING_SNAKE_CASE		)
    a__			:      Tuple            =src_tracker(SCREAMING_SNAKE_CASE		).parametrized

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

  a__			:      Union[str, Any]            ={}

  a__			:      Optional[int]            =to_params_dict(SCREAMING_SNAKE_CASE		)
  a__			:      List[Any]            =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__			:      List[str]            =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__			:      str            ="classifier.1.weight"
    a__			:      Optional[Any]            ="classifier.1.bias"

  return from_to_ours_keys
def   _A (	SCREAMING_SNAKE_CASE  :     Path , SCREAMING_SNAKE_CASE  :     str = None , SCREAMING_SNAKE_CASE  :     bool = True		):





  """simple docstring"""



  a__			:      int            ="imagenet-1k-id2label.json"
  a__			:      int            =1_000

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

  a__			:      Any            =idalabel
  a__			:      Optional[int]            ={v: k for k, v in idalabel.items()}

  a__			:      str            =partial(SCREAMING_SNAKE_CASE , num_labels=SCREAMING_SNAKE_CASE , idalabel=SCREAMING_SNAKE_CASE , labelaid=SCREAMING_SNAKE_CASE		)

  a__			:      Tuple            ={
      "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  :     str		) -> Tuple[Dict, Dict]:
    a__			:      Optional[int]            =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__			:      Optional[Any]            =files["classy_state_dict"]["base_model"]["model"]
    return model_state_dict["trunk"], model_state_dict["heads"]

  a__			:      int            ={
      "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__			:      List[Any]            =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__			:      Optional[int]            =names_to_from_model[model_name]()
    a__			:      Dict            =from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      a__			:      Optional[int]            ={**from_state_dict_trunk, **from_state_dict_head}
    logger.info("Done!"		)

    a__			:      List[str]            ={}

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






