code: 
  
  
  
  
  
  '''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()
  lowercase_			       =				logging.get_logger()
  
  
  @dataclass
  class       a_  :
  
      '''simple docstring'''
      UpperCamelCase									=				42
      UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )
      UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )
      UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )
  
  
      def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Union[str, Any]:
          _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 )	->     Any:
  
  
  
  
  
          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     snake_case_(	self )	->     str:
          return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys() ) ) > 0}
  
  
  
  
  class       a_  (       nn.Module ):
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A )	->     Optional[int]:
          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     snake_case_(	self				,	A )	->     Dict:
          return get_trunk_forward_outputs(
              __a				,	out_feat_keys=__a				,	feature_blocks=self._feature_blocks				,	)
  
  
  
  
  class       a_  (       lowerCAmelCase_ ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     List[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		(  __lowerCamelCase				:					str					)		->Dict:
      _SCREAMING_SNAKE_CASE             =    RegNetConfig(depths=[2, 7, 17, 1]      , hidden_sizes=[8, 8, 8, 8]      , groups_width=8					)
      if "in1k" in model_name:
          _SCREAMING_SNAKE_CASE             =    RegNetForImageClassification(_SCREAMING_SNAKE_CASE					)
      else:
          _SCREAMING_SNAKE_CASE             =    RegNetModel(_SCREAMING_SNAKE_CASE					)
      # create from model (with small weights)
      _SCREAMING_SNAKE_CASE             =    FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27      , group_width=1010      , w_a=1744      , w_a=620.83      , 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, 32, 32)					)
          # trace both
          _SCREAMING_SNAKE_CASE             =    Tracker(_SCREAMING_SNAKE_CASE					)
          _SCREAMING_SNAKE_CASE             =    dest_tracker(_SCREAMING_SNAKE_CASE					).parametrized
  
          pprint(dest_tracker.nameamodule					)
          _SCREAMING_SNAKE_CASE             =    Tracker(_SCREAMING_SNAKE_CASE					)
          _SCREAMING_SNAKE_CASE             =    src_tracker(_SCREAMING_SNAKE_CASE					).parametrized
  
      # convert the keys -> module dict to keys -> params
      def to_params_dict(__lowerCamelCase				:					List[Any]					):
          _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(_SCREAMING_SNAKE_CASE					)
      _SCREAMING_SNAKE_CASE             =    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()					):
          _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 lowerCamelCase		(  __lowerCamelCase				:					Path      , __lowerCamelCase				:					str = None      , __lowerCamelCase				:					bool = True					)		->Tuple:
      _SCREAMING_SNAKE_CASE             =    'imagenet-1k-id2label.json'
      _SCREAMING_SNAKE_CASE             =    1000
  
      _SCREAMING_SNAKE_CASE             =    'huggingface/label-files'
      _SCREAMING_SNAKE_CASE             =    num_labels
      _SCREAMING_SNAKE_CASE             =    json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , repo_type="""dataset"""					)					)      , """r"""					)					)
      _SCREAMING_SNAKE_CASE             =    {int(_SCREAMING_SNAKE_CASE					): 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(_SCREAMING_SNAKE_CASE      , num_labels=_SCREAMING_SNAKE_CASE      , idalabel=_SCREAMING_SNAKE_CASE      , labelaid=_SCREAMING_SNAKE_CASE					)
  
      _SCREAMING_SNAKE_CASE             =    {
          '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(__lowerCamelCase				:					str					) -> Tuple[Dict, Dict]:
          _SCREAMING_SNAKE_CASE             =    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
          _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(
              _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"""      , ),
      }
  
      _SCREAMING_SNAKE_CASE             =    get_from_to_our_keys(_SCREAMING_SNAKE_CASE					)
  
      if not (save_directory / F'{model_name}.pth').exists():
          logger.info("""Loading original state_dict."""					)
          _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(_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
              _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(_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
          _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(_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
          _SCREAMING_SNAKE_CASE             =    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      , )
          _SCREAMING_SNAKE_CASE             =    384
          # we can use the convnext one
          _SCREAMING_SNAKE_CASE             =    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__":
         lowercase_			       =				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.""",
         )
  
         lowercase_			       =				parser.parse_args()
  
         lowercase_			       =				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)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					float      , __lowerCamelCase				:					float      , __lowerCamelCase				:					float					)		->list[int]:
      if hue < 0 or hue > 360:
          raise Exception("""hue should be between 0 and 360"""					)
  
      if saturation < 0 or saturation > 1:
          raise Exception("""saturation should be between 0 and 1"""					)
  
      if value < 0 or value > 1:
          raise Exception("""value should be between 0 and 1"""					)
  
      _SCREAMING_SNAKE_CASE             =    value * saturation
      _SCREAMING_SNAKE_CASE             =    hue / 60
      _SCREAMING_SNAKE_CASE             =    chroma * (1 - abs(hue_section % 2 - 1					))
      _SCREAMING_SNAKE_CASE             =    value - chroma
  
      if hue_section >= 0 and hue_section <= 1:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 1 and hue_section <= 2:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
      elif hue_section > 2 and hue_section <= 3:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
      elif hue_section > 3 and hue_section <= 4:
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      elif hue_section > 4 and hue_section <= 5:
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
      else:
          _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
          _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
  
      return [red, green, blue]
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int					)		->list[float]:
      if red < 0 or red > 255:
          raise Exception("""red should be between 0 and 255"""					)
  
      if green < 0 or green > 255:
          raise Exception("""green should be between 0 and 255"""					)
  
      if blue < 0 or blue > 255:
          raise Exception("""blue should be between 0 and 255"""					)
  
      _SCREAMING_SNAKE_CASE             =    red / 255
      _SCREAMING_SNAKE_CASE             =    green / 255
      _SCREAMING_SNAKE_CASE             =    blue / 255
      _SCREAMING_SNAKE_CASE             =    max(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    value - min(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
      _SCREAMING_SNAKE_CASE             =    0 if value == 0 else chroma / value
  
      if chroma == 0:
          _SCREAMING_SNAKE_CASE             =    0.0
      elif value == float_red:
          _SCREAMING_SNAKE_CASE             =    60 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
          _SCREAMING_SNAKE_CASE             =    60 * (2 + (float_blue - float_red) / chroma)
      else:
          _SCREAMING_SNAKE_CASE             =    60 * (4 + (float_red - float_green) / chroma)
  
      _SCREAMING_SNAKE_CASE             =    (hue + 360) % 360
  
      return [hue, saturation, value]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					list[float]      , __lowerCamelCase				:					list[float]					)		->bool:
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[0] - hsv_a[0]					) < 0.2
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[1] - hsv_a[1]					) < 0.002
      _SCREAMING_SNAKE_CASE             =    abs(hsv_a[2] - hsv_a[2]					) < 0.002
  
      return check_hue and check_saturation and check_value
  
  
  
  