



"""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()
_lowerCAmelCase       :      List[str]							=							logging.get_logger()






@dataclass
class 			UpperCAmelCase_						:
      __SCREAMING_SNAKE_CASE		:     nn.Module
      __SCREAMING_SNAKE_CASE		:     List[nn.Module]				  =				field(default_factory=_UpperCamelCase		)
      __SCREAMING_SNAKE_CASE		:     list				  =				field(default_factory=_UpperCamelCase		)
      __SCREAMING_SNAKE_CASE		:     Dict[str, nn.Module]				  =				field(default_factory=_UpperCamelCase		)



      def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Optional[Any]  ,			A  :			Tensor  ,			A  :			Tensor  ,			A  :			str		):
           _UpperCAmelCase    :			Optional[Any]              =					len(list(m.modules()		)		) == 1 or isinstance(A  ,			nn.Convad		) or isinstance(A  ,			nn.BatchNormad		)
           if has_not_submodules:
                self.traced.append(A		)
                _UpperCAmelCase    :			int              =					m



      def __call__(  self  :			Optional[int]  ,			A  :			Tensor		):
           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		):
           # 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 			UpperCAmelCase_						(       nn.Module		):



      def __init__(  self  :			int  ,			A  :			nn.Module		):
           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    :			int              =					len(A		) + 1
                feature_blocks.append((f'res{block_index}', v)		)

           _UpperCAmelCase    :			int              =					nn.ModuleDict(A		)



      def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Tensor		):
           return get_trunk_forward_outputs(
               A  ,			out_feat_keys=A  ,			feature_blocks=self._feature_blocks  ,			)







class 			UpperCAmelCase_						(       _UpperCamelCase		):



      def 		snake_case_      (  self  :			Optional[int]		):
           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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str       )						->     Dict[str, str]:





     '''simple docstring'''

     _UpperCAmelCase    :			List[Any]              =					RegNetConfig(depths=[2, 7, 17, 1]		,						hidden_sizes=[8, 8, 8, 8]		,						groups_width=8       )
     if "in1k" in model_name:
          _UpperCAmelCase    :			Union[str, Any]              =					RegNetForImageClassification(SCREAMING_SNAKE_CASE__       )
     else:
          _UpperCAmelCase    :			List[Any]              =					RegNetModel(SCREAMING_SNAKE_CASE__       )
     # create from model (with small weights)
     _UpperCAmelCase    :			Optional[Any]              =					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():
          _UpperCAmelCase    :			List[str]              =					from_model.eval()
          _UpperCAmelCase    :			Any              =					our_model.eval()

          _UpperCAmelCase    :			List[Any]              =					torch.randn((1, 3, 32, 32)       )
          # trace both
          _UpperCAmelCase    :			Any              =					Tracker(SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			List[Any]              =					dest_tracker(SCREAMING_SNAKE_CASE__       ).parametrized

          pprint(dest_tracker.nameamodule       )
          _UpperCAmelCase    :			Union[str, Any]              =					Tracker(SCREAMING_SNAKE_CASE__       )
          _UpperCAmelCase    :			List[str]              =					src_tracker(SCREAMING_SNAKE_CASE__       ).parametrized

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

     _UpperCAmelCase    :			Union[str, Any]              =					{}

     _UpperCAmelCase    :			Union[str, Any]              =					to_params_dict(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			Union[str, 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()       ):
          _UpperCAmelCase    :			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:
          _UpperCAmelCase    :			Any              =					"classifier.1.weight"
          _UpperCAmelCase    :			str              =					"classifier.1.bias"

     return from_to_ours_keys

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		str = None		,						SCREAMING_SNAKE_CASE__			:		bool = True       )						->     Tuple:





     '''simple docstring'''

     _UpperCAmelCase    :			Optional[Any]              =					"imagenet-1k-id2label.json"
     _UpperCAmelCase    :			List[Any]              =					1_000

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

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

     _UpperCAmelCase    :			int              =					partial(SCREAMING_SNAKE_CASE__		,						num_labels=SCREAMING_SNAKE_CASE__		,						idalabel=SCREAMING_SNAKE_CASE__		,						labelaid=SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase    :			int              =					{
         "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]:
          _UpperCAmelCase    :			Optional[Any]              =					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
          _UpperCAmelCase    :			int              =					files["classy_state_dict"]["base_model"]["model"]
          return model_state_dict["trunk"], model_state_dict["heads"]

     _UpperCAmelCase    :			List[str]              =					{
         "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"		,						),
     }

     _UpperCAmelCase    :			Union[str, Any]              =					get_from_to_our_keys(SCREAMING_SNAKE_CASE__       )

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

          _UpperCAmelCase    :			Optional[Any]              =					{}

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

 _lowerCAmelCase       :      int							=							parser.parse_args()

 _lowerCAmelCase       :      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)





"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:





     '''simple docstring'''

     if right <= left:
          return
     _UpperCAmelCase    :			Any              =					left
     _UpperCAmelCase    :			Tuple              =					right
     _UpperCAmelCase    :			List[Any]              =					sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
     quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:





     '''simple docstring'''

     if left < right:
          _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
          quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
          quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					sorting[right]
     _UpperCAmelCase    :			Any              =					left
     for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          if sorting[i] < pivot:
               _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
               store_index += 1
     _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
     return store_index

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__       ) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)
