



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      __lowerCAmelCase       :
			UpperCamelCase     =	42
			UpperCamelCase     =	field(default_factory=A		)
			UpperCamelCase     =	field(default_factory=A		)
			UpperCamelCase     =	field(default_factory=A		)



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    int					,						A  :    Tensor					,						A  :    Tensor					,						A  :    str)  ->       Dict:



						"""simple docstring"""






						_UpperCAmelCase									=  len(list(m.modules())) == 1 or isinstance(A					,						nn.Convad) or isinstance(A					,						nn.BatchNormad)
						if has_not_submodules:
									self.traced.append(A)
									_UpperCAmelCase									=  m



			def __call__(		self  :    Dict					,						A  :    Tensor)  ->       str:



						"""simple docstring"""






						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        _lowerCamelCase			(		self  :    List[str])  ->       int:



						"""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  :    Union[str, Any]					,						A  :    nn.Module)  ->       Optional[Any]:



						"""simple docstring"""






						super().__init__()

						_UpperCAmelCase									=  []
						# - 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									=  len(A) + 1
									feature_blocks.append((F"res{block_index}", v))

						_UpperCAmelCase									=  nn.ModuleDict(A)



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    Tensor)  ->       Optional[Any]:



						"""simple docstring"""






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







class      __lowerCAmelCase       (     A		):



			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       str:



						"""simple docstring"""






						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 				A      (	_UpperCAmelCase							:  str       )							->					Dict[str, str]:







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

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

						pprint(dest_tracker.nameamodule       )
						_UpperCAmelCase									=  Tracker(_UpperCAmelCase       )
						_UpperCAmelCase									=  src_tracker(_UpperCAmelCase       ).parametrized

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

			_UpperCAmelCase									=  {}

			_UpperCAmelCase									=  to_params_dict(_UpperCAmelCase       )
			_UpperCAmelCase									=  to_params_dict(_UpperCAmelCase       )

			for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()				,  dst_state_dict.items()       ):
						_UpperCAmelCase									=  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									=  'classifier.1.weight'
						_UpperCAmelCase									=  'classifier.1.bias'

			return from_to_ours_keys





def 				A      (	_UpperCAmelCase							:  Path				,  _UpperCAmelCase							:  str = None				,  _UpperCAmelCase							:  bool = True       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  'imagenet-1k-id2label.json'
			_UpperCAmelCase									=  1_000

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

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

			_UpperCAmelCase									=  partial(_UpperCAmelCase				,  num_labels=_UpperCAmelCase				,  idalabel=_UpperCAmelCase				,  labelaid=_UpperCAmelCase       )

			_UpperCAmelCase									=  {
			    '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(_UpperCAmelCase							:  str       ) -> Tuple[Dict, Dict]:
						_UpperCAmelCase									=  torch.hub.load_state_dict_from_url(_UpperCAmelCase				,  model_dir=str(_UpperCAmelCase       )				,  map_location='cpu'       )
						# check if we have a head, if yes add it
						_UpperCAmelCase									=  files['classy_state_dict']['base_model']['model']
						return model_state_dict["trunk"], model_state_dict["heads"]

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

			_UpperCAmelCase									=  get_from_to_our_keys(_UpperCAmelCase       )

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

						_UpperCAmelCase									=  {}

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

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

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





def 				A      (	_UpperCAmelCase							:  List[Any]       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  len(_UpperCAmelCase       )
			for _ in range(
			    0				,  int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
						for j in range(0				,  no_of_elements - 1       ):
									if (
									    lst[j + 1] < lst[j]
									):  # applying bubble sort algorithm from left to right (or forwards)
												_UpperCAmelCase									=  lst[j + 1]
												_UpperCAmelCase									=  lst[j]
												_UpperCAmelCase									=  temp
									if (
									    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
									):  # applying bubble sort algorithm from right to left (or backwards)
												_UpperCAmelCase									=  lst[no_of_elements - 1 - j]
												_UpperCAmelCase									=  lst[no_of_elements - 2 - j]
												_UpperCAmelCase									=  temp
			return lst


if __name__ == "__main__":
							print("enter the list to be sorted")
							UpperCAmelCase__  =			[int(x) for x in input().split()]  # inputing elements of the list in one line
							UpperCAmelCase__  =			double_sort(lst)
							print("the sorted list is")
							print(sorted_lst)
