





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     =			logging.get_logger()




@dataclass
class   _UpperCAmelCase	:
					__A          =   42
					__A          =   field(default_factory=_lowercase					)
					__A          =   field(default_factory=_lowercase					)
					__A          =   field(default_factory=_lowercase					)




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
								UpperCamelCase__         =	len(list(m.modules()       )       ) == 1 or isinstance(__lowerCAmelCase    ,      nn.Convad       ) or isinstance(__lowerCAmelCase    ,      nn.BatchNormad       )
								if has_not_submodules:
											self.traced.append(__lowerCAmelCase       )
											UpperCamelCase__         =	m




					def __call__(						self    ,      __lowerCAmelCase       )						->  Any:
								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 	snake_case_		(						self       )						->  Dict:
								# 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    ,      __lowerCAmelCase       )						->  Optional[Any]:
								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(__lowerCAmelCase       ) + 1
											feature_blocks.append((F"""res{block_index}""", v)       )

								UpperCamelCase__         =	nn.ModuleDict(__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
								return get_trunk_forward_outputs(
								    __lowerCAmelCase    ,      out_feat_keys=__lowerCAmelCase    ,      feature_blocks=self._feature_blocks    ,      )




class   _UpperCAmelCase	(   _lowercase					):




					def 	snake_case_		(						self       )						->  Tuple:
								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						(      _lowercase   :     str       )     ->Dict[str, str]:
			UpperCamelCase__         =	RegNetConfig(depths=[2, 7, 1_7, 1] ,						hidden_sizes=[8, 8, 8, 8] ,						groups_width=8       )
			if "in1k" in model_name:
						UpperCamelCase__         =	RegNetForImageClassification(_lowercase       )
			else:
						UpperCamelCase__         =	RegNetModel(_lowercase       )
			# create from model (with small weights)
			UpperCamelCase__         =	FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=2_7 ,						group_width=1_0_1_0 ,						w_a=1_7_4_4 ,						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, 3_2, 3_2)       )
						# trace both
						UpperCamelCase__         =	Tracker(_lowercase       )
						UpperCamelCase__         =	dest_tracker(_lowercase       ).parametrized

						pprint(dest_tracker.nameamodule       )
						UpperCamelCase__         =	Tracker(_lowercase       )
						UpperCamelCase__         =	src_tracker(_lowercase       ).parametrized

			# convert the keys -> module dict to keys -> params
			def to_params_dict(_lowercase   :     Union[str, Any]       ):
						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(_lowercase       )
			UpperCamelCase__         =	to_params_dict(_lowercase       )

			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						(      _lowercase   :     Path ,						_lowercase   :     str = None ,						_lowercase   :     bool = True       )     ->Tuple:
			UpperCamelCase__         =	"""imagenet-1k-id2label.json"""
			UpperCamelCase__         =	1_0_0_0

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

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

			UpperCamelCase__         =	partial(_lowercase ,						num_labels=_lowercase ,						idalabel=_lowercase ,						labelaid=_lowercase       )

			UpperCamelCase__         =	{
			    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 1_7, 1] ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,						groups_width=1_0_1_0       ),
			    # finetuned on imagenet
			    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
			        depths=[2, 7, 1_7, 1] ,						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,						groups_width=1_0_1_0       ),
			}

			# add seer weights logic
			def load_using_classy_vision(_lowercase   :     str       ) -> Tuple[Dict, Dict]:
						UpperCamelCase__         =	torch.hub.load_state_dict_from_url(_lowercase ,						model_dir=str(_lowercase       ) ,						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(
			        _lowercase ,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,						),
			    """regnet-y-10b-seer-in1k""": partial(
			        _lowercase ,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" ,						),
			}

			UpperCamelCase__         =	get_from_to_our_keys(_lowercase       )

			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(_lowercase ,						"""""" ,						_lowercase       )
									# 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(_lowercase       )
						# check that all keys have been updated
						assert len(_lowercase       ) == 0, F"""Some keys where not used {','.join(_lowercase       )}"""

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

						# save our state dict to disk
						torch.save(_lowercase ,						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(_lowercase       )
						# 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(
						    _lowercase ,						_lowercase ,						[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__         =	3_8_4
						# we can use the convnext one
						UpperCamelCase__         =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" ,						size=_lowercase       )
						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     =			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     =			parser.parse_args()

				__lowerCAmelCase     =			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						(      _lowercase   :     Optional[int]       )     ->List[str]:
		UpperCamelCase__         =	len(_lowercase       )
		for i in range(length - 1       ):
				UpperCamelCase__         =	False
				for j in range(length - 1 - i       ):
						if collection[j] > collection[j + 1]:
								UpperCamelCase__         =	True
								UpperCamelCase__  ,			UpperCamelCase__         =	collection[j + 1], collection[j]
				if not swapped:
						break  # Stop iteration if the collection is sorted.
		return collection


if __name__ == "__main__":
				import doctest
				import time

				doctest.testmod()

				__lowerCAmelCase     =			input("Enter numbers separated by a comma:").strip()
				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
				__lowerCAmelCase     =			time.process_time()
				print(*bubble_sort(unsorted), sep=",")
				print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")





