



'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
UpperCAmelCase     		=   logging.get_logger()

@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   nn.Module
						UpperCAmelCase							:   List[nn.Module]									=		field(default_factory=__SCREAMING_SNAKE_CASE )
						UpperCAmelCase							:   list									=		field(default_factory=__SCREAMING_SNAKE_CASE )


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     len(list(m.modules()				)				) == 1 or isinstance(__UpperCAmelCase     ,	nn.Convad				) or isinstance(__UpperCAmelCase     ,	nn.BatchNormad				)
							if has_not_submodules:
								self.traced.append(__UpperCAmelCase				)


						def __call__( self     ,	A_				)     ->     str:
							for m in self.module.modules():
								self.handles.append(m.register_forward_hook(self._forward_hook				)				)
							self.module(__UpperCAmelCase				)
							[x.remove() for x in self.handles]
							return self




						@property
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							return list(filter(lambda A_				: len(list(x.state_dict().keys()				)				) > 0     ,	self.traced				)				)







@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   nn.Module
						UpperCAmelCase							:   nn.Module
						UpperCAmelCase							:   int									=		1
						UpperCAmelCase							:   List									=		field(default_factory=__SCREAMING_SNAKE_CASE )
						UpperCAmelCase							:   List									=		field(default_factory=__SCREAMING_SNAKE_CASE )
						UpperCAmelCase							:   bool									=		True


						def __call__( self     ,	A_				)     ->     Any:
							lowerCAmelCase   =     Tracker(self.dest				)(__UpperCAmelCase				).parametrized
							lowerCAmelCase   =     Tracker(self.src				)(__UpperCAmelCase				).parametrized

							lowerCAmelCase   =     list(filter(lambda A_				: type(__UpperCAmelCase				) not in self.src_skip     ,	__UpperCAmelCase				)				)
							lowerCAmelCase   =     list(filter(lambda A_				: type(__UpperCAmelCase				) not in self.dest_skip     ,	__UpperCAmelCase				)				)

							if len(__UpperCAmelCase				) != len(__UpperCAmelCase				) and self.raise_if_mismatch:
								raise Exception(
								    f'Numbers of operations are different. Source module has {len(__UpperCAmelCase				)} operations while'
								    f' destination module has {len(__UpperCAmelCase				)}.'				)

							for dest_m, src_m in zip(__UpperCAmelCase     ,	__UpperCAmelCase				):
								dest_m.load_state_dict(src_m.state_dict()				)
								if self.verbose == 1:
									print(f'Transfered from={src_m} to={dest_m}'				)







class __snake_case(				nn.Module ):


						'''simple docstring'''



						def __init__( self     ,	A_				)     ->     Optional[int]:
							super().__init__()

							lowerCAmelCase   =     []
							# - 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}'
								lowerCAmelCase   =     len(__UpperCAmelCase				) + 1
								feature_blocks.append((f'res{block_index}', v)				)

							lowerCAmelCase   =     nn.ModuleDict(__UpperCAmelCase				)




						def     __snake_case   ( self     ,	A_				)     ->     List[Any]:
							return get_trunk_forward_outputs(
							    __UpperCAmelCase     ,	out_feat_keys=__UpperCAmelCase     ,	feature_blocks=self._feature_blocks     ,	)







class __snake_case(				__SCREAMING_SNAKE_CASE ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     x.split("""-"""				)
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)




						def __getitem__( self     ,	A_				)     ->     Callable[[], Tuple[nn.Module, Dict]]:
							# default to timm!
							if x not in self:
								lowerCAmelCase   =     self.convert_name_to_timm(__UpperCAmelCase				)
								lowerCAmelCase   =     partial(lambda: (timm.create_model(__UpperCAmelCase     ,	pretrained=__UpperCAmelCase				).eval(), None)				)

							else:
								lowerCAmelCase   =     super().__getitem__(__UpperCAmelCase				)

							return val







class __snake_case(				__SCREAMING_SNAKE_CASE ):


						'''simple docstring'''



						def __getitem__( self     ,	A_				)     ->     Callable[[], nn.Module]:
							if "seer" in x and "in1k" not in x:
								lowerCAmelCase   =     RegNetModel
							else:
								lowerCAmelCase   =     RegNetForImageClassification
							return val







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						List[Tuple[str, str]]							)      ->						Dict:







	"""simple docstring"""





	for from_key, to_key in keys:
		lowerCAmelCase   =     from_state_dict[from_key].clone()
		print(f'Copied key={from_key} to={to_key}'							)
	return to_state_dict






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Callable[[], nn.Module]							,		_SCREAMING_SNAKE_CASE					:						Callable[[], nn.Module]							,		_SCREAMING_SNAKE_CASE					:						RegNetConfig							,		_SCREAMING_SNAKE_CASE					:						Path							,		_SCREAMING_SNAKE_CASE					:						bool = True							,		)      ->						Optional[Any]:







	"""simple docstring"""
	print(f'Converting {name}...'							)
	with torch.no_grad():
		lowerCAmelCase   =     from_model_func()
		lowerCAmelCase   =     our_model_func(lowerCAmelCase__							).eval()
		lowerCAmelCase   =     ModuleTransfer(src=lowerCAmelCase__							,		dest=lowerCAmelCase__							,		raise_if_mismatch=lowerCAmelCase__							)
		lowerCAmelCase   =     torch.randn((1, 3, 224, 224)							)
		module_transfer(lowerCAmelCase__							)

	if from_state_dict is not None:
		lowerCAmelCase   =     []
		# for seer - in1k finetuned we have to manually copy the head
		if "seer" in name and "in1k" in name:
			lowerCAmelCase   =     [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
		lowerCAmelCase   =     manually_copy_vissl_head(lowerCAmelCase__							,		our_model.state_dict()							,		lowerCAmelCase__							)
		our_model.load_state_dict(lowerCAmelCase__							)

	lowerCAmelCase   =     our_model(lowerCAmelCase__							,		output_hidden_states=lowerCAmelCase__							)
	lowerCAmelCase   =     (
	    our_outputs.logits if isinstance(lowerCAmelCase__							,		lowerCAmelCase__							) else our_outputs.last_hidden_state
	)

	lowerCAmelCase   =     from_model(lowerCAmelCase__							)
	lowerCAmelCase   =     from_output[-1] if type(lowerCAmelCase__							) is list else from_output

	# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
	if "seer" in name and "in1k" in name:
		lowerCAmelCase   =     our_outputs.hidden_states[-1]

	assert torch.allclose(lowerCAmelCase__							,		lowerCAmelCase__							), "The model logits don't match the original one."

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / name							,		commit_message="""Add model"""							,		use_temp_dir=lowerCAmelCase__							,		)

		lowerCAmelCase   =     224 if """seer""" not in name else 384
		# we can use the convnext one
		lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							,		size=lowerCAmelCase__							)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / name							,		commit_message="""Add image processor"""							,		use_temp_dir=lowerCAmelCase__							,		)

		print(f'Pushed {name}'							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Path							,		_SCREAMING_SNAKE_CASE					:						str = None							,		_SCREAMING_SNAKE_CASE					:						bool = True							)      ->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase   =     """imagenet-1k-id2label.json"""
	lowerCAmelCase   =     1_000
	lowerCAmelCase   =     (1, num_labels)

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

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

	lowerCAmelCase   =     partial(lowerCAmelCase__							,		num_labels=lowerCAmelCase__							,		idalabel=lowerCAmelCase__							,		labelaid=lowerCAmelCase__							)

	lowerCAmelCase   =     {
	    """regnet-x-002""": ImageNetPreTrainedConfig(
	        depths=[1, 1, 4, 7]							,		hidden_sizes=[24, 56, 152, 368]							,		groups_width=8							,		layer_type="""x"""							),
	    """regnet-x-004""": ImageNetPreTrainedConfig(
	        depths=[1, 2, 7, 12]							,		hidden_sizes=[32, 64, 160, 384]							,		groups_width=16							,		layer_type="""x"""							),
	    """regnet-x-006""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 5, 7]							,		hidden_sizes=[48, 96, 240, 528]							,		groups_width=24							,		layer_type="""x"""							),
	    """regnet-x-008""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 5]							,		hidden_sizes=[64, 128, 288, 672]							,		groups_width=16							,		layer_type="""x"""							),
	    """regnet-x-016""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 2]							,		hidden_sizes=[72, 168, 408, 912]							,		groups_width=24							,		layer_type="""x"""							),
	    """regnet-x-032""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 15, 2]							,		hidden_sizes=[96, 192, 432, 1_008]							,		groups_width=48							,		layer_type="""x"""							),
	    """regnet-x-040""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 14, 2]							,		hidden_sizes=[80, 240, 560, 1_360]							,		groups_width=40							,		layer_type="""x"""							),
	    """regnet-x-064""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]							,		hidden_sizes=[168, 392, 784, 1_624]							,		groups_width=56							,		layer_type="""x"""							),
	    """regnet-x-080""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 15, 1]							,		hidden_sizes=[80, 240, 720, 1_920]							,		groups_width=120							,		layer_type="""x"""							),
	    """regnet-x-120""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]							,		hidden_sizes=[224, 448, 896, 2_240]							,		groups_width=112							,		layer_type="""x"""							),
	    """regnet-x-160""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 13, 1]							,		hidden_sizes=[256, 512, 896, 2_048]							,		groups_width=128							,		layer_type="""x"""							),
	    """regnet-x-320""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 13, 1]							,		hidden_sizes=[336, 672, 1_344, 2_520]							,		groups_width=168							,		layer_type="""x"""							),
	    # y variant
	    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]							,		hidden_sizes=[24, 56, 152, 368]							,		groups_width=8							),
	    """regnet-y-004""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 6, 6]							,		hidden_sizes=[48, 104, 208, 440]							,		groups_width=8							),
	    """regnet-y-006""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 7, 4]							,		hidden_sizes=[48, 112, 256, 608]							,		groups_width=16							),
	    """regnet-y-008""": ImageNetPreTrainedConfig(
	        depths=[1, 3, 8, 2]							,		hidden_sizes=[64, 128, 320, 768]							,		groups_width=16							),
	    """regnet-y-016""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 17, 2]							,		hidden_sizes=[48, 120, 336, 888]							,		groups_width=24							),
	    """regnet-y-032""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 13, 1]							,		hidden_sizes=[72, 216, 576, 1_512]							,		groups_width=24							),
	    """regnet-y-040""": ImageNetPreTrainedConfig(
	        depths=[2, 6, 12, 2]							,		hidden_sizes=[128, 192, 512, 1_088]							,		groups_width=64							),
	    """regnet-y-064""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 14, 2]							,		hidden_sizes=[144, 288, 576, 1_296]							,		groups_width=72							),
	    """regnet-y-080""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 10, 1]							,		hidden_sizes=[168, 448, 896, 2_016]							,		groups_width=56							),
	    """regnet-y-120""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 11, 1]							,		hidden_sizes=[224, 448, 896, 2_240]							,		groups_width=112							),
	    """regnet-y-160""": ImageNetPreTrainedConfig(
	        depths=[2, 4, 11, 1]							,		hidden_sizes=[224, 448, 1_232, 3_024]							,		groups_width=112							),
	    """regnet-y-320""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,		hidden_sizes=[232, 696, 1_392, 3_712]							,		groups_width=232							),
	    # models created by SEER -> https://arxiv.org/abs/2202.08360
	    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]							,		hidden_sizes=[232, 696, 1_392, 3_712]							,		groups_width=232							),
	    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]							,		hidden_sizes=[328, 984, 1_968, 4_920]							,		groups_width=328							),
	    """regnet-y-1280-seer""": RegNetConfig(
	        depths=[2, 7, 17, 1]							,		hidden_sizes=[528, 1_056, 2_904, 7_392]							,		groups_width=264							),
	    """regnet-y-2560-seer""": RegNetConfig(
	        depths=[3, 7, 16, 1]							,		hidden_sizes=[640, 1_696, 2_544, 5_088]							,		groups_width=640							),
	    """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-320-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,		hidden_sizes=[232, 696, 1_392, 3_712]							,		groups_width=232							),
	    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 5, 12, 1]							,		hidden_sizes=[328, 984, 1_968, 4_920]							,		groups_width=328							),
	    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,		hidden_sizes=[528, 1_056, 2_904, 7_392]							,		groups_width=264							),
	    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[3, 7, 16, 1]							,		hidden_sizes=[640, 1_696, 2_544, 5_088]							,		groups_width=640							),
	    """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							),
	}

	lowerCAmelCase   =     NameToOurModelFuncMap()
	lowerCAmelCase   =     NameToFromModelFuncMap()
	# add seer weights logic

	def load_using_classy_vision(_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Callable[[], nn.Module]							) -> Tuple[nn.Module, Dict]:
		lowerCAmelCase   =     torch.hub.load_state_dict_from_url(lowerCAmelCase__							,		model_dir=str(lowerCAmelCase__							)							,		map_location="""cpu"""							)
		lowerCAmelCase   =     model_func()
		# check if we have a head, if yes add it
		lowerCAmelCase   =     files["""classy_state_dict"""]["""base_model"""]["""model"""]
		lowerCAmelCase   =     model_state_dict["""trunk"""]
		model.load_state_dict(lowerCAmelCase__							)
		return model.eval(), model_state_dict["heads"]

	# pretrained
	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,		lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27							,		group_width=1_010							,		w_a=1_744							,		w_a=620.83							,		w_m=2.52							)							)							)							,		)

	# IN1K finetuned
	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()							)							,		)

	lowerCAmelCase   =     partial(
	    lowerCAmelCase__							,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,		lambda: FakeRegNetVisslWrapper(
	        RegNet(RegNetParams(depth=27							,		group_width=1_010							,		w_a=1_744							,		w_a=620.83							,		w_m=2.52							)							)							)							,		)

	if model_name:
		convert_weight_and_push(
		    lowerCAmelCase__							,		names_to_from_model_map[model_name]							,		names_to_ours_model_map[model_name]							,		names_to_config[model_name]							,		lowerCAmelCase__							,		lowerCAmelCase__							,		)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    lowerCAmelCase__							,		names_to_from_model_map[model_name]							,		names_to_ours_model_map[model_name]							,		lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							,		)
	return config, expected_shape


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)




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))