


"""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()
A	:  List[str]            =     logging.get_logger()
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       nn.Module
							__UpperCAmelCase		:       List[nn.Module]							 =field(default_factory=lowerCAmelCase__						)
							__UpperCAmelCase		:       list							 =field(default_factory=lowerCAmelCase__						)

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     len(list(m.modules()      )      ) == 1 or isinstance(__a       ,  nn.Convad      ) or isinstance(__a       ,  nn.BatchNormad      )
										if has_not_submodules:
													self.traced.append(__a      )

							def __call__(	self       ,  __a      ):
										for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook      )      )
										self.module(__a      )
										[x.remove() for x in self.handles]
										return self



							@property
							def      snake_case							(	self      ):
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda __a      : len(list(x.state_dict().keys()      )      ) > 0       ,  self.traced      )      )
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       nn.Module
							__UpperCAmelCase		:       nn.Module
							__UpperCAmelCase		:       int							 =1
							__UpperCAmelCase		:       List							 =field(default_factory=lowerCAmelCase__						)
							__UpperCAmelCase		:       List							 =field(default_factory=lowerCAmelCase__						)
							__UpperCAmelCase		:       bool							 =True

							def __call__(	self       ,  __a      ):
										__lowerCAmelCase							       =     Tracker(self.dest      )(__a      ).parametrized
										__lowerCAmelCase							       =     Tracker(self.src      )(__a      ).parametrized

										__lowerCAmelCase							       =     list(filter(lambda __a      : type(__a      ) not in self.src_skip       ,  __a      )      )
										__lowerCAmelCase							       =     list(filter(lambda __a      : type(__a      ) not in self.dest_skip       ,  __a      )      )

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

										for dest_m, src_m in zip(__a       ,  __a      ):
													dest_m.load_state_dict(src_m.state_dict()      )
													if self.verbose == 1:
																print(f"Transfered from={src_m} to={dest_m}"      )
class     _UpperCamelCase    (  nn.Module						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										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(__a      ) + 1
													feature_blocks.append((f"res{block_index}", v)      )

										__lowerCAmelCase							       =     nn.ModuleDict(__a      )



							def      snake_case							(	self       ,  __a      ):
										return get_trunk_forward_outputs(
										    __a       ,  out_feat_keys=__a       ,  feature_blocks=self._feature_blocks       ,  )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     x.split("-"      )
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )



							def __getitem__(	self       ,  __a      ):
										# default to timm!
										if x not in self:
													__lowerCAmelCase							       =     self.convert_name_to_timm(__a      )
													__lowerCAmelCase							       =     partial(lambda: (timm.create_model(__a       ,  pretrained=__a      ).eval(), None)      )

										else:
													__lowerCAmelCase							       =     super().__getitem__(__a      )

										return val
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __getitem__(	self       ,  __a      ):
										if "seer" in x and "in1k" not in x:
													__lowerCAmelCase							       =     RegNetModel
										else:
													__lowerCAmelCase							       =     RegNetForImageClassification
										return val


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase = True       ,					):

			'''simple docstring'''




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

			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(_UpperCamelCase       ,					our_model.state_dict()       ,					_UpperCamelCase )
						our_model.load_state_dict(_UpperCamelCase )

			__lowerCAmelCase							       =     our_model(_UpperCamelCase       ,					output_hidden_states=_UpperCamelCase )
			__lowerCAmelCase							       =     (
			    our_outputs.logits if isinstance(_UpperCamelCase       ,					_UpperCamelCase ) else our_outputs.last_hidden_state
			)

			__lowerCAmelCase							       =     from_model(_UpperCamelCase )
			__lowerCAmelCase							       =     from_output[-1] if type(_UpperCamelCase ) 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(_UpperCamelCase       ,					_UpperCamelCase ), "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=_UpperCamelCase       ,					)

						__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=_UpperCamelCase )
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / name       ,					commit_message="Add image processor"       ,					use_temp_dir=_UpperCamelCase       ,					)

						print(f"Pushed {name}" )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase = None       ,					_UpperCamelCase = True ):

			'''simple docstring'''




			__lowerCAmelCase							       =     "imagenet-1k-id2label.json"
			__lowerCAmelCase							       =     1000
			__lowerCAmelCase							       =     (1, num_labels)

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

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

			__lowerCAmelCase							       =     partial(_UpperCamelCase       ,					num_labels=_UpperCamelCase       ,					idalabel=_UpperCamelCase       ,					labelaid=_UpperCamelCase )

			__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, 1008]       ,					groups_width=48       ,					layer_type="x" ),
			    "regnet-x-040": ImageNetPreTrainedConfig(
			        depths=[2, 5, 14, 2]       ,					hidden_sizes=[80, 240, 560, 1360]       ,					groups_width=40       ,					layer_type="x" ),
			    "regnet-x-064": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]       ,					hidden_sizes=[168, 392, 784, 1624]       ,					groups_width=56       ,					layer_type="x" ),
			    "regnet-x-080": ImageNetPreTrainedConfig(
			        depths=[2, 5, 15, 1]       ,					hidden_sizes=[80, 240, 720, 1920]       ,					groups_width=120       ,					layer_type="x" ),
			    "regnet-x-120": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]       ,					hidden_sizes=[224, 448, 896, 2240]       ,					groups_width=112       ,					layer_type="x" ),
			    "regnet-x-160": ImageNetPreTrainedConfig(
			        depths=[2, 6, 13, 1]       ,					hidden_sizes=[256, 512, 896, 2048]       ,					groups_width=128       ,					layer_type="x" ),
			    "regnet-x-320": ImageNetPreTrainedConfig(
			        depths=[2, 7, 13, 1]       ,					hidden_sizes=[336, 672, 1344, 2520]       ,					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, 1512]       ,					groups_width=24 ),
			    "regnet-y-040": ImageNetPreTrainedConfig(
			        depths=[2, 6, 12, 2]       ,					hidden_sizes=[128, 192, 512, 1088]       ,					groups_width=64 ),
			    "regnet-y-064": ImageNetPreTrainedConfig(
			        depths=[2, 7, 14, 2]       ,					hidden_sizes=[144, 288, 576, 1296]       ,					groups_width=72 ),
			    "regnet-y-080": ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]       ,					hidden_sizes=[168, 448, 896, 2016]       ,					groups_width=56 ),
			    "regnet-y-120": ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]       ,					hidden_sizes=[224, 448, 896, 2240]       ,					groups_width=112 ),
			    "regnet-y-160": ImageNetPreTrainedConfig(
			        depths=[2, 4, 11, 1]       ,					hidden_sizes=[224, 448, 1232, 3024]       ,					groups_width=112 ),
			    "regnet-y-320": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]       ,					hidden_sizes=[232, 696, 1392, 3712]       ,					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, 1392, 3712]       ,					groups_width=232 ),
			    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]       ,					hidden_sizes=[328, 984, 1968, 4920]       ,					groups_width=328 ),
			    "regnet-y-1280-seer": RegNetConfig(
			        depths=[2, 7, 17, 1]       ,					hidden_sizes=[528, 1056, 2904, 7392]       ,					groups_width=264 ),
			    "regnet-y-2560-seer": RegNetConfig(
			        depths=[3, 7, 16, 1]       ,					hidden_sizes=[640, 1696, 2544, 5088]       ,					groups_width=640 ),
			    "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-320-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]       ,					hidden_sizes=[232, 696, 1392, 3712]       ,					groups_width=232 ),
			    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]       ,					hidden_sizes=[328, 984, 1968, 4920]       ,					groups_width=328 ),
			    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]       ,					hidden_sizes=[528, 1056, 2904, 7392]       ,					groups_width=264 ),
			    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[3, 7, 16, 1]       ,					hidden_sizes=[640, 1696, 2544, 5088]       ,					groups_width=640 ),
			    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]       ,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,					groups_width=1010 ),
			}

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

			def load_using_classy_vision(_UpperCamelCase       ,					_UpperCamelCase ) -> Tuple[nn.Module, Dict]:
						__lowerCAmelCase							       =     torch.hub.load_state_dict_from_url(_UpperCamelCase       ,					model_dir=str(_UpperCamelCase )       ,					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(_UpperCamelCase )
						return model.eval(), model_state_dict["heads"]

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

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

			__lowerCAmelCase							       =     partial(
			    _UpperCamelCase       ,					"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(
			    _UpperCamelCase       ,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"       ,					lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27       ,					group_width=1010       ,					w_a=1744       ,					w_a=6_20.83       ,					w_m=2.52 ) ) )       ,					)

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

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

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

			__lowerCAmelCase							       =     partial(
			    _UpperCamelCase       ,					"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=1010       ,					w_a=1744       ,					w_a=6_20.83       ,					w_m=2.52 ) ) )       ,					)

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


if __name__ == "__main__":
	A	:  Union[str, 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.",
	)

	A	:  List[str]            =     parser.parse_args()

	A	:  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 _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase							       =     True
						for i in range(0       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False

						for i in range(1       ,					len(_UpperCamelCase ) - 1       ,					2 ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase       ,      __lowerCAmelCase							       =     input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase							       =     False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A	:  Tuple            =     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A	:  Dict            =     odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




