

"""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()
__A     :    Optional[int]    =		logging.get_logger()





@dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    field(default_factory=UpperCamelCase__						)
		SCREAMING_SNAKE_CASE								=    field(default_factory=UpperCamelCase__						)
		SCREAMING_SNAKE_CASE								=    field(default_factory=UpperCamelCase__						)


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


		def __call__(self :		str     ,     __SCREAMING_SNAKE_CASE :		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 					SCREAMING_SNAKE_CASE__   (self :		str):
							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 :		Tuple     ,     __SCREAMING_SNAKE_CASE :		nn.Module):
							super().__init__()

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

							A			=					nn.ModuleDict(__A)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		Tensor):
							return get_trunk_forward_outputs(
							    __A     ,     out_feat_keys=__A     ,     feature_blocks=self._feature_blocks     ,     )





class 			__UpperCamelCase (   UpperCamelCase__						):


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							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    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					RegNetConfig(depths=[2, 7, 17, 1]			,							hidden_sizes=[8, 8, 8, 8]			,							groups_width=8      )
					if "in1k" in model_name:
										A			=					RegNetForImageClassification(UpperCamelCase__      )
					else:
										A			=					RegNetModel(UpperCamelCase__      )
					# create from model (with small weights)
					A			=					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():
										A			=					from_model.eval()
										A			=					our_model.eval()

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

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

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

					A			=					{}

					A			=					to_params_dict(UpperCamelCase__      )
					A			=					to_params_dict(UpperCamelCase__      )

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

					return from_to_ours_keys
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__ = None			,							lowercase__ = True      ):
					"""simple docstring"""
					A			=					"imagenet-1k-id2label.json"
					A			=					1_000

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

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

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

					A			=					{
					    "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(lowercase__      ) -> Tuple[Dict, Dict]:
										A			=					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
										A			=					files["classy_state_dict"]["base_model"]["model"]
										return model_state_dict["trunk"], model_state_dict["heads"]

					A			=					{
					    "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"			,							),
					}

					A			=					get_from_to_our_keys(UpperCamelCase__      )

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

										A			=					{}

										A			=					list(from_state_dict.keys()      )
										A			=					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
															A			=					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
															A			=					from_to_ours_keys[src_key]
															# store the parameter with our key
															A			=					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
										A			=					names_to_config[model_name]
										A			=					RegNetModel
										if "in1k" in model_name:
															A			=					RegNetForImageClassification
										A			=					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
										A			=					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			,							)
										A			=					384
										# we can use the convnext one
										A			=					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__":
	__A     :    Optional[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     :    Any    =		parser.parse_args()

	__A     :    Dict    =		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)












from __future__ import annotations

import math
import random






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							int    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					d * e - 1
		SCREAMING_SNAKE_CASE__								=					0
		SCREAMING_SNAKE_CASE__								=					0
		while p == 0:
				SCREAMING_SNAKE_CASE__								=					random.randint(2    ,      n - 1			)
				SCREAMING_SNAKE_CASE__								=					k
				while True:
						if t % 2 == 0:
								SCREAMING_SNAKE_CASE__								=					t // 2
								SCREAMING_SNAKE_CASE__								=					(g**t) % n
								SCREAMING_SNAKE_CASE__								=					math.gcd(x - 1    ,      UpperCamelCase__			)
								if x > 1 and y > 1:
										SCREAMING_SNAKE_CASE__								=					y
										SCREAMING_SNAKE_CASE__								=					n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()