





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_				:
							lowerCamelCase_							=    42
							lowerCamelCase_							=    field(default_factory=UpperCamelCase__			)
							lowerCamelCase_							=    field(default_factory=UpperCamelCase__			)
							lowerCamelCase_							=    field(default_factory=UpperCamelCase__			)


							def    _snake_case     (							self			:Optional[int]							, __A			:List[Any]							, __A			:Tensor							, __A			:Tensor							, __A			:str						)      ->      str:



									"""simple docstring"""



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


							def __call__(							self			:str							, __A			:Tensor						)      ->      Any:



									"""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    _snake_case     (							self			:str						)      ->      Optional[Any]:



									"""simple docstring"""



									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							, __A			:nn.Module						)      ->      Union[str, Any]:



									"""simple docstring"""



									super().__init__()

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

									SCREAMING_SNAKE_CASE__								=					nn.ModuleDict(__A						)


							def    _snake_case     (							self			:Optional[Any]							, __A			:Tensor						)      ->      Dict:



									"""simple docstring"""



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




class      UpperCamelCase_				(   UpperCamelCase__			):


							def    _snake_case     (							self			:List[str]						)      ->      Dict:



									"""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  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					RegNetConfig(depths=[2, 7, 17, 1]    ,      hidden_sizes=[8, 8, 8, 8]    ,      groups_width=8			)
		if "in1k" in model_name:
				SCREAMING_SNAKE_CASE__								=					RegNetForImageClassification(UpperCamelCase__			)
		else:
				SCREAMING_SNAKE_CASE__								=					RegNetModel(UpperCamelCase__			)
		# create from model (with small weights)
		SCREAMING_SNAKE_CASE__								=					FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27    ,      group_width=1_010    ,      w_a=1_744    ,      w_a=6_2_0.8_3    ,      w_m=2.5_2			)			)			)

		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								=					from_model.eval()
				SCREAMING_SNAKE_CASE__								=					our_model.eval()

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

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

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

		SCREAMING_SNAKE_CASE__								=					{}

		SCREAMING_SNAKE_CASE__								=					to_params_dict(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					to_params_dict(UpperCamelCase__			)

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

		return from_to_ours_keys






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Path    ,      UpperCamelCase__:							str = None    ,      UpperCamelCase__:							bool = True			):
		SCREAMING_SNAKE_CASE__								=					"""imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__								=					1_000

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

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

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

		SCREAMING_SNAKE_CASE__								=					{
		    """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]:
				SCREAMING_SNAKE_CASE__								=					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
				SCREAMING_SNAKE_CASE__								=					files["""classy_state_dict"""]["""base_model"""]["""model"""]
				return model_state_dict["trunk"], model_state_dict["heads"]

		SCREAMING_SNAKE_CASE__								=					{
		    """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"""    ,      ),
		}

		SCREAMING_SNAKE_CASE__								=					get_from_to_our_keys(UpperCamelCase__			)

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

				SCREAMING_SNAKE_CASE__								=					{}

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






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()