


"""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()
__SCREAMING_SNAKE_CASE				:     str		=     logging.get_logger()





@dataclass
class   __A  :





							'''simple docstring'''

							__lowercase:		Any				   =     42
							__lowercase:		List[Any]				   =     field(default_factory=snake_case__)
							__lowercase:		str				   =     field(default_factory=snake_case__)
							__lowercase:		List[str]				   =     field(default_factory=snake_case__)



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Tensor		,							UpperCAmelCase_				:   Tensor		,							UpperCAmelCase_				:   str							)       ->int:



														"""simple docstring"""



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



							def __call__(					self				:   List[str]		,							UpperCAmelCase_				:   Tensor							)       ->Any:



														"""simple docstring"""



														for name, m in self.module.named_modules():
																					self.handles.append(m.register_forward_hook(partial(self._forward_hook		,							name=UpperCamelCase_							)							)							)
														self.module(UpperCamelCase_							)
														[x.remove() for x in self.handles]
														return self



							@property
							def 		lowerCAmelCase							(					self				:   List[str]							)       ->List[str]:



														"""simple docstring"""



														return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}





class   __A  (nn.Module):





							'''simple docstring'''

							def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   nn.Module							)       ->Optional[int]:



														"""simple docstring"""



														super().__init__()

														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}"""
																					snake_case_     							=		len(UpperCamelCase_							) + 1
																					feature_blocks.append((F"""res{block_index}""", v)							)

														snake_case_     							=		nn.ModuleDict(UpperCamelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Tensor							)       ->Tuple:



														"""simple docstring"""



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





class   __A  (snake_case__):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   List[str]							)       ->List[Any]:



														"""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     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Dict[str, str]:
							snake_case_     							=		RegNetConfig(depths=[2, 7, 17, 1]					,			hidden_sizes=[8, 8, 8, 8]					,			groups_width=8					)
							if "in1k" in model_name:
														snake_case_     							=		RegNetForImageClassification(_lowercase					)
							else:
														snake_case_     							=		RegNetModel(_lowercase					)
							# create from model (with small weights)
							snake_case_     							=		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():
														snake_case_     							=		from_model.eval()
														snake_case_     							=		our_model.eval()

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

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

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

							snake_case_     							=		{}

							snake_case_     							=		to_params_dict(_lowercase					)
							snake_case_     							=		to_params_dict(_lowercase					)

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

							return from_to_ours_keys







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = None					,			_SCREAMING_SNAKE_CASE = True					)     ->					Dict:
							snake_case_     							=		'imagenet-1k-id2label.json'
							snake_case_     							=		1_000

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

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

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

							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(_SCREAMING_SNAKE_CASE					) -> Tuple[Dict, Dict]:
														snake_case_     							=		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
														snake_case_     							=		files['classy_state_dict']['base_model']['model']
														return model_state_dict["trunk"], model_state_dict["heads"]

							snake_case_     							=		{
							    '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"""					,			),
							}

							snake_case_     							=		get_from_to_our_keys(_lowercase					)

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

														snake_case_     							=		{}

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

				__SCREAMING_SNAKE_CASE				:     List[Any]		=     parser.parse_args()

				__SCREAMING_SNAKE_CASE				:     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'''

from __future__ import annotations

import sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
