
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()
_UpperCAmelCase   :					Tuple    =			logging.get_logger()


@dataclass
class 							lowercase   :
		__lowercase			:			nn.Module
		__lowercase			:			List[nn.Module]      	=							field(default_factory=_SCREAMING_SNAKE_CASE  )
		__lowercase			:			list      	=							field(default_factory=_SCREAMING_SNAKE_CASE  )
		__lowercase			:			Dict[str, nn.Module]      	=							field(default_factory=_SCREAMING_SNAKE_CASE  )



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_			)    ->   int:
				"""simple docstring"""
				UpperCamelCase            =	len(list(m.modules()			)			) == 1 or isinstance(A_							, nn.Convad			) or isinstance(A_							, nn.BatchNormad			)
				if has_not_submodules:
						self.traced.append(A_			)
						UpperCamelCase            =	m



		def __call__(					self							, A_			)    ->   Tuple:
				"""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 __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				# check the len of the state_dict keys to see if we have learnable params
				return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()			)			) > 0}






class 							lowercase   (						nn.Module  ):



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

				UpperCamelCase            =	[]
				# - 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}'''
						UpperCamelCase            =	len(A_			) + 1
						feature_blocks.append((F'''res{block_index}''', v)			)

				UpperCamelCase            =	nn.ModuleDict(A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				return get_trunk_forward_outputs(
				    A_							, out_feat_keys=A_							, feature_blocks=self._feature_blocks							, )






class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __UpperCamelCase					(					self			)    ->   Union[str, 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			(   lowercase					)  ->    Dict[str, str]:
		'''simple docstring'''

		UpperCamelCase            =	RegNetConfig(depths=[2, 7, 17, 1]       ,			hidden_sizes=[8, 8, 8, 8]       ,			groups_width=8					)
		if "in1k" in model_name:
				UpperCamelCase            =	RegNetForImageClassification(lowercase					)
		else:
				UpperCamelCase            =	RegNetModel(lowercase					)
		# create from model (with small weights)
		UpperCamelCase            =	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():
				UpperCamelCase            =	from_model.eval()
				UpperCamelCase            =	our_model.eval()

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

				pprint(dest_tracker.nameamodule					)
				UpperCamelCase            =	Tracker(lowercase					)
				UpperCamelCase            =	src_tracker(lowercase					).parametrized

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

		UpperCamelCase            =	{}

		UpperCamelCase            =	to_params_dict(lowercase					)
		UpperCamelCase            =	to_params_dict(lowercase					)

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

		return from_to_ours_keys


def  A			(   lowercase       ,			lowercase = None       ,			lowercase = True					)  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	'imagenet-1k-id2label.json'
		UpperCamelCase            =	1_000

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

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

		UpperCamelCase            =	partial(lowercase       ,			num_labels=lowercase       ,			idalabel=lowercase       ,			labelaid=lowercase					)

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

		UpperCamelCase            =	{
		    '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'       ,			),
		}

		UpperCamelCase            =	get_from_to_our_keys(lowercase					)

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

				UpperCamelCase            =	{}

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

					_UpperCAmelCase   :					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)


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		barcode //= 10  # exclude the last digit
		UpperCamelCase            =	False
		UpperCamelCase            =	0

		# extract and check each digit
		while barcode != 0:
				UpperCamelCase            =	1 if checker else 3
				s += mult * (barcode % 10)
				barcode //= 10
				UpperCamelCase            =	not checker

		return (10 - (s % 10)) % 10


def  A			(   lowercase					)  ->    bool:
		'''simple docstring'''

		return len(str(lowercase					)					) == 13 and get_check_digit(lowercase					) == barcode % 10


def  A			(   lowercase					)  ->    int:
		'''simple docstring'''

		if str(lowercase					).isalpha():
				UpperCamelCase            =	f'''Barcode \'{barcode}\' has alphabetic characters.'''
				raise ValueError(lowercase					)
		elif int(lowercase					) < 0:
				raise ValueError('The entered barcode has a negative value. Try again.'					)
		else:
				return int(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					_UpperCAmelCase   :					List[str]    =			get_barcode(input("Barcode: ").strip())

					if is_valid(barcode):
										print(F'''\'{barcode}\' is a valid barcode.''')
					else:
										print(F'''\'{barcode}\' is NOT a valid barcode.''')
