


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 :	Union[str, Any]												=     logging.get_logger()



@dataclass
class 							A					:
					'''simple docstring'''






					A__    =				42
					A__    =				field(default_factory=UpperCamelCase_							)
					A__    =				field(default_factory=UpperCamelCase_							)
					A__    =				field(default_factory=UpperCamelCase_							)

					def 						lowerCamelCase__  (self					:		Dict   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		Any   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		str	)		->     List[Any]:




							"""simple docstring"""


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

					def __call__(self					:		Any   ,      _UpperCAmelCase					:		List[str]	)		->     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					:		str	)		->     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					:		List[Any]   ,      _UpperCAmelCase					:		int	)		->     Optional[Any]:




							"""simple docstring"""


							super().__init__()

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

							lowercase__      =	nn.ModuleDict(__UpperCamelCase	)


					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		List[str]	)		->     List[str]:




							"""simple docstring"""


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






class 							A					( UpperCamelCase_							):
					'''simple docstring'''






					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     int:




							"""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 				UpperCamelCase      (			__magic_name__  :							str  )     ->						Dict[str, str]:
		"""simple docstring"""




		lowercase__      =	RegNetConfig(depths=[2, 7, 17, 1]					,					hidden_sizes=[8, 8, 8, 8]					,					groups_width=8  )
		if "in1k" in model_name:
				lowercase__      =	RegNetForImageClassification(snake_case_  )
		else:
				lowercase__      =	RegNetModel(snake_case_  )
		# create from model (with small weights)
		lowercase__      =	FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27					,					group_width=1010					,					w_a=1744					,					w_a=620.83					,					w_m=2.5_2  )  )  )

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

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

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

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

		lowercase__      =	{}

		lowercase__      =	to_params_dict(snake_case_  )
		lowercase__      =	to_params_dict(snake_case_  )

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

		return from_to_ours_keys






def 				UpperCamelCase      (			__magic_name__  :							List[str]					,					__magic_name__  :							List[Any] = None					,					__magic_name__  :							int = True  )     ->						int:
		"""simple docstring"""




		lowercase__      =	'''imagenet-1k-id2label.json'''
		lowercase__      =	1000

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

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

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

		lowercase__      =	{
		    '''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-10b-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]					,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,					groups_width=1010  ),
		}

		# add seer weights logic
		def load_using_classy_vision(__magic_name__  :							Optional[int]  ) -> Tuple[Dict, Dict]:
				lowercase__      =	torch.hub.load_state_dict_from_url(snake_case_					,					model_dir=str(snake_case_  )					,					map_location="""cpu"""  )
				# check if we have a head, if yes add it
				lowercase__      =	files['''classy_state_dict''']['''base_model''']['''model''']
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowercase__      =	{
		    '''regnet-y-10b-seer''': partial(
		        snake_case_					,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""					,					),
		    '''regnet-y-10b-seer-in1k''': partial(
		        snake_case_					,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""					,					),
		}

		lowercase__      =	get_from_to_our_keys(snake_case_  )

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

				lowercase__      =	{}

				lowercase__      =	list(from_state_dict.keys()  )
				lowercase__      =	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
						lowercase__      =	re.sub(snake_case_					,					""""""					,					snake_case_  )
						# 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
						lowercase__      =	from_to_ours_keys[src_key]
						# store the parameter with our key
						lowercase__      =	from_state_dict[key]
						not_used_keys.remove(snake_case_  )
				# check that all keys have been updated
				assert len(snake_case_  ) == 0, f'''Some keys where not used {",".join(snake_case_  )}'''

				logger.info(f'''The following keys were not used: {",".join(snake_case_  )}'''  )

				# save our state dict to disk
				torch.save(snake_case_					,					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
				lowercase__      =	names_to_config[model_name]
				lowercase__      =	RegNetModel
				if "in1k" in model_name:
						lowercase__      =	RegNetForImageClassification
				lowercase__      =	our_model_func(snake_case_  )
				# 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
				lowercase__      =	our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    snake_case_					,					snake_case_					,					[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					,					)
				lowercase__      =	384
				# we can use the convnext one
				lowercase__      =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""					,					size=snake_case_  )
				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 :	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)





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A :	Optional[Any]												=     logging.get_logger(__name__)



class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      *_UpperCAmelCase					:		Union[str, Any]   ,      **_UpperCAmelCase					:		Any	)		->     None:




							"""simple docstring"""


							warnings.warn(
							    """The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV1ImageProcessor instead."""   ,      _UpperCAmelCase   ,      )
							super().__init__(*_UpperCAmelCase   ,      **_UpperCAmelCase	)

