


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







@dataclass
class 					snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	nn.Module
						SCREAMING_SNAKE_CASE__   :	List[nn.Module]       =				field(default_factory=UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__   :	list       =				field(default_factory=UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__   :	Dict[str, nn.Module]       =				field(default_factory=UpperCAmelCase__				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Tuple:
										lowerCAmelCase_			:   List[str]     					=					len(list(m.modules()				)				) == 1 or isinstance(__lowercase ,    nn.Convad				) or isinstance(__lowercase ,    nn.BatchNormad				)
										if has_not_submodules:
														self.traced.append(__lowercase				)
														lowerCAmelCase_			:   Optional[int]     					=					m




						def __call__(					self ,    __lowercase				)       ->     Tuple:
										for name, m in self.module.named_modules():
														self.handles.append(m.register_forward_hook(partial(self._forward_hook ,    name=__lowercase				)				)				)
										self.module(__lowercase				)
										[x.remove() for x in self.handles]
										return self




						@property
						def     lowercase_			(					self				)       ->     str:
										# 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 					snake_case__(				nn.Module				):







						'''simple docstring'''




						def __init__(					self ,    __lowercase				)       ->     List[Any]:
										super().__init__()

										lowerCAmelCase_			:   List[Tuple[str, nn.Module]]     					=					[]
										# - 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}"""
														lowerCAmelCase_			:   List[str]     					=					len(__lowercase				) + 1
														feature_blocks.append((f"""res{block_index}""", v)				)

										lowerCAmelCase_			:   Any     					=					nn.ModuleDict(__lowercase				)




						def     lowercase_			(					self ,    __lowercase				)       ->     Optional[int]:
										return get_trunk_forward_outputs(
										    __lowercase ,    out_feat_keys=__lowercase ,    feature_blocks=self._feature_blocks ,    )







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     Optional[Any]:
										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       lowerCAmelCase			(		lowerCAmelCase_			)->			Dict[str, str]:
				lowerCAmelCase_			:   Optional[int]     					=					RegNetConfig(depths=[2, 7, 17, 1]			, hidden_sizes=[8, 8, 8, 8]			, groups_width=8			)
				if "in1k" in model_name:
								lowerCAmelCase_			:   int     					=					RegNetForImageClassification(lowerCAmelCase_			)
				else:
								lowerCAmelCase_			:   Optional[Any]     					=					RegNetModel(lowerCAmelCase_			)
				# create from model (with small weights)
				lowerCAmelCase_			:   Tuple     					=					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():
								lowerCAmelCase_			:   Union[str, Any]     					=					from_model.eval()
								lowerCAmelCase_			:   int     					=					our_model.eval()

								lowerCAmelCase_			:   Optional[int]     					=					torch.randn((1, 3, 32, 32)			)
								# trace both
								lowerCAmelCase_			:   str     					=					Tracker(lowerCAmelCase_			)
								lowerCAmelCase_			:   Union[str, Any]     					=					dest_tracker(lowerCAmelCase_			).parametrized

								pprint(dest_tracker.nameamodule			)
								lowerCAmelCase_			:   str     					=					Tracker(lowerCAmelCase_			)
								lowerCAmelCase_			:   List[Any]     					=					src_tracker(lowerCAmelCase_			).parametrized

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

				lowerCAmelCase_			:   Any     					=					{}

				lowerCAmelCase_			:   str     					=					to_params_dict(lowerCAmelCase_			)
				lowerCAmelCase_			:   str     					=					to_params_dict(lowerCAmelCase_			)

				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()			, dst_state_dict.items()			):
								lowerCAmelCase_			:   str     					=					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:
								lowerCAmelCase_			:   Optional[int]     					=					'''classifier.1.weight'''
								lowerCAmelCase_			:   Tuple     					=					'''classifier.1.bias'''

				return from_to_ours_keys



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_ = None			, lowerCAmelCase_ = True			)->			int:
				lowerCAmelCase_			:   Optional[Any]     					=					'''imagenet-1k-id2label.json'''
				lowerCAmelCase_			:   List[Any]     					=					1_000

				lowerCAmelCase_			:   List[Any]     					=					'''huggingface/label-files'''
				lowerCAmelCase_			:   Optional[Any]     					=					num_labels
				lowerCAmelCase_			:   Tuple     					=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase_			, lowerCAmelCase_			, repo_type='''dataset'''			)			)			, '''r'''			)			)
				lowerCAmelCase_			:   Union[str, Any]     					=					{int(lowerCAmelCase_			): v for k, v in idalabel.items()}

				lowerCAmelCase_			:   List[str]     					=					idalabel
				lowerCAmelCase_			:   List[Any]     					=					{v: k for k, v in idalabel.items()}

				lowerCAmelCase_			:   Optional[Any]     					=					partial(lowerCAmelCase_			, num_labels=lowerCAmelCase_			, idalabel=lowerCAmelCase_			, labelaid=lowerCAmelCase_			)

				lowerCAmelCase_			:   Optional[int]     					=					{
				    '''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(lowerCAmelCase_			) -> Tuple[Dict, Dict]:
								lowerCAmelCase_			:   Tuple     					=					torch.hub.load_state_dict_from_url(lowerCAmelCase_			, model_dir=str(lowerCAmelCase_			)			, map_location='''cpu'''			)
								# check if we have a head, if yes add it
								lowerCAmelCase_			:   Optional[Any]     					=					files['''classy_state_dict''']['''base_model''']['''model''']
								return model_state_dict["trunk"], model_state_dict["heads"]

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

				lowerCAmelCase_			:   Any     					=					get_from_to_our_keys(lowerCAmelCase_			)

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

								lowerCAmelCase_			:   Optional[int]     					=					{}

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

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

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

						_UpperCAmelCase      :	str  =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)



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
						import transformer_engine.pytorch as te



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_=True			, lowerCAmelCase_=True			, lowerCAmelCase_=True			)->			Union[str, Any]:
				if not is_fpa_available():
								raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''			)
				for name, module in model.named_children():
								if isinstance(lowerCAmelCase_			, nn.Linear			) and to_transformer_engine and _convert_linear:
												# Return early if the linear layer weights are not multiples of 16
												if any(p % 16 != 0 for p in module.weight.shape			):
																return
												lowerCAmelCase_			:   Optional[int]     					=					module.bias is not None
												lowerCAmelCase_			:   Any     					=					te.Linear(
												    module.in_features			, module.out_features			, bias=lowerCAmelCase_			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Dict     					=					module.weight.data.clone()
												if has_bias:
																lowerCAmelCase_			:   Any     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								elif isinstance(lowerCAmelCase_			, nn.LayerNorm			) and to_transformer_engine and _convert_ln:
												lowerCAmelCase_			:   List[str]     					=					te.LayerNorm(module.normalized_shape[0]			, eps=module.eps			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Union[str, Any]     					=					module.weight.data.clone()
												lowerCAmelCase_			:   List[Any]     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								elif isinstance(lowerCAmelCase_			, te.Linear			) and not to_transformer_engine and _convert_linear:
												lowerCAmelCase_			:   Dict     					=					module.bias is not None
												lowerCAmelCase_			:   Union[str, Any]     					=					nn.Linear(
												    module.in_features			, module.out_features			, bias=lowerCAmelCase_			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Optional[Any]     					=					module.weight.data.clone()
												if has_bias:
																lowerCAmelCase_			:   Union[str, Any]     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								elif isinstance(lowerCAmelCase_			, te.LayerNorm			) and not to_transformer_engine and _convert_ln:
												lowerCAmelCase_			:   Union[str, Any]     					=					nn.LayerNorm(module.normalized_shape[0]			, eps=module.eps			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   List[str]     					=					module.weight.data.clone()
												lowerCAmelCase_			:   List[Any]     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								else:
												convert_model(
												    lowerCAmelCase_			, to_transformer_engine=lowerCAmelCase_			, _convert_linear=_convert_linear			, _convert_ln=_convert_ln			, )




def       lowerCAmelCase			(		lowerCAmelCase_			)->			str:
				if not is_fpa_available():
								raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''			)
				for m in model.modules():
								if isinstance(lowerCAmelCase_			, (te.LayerNorm, te.Linear)			):
												return True
				return False