



from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
	import accelerate







def     UpperCAmelCase_		(					snake_case__					)	->     Any:



				"""simple docstring"""





				if not is_accelerate_available():
								return method
				lowerCAmelCase__		  =  version.parse(accelerate.__version__					).base_version
				if version.parse(snake_case__					) < version.parse('0.17.0'					):
								return method

				def wrapper(self    ,			*snake_case__    ,			**snake_case__					):
								if hasattr(self    ,			'_hf_hook'					) and hasattr(self._hf_hook    ,			'pre_forward'					):
												self._hf_hook.pre_forward(self					)
								return method(self    ,			*snake_case__    ,			**snake_case__					)

				return wrapper





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:   Optional[Any]    =   logging.get_logger()


@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  len(list(m.modules()						)						) == 1 or isinstance(a_    ,nn.Convad						) or isinstance(a_    ,nn.BatchNormad						)
						if has_not_submodules:
										self.traced.append(a_						)
		def __call__(							self    ,a_						):







						"""simple docstring"""
						for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook						)						)
						self.module(a_						)
						[x.remove() for x in self.handles]
						return self



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# check the len of the state_dict keys to see if we have learnable params
						return list(filter(lambda a_						: len(list(x.state_dict().keys()						)						) > 0    ,self.traced						)						)



@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	1
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	True
		def __call__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Tracker(self.dest						)(a_						).parametrized
						lowerCAmelCase__		  =  Tracker(self.src						)(a_						).parametrized

						lowerCAmelCase__		  =  list(filter(lambda a_						: type(a_						) not in self.src_skip    ,a_						)						)
						lowerCAmelCase__		  =  list(filter(lambda a_						: type(a_						) not in self.dest_skip    ,a_						)						)

						if len(a_						) != len(a_						) and self.raise_if_mismatch:
										raise Exception(
										    f'Numbers of operations are different. Source module has {len(a_						)} operations while'
										    f' destination module has {len(a_						)}.'						)

						for dest_m, src_m in zip(a_    ,a_						):
										dest_m.load_state_dict(src_m.state_dict()						)
										if self.verbose == 1:
														print(f'Transfered from={src_m} to={dest_m}'						)



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__()

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

						lowerCAmelCase__		  =  nn.ModuleDict(a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return get_trunk_forward_outputs(
						    a_    ,out_feat_keys=a_    ,feature_blocks=self._feature_blocks    ,)



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  x.split('-'						)
						return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						# default to timm!
						if x not in self:
										lowerCAmelCase__		  =  self.convert_name_to_timm(a_						)
										lowerCAmelCase__		  =  partial(lambda: (timm.create_model(a_    ,pretrained=a_						).eval(), None)						)

						else:
										lowerCAmelCase__		  =  super().__getitem__(a_						)

						return val



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						if "seer" in x and "in1k" not in x:
										lowerCAmelCase__		  =  RegNetModel
						else:
										lowerCAmelCase__		  =  RegNetForImageClassification
						return val







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[str]:



				"""simple docstring"""





				for from_key, to_key in keys:
								lowerCAmelCase__		  =  from_state_dict[from_key].clone()
								print(f'Copied key={from_key} to={to_key}'					)
				return to_state_dict







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__ = True    ,			)	->     Optional[int]:



				"""simple docstring"""





				print(f'Converting {name}...'					)
				with torch.no_grad():
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  from_model_func()
								lowerCAmelCase__		  =  our_model_func(snake_case__					).eval()
								lowerCAmelCase__		  =  ModuleTransfer(src=snake_case__    ,			dest=snake_case__    ,			raise_if_mismatch=snake_case__					)
								lowerCAmelCase__		  =  torch.randn((1, 3, 224, 224)					)
								module_transfer(snake_case__					)

				if from_state_dict is not None:
								lowerCAmelCase__		  =  []
								# for seer - in1k finetuned we have to manually copy the head
								if "seer" in name and "in1k" in name:
												lowerCAmelCase__		  =  [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
								lowerCAmelCase__		  =  manually_copy_vissl_head(snake_case__    ,			our_model.state_dict()    ,			snake_case__					)
								our_model.load_state_dict(snake_case__					)

				lowerCAmelCase__		  =  our_model(snake_case__    ,			output_hidden_states=snake_case__					)
				lowerCAmelCase__		  =  (
				    our_outputs.logits if isinstance(snake_case__    ,			snake_case__					) else our_outputs.last_hidden_state
				)

				lowerCAmelCase__		  =  from_model(snake_case__					)
				lowerCAmelCase__		  =  from_output[-1] if type(snake_case__					) is list else from_output

				# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
				if "seer" in name and "in1k" in name:
								lowerCAmelCase__		  =  our_outputs.hidden_states[-1]

				assert torch.allclose(snake_case__    ,			snake_case__					), "The model logits don't match the original one."

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / name    ,			commit_message='Add model'    ,			use_temp_dir=snake_case__    ,			)

								lowerCAmelCase__		  =  224 if 'seer' not in name else 384
								# we can use the convnext one
								lowerCAmelCase__		  =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    ,			size=snake_case__					)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name    ,			commit_message='Add image processor'    ,			use_temp_dir=snake_case__    ,			)

								print(f'Pushed {name}'					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__ = None    ,			snake_case__ = True					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  'imagenet-1k-id2label.json'
				lowerCAmelCase__		  =  1000
				lowerCAmelCase__		  =  (1, num_labels)

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

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

				lowerCAmelCase__		  =  partial(snake_case__    ,			num_labels=snake_case__    ,			idalabel=snake_case__    ,			labelaid=snake_case__					)

				lowerCAmelCase__		  =  {
				    'regnet-x-002': ImageNetPreTrainedConfig(
				        depths=[1, 1, 4, 7]    ,			hidden_sizes=[24, 56, 152, 368]    ,			groups_width=8    ,			layer_type='x'					),
				    'regnet-x-004': ImageNetPreTrainedConfig(
				        depths=[1, 2, 7, 12]    ,			hidden_sizes=[32, 64, 160, 384]    ,			groups_width=16    ,			layer_type='x'					),
				    'regnet-x-006': ImageNetPreTrainedConfig(
				        depths=[1, 3, 5, 7]    ,			hidden_sizes=[48, 96, 240, 528]    ,			groups_width=24    ,			layer_type='x'					),
				    'regnet-x-008': ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 5]    ,			hidden_sizes=[64, 128, 288, 672]    ,			groups_width=16    ,			layer_type='x'					),
				    'regnet-x-016': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 2]    ,			hidden_sizes=[72, 168, 408, 912]    ,			groups_width=24    ,			layer_type='x'					),
				    'regnet-x-032': ImageNetPreTrainedConfig(
				        depths=[2, 6, 15, 2]    ,			hidden_sizes=[96, 192, 432, 1008]    ,			groups_width=48    ,			layer_type='x'					),
				    'regnet-x-040': ImageNetPreTrainedConfig(
				        depths=[2, 5, 14, 2]    ,			hidden_sizes=[80, 240, 560, 1360]    ,			groups_width=40    ,			layer_type='x'					),
				    'regnet-x-064': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]    ,			hidden_sizes=[168, 392, 784, 1624]    ,			groups_width=56    ,			layer_type='x'					),
				    'regnet-x-080': ImageNetPreTrainedConfig(
				        depths=[2, 5, 15, 1]    ,			hidden_sizes=[80, 240, 720, 1920]    ,			groups_width=120    ,			layer_type='x'					),
				    'regnet-x-120': ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]    ,			hidden_sizes=[224, 448, 896, 2240]    ,			groups_width=112    ,			layer_type='x'					),
				    'regnet-x-160': ImageNetPreTrainedConfig(
				        depths=[2, 6, 13, 1]    ,			hidden_sizes=[256, 512, 896, 2048]    ,			groups_width=128    ,			layer_type='x'					),
				    'regnet-x-320': ImageNetPreTrainedConfig(
				        depths=[2, 7, 13, 1]    ,			hidden_sizes=[336, 672, 1344, 2520]    ,			groups_width=168    ,			layer_type='x'					),
				    # y variant
				    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,			hidden_sizes=[24, 56, 152, 368]    ,			groups_width=8					),
				    'regnet-y-004': ImageNetPreTrainedConfig(
				        depths=[1, 3, 6, 6]    ,			hidden_sizes=[48, 104, 208, 440]    ,			groups_width=8					),
				    'regnet-y-006': ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 4]    ,			hidden_sizes=[48, 112, 256, 608]    ,			groups_width=16					),
				    'regnet-y-008': ImageNetPreTrainedConfig(
				        depths=[1, 3, 8, 2]    ,			hidden_sizes=[64, 128, 320, 768]    ,			groups_width=16					),
				    'regnet-y-016': ImageNetPreTrainedConfig(
				        depths=[2, 6, 17, 2]    ,			hidden_sizes=[48, 120, 336, 888]    ,			groups_width=24					),
				    'regnet-y-032': ImageNetPreTrainedConfig(
				        depths=[2, 5, 13, 1]    ,			hidden_sizes=[72, 216, 576, 1512]    ,			groups_width=24					),
				    'regnet-y-040': ImageNetPreTrainedConfig(
				        depths=[2, 6, 12, 2]    ,			hidden_sizes=[128, 192, 512, 1088]    ,			groups_width=64					),
				    'regnet-y-064': ImageNetPreTrainedConfig(
				        depths=[2, 7, 14, 2]    ,			hidden_sizes=[144, 288, 576, 1296]    ,			groups_width=72					),
				    'regnet-y-080': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]    ,			hidden_sizes=[168, 448, 896, 2016]    ,			groups_width=56					),
				    'regnet-y-120': ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]    ,			hidden_sizes=[224, 448, 896, 2240]    ,			groups_width=112					),
				    'regnet-y-160': ImageNetPreTrainedConfig(
				        depths=[2, 4, 11, 1]    ,			hidden_sizes=[224, 448, 1232, 3024]    ,			groups_width=112					),
				    'regnet-y-320': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1392, 3712]    ,			groups_width=232					),
				    # models created by SEER -> https://arxiv.org/abs/2202.08360
				    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1392, 3712]    ,			groups_width=232					),
				    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]    ,			hidden_sizes=[328, 984, 1968, 4920]    ,			groups_width=328					),
				    'regnet-y-1280-seer': RegNetConfig(
				        depths=[2, 7, 17, 1]    ,			hidden_sizes=[528, 1056, 2904, 7392]    ,			groups_width=264					),
				    'regnet-y-2560-seer': RegNetConfig(
				        depths=[3, 7, 16, 1]    ,			hidden_sizes=[640, 1696, 2544, 5088]    ,			groups_width=640					),
				    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,			hidden_sizes=[2020, 4040, 11110, 28280]    ,			groups_width=1010					),
				    # finetuned on imagenet
				    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,			hidden_sizes=[232, 696, 1392, 3712]    ,			groups_width=232					),
				    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,			hidden_sizes=[328, 984, 1968, 4920]    ,			groups_width=328					),
				    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,			hidden_sizes=[528, 1056, 2904, 7392]    ,			groups_width=264					),
				    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[3, 7, 16, 1]    ,			hidden_sizes=[640, 1696, 2544, 5088]    ,			groups_width=640					),
				    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,			hidden_sizes=[2020, 4040, 11110, 28280]    ,			groups_width=1010					),
				}

				lowerCAmelCase__		  =  NameToOurModelFuncMap()
				lowerCAmelCase__		  =  NameToFromModelFuncMap()
				# add seer weights logic

				def load_using_classy_vision(snake_case__    ,			snake_case__					) -> Tuple[nn.Module, Dict]:
								lowerCAmelCase__		  =  torch.hub.load_state_dict_from_url(snake_case__    ,			model_dir=str(snake_case__					)    ,			map_location='cpu'					)
								lowerCAmelCase__		  =  model_func()
								# check if we have a head, if yes add it
								lowerCAmelCase__		  =  files['classy_state_dict']['base_model']['model']
								lowerCAmelCase__		  =  model_state_dict['trunk']
								model.load_state_dict(snake_case__					)
								return model.eval(), model_state_dict["heads"]

				# pretrained
				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'    ,			lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27    ,			group_width=1010    ,			w_a=1744    ,			w_a=620.83    ,			w_m=2.52					)					)					)    ,			)

				# IN1K finetuned
				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'    ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    snake_case__    ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'    ,			lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27    ,			group_width=1010    ,			w_a=1744    ,			w_a=620.83    ,			w_m=2.52					)					)					)    ,			)

				if model_name:
								convert_weight_and_push(
								    snake_case__    ,			names_to_from_model_map[model_name]    ,			names_to_ours_model_map[model_name]    ,			names_to_config[model_name]    ,			snake_case__    ,			snake_case__    ,			)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    snake_case__    ,			names_to_from_model_map[model_name]    ,			names_to_ours_model_map[model_name]    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			)
				return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase	:   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.",
	)

	_lowerCAmelCase	:   int    =   parser.parse_args()

	_lowerCAmelCase	:   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)
