




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Optional[int]:




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				SCREAMING_SNAKE_CASE_					      =							flax_key_tuple[:-1] + ("weight",)
				SCREAMING_SNAKE_CASE_					      =							torch.permute(lowerCAmelCase    ,						(0, 2, 1) )
		elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase ):
				# linear layer
				SCREAMING_SNAKE_CASE_					      =							flax_key_tuple[:-1] + ("weight",)
				SCREAMING_SNAKE_CASE_					      =							flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				SCREAMING_SNAKE_CASE_					      =							flax_key_tuple[:-1] + ("weight",)

		return flax_key_tuple, flax_tensor


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Union[str, Any]:




		if "metadata" in layer:
				SCREAMING_SNAKE_CASE_					      =							layer.split('metadata' )
				SCREAMING_SNAKE_CASE_					      =							"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_					      =							[tuple(('metadata' + split_layer[1]).split('/' ) )]
		elif "kvstore" in layer:
				SCREAMING_SNAKE_CASE_					      =							layer.split('kvstore' )
				SCREAMING_SNAKE_CASE_					      =							"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_					      =							[tuple(('kvstore' + split_layer[1]).split('/' ) )]

		else:
				SCREAMING_SNAKE_CASE_					      =							layer.split('/' )
				SCREAMING_SNAKE_CASE_					      =							"/".join(split_layer[:-1] )
				SCREAMING_SNAKE_CASE_					      =							(split_layer[-1],)

		if "kvstore/path" in layer:
				SCREAMING_SNAKE_CASE_					      =							F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
		elif "kvstore/driver" in layer:
				SCREAMING_SNAKE_CASE_					      =							"file"
		else:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							rename_keys(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							{}
		for k, v in current_block.items():
				SCREAMING_SNAKE_CASE_					      =							v
		SCREAMING_SNAKE_CASE_					      =							new_current_block
		torch.save(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase = WEIGHTS_NAME )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							convert_file_size_to_int(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							0
		SCREAMING_SNAKE_CASE_					      =							0

		os.makedirs(lowerCAmelCase    ,						exist_ok=lowerCAmelCase )
		with gfile.GFile(switch_checkpoint_path + '/checkpoint'    ,						'rb' ) as fp:
				SCREAMING_SNAKE_CASE_					      =							serialization.msgpack_restore(fp.read() )["optimizer"]["target"]
				SCREAMING_SNAKE_CASE_					      =							flatten_dict(lowerCAmelCase    ,						sep='/' )

		SCREAMING_SNAKE_CASE_					      =							{}
		for layer in checkpoint_info.keys():
				SCREAMING_SNAKE_CASE_					      =							get_key_and_tensorstore_dict(
				    lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				if curr_real_layer_name in all_layers:
						SCREAMING_SNAKE_CASE_					      =							content
				else:
						SCREAMING_SNAKE_CASE_					      =							{split_layer[-1]: content}

		for key in all_layers.keys():
				# open tensorstore file
				SCREAMING_SNAKE_CASE_					      =							ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
				SCREAMING_SNAKE_CASE_					      =							torch.tensor(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

				# use the renaming pattern from the small conversion scripts
				SCREAMING_SNAKE_CASE_					      =							rename_base_flax_keys(tuple(key.split('/' ) )    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							"/".join(lowerCAmelCase )

				# If this weight is going to tip up over the maximal size, we split.
				if current_block_size + weight_size > max_shard_size:
						SCREAMING_SNAKE_CASE_					      =							os.path.join(
						    lowerCAmelCase    ,						weights_name.replace('.bin'    ,						F'''-{len(lowerCAmelCase )+1:05d}-of-???.bin''' ) )
						rename_and_save_block(lowerCAmelCase    ,						lowerCAmelCase )
						sharded_state_dicts.append(current_block.keys() )
						del current_block
						SCREAMING_SNAKE_CASE_					      =							{}
						SCREAMING_SNAKE_CASE_					      =							0

				SCREAMING_SNAKE_CASE_					      =							raw_weights.to(getattr(lowerCAmelCase    ,						lowerCAmelCase ) )
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		SCREAMING_SNAKE_CASE_					      =							os.path.join(lowerCAmelCase    ,						weights_name.replace('.bin'    ,						F'''-{len(lowerCAmelCase )+1:05d}-of-???.bin''' ) )
		rename_and_save_block(lowerCAmelCase    ,						lowerCAmelCase )
		sharded_state_dicts.append(current_block.keys() )

		# If we only have one shard, we return it
		if len(lowerCAmelCase ) == 1:
				return {weights_name: sharded_state_dicts[0]}, None

		# Otherwise, let's build the index
		SCREAMING_SNAKE_CASE_					      =							{}
		SCREAMING_SNAKE_CASE_					      =							{}
		for idx, shard in enumerate(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							weights_name.replace(
				    '.bin'    ,						F'''-{idx+1:05d}-of-{len(lowerCAmelCase ):05d}.bin''' )  # len(sharded_state_dicts):05d}
				SCREAMING_SNAKE_CASE_					      =							os.path.join(lowerCAmelCase    ,						weights_name.replace('.bin'    ,						F'''-{idx+1:05d}-of-???.bin''' ) )
				os.rename(lowerCAmelCase    ,						os.path.join(lowerCAmelCase    ,						lowerCAmelCase ) )
				SCREAMING_SNAKE_CASE_					      =							shard
				for key in shard:
						SCREAMING_SNAKE_CASE_					      =							shard_file

    # Add the metadata
		SCREAMING_SNAKE_CASE_					      =							{"total_size": total_size}
		SCREAMING_SNAKE_CASE_					      =							{"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(lowerCAmelCase    ,						lowerCAmelCase )    ,						'w'    ,						encoding='utf-8' ) as f:
				SCREAMING_SNAKE_CASE_					      =							json.dumps(lowerCAmelCase    ,						indent=2    ,						sort_keys=lowerCAmelCase ) + "\n"
				f.write(lowerCAmelCase )

		return metadata, index


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--switch_t5x_checkpoint_path''',
						    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
						    type=str,
						    required=False,
						    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
						)
						parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
						parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
						    type=str,
						    required=False,
						    help='''Path to the output pytorch model.''',
						)
						SCREAMING_SNAKE_CASE:     Tuple									= parser.parse_args()
						shard_on_the_fly(
						    args.switch_tax_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.max_shard_size,
						    args.dtype,
						)




def 					_a							(					)->	List[Any]:
		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

		SCREAMING_SNAKE_CASE_					      =							SwitchTransformersConfig.from_pretrained('google/switch-base-8' )
		config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted' )
		SCREAMING_SNAKE_CASE_					      =							SwitchTransformersForConditionalGeneration.from_pretrained(
		    '/home/arthur_huggingface_co/transformers/switch_converted'    ,						device_map='auto' )

		SCREAMING_SNAKE_CASE_					      =							TaTokenizer.from_pretrained('t5-small' )
		SCREAMING_SNAKE_CASE_					      =							"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

		SCREAMING_SNAKE_CASE_					      =							tokenizer(lowerCAmelCase    ,						return_tensors='pt' ).input_ids
		SCREAMING_SNAKE_CASE_					      =							model.generate(lowerCAmelCase    ,						decoder_start_token_id=0 )
		print(tokenizer.decode(out[0] ) )



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()
SCREAMING_SNAKE_CASE__     					=			logging.get_logger()



@dataclass
class 						_UpperCAmelCase      :
		lowerCamelCase_	:			nn.Module
		lowerCamelCase_	:			List[nn.Module]       			=  field(default_factory=lowercase							)
		lowerCamelCase_	:			list       			=  field(default_factory=lowercase							)






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Tensor   ,  UpperCAmelCase				:     Tensor):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						len(list(m.modules())) == 1 or isinstance(UpperCAmelCase   ,  nn.Convad) or isinstance(UpperCAmelCase   ,  nn.BatchNormad)
				if has_not_submodules:
						self.traced.append(UpperCAmelCase)






		def __call__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Tensor):
				for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
				self.module(UpperCAmelCase)
				[x.remove() for x in self.handles]
				return self







		@property
		def _snake_case		(						self				:     Optional[Any]):
				# check the len of the state_dict keys to see if we have learnable params
				return list(filter(lambda UpperCAmelCase: len(list(x.state_dict().keys())) > 0   ,  self.traced))




@dataclass
class 						_UpperCAmelCase      :
		lowerCamelCase_	:			nn.Module
		lowerCamelCase_	:			nn.Module
		lowerCamelCase_	:			int       			=  1
		lowerCamelCase_	:			List       			=  field(default_factory=lowercase							)
		lowerCamelCase_	:			List       			=  field(default_factory=lowercase							)
		lowerCamelCase_	:			bool       			=  True






		def __call__(						self				:     List[str]   ,  UpperCAmelCase				:     Tensor):
				SCREAMING_SNAKE_CASE_				:str				     =						Tracker(self.dest)(UpperCAmelCase).parametrized
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						Tracker(self.src)(UpperCAmelCase).parametrized

				SCREAMING_SNAKE_CASE_				:str				     =						list(filter(lambda UpperCAmelCase: type(UpperCAmelCase) not in self.src_skip   ,  UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Tuple				     =						list(filter(lambda UpperCAmelCase: type(UpperCAmelCase) not in self.dest_skip   ,  UpperCAmelCase))

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

				for dest_m, src_m in zip(UpperCAmelCase   ,  UpperCAmelCase):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
								print(F"Transfered from={src_m} to={dest_m}")




class 						_UpperCAmelCase      (     nn.Module							):






		def __init__(						self				:     Optional[Any]   ,  UpperCAmelCase				:     nn.Module):
				super().__init__()

				SCREAMING_SNAKE_CASE_				: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}"
						SCREAMING_SNAKE_CASE_				:Any				     =						len(UpperCAmelCase) + 1
						feature_blocks.append((F"res{block_index}", v))

				SCREAMING_SNAKE_CASE_				:List[str]				     =						nn.ModuleDict(UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     Tensor):
				return get_trunk_forward_outputs(
				    UpperCAmelCase   ,  out_feat_keys=UpperCAmelCase   ,  feature_blocks=self._feature_blocks   ,  )




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						x.split("-")
				return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])







		def __getitem__(						self				:     str   ,  UpperCAmelCase				:     str):
				# default to timm!
				if x not in self:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.convert_name_to_timm(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						partial(lambda: (timm.create_model(UpperCAmelCase   ,  pretrained=UpperCAmelCase).eval(), None))

				else:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						super().__getitem__(UpperCAmelCase)

				return val




class 						_UpperCAmelCase      (     lowercase							):






		def __getitem__(						self				:     Any   ,  UpperCAmelCase				:     str):
				if "seer" in x and "in1k" not in x:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						RegNetModel
				else:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						RegNetForImageClassification
				return val




def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		for from_key, to_key in keys:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						from_state_dict[from_key].clone()
				print(F"Copied key={from_key} to={to_key}" )
		return to_state_dict


def     lowercase	(    a     , a     , a     , a     , a     , a = True     , ):



		'''simple docstring'''




		print(F"Converting {name}..." )
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						from_model_func()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						our_model_func(a ).eval()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ModuleTransfer(src=a     , dest=a     , raise_if_mismatch=a )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.randn((1, 3, 224, 224) )
				module_transfer(a )

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

		SCREAMING_SNAKE_CASE_				:str				     =						our_model(a     , output_hidden_states=a )
		SCREAMING_SNAKE_CASE_				:Dict				     =						(
		    our_outputs.logits if isinstance(a     , a ) else our_outputs.last_hidden_state
		)

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						from_model(a )
		SCREAMING_SNAKE_CASE_				:int				     =						from_output[-1] if type(a ) 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:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						our_outputs.hidden_states[-1]

		assert torch.allclose(a     , a ), "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=a     , )

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						224 if "seer" not in name else 384
				# we can use the convnext one
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     , size=a )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name     , commit_message="Add image processor"     , use_temp_dir=a     , )

				print(F"Pushed {name}" )


def     lowercase	(    a     , a = None     , a = True ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						"imagenet-1k-id2label.json"
		SCREAMING_SNAKE_CASE_				:str				     =						1000
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						(1, num_labels)

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

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

		SCREAMING_SNAKE_CASE_				:int				     =						partial(a     , num_labels=a     , idalabel=a     , labelaid=a )

		SCREAMING_SNAKE_CASE_				:str				     =						{
		    "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, 1_1110, 2_8280]     , 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, 1_1110, 2_8280]     , groups_width=1010 ),
		}

		SCREAMING_SNAKE_CASE_				:List[str]				     =						NameToOurModelFuncMap()
		SCREAMING_SNAKE_CASE_				:Tuple				     =						NameToFromModelFuncMap()
		# add seer weights logic

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

		# pretrained
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     , )

		SCREAMING_SNAKE_CASE_				:int				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     , )

		SCREAMING_SNAKE_CASE_				:int				     =						partial(
		    a     , "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() )     , )

		SCREAMING_SNAKE_CASE_				:Tuple				     =						partial(
		    a     , "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
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     , )

		SCREAMING_SNAKE_CASE_				:int				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     , )

		SCREAMING_SNAKE_CASE_				:List[str]				     =						partial(
		    a     , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"     , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )     , )

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						partial(
		    a     , "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(
				    a     , names_to_from_model_map[model_name]     , names_to_ours_model_map[model_name]     , names_to_config[model_name]     , a     , a     , )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    a     , names_to_from_model_map[model_name]     , names_to_ours_model_map[model_name]     , a     , a     , a     , )
		return config, expected_shape


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			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__     					=			parser.parse_args()

		SCREAMING_SNAKE_CASE__     					=			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)






