


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor



class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.config_class(**self.inputs_dict)
				self.parent.assertTrue(hasattr(UpperCAmelCase   ,  "tf_padding"))
				self.parent.assertTrue(hasattr(UpperCAmelCase   ,  "depth_multiplier"))




class 						_UpperCAmelCase      :






		def __init__(						self				:     Tuple   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Tuple=13   ,  UpperCAmelCase				:     Dict=3   ,  UpperCAmelCase				:     int=32   ,  UpperCAmelCase				:     Any=0.25   ,  UpperCAmelCase				:     Optional[int]=8   ,  UpperCAmelCase				:     Dict=True   ,  UpperCAmelCase				:     str=10_24   ,  UpperCAmelCase				:     int=32   ,  UpperCAmelCase				:     Union[str, Any]="relu6"   ,  UpperCAmelCase				:     Optional[int]=0.1   ,  UpperCAmelCase				:     List[Any]=0.02   ,  UpperCAmelCase				:     int=True   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     Optional[int]=10   ,  UpperCAmelCase				:     List[str]=None   ,  ):
				SCREAMING_SNAKE_CASE_				:Dict				     =						parent
				SCREAMING_SNAKE_CASE_				:int				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						num_channels
				SCREAMING_SNAKE_CASE_				:Any				     =						image_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						depth_multiplier
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						min_depth
				SCREAMING_SNAKE_CASE_				:int				     =						tf_padding
				SCREAMING_SNAKE_CASE_				:int				     =						int(last_hidden_size * depth_multiplier)
				SCREAMING_SNAKE_CASE_				:Any				     =						output_stride
				SCREAMING_SNAKE_CASE_				:Any				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:Tuple				     =						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_				:Dict				     =						use_labels
				SCREAMING_SNAKE_CASE_				:Dict				     =						is_training
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						num_labels
				SCREAMING_SNAKE_CASE_				:Tuple				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:int				     =						scope






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				SCREAMING_SNAKE_CASE_				:Tuple				     =						None
				SCREAMING_SNAKE_CASE_				:int				     =						None
				if self.use_labels:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ids_tensor([self.batch_size]   ,  self.num_labels)
						SCREAMING_SNAKE_CASE_				:Any				     =						ids_tensor([self.batch_size, self.image_size, self.image_size]   ,  self.num_labels)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_config()

				return config, pixel_values, labels, pixel_labels






		def _snake_case		(						self				:     Optional[int]):
				return MobileNetVaConfig(
				    num_channels=self.num_channels   ,  image_size=self.image_size   ,  depth_multiplier=self.depth_multiplier   ,  min_depth=self.min_depth   ,  tf_padding=self.tf_padding   ,  hidden_act=self.hidden_act   ,  classifier_dropout_prob=self.classifier_dropout_prob   ,  initializer_range=self.initializer_range   ,  )






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						MobileNetVaModel(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Any				     =						model(UpperCAmelCase)
				self.parent.assertEqual(
				    result.last_hidden_state.shape   ,  (
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )   ,  )






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     int):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.num_labels
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						MobileNetVaForImageClassification(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:str				     =						model(UpperCAmelCase   ,  labels=UpperCAmelCase)
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels))







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Any       			=  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
		lowerCamelCase_	:			Union[str, Any]       			=  (
		    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase_	:			Tuple       			=  False
		lowerCamelCase_	:			Any       			=  False
		lowerCamelCase_	:			int       			=  False
		lowerCamelCase_	:			List[Any]       			=  False






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						MobileNetVaModelTester(self)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						MobileNetVaConfigTester(self   ,  config_class=UpperCAmelCase   ,  has_text_modality=UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]):
				self.config_tester.run_common_tests()






		@unittest.skip(reason="MobileNetV1 does not use inputs_embeds")
		def _snake_case		(						self				:     Tuple):
				pass






		@unittest.skip(reason="MobileNetV1 does not support input and output embeddings")
		def _snake_case		(						self				:     Tuple):
				pass






		@unittest.skip(reason="MobileNetV1 does not output attentions")
		def _snake_case		(						self				:     List[str]):
				pass






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:str				     =						model_class(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:str				     =						inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[*signature.parameters.keys()]

						SCREAMING_SNAKE_CASE_				:Dict				     =						["pixel_values"]
						self.assertListEqual(arg_names[:1]   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:int				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase)






		def _snake_case		(						self				:     Tuple):
				def check_hidden_states_output(UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Optional[int]):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class(UpperCAmelCase)
						model.to(UpperCAmelCase)
						model.eval()

						with torch.no_grad():
								SCREAMING_SNAKE_CASE_				:str				     =						model(**self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase))

						SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.hidden_states

						SCREAMING_SNAKE_CASE_				:str				     =						26
						self.assertEqual(len(UpperCAmelCase)   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						True
						check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						SCREAMING_SNAKE_CASE_				:int				     =						True

						check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)







		@slow
		def _snake_case		(						self				:     Tuple):
				for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						MobileNetVaModel.from_pretrained(UpperCAmelCase)
						self.assertIsNotNone(UpperCAmelCase)




def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image



@require_torch
@require_vision
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@cached_property
		def _snake_case		(						self				:     Any):
				return (
				    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224") if is_vision_available() else None
				)







		@slow
		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224").to(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:str				     =						self.default_image_processor
				SCREAMING_SNAKE_CASE_				:Any				     =						prepare_img()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						image_processor(images=UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:str				     =						model(**UpperCAmelCase)

				# verify the logits
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.Size((1, 10_01))
				self.assertEqual(outputs.logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Any				     =						torch.tensor([-4.1739, -1.1233, 3.1205]).to(UpperCAmelCase)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,  UpperCAmelCase   ,  atol=1E-4))










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)






