
import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase=False )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	OrderedDict()
		for key, value in state_dict.items():
				if encoder_only and not key.startswith("""head""" ):
						_UpperCamelCase						    =	"""segformer.encoder.""" + key
				if key.startswith("""backbone""" ):
						_UpperCamelCase						    =	key.replace("""backbone"""						,  """segformer.encoder""" )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						_UpperCamelCase						    =	key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
						_UpperCamelCase						    =	key.replace(F'patch_embed{idx}'						,  F'patch_embeddings.{int(lowerCAmelCase )-1}' )
				if "norm" in key:
						_UpperCamelCase						    =	key.replace("""norm"""						,  """layer_norm""" )
				if "segformer.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						_UpperCamelCase						    =	key[key.find("""segformer.encoder.layer_norm""" ) + len("""segformer.encoder.layer_norm""" )]
						_UpperCamelCase						    =	key.replace(F'layer_norm{idx}'						,  F'layer_norm.{int(lowerCAmelCase )-1}' )
				if "layer_norm1" in key:
						_UpperCamelCase						    =	key.replace("""layer_norm1"""						,  """layer_norm_1""" )
				if "layer_norm2" in key:
						_UpperCamelCase						    =	key.replace("""layer_norm2"""						,  """layer_norm_2""" )
				if "block" in key:
						# replace for example block1 by block.0
						_UpperCamelCase						    =	key[key.find("""block""" ) + len("""block""" )]
						_UpperCamelCase						    =	key.replace(F'block{idx}'						,  F'block.{int(lowerCAmelCase )-1}' )
				if "attn.q" in key:
						_UpperCamelCase						    =	key.replace("""attn.q"""						,  """attention.self.query""" )
				if "attn.proj" in key:
						_UpperCamelCase						    =	key.replace("""attn.proj"""						,  """attention.output.dense""" )
				if "attn" in key:
						_UpperCamelCase						    =	key.replace("""attn"""						,  """attention.self""" )
				if "fc1" in key:
						_UpperCamelCase						    =	key.replace("""fc1"""						,  """dense1""" )
				if "fc2" in key:
						_UpperCamelCase						    =	key.replace("""fc2"""						,  """dense2""" )
				if "linear_pred" in key:
						_UpperCamelCase						    =	key.replace("""linear_pred"""						,  """classifier""" )
				if "linear_fuse" in key:
						_UpperCamelCase						    =	key.replace("""linear_fuse.conv"""						,  """linear_fuse""" )
						_UpperCamelCase						    =	key.replace("""linear_fuse.bn"""						,  """batch_norm""" )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						_UpperCamelCase						    =	key[key.find("""linear_c""" ) + len("""linear_c""" )]
						_UpperCamelCase						    =	key.replace(F'linear_c{idx}'						,  F'linear_c.{int(lowerCAmelCase )-1}' )
				if key.startswith("""head""" ):
						_UpperCamelCase						    =	key.replace("""head"""						,  """classifier""" )
				_UpperCamelCase						    =	value

		return new_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for i in range(config.num_encoder_blocks ):
				for j in range(config.depths[i] ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						_UpperCamelCase						    =	state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
						_UpperCamelCase						    =	state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.bias' )
						# next, add keys and values (in that order) to the state dict
						_UpperCamelCase						    =	kv_weight[
						    : config.hidden_sizes[i], :
						]
						_UpperCamelCase						    =	kv_bias[: config.hidden_sizes[i]]
						_UpperCamelCase						    =	kv_weight[
						    config.hidden_sizes[i] :, :
						]
						_UpperCamelCase						    =	kv_bias[
						    config.hidden_sizes[i] :
						]


def 							__A()		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		_UpperCamelCase						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw )

		return image


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	SegformerConfig()
		_UpperCamelCase						    =	False

		# set attributes based on model_name
		_UpperCamelCase						    =	"""huggingface/label-files"""
		if "segformer" in model_name:
				_UpperCamelCase						    =	model_name[len("""segformer.""" ) : len("""segformer.""" ) + 2]
				if "ade" in model_name:
						_UpperCamelCase						    =	1_5_0
						_UpperCamelCase						    =	"""ade20k-id2label.json"""
						_UpperCamelCase						    =	(1, 1_5_0, 1_2_8, 1_2_8)
				elif "city" in model_name:
						_UpperCamelCase						    =	1_9
						_UpperCamelCase						    =	"""cityscapes-id2label.json"""
						_UpperCamelCase						    =	(1, 1_9, 1_2_8, 1_2_8)
				else:
						raise ValueError(F'Model {model_name} not supported' )
		elif "mit" in model_name:
				_UpperCamelCase						    =	True
				_UpperCamelCase						    =	model_name[4:6]
				_UpperCamelCase						    =	1_0_0_0
				_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
				_UpperCamelCase						    =	(1, 1_0_0_0)
		else:
				raise ValueError(F'Model {model_name} not supported' )

		# set config attributes
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}
		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}
		if size == "b0":
				pass
		elif size == "b1":
				_UpperCamelCase						    =	[6_4, 1_2_8, 3_2_0, 5_1_2]
				_UpperCamelCase						    =	2_5_6
		elif size == "b2":
				_UpperCamelCase						    =	[6_4, 1_2_8, 3_2_0, 5_1_2]
				_UpperCamelCase						    =	7_6_8
				_UpperCamelCase						    =	[3, 4, 6, 3]
		elif size == "b3":
				_UpperCamelCase						    =	[6_4, 1_2_8, 3_2_0, 5_1_2]
				_UpperCamelCase						    =	7_6_8
				_UpperCamelCase						    =	[3, 4, 1_8, 3]
		elif size == "b4":
				_UpperCamelCase						    =	[6_4, 1_2_8, 3_2_0, 5_1_2]
				_UpperCamelCase						    =	7_6_8
				_UpperCamelCase						    =	[3, 8, 2_7, 3]
		elif size == "b5":
				_UpperCamelCase						    =	[6_4, 1_2_8, 3_2_0, 5_1_2]
				_UpperCamelCase						    =	7_6_8
				_UpperCamelCase						    =	[3, 6, 4_0, 3]
		else:
				raise ValueError(F'Size {size} not supported' )

		# load image processor (only resize + normalize)
		_UpperCamelCase						    =	SegformerImageProcessor(
		    image_scale=(5_1_2, 5_1_2)						,  keep_ratio=lowerCAmelCase						,  align=lowerCAmelCase						,  do_random_crop=lowerCAmelCase )

		# prepare image
		_UpperCamelCase						    =	prepare_img()
		_UpperCamelCase						    =	image_processor(images=lowerCAmelCase						,  return_tensors="""pt""" ).pixel_values

		logger.info(F'Converting model {model_name}...' )

		# load original state dict
		if encoder_only:
				_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location=torch.device("""cpu""" ) )
		else:
				_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location=torch.device("""cpu""" ) )["""state_dict"""]

		# rename keys
		_UpperCamelCase						    =	rename_keys(lowerCAmelCase						,  encoder_only=lowerCAmelCase )
		if not encoder_only:
				del state_dict["decode_head.conv_seg.weight"]
				del state_dict["decode_head.conv_seg.bias"]

		# key and value matrices need special treatment
		read_in_k_v(lowerCAmelCase						,  lowerCAmelCase )

		# create HuggingFace model and load state dict
		if encoder_only:
				_UpperCamelCase						    =	False
				_UpperCamelCase						    =	SegformerForImageClassification(lowerCAmelCase )
		else:
				_UpperCamelCase						    =	SegformerForSemanticSegmentation(lowerCAmelCase )
		model.load_state_dict(lowerCAmelCase )
		model.eval()

		# forward pass
		_UpperCamelCase						    =	model(lowerCAmelCase )
		_UpperCamelCase						    =	outputs.logits

		# set expected_slice based on model name
		# ADE20k checkpoints
		if model_name == "segformer.b0.512x512.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
				        [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
				        [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
				    ] )
		elif model_name == "segformer.b1.512x512.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
				        [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
				        [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
				    ] )
		elif model_name == "segformer.b2.512x512.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
				        [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
				        [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
				    ] )
		elif model_name == "segformer.b3.512x512.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
				        [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
				        [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
				    ] )
		elif model_name == "segformer.b4.512x512.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
				        [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
				        [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
				    ] )
		elif model_name == "segformer.b5.640x640.ade.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
				        [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
				        [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
				    ] )
		# Cityscapes checkpoints
		elif model_name == "segformer.b0.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
				        [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
				        [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
				    ] )
		elif model_name == "segformer.b0.512x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
				        [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
				        [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
				    ] )
		elif model_name == "segformer.b0.640x1280.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [
				            [-1.13_72e01, -1.27_87e01, -1.34_77e01],
				            [-1.25_36e01, -1.41_94e01, -1.44_09e01],
				            [-1.32_17e01, -1.48_88e01, -1.53_27e01],
				        ],
				        [
				            [-1.47_91e01, -1.71_22e01, -1.82_77e01],
				            [-1.71_63e01, -1.91_92e01, -1.95_33e01],
				            [-1.78_97e01, -1.99_91e01, -2.03_15e01],
				        ],
				        [
				            [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
				            [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
				            [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
				        ],
				    ] )
		elif model_name == "segformer.b0.768x768.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
				        [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
				        [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
				    ] )
		elif model_name == "segformer.b1.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
				        [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
				        [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
				    ] )
		elif model_name == "segformer.b2.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
				        [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
				        [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
				    ] )
		elif model_name == "segformer.b3.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
				        [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
				        [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
				    ] )
		elif model_name == "segformer.b4.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
				        [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
				        [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
				    ] )
		elif model_name == "segformer.b5.1024x1024.city.160k":
				_UpperCamelCase						    =	torch.tensor(
				    [
				        [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
				        [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
				        [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
				    ] )
		else:
				_UpperCamelCase						    =	logits.argmax(-1 ).item()
				print("""Predicted class:"""						,  model.config.idalabel[predicted_class_idx] )

		# verify logits
		if not encoder_only:
				assert logits.shape == expected_shape
				assert torch.allclose(logits[0, :3, :3, :3]						,  lowerCAmelCase						,  atol=1e-2 )

		# finally, save model and image processor
		logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )
		image_processor.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    default="segformer.b0.512x512.ade.160k",
		    type=str,
		    help="Name of the model you'd like to convert.",
		)
		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	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				)					->      Optional[int]:


								'''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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				):
										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}'				)




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

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

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add model"""						,  use_temp_dir=lowerCAmelCase						,  )

				# we can use the convnext one
				_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add image processor"""						,  use_temp_dir=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

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

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		}

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


if __name__ == "__main__":
		lowerCamelCase__	=							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 resnet* architecture,"
		        " currently: resnet18,26,34,50,101,152. 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__	=							parser.parse_args()
		lowerCamelCase__	=							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)



