





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()
UpperCAmelCase__						:			Any									=logging.get_logger(__name__)





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

				return new_state_dict





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				# for each of the encoder blocks:
				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)
												lowerCamelCase	 =state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""				)
												lowerCamelCase	 =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
												lowerCamelCase	 =kv_weight[
												    : config.hidden_sizes[i], :
												]
												lowerCamelCase	 =kv_bias[: config.hidden_sizes[i]]
												lowerCamelCase	 =kv_weight[
												    config.hidden_sizes[i] :, :
												]
												lowerCamelCase	 =kv_bias[
												    config.hidden_sizes[i] :
												]





def      _lowercase					(						)						->  List[Any]:
				lowerCamelCase	 ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase	 =Image.open(requests.get(_UpperCAmelCase			,      stream=_UpperCAmelCase				).raw				)

				return image





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[int]:
				lowerCamelCase	 =SegformerConfig()
				lowerCamelCase	 =False

				# set attributes based on model_name
				lowerCamelCase	 ="""huggingface/label-files"""
				if "segformer" in model_name:
								lowerCamelCase	 =model_name[len("""segformer."""				) : len("""segformer."""				) + 2]
								if "ade" in model_name:
												lowerCamelCase	 =1_50
												lowerCamelCase	 ="""ade20k-id2label.json"""
												lowerCamelCase	 =(1, 1_50, 1_28, 1_28)
								elif "city" in model_name:
												lowerCamelCase	 =19
												lowerCamelCase	 ="""cityscapes-id2label.json"""
												lowerCamelCase	 =(1, 19, 1_28, 1_28)
								else:
												raise ValueError(F"""Model {model_name} not supported"""				)
				elif "mit" in model_name:
								lowerCamelCase	 =True
								lowerCamelCase	 =model_name[4:6]
								lowerCamelCase	 =10_00
								lowerCamelCase	 ="""imagenet-1k-id2label.json"""
								lowerCamelCase	 =(1, 10_00)
				else:
								raise ValueError(F"""Model {model_name} not supported"""				)

				# set config attributes
				lowerCamelCase	 =json.load(open(hf_hub_download(_UpperCAmelCase			,      _UpperCAmelCase			,      repo_type="""dataset"""				)			,      """r"""				)				)
				lowerCamelCase	 ={int(_UpperCAmelCase				): v for k, v in idalabel.items()}
				lowerCamelCase	 =idalabel
				lowerCamelCase	 ={v: k for k, v in idalabel.items()}
				if size == "b0":
								pass
				elif size == "b1":
								lowerCamelCase	 =[64, 1_28, 3_20, 5_12]
								lowerCamelCase	 =2_56
				elif size == "b2":
								lowerCamelCase	 =[64, 1_28, 3_20, 5_12]
								lowerCamelCase	 =7_68
								lowerCamelCase	 =[3, 4, 6, 3]
				elif size == "b3":
								lowerCamelCase	 =[64, 1_28, 3_20, 5_12]
								lowerCamelCase	 =7_68
								lowerCamelCase	 =[3, 4, 18, 3]
				elif size == "b4":
								lowerCamelCase	 =[64, 1_28, 3_20, 5_12]
								lowerCamelCase	 =7_68
								lowerCamelCase	 =[3, 8, 27, 3]
				elif size == "b5":
								lowerCamelCase	 =[64, 1_28, 3_20, 5_12]
								lowerCamelCase	 =7_68
								lowerCamelCase	 =[3, 6, 40, 3]
				else:
								raise ValueError(F"""Size {size} not supported"""				)

				# load image processor (only resize + normalize)
				lowerCamelCase	 =SegformerImageProcessor(
				    image_scale=(5_12, 5_12)			,      keep_ratio=_UpperCAmelCase			,      align=_UpperCAmelCase			,      do_random_crop=_UpperCAmelCase				)

				# prepare image
				lowerCamelCase	 =prepare_img()
				lowerCamelCase	 =image_processor(images=_UpperCAmelCase			,      return_tensors="""pt"""				).pixel_values

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

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

				# rename keys
				lowerCamelCase	 =rename_keys(_UpperCAmelCase			,      encoder_only=_UpperCAmelCase				)
				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(_UpperCAmelCase			,      _UpperCAmelCase				)

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

				# forward pass
				lowerCamelCase	 =model(_UpperCAmelCase				)
				lowerCamelCase	 =outputs.logits

				# set expected_slice based on model name
				# ADE20k checkpoints
				if model_name == "segformer.b0.512x512.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
								        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
								        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
								    ]				)
				elif model_name == "segformer.b1.512x512.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
								        [[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
								        [[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
								    ]				)
				elif model_name == "segformer.b2.512x512.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
								        [[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
								        [[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
								    ]				)
				elif model_name == "segformer.b3.512x512.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
								        [[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
								        [[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
								    ]				)
				elif model_name == "segformer.b4.512x512.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
								        [[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
								        [[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
								    ]				)
				elif model_name == "segformer.b5.640x640.ade.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
								        [[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
								        [[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
								    ]				)
				# Cityscapes checkpoints
				elif model_name == "segformer.b0.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
								        [[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
								        [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
								    ]				)
				elif model_name == "segformer.b0.512x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
								        [[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
								        [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
								    ]				)
				elif model_name == "segformer.b0.640x1280.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [
								            [-1.1_3_7_2e0_1, -1.2_7_8_7e0_1, -1.3_4_7_7e0_1],
								            [-1.2_5_3_6e0_1, -1.4_1_9_4e0_1, -1.4_4_0_9e0_1],
								            [-1.3_2_1_7e0_1, -1.4_8_8_8e0_1, -1.5_3_2_7e0_1],
								        ],
								        [
								            [-1.4_7_9_1e0_1, -1.7_1_2_2e0_1, -1.8_2_7_7e0_1],
								            [-1.7_1_6_3e0_1, -1.9_1_9_2e0_1, -1.9_5_3_3e0_1],
								            [-1.7_8_9_7e0_1, -1.9_9_9_1e0_1, -2.0_3_1_5e0_1],
								        ],
								        [
								            [7.6_7_2_3e-0_1, 4.1_9_2_1e-0_1, -7.7_8_7_8e-0_2],
								            [4.7_7_7_2e-0_1, 9.5_5_5_7e-0_3, -2.8_0_8_2e-0_1],
								            [3.6_0_3_2e-0_1, -2.4_8_2_6e-0_1, -5.1_1_6_8e-0_1],
								        ],
								    ]				)
				elif model_name == "segformer.b0.768x768.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
								        [[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
								        [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
								    ]				)
				elif model_name == "segformer.b1.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
								        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
								        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
								    ]				)
				elif model_name == "segformer.b2.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
								        [[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
								        [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
								    ]				)
				elif model_name == "segformer.b3.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
								        [[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
								        [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
								    ]				)
				elif model_name == "segformer.b4.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
								        [[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
								        [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
								    ]				)
				elif model_name == "segformer.b5.1024x1024.city.160k":
								lowerCamelCase	 =torch.tensor(
								    [
								        [[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
								        [[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
								        [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
								    ]				)
				else:
								lowerCamelCase	 =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]			,      _UpperCAmelCase			,      atol=1e-2				)

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


if __name__ == "__main__":
							UpperCAmelCase__						:			str									=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.'''
							)
							UpperCAmelCase__						:			List[str]									=parser.parse_args()
							convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












from __future__ import annotations

UpperCAmelCase__						:			str									=tuple[int, int, int]
UpperCAmelCase__						:			str									=tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase__						:			int									='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''

# -------------------------- default selection --------------------------
# rotors --------------------------
UpperCAmelCase__						:			Union[str, Any]									='''EGZWVONAHDCLFQMSIPJBYUKXTR'''
UpperCAmelCase__						:			Union[str, Any]									='''FOBHMDKEXQNRAULPGSJVTYICZW'''
UpperCAmelCase__						:			Union[str, Any]									='''ZJXESIUQLHAVRMDOYGTNFWPBKC'''
# reflector --------------------------
UpperCAmelCase__						:			Optional[Any]									={
    '''A''': '''N''',
    '''N''': '''A''',
    '''B''': '''O''',
    '''O''': '''B''',
    '''C''': '''P''',
    '''P''': '''C''',
    '''D''': '''Q''',
    '''Q''': '''D''',
    '''E''': '''R''',
    '''R''': '''E''',
    '''F''': '''S''',
    '''S''': '''F''',
    '''G''': '''T''',
    '''T''': '''G''',
    '''H''': '''U''',
    '''U''': '''H''',
    '''I''': '''V''',
    '''V''': '''I''',
    '''J''': '''W''',
    '''W''': '''J''',
    '''K''': '''X''',
    '''X''': '''K''',
    '''L''': '''Y''',
    '''Y''': '''L''',
    '''M''': '''Z''',
    '''Z''': '''M''',
}

# -------------------------- extra rotors --------------------------
UpperCAmelCase__						:			Dict									='''RMDJXFUWGISLHVTCQNKYPBEZOA'''
UpperCAmelCase__						:			Any									='''SGLCPQWZHKXAREONTFBVIYJUDM'''
UpperCAmelCase__						:			List[Any]									='''HVSICLTYKQUBXDWAJZOMFGPREN'''
UpperCAmelCase__						:			Tuple									='''RZWQHFMVDBKICJLNTUXAGYPSOE'''
UpperCAmelCase__						:			Optional[Any]									='''LFKIJODBEGAMQPXVUHYSTCZRWN'''
UpperCAmelCase__						:			Dict									='''KOAEGVDHXPQZMLFTYWJNBRCIUS'''





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
				# Checks if there are 3 unique rotors

				if (unique_rotsel := len(set(_UpperCAmelCase				)				)) < 3:
								lowerCamelCase	 =F"""Please use 3 unique rotors (not {unique_rotsel})"""
								raise Exception(_UpperCAmelCase				)

				# Checks if rotor positions are valid
				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =rotpos
				if not 0 < rotorposa <= len(_UpperCAmelCase				):
								lowerCamelCase	 =F"""First rotor position is not within range of 1..26 ({rotorposa}"""
								raise ValueError(_UpperCAmelCase				)
				if not 0 < rotorposa <= len(_UpperCAmelCase				):
								lowerCamelCase	 =F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
								raise ValueError(_UpperCAmelCase				)
				if not 0 < rotorposa <= len(_UpperCAmelCase				):
								lowerCamelCase	 =F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
								raise ValueError(_UpperCAmelCase				)

				# Validates string and returns dict
				lowerCamelCase	 =_plugboard(_UpperCAmelCase				)

				return rotpos, rotsel, pbdict





def      _lowercase					(						_UpperCAmelCase				)						->  dict[str, str]:

				# tests the input string if it
				# a) is type string
				# b) has even length (so pairs can be made)
				if not isinstance(_UpperCAmelCase			,      _UpperCAmelCase				):
								lowerCamelCase	 =F"""Plugboard setting isn't type string ({type(_UpperCAmelCase				)})"""
								raise TypeError(_UpperCAmelCase				)
				elif len(_UpperCAmelCase				) % 2 != 0:
								lowerCamelCase	 =F"""Odd number of symbols ({len(_UpperCAmelCase				)})"""
								raise Exception(_UpperCAmelCase				)
				elif pbstring == "":
								return {}

				pbstring.replace(""" """			,      """"""				)

				# Checks if all characters are unique
				lowerCamelCase	 =set()
				for i in pbstring:
								if i not in abc:
												lowerCamelCase	 =F"""'{i}' not in list of symbols"""
												raise Exception(_UpperCAmelCase				)
								elif i in tmppbl:
												lowerCamelCase	 =F"""Duplicate symbol ({i})"""
												raise Exception(_UpperCAmelCase				)
								else:
												tmppbl.add(_UpperCAmelCase				)
				del tmppbl

				# Created the dictionary
				lowerCamelCase	 ={}
				for j in range(0			,      len(_UpperCAmelCase				) - 1			,      2				):
								lowerCamelCase	 =pbstring[j + 1]
								lowerCamelCase	 =pbstring[j]

				return pb





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = (rotora, rotora, rotora)			,      _UpperCAmelCase = ""			,      )						->  str:
				lowerCamelCase	 =text.upper()
				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =_validator(
				    _UpperCAmelCase			,      _UpperCAmelCase			,      plugb.upper()				)

				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =rotor_position
				lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =rotor_selection
				rotorposa -= 1
				rotorposa -= 1
				rotorposa -= 1

				lowerCamelCase	 =[]

				# encryption/decryption process --------------------------
				for symbol in text:
								if symbol in abc:
												# 1st plugboard --------------------------
												if symbol in plugboard:
																lowerCamelCase	 =plugboard[symbol]

												# rotor ra --------------------------
												lowerCamelCase	 =abc.index(_UpperCAmelCase				) + rotorposa
												lowerCamelCase	 =rotora[index % len(_UpperCAmelCase				)]

												# rotor rb --------------------------
												lowerCamelCase	 =abc.index(_UpperCAmelCase				) + rotorposa
												lowerCamelCase	 =rotora[index % len(_UpperCAmelCase				)]

												# rotor rc --------------------------
												lowerCamelCase	 =abc.index(_UpperCAmelCase				) + rotorposa
												lowerCamelCase	 =rotora[index % len(_UpperCAmelCase				)]

												# reflector --------------------------
												# this is the reason you don't need another machine to decipher

												lowerCamelCase	 =reflector[symbol]

												# 2nd rotors
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]
												lowerCamelCase	 =abc[rotora.index(_UpperCAmelCase				) - rotorposa]

												# 2nd plugboard
												if symbol in plugboard:
																lowerCamelCase	 =plugboard[symbol]

												# moves/resets rotor positions
												rotorposa += 1
												if rotorposa >= len(_UpperCAmelCase				):
																lowerCamelCase	 =0
																rotorposa += 1
												if rotorposa >= len(_UpperCAmelCase				):
																lowerCamelCase	 =0
																rotorposa += 1
												if rotorposa >= len(_UpperCAmelCase				):
																lowerCamelCase	 =0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
								result.append(_UpperCAmelCase				)

				return "".join(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									='''This is my Python script that emulates the Enigma machine from WWII.'''
							UpperCAmelCase__						:			List[Any]									=(1, 1, 1)
							UpperCAmelCase__						:			Optional[Any]									='''pictures'''
							UpperCAmelCase__						:			Any									=(rotora, rotora, rotora)
							UpperCAmelCase__						:			str									=enigma(message, rotor_pos, rotor_sel, pb)

							print('''Encrypted message:''', en)
							print('''Decrypted message:''', enigma(en, rotor_pos, rotor_sel, pb))





