




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__ :   Optional[Any]						=							logging.get_logger(__name__)





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Dict=False  )       ->  Dict:







			"""simple docstring"""





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

			return new_state_dict





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Any  )       ->  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)
									SCREAMING_SNAKE_CASE_   : List[str]					  =      state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      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
									SCREAMING_SNAKE_CASE_   : Tuple					  =      kv_weight[
									    : config.hidden_sizes[i], :
									]
									SCREAMING_SNAKE_CASE_   : Dict					  =      kv_bias[: config.hidden_sizes[i]]
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      kv_weight[
									    config.hidden_sizes[i] :, :
									]
									SCREAMING_SNAKE_CASE_   : Dict					  =      kv_bias[
									    config.hidden_sizes[i] :
									]





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_   : int					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  )

			return image





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Optional[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      SegformerConfig()
			SCREAMING_SNAKE_CASE_   : Dict					  =      False

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

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

			# load image processor (only resize + normalize)
			SCREAMING_SNAKE_CASE_   : Any					  =      SegformerImageProcessor(
			    image_scale=(5_12, 5_12)  ,							keep_ratio=lowerCamelCase_  ,							align=lowerCamelCase_  ,							do_random_crop=lowerCamelCase_  )

			# prepare image
			SCREAMING_SNAKE_CASE_   : str					  =      prepare_img()
			SCREAMING_SNAKE_CASE_   : Tuple					  =      image_processor(images=lowerCamelCase_  ,							return_tensors='pt'  ).pixel_values

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

			# load original state dict
			if encoder_only:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.load(lowerCamelCase_  ,							map_location=torch.device('cpu'  )  )
			else:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.load(lowerCamelCase_  ,							map_location=torch.device('cpu'  )  )['state_dict']

			# rename keys
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      False
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      SegformerForImageClassification(lowerCamelCase_  )
			else:
						SCREAMING_SNAKE_CASE_   : int					  =      SegformerForSemanticSegmentation(lowerCamelCase_  )
			model.load_state_dict(lowerCamelCase_  )
			model.eval()

			# forward pass
			SCREAMING_SNAKE_CASE_   : Any					  =      model(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      outputs.logits

			# set expected_slice based on model name
			# ADE20k checkpoints
			if model_name == "segformer.b0.512x512.ade.160k":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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":
						SCREAMING_SNAKE_CASE_   : Dict					  =      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":
						SCREAMING_SNAKE_CASE_   : Any					  =      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":
						SCREAMING_SNAKE_CASE_   : Dict					  =      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":
						SCREAMING_SNAKE_CASE_   : Tuple					  =      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":
						SCREAMING_SNAKE_CASE_   : int					  =      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":
						SCREAMING_SNAKE_CASE_   : str					  =      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":
						SCREAMING_SNAKE_CASE_   : int					  =      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":
						SCREAMING_SNAKE_CASE_   : Tuple					  =      torch.tensor(
						    [
						        [
						            [-1.1372E01, -1.2787E01, -1.3477E01],
						            [-1.2536E01, -1.4194E01, -1.4409E01],
						            [-1.3217E01, -1.4888E01, -1.5327E01],
						        ],
						        [
						            [-1.4791E01, -1.7122E01, -1.8277E01],
						            [-1.7163E01, -1.9192E01, -1.9533E01],
						            [-1.7897E01, -1.9991E01, -2.0315E01],
						        ],
						        [
						            [7.6723E-01, 4.1921E-01, -7.7878E-02],
						            [4.7772E-01, 9.5557E-03, -2.8082E-01],
						            [3.6032E-01, -2.4826E-01, -5.1168E-01],
						        ],
						    ]  )
			elif model_name == "segformer.b0.768x768.city.160k":
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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":
						SCREAMING_SNAKE_CASE_   : int					  =      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":
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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":
						SCREAMING_SNAKE_CASE_   : Dict					  =      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":
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      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__":
						UpperCamelCase__ :   Dict						=							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__ :   Optional[int]						=							parser.parse_args()
						convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                      ],
                      """attention_mask""": [
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






