






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 	UpperCamelCase	(							snake_case__  : Optional[Any]						,snake_case__  : Optional[Any]=False	):




     '''simple docstring'''






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

     return new_state_dict






def 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : Optional[int]	):




     '''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)
               __snake_case      :Any 							=    state_dict.pop(f'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''	)
               __snake_case      :Dict 							=    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
               __snake_case      :Optional[int] 							=    kv_weight[
                   : config.hidden_sizes[i], :
               ]
               __snake_case      :List[Any] 							=    kv_bias[: config.hidden_sizes[i]]
               __snake_case      :Tuple 							=    kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               __snake_case      :Dict 							=    kv_bias[
                   config.hidden_sizes[i] :
               ]






def 	UpperCamelCase	(							):




     '''simple docstring'''






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

     return image






@torch.no_grad()
def 	UpperCamelCase	(							snake_case__  : Union[str, Any]						,snake_case__  : Tuple						,snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :Any 							=    SegformerConfig()
     __snake_case      :Optional[Any] 							=    False

     # set attributes based on model_name
     __snake_case      :Optional[int] 							=    """huggingface/label-files"""
     if "segformer" in model_name:
          __snake_case      :str 							=    model_name[len("""segformer."""	) : len("""segformer."""	) + 2]
          if "ade" in model_name:
               __snake_case      :int 							=    150
               __snake_case      :Any 							=    """ade20k-id2label.json"""
               __snake_case      :Any 							=    (1, 150, 128, 128)
          elif "city" in model_name:
               __snake_case      :Optional[int] 							=    19
               __snake_case      :Tuple 							=    """cityscapes-id2label.json"""
               __snake_case      :Optional[Any] 							=    (1, 19, 128, 128)
          else:
               raise ValueError(f'''Model {model_name} not supported'''	)
     elif "mit" in model_name:
          __snake_case      :Optional[int] 							=    True
          __snake_case      :Optional[int] 							=    model_name[4:6]
          __snake_case      :List[str] 							=    1000
          __snake_case      :Tuple 							=    """imagenet-1k-id2label.json"""
          __snake_case      :Dict 							=    (1, 1000)
     else:
          raise ValueError(f'''Model {model_name} not supported'''	)

     # set config attributes
     __snake_case      :Tuple 							=    json.load(open(hf_hub_download(snake_case__						,snake_case__						,repo_type="""dataset"""	)						,"""r"""	)	)
     __snake_case      :str 							=    {int(snake_case__	): v for k, v in idalabel.items()}
     __snake_case      :Union[str, Any] 							=    idalabel
     __snake_case      :Union[str, Any] 							=    {v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          __snake_case      :Dict 							=    [64, 128, 320, 512]
          __snake_case      :Optional[int] 							=    256
     elif size == "b2":
          __snake_case      :List[str] 							=    [64, 128, 320, 512]
          __snake_case      :Any 							=    768
          __snake_case      :List[str] 							=    [3, 4, 6, 3]
     elif size == "b3":
          __snake_case      :Optional[int] 							=    [64, 128, 320, 512]
          __snake_case      :List[Any] 							=    768
          __snake_case      :List[str] 							=    [3, 4, 18, 3]
     elif size == "b4":
          __snake_case      :Tuple 							=    [64, 128, 320, 512]
          __snake_case      :Optional[int] 							=    768
          __snake_case      :str 							=    [3, 8, 27, 3]
     elif size == "b5":
          __snake_case      :Tuple 							=    [64, 128, 320, 512]
          __snake_case      :Optional[Any] 							=    768
          __snake_case      :Optional[int] 							=    [3, 6, 40, 3]
     else:
          raise ValueError(f'''Size {size} not supported'''	)

     # load image processor (only resize + normalize)
     __snake_case      :int 							=    SegformerImageProcessor(
         image_scale=(512, 512)						,keep_ratio=snake_case__						,align=snake_case__						,do_random_crop=snake_case__	)

     # prepare image
     __snake_case      :int 							=    prepare_img()
     __snake_case      :List[str] 							=    image_processor(images=snake_case__						,return_tensors="""pt"""	).pixel_values

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

     # load original state dict
     if encoder_only:
          __snake_case      :int 							=    torch.load(snake_case__						,map_location=torch.device("""cpu"""	)	)
     else:
          __snake_case      :Union[str, Any] 							=    torch.load(snake_case__						,map_location=torch.device("""cpu"""	)	)["""state_dict"""]

     # rename keys
     __snake_case      :Any 							=    rename_keys(snake_case__						,encoder_only=snake_case__	)
     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(snake_case__						,snake_case__	)

     # create HuggingFace model and load state dict
     if encoder_only:
          __snake_case      :Dict 							=    False
          __snake_case      :Dict 							=    SegformerForImageClassification(snake_case__	)
     else:
          __snake_case      :Any 							=    SegformerForSemanticSegmentation(snake_case__	)
     model.load_state_dict(snake_case__	)
     model.eval()

     # forward pass
     __snake_case      :str 							=    model(snake_case__	)
     __snake_case      :Optional[int] 							=    outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          __snake_case      :List[str] 							=    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":
          __snake_case      :List[Any] 							=    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":
          __snake_case      :int 							=    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":
          __snake_case      :Tuple 							=    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":
          __snake_case      :Any 							=    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":
          __snake_case      :Optional[Any] 							=    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":
          __snake_case      :Dict 							=    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":
          __snake_case      :Dict 							=    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":
          __snake_case      :List[Any] 							=    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":
          __snake_case      :Union[str, 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":
          __snake_case      :Dict 							=    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":
          __snake_case      :str 							=    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":
          __snake_case      :Union[str, Any] 							=    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":
          __snake_case      :List[Any] 							=    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":
          __snake_case      :Optional[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:
          __snake_case      :str 							=    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]						,snake_case__						,atol=1e-2	)

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


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)










def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


