






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()
a		:       Optional[int]			     = logging.get_logger(__name__)






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

  return new_state_dict






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     Tuple		):
  # 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)
      __UpperCAmelCase							:	Any           =  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""		)
      __UpperCAmelCase							:	str           =  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							:	List[Any]           =  kv_weight[
          : config.hidden_sizes[i], :
      ]
      __UpperCAmelCase							:	Union[str, Any]           =  kv_bias[: config.hidden_sizes[i]]
      __UpperCAmelCase							:	Optional[int]           =  kv_weight[
          config.hidden_sizes[i] :, :
      ]
      __UpperCAmelCase							:	Optional[int]           =  kv_bias[
          config.hidden_sizes[i] :
      ]






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	List[Any]           =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:	int           =  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		)

  return image






@torch.no_grad()
def 				lowerCamelCase__      (    __lowerCamelCase			:     List[Any]  ,		__lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     List[Any]		):
  __UpperCAmelCase							:	List[Any]           =  SegformerConfig()
  __UpperCAmelCase							:	List[Any]           =  False

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

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

  # load image processor (only resize + normalize)
  __UpperCAmelCase							:	Optional[Any]           =  SegformerImageProcessor(
      image_scale=(512, 512)  ,		keep_ratio=__lowerCamelCase  ,		align=__lowerCamelCase  ,		do_random_crop=__lowerCamelCase		)

  # prepare image
  __UpperCAmelCase							:	Union[str, Any]           =  prepare_img()
  __UpperCAmelCase							:	Dict           =  image_processor(images=__lowerCamelCase  ,		return_tensors="""pt"""		).pixel_values

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

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

  # rename keys
  __UpperCAmelCase							:	Union[str, 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:
    __UpperCAmelCase							:	int           =  False
    __UpperCAmelCase							:	Any           =  SegformerForImageClassification(__lowerCamelCase		)
  else:
    __UpperCAmelCase							:	Tuple           =  SegformerForSemanticSegmentation(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)
  model.eval()

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

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    __UpperCAmelCase							:	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":
    __UpperCAmelCase							:	int           =  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":
    __UpperCAmelCase							:	Tuple           =  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":
    __UpperCAmelCase							:	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":
    __UpperCAmelCase							:	List[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":
    __UpperCAmelCase							:	Union[str, 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":
    __UpperCAmelCase							:	Any           =  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":
    __UpperCAmelCase							:	Optional[Any]           =  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":
    __UpperCAmelCase							:	int           =  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":
    __UpperCAmelCase							:	int           =  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":
    __UpperCAmelCase							:	Optional[int]           =  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":
    __UpperCAmelCase							:	Optional[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":
    __UpperCAmelCase							:	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":
    __UpperCAmelCase							:	int           =  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":
    __UpperCAmelCase							:	int           =  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:
    __UpperCAmelCase							:	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__":
       a		:       Any			     = 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."
       )
       a		:       List[str]			     = parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			Optional[NestedDataStructureLike[PathLike]] = None		,      __lowercase					:			Optional[NamedSplit] = None		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			List[str]		,      )							->      Tuple:
        __UpperCAmelCase							:	Any           =  path_or_paths
        __UpperCAmelCase							:	Dict           =  split if split or isinstance(__lowercase		,      __lowercase					) else """train"""
        __UpperCAmelCase							:	Optional[int]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	str           =  keep_in_memory
        __UpperCAmelCase							:	Optional[int]           =  streaming
        __UpperCAmelCase							:	Dict           =  num_proc
        __UpperCAmelCase							:	Tuple           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
        pass


class     a    ( lowercase__   ):



      """simple docstring"""


      def __init__(      self					:			List[str]		,      __lowercase					:			Optional[Features] = None		,      __lowercase					:			str = None		,      __lowercase					:			bool = False		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[int] = None		,      **__lowercase					:			Optional[Any]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	Optional[Any]           =  features
        __UpperCAmelCase							:	str           =  cache_dir
        __UpperCAmelCase							:	Optional[int]           =  keep_in_memory
        __UpperCAmelCase							:	Dict           =  streaming
        __UpperCAmelCase							:	Optional[Any]           =  num_proc
        __UpperCAmelCase							:	Union[str, Any]           =  kwargs




      @abstractmethod
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Union[Dataset, IterableDataset]:
        pass


