




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	:	Union[str, Any]				=   logging.get_logger(__name__)




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

   return new_state_dict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:





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




def 						SCREAMING_SNAKE_CASE__ (   )  ->       List[str]:
   snake_case				:    List[str]           =	"""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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       List[Any]:
   snake_case				:    List[Any]           =	SegformerConfig()
   snake_case				:    Tuple           =	False

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

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

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

   # prepare image
   snake_case				:    Any           =	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				:    int           =	torch.load(__snake_case						,map_location=torch.device("""cpu"""					)					)["""state_dict"""]

   # rename keys
   snake_case				:    Tuple           =	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				:    List[str]           =	False
      snake_case				:    Union[str, Any]           =	SegformerForImageClassification(__snake_case					)
   else:
      snake_case				:    Union[str, Any]           =	SegformerForSemanticSegmentation(__snake_case					)
   model.load_state_dict(__snake_case					)
   model.eval()

   # forward pass
   snake_case				:    Optional[int]           =	model(__snake_case					)
   snake_case				:    List[Any]           =	outputs.logits

   # set expected_slice based on model name
   # ADE20k checkpoints
   if model_name == "segformer.b0.512x512.ade.160k":
      snake_case				:    Any           =	torch.tensor(
          [
              [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
              [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
              [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
          ]					)
   elif model_name == "segformer.b1.512x512.ade.160k":
      snake_case				:    str           =	torch.tensor(
          [
              [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
              [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
              [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
          ]					)
   elif model_name == "segformer.b2.512x512.ade.160k":
      snake_case				:    str           =	torch.tensor(
          [
              [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
              [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
              [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
          ]					)
   elif model_name == "segformer.b3.512x512.ade.160k":
      snake_case				:    int           =	torch.tensor(
          [
              [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
              [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
              [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
          ]					)
   elif model_name == "segformer.b4.512x512.ade.160k":
      snake_case				:    Any           =	torch.tensor(
          [
              [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
              [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
              [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
          ]					)
   elif model_name == "segformer.b5.640x640.ade.160k":
      snake_case				:    int           =	torch.tensor(
          [
              [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
              [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
              [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
          ]					)
   # Cityscapes checkpoints
   elif model_name == "segformer.b0.1024x1024.city.160k":
      snake_case				:    List[Any]           =	torch.tensor(
          [
              [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
              [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
              [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
          ]					)
   elif model_name == "segformer.b0.512x1024.city.160k":
      snake_case				:    List[str]           =	torch.tensor(
          [
              [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
              [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
              [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
          ]					)
   elif model_name == "segformer.b0.640x1280.city.160k":
      snake_case				:    Dict           =	torch.tensor(
          [
              [
                  [-1.1_372E01, -1.2_787E01, -1.3_477E01],
                  [-1.2_536E01, -1.4_194E01, -1.4_409E01],
                  [-1.3_217E01, -1.4_888E01, -1.5_327E01],
              ],
              [
                  [-1.4_791E01, -1.7_122E01, -1.8_277E01],
                  [-1.7_163E01, -1.9_192E01, -1.9_533E01],
                  [-1.7_897E01, -1.9_991E01, -2.0_315E01],
              ],
              [
                  [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
                  [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
                  [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
              ],
          ]					)
   elif model_name == "segformer.b0.768x768.city.160k":
      snake_case				:    List[str]           =	torch.tensor(
          [
              [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
              [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
              [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
          ]					)
   elif model_name == "segformer.b1.1024x1024.city.160k":
      snake_case				:    Optional[Any]           =	torch.tensor(
          [
              [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
              [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
              [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
          ]					)
   elif model_name == "segformer.b2.1024x1024.city.160k":
      snake_case				:    str           =	torch.tensor(
          [
              [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
              [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
              [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
          ]					)
   elif model_name == "segformer.b3.1024x1024.city.160k":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [
              [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
              [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
              [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
          ]					)
   elif model_name == "segformer.b4.1024x1024.city.160k":
      snake_case				:    List[Any]           =	torch.tensor(
          [
              [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
              [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
              [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
          ]					)
   elif model_name == "segformer.b5.1024x1024.city.160k":
      snake_case				:    Optional[int]           =	torch.tensor(
          [
              [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
              [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
              [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
          ]					)
   else:
      snake_case				:    List[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	:	int				=   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	:	int				=   parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import warnings
from functools import wraps
from typing import Callable




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Callable:

   @wraps(lowercase					)
   def _inner_fn(*lowercase						,**lowercase					):
      warnings.warn(
          (f"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")						,lowercase						,)
      return fn(*lowercase						,**lowercase					)

   return _inner_fn





