





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__      :     List[str]         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:Any=False				)	->				Optional[Any]:
     '''simple docstring'''


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

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Optional[int]				)	->				List[Any]:
     '''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)
               A_          =       state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'				)
               A_          =       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
               A_          =       kv_weight[
                   : config.hidden_sizes[i], :
               ]
               A_          =       kv_bias[: config.hidden_sizes[i]]
               A_          =       kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               A_          =       kv_bias[
                   config.hidden_sizes[i] :
               ]

def      UpperCAmelCase_    (    )	->				Any:
     '''simple docstring'''


     A_          =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_          =       Image.open(requests.get(_UpperCAmelCase  ,						stream=_UpperCAmelCase				).raw				)

     return image

@torch.no_grad()
def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:Tuple  ,						_UpperCAmelCase							:Optional[int]				)	->				List[Any]:
     '''simple docstring'''


     A_          =       SegformerConfig()
     A_          =       False

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

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

     # load image processor (only resize + normalize)
     A_          =       SegformerImageProcessor(
         image_scale=(512, 512)  ,						keep_ratio=_UpperCAmelCase  ,						align=_UpperCAmelCase  ,						do_random_crop=_UpperCAmelCase				)

     # prepare image
     A_          =       prepare_img()
     A_          =       image_processor(images=_UpperCAmelCase  ,						return_tensors='''pt'''				).pixel_values

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

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

     # rename keys
     A_          =       rename_keys(_UpperCAmelCase  ,						encoder_only=_UpperCAmelCase				)
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(_UpperCAmelCase  ,						_UpperCAmelCase				)

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

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

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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":
          A_          =       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:
          A_          =       logits.argmax(-1				).item()
          print('''Predicted class:'''  ,						model.config.idalabel[predicted_class_idx]				)

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]  ,						_UpperCAmelCase  ,						atol=1e-2				)

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


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










def      UpperCAmelCase_    (    _UpperCAmelCase							:Any  ,						_UpperCAmelCase							:Dict				)	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       [1]
     for i in range(2  ,						_UpperCAmelCase				):
          factorials.append(factorials[-1] * i				)
     assert 0 <= k < factorials[-1] * n, "k out of bounds"

     A_          =       []
     A_          =       list(range(_UpperCAmelCase				)				)

     # Find permutation
     while factorials:
          A_          =       factorials.pop()
          A_   ,    A_          =       divmod(_UpperCAmelCase  ,						_UpperCAmelCase				)
          permutation.append(elements[number]				)
          elements.remove(elements[number]				)
     permutation.append(elements[0]				)

     return permutation


if __name__ == "__main__":
      import doctest

      doctest.testmod()



