






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    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					List[Any]=False							)     ->  int:
   __UpperCamelCase	:			Union[str, 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	:			int					=					key.replace("""backbone"""					,							"""segformer.encoder"""							)
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         __UpperCamelCase	:			Any					=					key[key.find("""patch_embed"""							) + len("""patch_embed"""							)]
         __UpperCamelCase	:			Dict					=					key.replace(f'patch_embed{idx}'					,							f'patch_embeddings.{int(__lowerCAmelCase							)-1}'							)
      if "norm" in key:
         __UpperCamelCase	:			Optional[int]					=					key.replace("""norm"""					,							"""layer_norm"""							)
      if "segformer.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         __UpperCamelCase	:			List[str]					=					key[key.find("""segformer.encoder.layer_norm"""							) + len("""segformer.encoder.layer_norm"""							)]
         __UpperCamelCase	:			int					=					key.replace(f'layer_norm{idx}'					,							f'layer_norm.{int(__lowerCAmelCase							)-1}'							)
      if "layer_norm1" in key:
         __UpperCamelCase	:			Any					=					key.replace("""layer_norm1"""					,							"""layer_norm_1"""							)
      if "layer_norm2" in key:
         __UpperCamelCase	:			Any					=					key.replace("""layer_norm2"""					,							"""layer_norm_2"""							)
      if "block" in key:
         # replace for example block1 by block.0
         __UpperCamelCase	:			Any					=					key[key.find("""block"""							) + len("""block"""							)]
         __UpperCamelCase	:			str					=					key.replace(f'block{idx}'					,							f'block.{int(__lowerCAmelCase							)-1}'							)
      if "attn.q" in key:
         __UpperCamelCase	:			Union[str, Any]					=					key.replace("""attn.q"""					,							"""attention.self.query"""							)
      if "attn.proj" in key:
         __UpperCamelCase	:			Tuple					=					key.replace("""attn.proj"""					,							"""attention.output.dense"""							)
      if "attn" in key:
         __UpperCamelCase	:			str					=					key.replace("""attn"""					,							"""attention.self"""							)
      if "fc1" in key:
         __UpperCamelCase	:			List[Any]					=					key.replace("""fc1"""					,							"""dense1"""							)
      if "fc2" in key:
         __UpperCamelCase	:			Tuple					=					key.replace("""fc2"""					,							"""dense2"""							)
      if "linear_pred" in key:
         __UpperCamelCase	:			List[Any]					=					key.replace("""linear_pred"""					,							"""classifier"""							)
      if "linear_fuse" in key:
         __UpperCamelCase	:			Optional[Any]					=					key.replace("""linear_fuse.conv"""					,							"""linear_fuse"""							)
         __UpperCamelCase	:			str					=					key.replace("""linear_fuse.bn"""					,							"""batch_norm"""							)
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         __UpperCamelCase	:			Optional[Any]					=					key[key.find("""linear_c"""							) + len("""linear_c"""							)]
         __UpperCamelCase	:			str					=					key.replace(f'linear_c{idx}'					,							f'linear_c.{int(__lowerCAmelCase							)-1}'							)
      if key.startswith("""head"""							):
         __UpperCamelCase	:			List[str]					=					key.replace("""head"""					,							"""classifier"""							)
      __UpperCamelCase	:			Tuple					=					value

   return new_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  int:
   # 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	:			Tuple					=					state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'							)
         __UpperCamelCase	:			Union[str, 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
         __UpperCamelCase	:			Union[str, Any]					=					kv_weight[
             : config.hidden_sizes[i], :
         ]
         __UpperCamelCase	:			Tuple					=					kv_bias[: config.hidden_sizes[i]]
         __UpperCamelCase	:			Optional[Any]					=					kv_weight[
             config.hidden_sizes[i] :, :
         ]
         __UpperCamelCase	:			Union[str, Any]					=					kv_bias[
             config.hidden_sizes[i] :
         ]


def 				__lowerCamelCase     (							)     ->  Tuple:
   __UpperCamelCase	:			Any					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __UpperCamelCase	:			str					=					Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase							).raw							)

   return image


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]							)     ->  Tuple:
   __UpperCamelCase	:			Tuple					=					SegformerConfig()
   __UpperCamelCase	:			str					=					False

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

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

   # load image processor (only resize + normalize)
   __UpperCamelCase	:			Optional[int]					=					SegformerImageProcessor(
       image_scale=(512, 512)					,							keep_ratio=__lowerCAmelCase					,							align=__lowerCAmelCase					,							do_random_crop=__lowerCAmelCase							)

   # prepare image
   __UpperCamelCase	:			Dict					=					prepare_img()
   __UpperCamelCase	:			List[str]					=					image_processor(images=__lowerCAmelCase					,							return_tensors="""pt"""							).pixel_values

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

   # load original state dict
   if encoder_only:
      __UpperCamelCase	:			Dict					=					torch.load(__lowerCAmelCase					,							map_location=torch.device("""cpu"""							)							)
   else:
      __UpperCamelCase	:			Any					=					torch.load(__lowerCAmelCase					,							map_location=torch.device("""cpu"""							)							)["""state_dict"""]

   # rename keys
   __UpperCamelCase	:			Tuple					=					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	:			Any					=					False
      __UpperCamelCase	:			Union[str, Any]					=					SegformerForImageClassification(__lowerCAmelCase							)
   else:
      __UpperCamelCase	:			Optional[Any]					=					SegformerForSemanticSegmentation(__lowerCAmelCase							)
   model.load_state_dict(__lowerCAmelCase							)
   model.eval()

   # forward pass
   __UpperCamelCase	:			Tuple					=					model(__lowerCAmelCase							)
   __UpperCamelCase	:			Tuple					=					outputs.logits

   # set expected_slice based on model name
   # ADE20k checkpoints
   if model_name == "segformer.b0.512x512.ade.160k":
      __UpperCamelCase	:			int					=					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":
      __UpperCamelCase	:			Any					=					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":
      __UpperCamelCase	:			List[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":
      __UpperCamelCase	:			List[Any]					=					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":
      __UpperCamelCase	:			Optional[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":
      __UpperCamelCase	:			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":
      __UpperCamelCase	:			str					=					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":
      __UpperCamelCase	:			Union[str, Any]					=					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":
      __UpperCamelCase	:			int					=					torch.tensor(
          [
              [
                  [-1.13_72e01, -1.27_87e01, -1.34_77e01],
                  [-1.25_36e01, -1.41_94e01, -1.44_09e01],
                  [-1.32_17e01, -1.48_88e01, -1.53_27e01],
              ],
              [
                  [-1.47_91e01, -1.71_22e01, -1.82_77e01],
                  [-1.71_63e01, -1.91_92e01, -1.95_33e01],
                  [-1.78_97e01, -1.99_91e01, -2.03_15e01],
              ],
              [
                  [7.67_23e-01, 4.19_21e-01, -7.78_78e-02],
                  [4.77_72e-01, 9.55_57e-03, -2.80_82e-01],
                  [3.60_32e-01, -2.48_26e-01, -5.11_68e-01],
              ],
          ]							)
   elif model_name == "segformer.b0.768x768.city.160k":
      __UpperCamelCase	:			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":
      __UpperCamelCase	:			Union[str, 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":
      __UpperCamelCase	:			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":
      __UpperCamelCase	:			List[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":
      __UpperCamelCase	:			Tuple					=					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":
      __UpperCamelCase	:			List[Any]					=					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:
      __UpperCamelCase	:			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]					,							__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    							=      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    							=      parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)











import re

from filelock import FileLock


try:
       import nltk

       UpperCamelCase    							=      True
except (ImportError, ModuleNotFoundError):
       UpperCamelCase    							=      False

if NLTK_AVAILABLE:
       with FileLock('.lock') as lock:
              nltk.download('punkt', quiet=True)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  str:
   re.sub("""<n>"""					,							""""""					,							__lowerCAmelCase							)  # remove pegasus newline char
   assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
   return "\n".join(nltk.sent_tokenize(__lowerCAmelCase							)							)



