



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()
_snake_case	=						logging.get_logger(__name__)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





       '''simple docstring'''




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

       return new_state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





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






def        A       (      ):





       '''simple docstring'''




       _lowerCAmelCase  :							Union[str, Any]				     =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       _lowerCAmelCase  :							Union[str, Any]				     =					Image.open(requests.get(_lowerCamelCase       ,				stream=_lowerCamelCase						).raw						)

       return image






@torch.no_grad()
def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




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

       # set attributes based on model_name
       _lowerCAmelCase  :							Tuple				     =					"huggingface/label-files"
       if "segformer" in model_name:
              _lowerCAmelCase  :							Dict				     =					model_name[len("segformer."						) : len("segformer."						) + 2]
              if "ade" in model_name:
                     _lowerCAmelCase  :							Tuple				     =					150
                     _lowerCAmelCase  :							int				     =					"ade20k-id2label.json"
                     _lowerCAmelCase  :							Optional[int]				     =					(1, 150, 128, 128)
              elif "city" in model_name:
                     _lowerCAmelCase  :							Union[str, Any]				     =					19
                     _lowerCAmelCase  :							Any				     =					"cityscapes-id2label.json"
                     _lowerCAmelCase  :							List[str]				     =					(1, 19, 128, 128)
              else:
                     raise ValueError(F"Model {model_name} not supported"						)
       elif "mit" in model_name:
              _lowerCAmelCase  :							str				     =					True
              _lowerCAmelCase  :							List[Any]				     =					model_name[4:6]
              _lowerCAmelCase  :							Optional[Any]				     =					1_000
              _lowerCAmelCase  :							int				     =					"imagenet-1k-id2label.json"
              _lowerCAmelCase  :							List[str]				     =					(1, 1_000)
       else:
              raise ValueError(F"Model {model_name} not supported"						)

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

       # load image processor (only resize + normalize)
       _lowerCAmelCase  :							Any				     =					SegformerImageProcessor(
           image_scale=(512, 512)       ,				keep_ratio=_lowerCamelCase       ,				align=_lowerCamelCase       ,				do_random_crop=_lowerCamelCase						)

       # prepare image
       _lowerCAmelCase  :							int				     =					prepare_img()
       _lowerCAmelCase  :							Tuple				     =					image_processor(images=_lowerCamelCase       ,				return_tensors="pt"						).pixel_values

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

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

       # rename keys
       _lowerCAmelCase  :							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:
              _lowerCAmelCase  :							Any				     =					False
              _lowerCAmelCase  :							List[str]				     =					SegformerForImageClassification(_lowerCamelCase						)
       else:
              _lowerCAmelCase  :							Tuple				     =					SegformerForSemanticSegmentation(_lowerCamelCase						)
       model.load_state_dict(_lowerCamelCase						)
       model.eval()

       # forward pass
       _lowerCAmelCase  :							List[str]				     =					model(_lowerCamelCase						)
       _lowerCAmelCase  :							str				     =					outputs.logits

       # set expected_slice based on model name
       # ADE20k checkpoints
       if model_name == "segformer.b0.512x512.ade.160k":
              _lowerCAmelCase  :							List[str]				     =					torch.tensor(
                  [
                      [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                      [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                      [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
                  ]						)
       elif model_name == "segformer.b1.512x512.ade.160k":
              _lowerCAmelCase  :							Union[str, Any]				     =					torch.tensor(
                  [
                      [[-7.58_20, -8.72_31, -8.32_15], [-8.06_00, -10.35_29, -10.03_04], [-7.52_08, -9.41_03, -9.62_39]],
                      [[-12.69_18, -13.89_94, -13.71_37], [-13.31_96, -15.75_23, -15.47_89], [-12.93_43, -14.87_57, -14.96_89]],
                      [[-11.19_11, -11.94_21, -11.32_43], [-11.33_42, -13.68_39, -13.35_81], [-10.39_09, -12.18_32, -12.48_58]],
                  ]						)
       elif model_name == "segformer.b2.512x512.ade.160k":
              _lowerCAmelCase  :							Dict				     =					torch.tensor(
                  [
                      [[-11.81_73, -14.38_50, -16.31_28], [-14.56_48, -16.58_04, -18.65_68], [-14.72_23, -15.73_87, -18.42_18]],
                      [[-15.72_90, -17.91_71, -19.44_23], [-18.31_05, -19.94_48, -21.46_61], [-17.92_96, -18.64_97, -20.79_10]],
                      [[-15.07_83, -17.03_36, -18.27_89], [-16.87_71, -18.68_70, -20.16_12], [-16.24_54, -17.14_26, -19.50_55]],
                  ]						)
       elif model_name == "segformer.b3.512x512.ade.160k":
              _lowerCAmelCase  :							Union[str, Any]				     =					torch.tensor(
                  [
                      [[-9.08_78, -10.20_81, -10.18_91], [-9.31_44, -10.79_41, -10.98_43], [-9.22_94, -10.38_55, -10.57_04]],
                      [[-12.23_16, -13.90_68, -13.61_02], [-12.91_61, -14.37_02, -14.32_35], [-12.52_33, -13.71_74, -13.79_32]],
                      [[-14.62_75, -15.24_90, -14.97_27], [-14.34_00, -15.96_87, -16.28_27], [-14.14_84, -15.40_33, -15.89_37]],
                  ]						)
       elif model_name == "segformer.b4.512x512.ade.160k":
              _lowerCAmelCase  :							Optional[Any]				     =					torch.tensor(
                  [
                      [[-12.31_44, -13.24_47, -14.08_02], [-13.36_14, -14.58_16, -15.61_17], [-13.33_40, -14.44_33, -16.22_19]],
                      [[-19.27_81, -20.41_28, -20.75_06], [-20.61_53, -21.65_66, -22.09_98], [-19.98_00, -21.04_30, -22.14_94]],
                      [[-18.87_39, -19.78_04, -21.18_34], [-20.12_33, -21.67_65, -23.29_44], [-20.03_15, -21.26_41, -23.69_44]],
                  ]						)
       elif model_name == "segformer.b5.640x640.ade.160k":
              _lowerCAmelCase  :							List[Any]				     =					torch.tensor(
                  [
                      [[-9.55_24, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.58_42, -12.88_51, -13.94_14]],
                      [[-15.34_32, -17.53_23, -17.08_18], [-16.33_30, -18.92_55, -19.21_01], [-15.13_40, -17.78_48, -18.39_71]],
                      [[-12.60_72, -14.94_86, -14.66_31], [-13.76_29, -17.09_07, -17.77_45], [-12.78_99, -16.16_95, -17.16_71]],
                  ]						)
       # Cityscapes checkpoints
       elif model_name == "segformer.b0.1024x1024.city.160k":
              _lowerCAmelCase  :							Any				     =					torch.tensor(
                  [
                      [[-11.92_95, -13.40_57, -14.81_06], [-13.34_31, -14.81_79, -15.37_81], [-14.28_36, -15.59_42, -16.15_88]],
                      [[-11.49_06, -12.80_67, -13.65_64], [-13.11_89, -14.05_00, -14.15_43], [-13.87_48, -14.51_36, -14.87_89]],
                      [[0.53_74, 0.10_67, -0.47_42], [0.11_41, -0.22_55, -0.70_99], [-0.30_00, -0.59_24, -1.31_05]],
                  ]						)
       elif model_name == "segformer.b0.512x1024.city.160k":
              _lowerCAmelCase  :							Union[str, Any]				     =					torch.tensor(
                  [
                      [[-7.82_17, -9.87_67, -10.17_17], [-9.44_38, -10.90_58, -11.40_47], [-9.79_39, -12.34_95, -12.10_79]],
                      [[-7.15_14, -9.53_36, -10.08_60], [-9.77_76, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
                      [[0.30_21, 0.08_05, -0.23_10], [-0.03_28, -0.16_05, -0.27_14], [-0.14_08, -0.54_77, -0.69_76]],
                  ]						)
       elif model_name == "segformer.b0.640x1280.city.160k":
              _lowerCAmelCase  :							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":
              _lowerCAmelCase  :							Optional[int]				     =					torch.tensor(
                  [
                      [[-9.49_59, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
                      [[-9.89_05, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
                      [[0.22_13, 0.01_92, -0.24_66], [-0.17_31, -0.42_13, -0.48_74], [-0.31_26, -0.65_41, -1.13_89]],
                  ]						)
       elif model_name == "segformer.b1.1024x1024.city.160k":
              _lowerCAmelCase  :							Dict				     =					torch.tensor(
                  [
                      [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                      [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                      [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
                  ]						)
       elif model_name == "segformer.b2.1024x1024.city.160k":
              _lowerCAmelCase  :							Tuple				     =					torch.tensor(
                  [
                      [[-16.09_76, -16.48_56, -17.39_62], [-16.62_34, -19.03_42, -19.76_85], [-16.09_00, -18.06_61, -19.11_80]],
                      [[-18.47_50, -18.84_88, -19.50_74], [-19.40_30, -22.15_70, -22.59_77], [-19.11_91, -20.84_86, -22.37_83]],
                      [[-4.51_78, -5.50_37, -6.51_09], [-5.08_84, -7.21_74, -8.03_34], [-4.41_56, -5.81_17, -7.29_70]],
                  ]						)
       elif model_name == "segformer.b3.1024x1024.city.160k":
              _lowerCAmelCase  :							List[str]				     =					torch.tensor(
                  [
                      [[-14.20_81, -14.47_32, -14.19_77], [-14.58_67, -16.44_23, -16.63_56], [-13.44_41, -14.96_85, -16.86_96]],
                      [[-14.45_76, -14.70_73, -15.04_51], [-15.08_16, -17.62_37, -17.98_73], [-14.42_13, -16.01_99, -18.59_92]],
                      [[-4.73_49, -4.95_88, -5.09_66], [-4.32_10, -6.93_25, -7.25_91], [-3.43_12, -4.74_84, -7.19_17]],
                  ]						)
       elif model_name == "segformer.b4.1024x1024.city.160k":
              _lowerCAmelCase  :							Optional[Any]				     =					torch.tensor(
                  [
                      [[-11.77_37, -11.95_26, -11.32_73], [-13.66_92, -14.45_74, -13.88_78], [-13.89_37, -14.69_24, -15.93_45]],
                      [[-14.67_06, -14.53_30, -14.13_06], [-16.15_02, -16.81_80, -16.42_69], [-16.83_38, -17.89_39, -20.17_46]],
                      [[1.04_91, 0.82_89, 1.03_10], [1.10_44, 0.52_19, 0.80_55], [1.08_99, 0.69_26, 0.55_90]],
                  ]						)
       elif model_name == "segformer.b5.1024x1024.city.160k":
              _lowerCAmelCase  :							str				     =					torch.tensor(
                  [
                      [[-12.56_41, -13.47_77, -13.06_84], [-13.95_87, -15.89_83, -16.65_57], [-13.31_09, -15.73_50, -16.31_41]],
                      [[-14.70_74, -15.43_52, -14.59_44], [-16.63_53, -18.16_63, -18.61_20], [-15.17_02, -18.03_29, -18.15_47]],
                      [[-1.79_90, -2.09_51, -1.77_84], [-2.63_97, -3.82_45, -3.96_86], [-1.52_64, -2.81_26, -2.93_16]],
                  ]						)
       else:
              _lowerCAmelCase  :							Optional[Any]				     =					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__":
      _snake_case	=						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."
      )
      _snake_case	=						parser.parse_args()
      convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return price * (1 + tax_rate)


if __name__ == "__main__":
						print(f'''{price_plus_tax(100, 0.25) = }''')
						print(f'''{price_plus_tax(125.50, 0.05) = }''')
