

'''simple docstring'''
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			:				Any 			=					logging.get_logger(__name__)


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					List[Any]=False    )				->      Union[str, Any]:
     """simple docstring"""




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

     return new_state_dict


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Tuple    )				->      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)
               _SCREAMING_SNAKE_CASE							   =state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"    )
               _SCREAMING_SNAKE_CASE							   =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
               _SCREAMING_SNAKE_CASE							   =kv_weight[
                   : config.hidden_sizes[i], :
               ]
               _SCREAMING_SNAKE_CASE							   =kv_bias[: config.hidden_sizes[i]]
               _SCREAMING_SNAKE_CASE							   =kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               _SCREAMING_SNAKE_CASE							   =kv_bias[
                   config.hidden_sizes[i] :
               ]


def 	_lowerCAmelCase (						)				->      Optional[int]:
     """simple docstring"""




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

     return image


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					List[str]    )				->      Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =SegformerConfig()
     _SCREAMING_SNAKE_CASE							   =False

     # set attributes based on model_name
     _SCREAMING_SNAKE_CASE							   ='huggingface/label-files'
     if "segformer" in model_name:
          _SCREAMING_SNAKE_CASE							   =model_name[len('segformer.'    ) : len('segformer.'    ) + 2]
          if "ade" in model_name:
               _SCREAMING_SNAKE_CASE							   =1_50
               _SCREAMING_SNAKE_CASE							   ='ade20k-id2label.json'
               _SCREAMING_SNAKE_CASE							   =(1, 1_50, 1_28, 1_28)
          elif "city" in model_name:
               _SCREAMING_SNAKE_CASE							   =19
               _SCREAMING_SNAKE_CASE							   ='cityscapes-id2label.json'
               _SCREAMING_SNAKE_CASE							   =(1, 19, 1_28, 1_28)
          else:
               raise ValueError(f"Model {model_name} not supported"    )
     elif "mit" in model_name:
          _SCREAMING_SNAKE_CASE							   =True
          _SCREAMING_SNAKE_CASE							   =model_name[4:6]
          _SCREAMING_SNAKE_CASE							   =10_00
          _SCREAMING_SNAKE_CASE							   ='imagenet-1k-id2label.json'
          _SCREAMING_SNAKE_CASE							   =(1, 10_00)
     else:
          raise ValueError(f"Model {model_name} not supported"    )

     # set config attributes
     _SCREAMING_SNAKE_CASE							   =json.load(open(hf_hub_download(_UpperCamelCase							,			_UpperCamelCase							,			repo_type='dataset'    )							,			'r'    )    )
     _SCREAMING_SNAKE_CASE							   ={int(_UpperCamelCase    ): v for k, v in idalabel.items()}
     _SCREAMING_SNAKE_CASE							   =idalabel
     _SCREAMING_SNAKE_CASE							   ={v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          _SCREAMING_SNAKE_CASE							   =[64, 1_28, 3_20, 5_12]
          _SCREAMING_SNAKE_CASE							   =2_56
     elif size == "b2":
          _SCREAMING_SNAKE_CASE							   =[64, 1_28, 3_20, 5_12]
          _SCREAMING_SNAKE_CASE							   =7_68
          _SCREAMING_SNAKE_CASE							   =[3, 4, 6, 3]
     elif size == "b3":
          _SCREAMING_SNAKE_CASE							   =[64, 1_28, 3_20, 5_12]
          _SCREAMING_SNAKE_CASE							   =7_68
          _SCREAMING_SNAKE_CASE							   =[3, 4, 18, 3]
     elif size == "b4":
          _SCREAMING_SNAKE_CASE							   =[64, 1_28, 3_20, 5_12]
          _SCREAMING_SNAKE_CASE							   =7_68
          _SCREAMING_SNAKE_CASE							   =[3, 8, 27, 3]
     elif size == "b5":
          _SCREAMING_SNAKE_CASE							   =[64, 1_28, 3_20, 5_12]
          _SCREAMING_SNAKE_CASE							   =7_68
          _SCREAMING_SNAKE_CASE							   =[3, 6, 40, 3]
     else:
          raise ValueError(f"Size {size} not supported"    )

     # load image processor (only resize + normalize)
     _SCREAMING_SNAKE_CASE							   =SegformerImageProcessor(
         image_scale=(5_12, 5_12)							,			keep_ratio=_UpperCamelCase							,			align=_UpperCamelCase							,			do_random_crop=_UpperCamelCase    )

     # prepare image
     _SCREAMING_SNAKE_CASE							   =prepare_img()
     _SCREAMING_SNAKE_CASE							   =image_processor(images=_UpperCamelCase							,			return_tensors='pt'    ).pixel_values

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

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

     # rename keys
     _SCREAMING_SNAKE_CASE							   =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:
          _SCREAMING_SNAKE_CASE							   =False
          _SCREAMING_SNAKE_CASE							   =SegformerForImageClassification(_UpperCamelCase    )
     else:
          _SCREAMING_SNAKE_CASE							   =SegformerForSemanticSegmentation(_UpperCamelCase    )
     model.load_state_dict(_UpperCamelCase    )
     model.eval()

     # forward pass
     _SCREAMING_SNAKE_CASE							   =model(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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":
          _SCREAMING_SNAKE_CASE							   =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:
          _SCREAMING_SNAKE_CASE							   =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__":
     lowerCamelCase			:				Tuple 			=					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			:				Any 			=					parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'    )
     return 0.5 * mass * abs(_UpperCamelCase    ) * abs(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
