


'''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()
a_   :      List[Any]				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Tuple=False						)				->					Dict:




     '''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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-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(lowerCAmelCase__						)-1}'						)
          if key.startswith('head'						):
               _a      =							key.replace('head'			,  'classifier'						)
          _a      =							value

     return new_state_dict






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					: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 		_A			()				->					Any:




     '''simple docstring'''



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

     return image






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:str			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Optional[Any]						)				->					Union[str, 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      =							1_50
               _a      =							'ade20k-id2label.json'
               _a      =							(1, 1_50, 1_28, 1_28)
          elif "city" in model_name:
               _a      =							19
               _a      =							'cityscapes-id2label.json'
               _a      =							(1, 19, 1_28, 1_28)
          else:
               raise ValueError(f'Model {model_name} not supported'						)
     elif "mit" in model_name:
          _a      =							True
          _a      =							model_name[4:6]
          _a      =							10_00
          _a      =							'imagenet-1k-id2label.json'
          _a      =							(1, 10_00)
     else:
          raise ValueError(f'Model {model_name} not supported'						)

     # set config attributes
     _a      =							json.load(open(hf_hub_download(lowerCAmelCase__			,  lowerCAmelCase__			,  repo_type='dataset'						)			,  'r'						)						)
     _a      =							{int(lowerCAmelCase__						): 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, 1_28, 3_20, 5_12]
          _a      =							2_56
     elif size == "b2":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 4, 6, 3]
     elif size == "b3":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 4, 18, 3]
     elif size == "b4":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 8, 27, 3]
     elif size == "b5":
          _a      =							[64, 1_28, 3_20, 5_12]
          _a      =							7_68
          _a      =							[3, 6, 40, 3]
     else:
          raise ValueError(f'Size {size} not supported'						)

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

     # prepare image
     _a      =							prepare_img()
     _a      =							image_processor(images=lowerCAmelCase__			,  return_tensors='pt'						).pixel_values

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

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

     # rename keys
     _a      =							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:
          _a      =							False
          _a      =							SegformerForImageClassification(lowerCAmelCase__						)
     else:
          _a      =							SegformerForSemanticSegmentation(lowerCAmelCase__						)
     model.load_state_dict(lowerCAmelCase__						)
     model.eval()

     # forward pass
     _a      =							model(lowerCAmelCase__						)
     _a      =							outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          _a      =							torch.tensor(
              [
                  [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
                  [[-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":
          _a      =							torch.tensor(
              [
                  [[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -10.35_29, -10.03_04], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
                  [[-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":
          _a      =							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":
          _a      =							torch.tensor(
              [
                  [[-9.0_8_7_8, -10.20_81, -10.18_91], [-9.3_1_4_4, -10.79_41, -10.98_43], [-9.2_2_9_4, -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":
          _a      =							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":
          _a      =							torch.tensor(
              [
                  [[-9.5_5_2_4, -12.08_35, -11.73_48], [-10.52_29, -13.64_46, -14.56_62], [-9.5_8_4_2, -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":
          _a      =							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]],
                  [[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
              ]						)
     elif model_name == "segformer.b0.512x1024.city.160k":
          _a      =							torch.tensor(
              [
                  [[-7.8_2_1_7, -9.8_7_6_7, -10.17_17], [-9.4_4_3_8, -10.90_58, -11.40_47], [-9.7_9_3_9, -12.34_95, -12.10_79]],
                  [[-7.1_5_1_4, -9.5_3_3_6, -10.08_60], [-9.7_7_7_6, -11.68_22, -11.84_39], [-10.14_11, -12.76_55, -12.89_72]],
                  [[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
              ]						)
     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(
              [
                  [[-9.4_9_5_9, -11.30_87, -11.74_79], [-11.00_25, -12.65_40, -12.33_19], [-11.40_64, -13.04_87, -12.99_05]],
                  [[-9.8_9_0_5, -11.30_84, -12.08_54], [-11.17_26, -12.76_98, -12.95_83], [-11.59_85, -13.32_78, -14.17_74]],
                  [[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
              ]						)
     elif model_name == "segformer.b1.1024x1024.city.160k":
          _a      =							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]],
                  [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
              ]						)
     elif model_name == "segformer.b2.1024x1024.city.160k":
          _a      =							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]],
                  [[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
              ]						)
     elif model_name == "segformer.b3.1024x1024.city.160k":
          _a      =							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]],
                  [[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
              ]						)
     elif model_name == "segformer.b4.1024x1024.city.160k":
          _a      =							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]],
                  [[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
              ]						)
     elif model_name == "segformer.b5.1024x1024.city.160k":
          _a      =							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]],
                  [[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
              ]						)
     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]			,  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__":
 a_   :      str				    =      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_   :      Optional[Any]				    =      parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # If this activity has start time greater than
          # or equal to the finish time of previously
          # selected activity, then select it
          if start[j] >= finish[i]:
               print(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

