



'''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()
lowercase							       =			logging.get_logger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''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(lowercase__       )-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(lowercase__       )-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(lowercase__       )-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(lowercase__       )-1}"""       )
          if key.startswith("head"       ):
               a_       		=key.replace("head"       ,	"classifier"       )
          a_       		=value

     return new_state_dict






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''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_       (		):


     '''simple docstring'''







     a_       		="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_       		=Image.open(requests.get(lowercase__       ,	stream=lowercase__       ).raw       )

     return image






@torch.no_grad()
def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''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_5_0
               a_       		="ade20k-id2label.json"
               a_       		=(1, 1_5_0, 1_2_8, 1_2_8)
          elif "city" in model_name:
               a_       		=1_9
               a_       		="cityscapes-id2label.json"
               a_       		=(1, 1_9, 1_2_8, 1_2_8)
          else:
               raise ValueError(F"""Model {model_name} not supported"""       )
     elif "mit" in model_name:
          a_       		=True
          a_       		=model_name[4:6]
          a_       		=1_0_0_0
          a_       		="imagenet-1k-id2label.json"
          a_       		=(1, 1_0_0_0)
     else:
          raise ValueError(F"""Model {model_name} not supported"""       )

     # set config attributes
     a_       		=json.load(open(hf_hub_download(lowercase__       ,	lowercase__       ,	repo_type="dataset"       )       ,	"r"       )       )
     a_       		={int(lowercase__       ): 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_       		=[6_4, 1_2_8, 3_2_0, 5_1_2]
          a_       		=2_5_6
     elif size == "b2":
          a_       		=[6_4, 1_2_8, 3_2_0, 5_1_2]
          a_       		=7_6_8
          a_       		=[3, 4, 6, 3]
     elif size == "b3":
          a_       		=[6_4, 1_2_8, 3_2_0, 5_1_2]
          a_       		=7_6_8
          a_       		=[3, 4, 1_8, 3]
     elif size == "b4":
          a_       		=[6_4, 1_2_8, 3_2_0, 5_1_2]
          a_       		=7_6_8
          a_       		=[3, 8, 2_7, 3]
     elif size == "b5":
          a_       		=[6_4, 1_2_8, 3_2_0, 5_1_2]
          a_       		=7_6_8
          a_       		=[3, 6, 4_0, 3]
     else:
          raise ValueError(F"""Size {size} not supported"""       )

     # load image processor (only resize + normalize)
     a_       		=SegformerImageProcessor(
         image_scale=(5_1_2, 5_1_2)       ,	keep_ratio=lowercase__       ,	align=lowercase__       ,	do_random_crop=lowercase__       )

     # prepare image
     a_       		=prepare_img()
     a_       		=image_processor(images=lowercase__       ,	return_tensors="pt"       ).pixel_values

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

     # load original state dict
     if encoder_only:
          a_       		=torch.load(lowercase__       ,	map_location=torch.device("cpu"       )       )
     else:
          a_       		=torch.load(lowercase__       ,	map_location=torch.device("cpu"       )       )["state_dict"]

     # rename keys
     a_       		=rename_keys(lowercase__       ,	encoder_only=lowercase__       )
     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(lowercase__       ,	lowercase__       )

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

     # forward pass
     a_       		=model(lowercase__       )
     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]       ,	lowercase__       ,	atol=1E-2       )

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


if __name__ == "__main__":
  lowercase							       =			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.'''
  )
  lowercase							       =			parser.parse_args()
  convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


from collections.abc import Sequence






def  UpperCAmelCase_       (		lowercase__ = None       ):


     '''simple docstring'''







     if nums is None or not nums:
          raise ValueError("Input sequence should not be empty"       )

     a_       		=nums[0]
     for i in range(1       ,	len(lowercase__       )       ):
          a_       		=nums[i]
          a_       		=max(lowercase__       ,	ans + num       ,	lowercase__       )

     return ans


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Try on a sample input from the user
  lowercase							       =			int(input('''Enter number of elements : ''').strip())
  lowercase							       =			list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
  print(max_subsequence_sum(array))


