

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

def 					__UpperCamelCase				(						lowercase__    :	Optional[int],       lowercase__    :	Dict=False			):






     '''simple docstring'''





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

     return new_state_dict

def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	Dict			):






     '''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)
               __lowercase      		=state_dict.pop(F'''segformer.encoder.block.{i}.{j}.attention.self.kv.weight'''			)
               __lowercase      		=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
               __lowercase      		=kv_weight[
                   : config.hidden_sizes[i], :
               ]
               __lowercase      		=kv_bias[: config.hidden_sizes[i]]
               __lowercase      		=kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               __lowercase      		=kv_bias[
                   config.hidden_sizes[i] :
               ]

def 					__UpperCamelCase				(						):






     '''simple docstring'''





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

     return image

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	List[str],       lowercase__    :	List[Any],       lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=SegformerConfig()
     __lowercase      		=False

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

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

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

     # prepare image
     __lowercase      		=prepare_img()
     __lowercase      		=image_processor(images=lowercase__,       return_tensors='pt'			).pixel_values

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

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

     # rename keys
     __lowercase      		=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:
          __lowercase      		=False
          __lowercase      		=SegformerForImageClassification(lowercase__			)
     else:
          __lowercase      		=SegformerForSemanticSegmentation(lowercase__			)
     model.load_state_dict(lowercase__			)
     model.eval()

     # forward pass
     __lowercase      		=model(lowercase__			)
     __lowercase      		=outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=torch.tensor(
              [
                  [
                      [-1.1_372E01, -1.2_787E01, -1.3_477E01],
                      [-1.2_536E01, -1.4_194E01, -1.4_409E01],
                      [-1.3_217E01, -1.4_888E01, -1.5_327E01],
                  ],
                  [
                      [-1.4_791E01, -1.7_122E01, -1.8_277E01],
                      [-1.7_163E01, -1.9_192E01, -1.9_533E01],
                      [-1.7_897E01, -1.9_991E01, -2.0_315E01],
                  ],
                  [
                      [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
                      [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
                      [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
                  ],
              ]			)
     elif model_name == "segformer.b0.768x768.city.160k":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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":
          __lowercase      		=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:
          __lowercase      		=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__":
       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)





'''simple docstring'''

# Function to print upper half of diamond (pyramid)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(0,       lowercase__			):
          for _ in range(0,       n - i - 1			):  # printing spaces
               print(' ',       end=''			)
          for _ in range(0,       i + 1			):  # printing stars
               print('* ',       end=''			)
          print()

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     for i in range(lowercase__,       0,       -1			):
          for _ in range(lowercase__,       0,       -1			):  # printing stars
               print('* ',       end=''			)
          print()
          for _ in range(n - i + 1,       0,       -1			):  # printing spaces
               print(' ',       end=''			)

def 					__UpperCamelCase				(						lowercase__    :	Any			):






     '''simple docstring'''





     if n <= 0:
          print('       ...       ....        nothing printing :('			)
          return
     floyd(lowercase__			)  # upper half
     reverse_floyd(lowercase__			)  # lower half


if __name__ == "__main__":
       print(r'''| /\ | |- |  |-  |--| |\  /| |-''')
       print(r'''|/  \| |- |_ |_  |__| | \/ | |_''')
       UpperCAmelCase					 =	1
       while K:
              UpperCAmelCase					 =	int(input('''enter the number and , and see the magic : '''))
              print()
              pretty_print(user_number)
              UpperCAmelCase					 =	int(input('''press 0 to exit... and 1 to continue...'''))

       print('''Good Bye...''')


