



'''simple docstring'''






from math import pow, sqrt







def     UpperCamelCase_      (   *A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       len(A__ ) > 0 and all(value > 0.0 for value in values )
  return result







def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Optional[int] ):
  '''simple docstring'''





  return (
      round(sqrt(molar_mass_a / molar_mass_a )							,   6 )
      if validate(A__							,   A__ )
      else ValueError("""Input Error: Molar mass values must greater than 0.""" )
  )







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Optional[Any]							,   A__ :       Tuple ):
  '''simple docstring'''





  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )							,   6 )
      if validate(A__							,   A__							,   A__ )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0.""" )
  )







def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       str							,   A__ :       Optional[Any] ):
  '''simple docstring'''





  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )							,   6 )
      if validate(A__							,   A__							,   A__ )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0.""" )
  )







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       str							,   A__ :       List[Any] ):
  '''simple docstring'''





  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a							,   2 )							,   6 )
      if validate(A__							,   A__							,   A__ )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0.""" )
  )







def     UpperCamelCase_      (   A__ :       int							,   A__ :       int							,   A__ :       List[str] ):
  '''simple docstring'''





  return (
      round(pow(effusion_rate_a / effusion_rate_a							,   2 ) / molar_mass							,   6 )
      if validate(A__							,   A__							,   A__ )
      else ValueError(
          """Input Error: Molar mass and effusion rate values must greater than 0.""" )
  )







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







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       str=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       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_      :				str      =       key.replace("""backbone"""							,   """segformer.encoder""" )
    if "patch_embed" in key:
      # replace for example patch_embed1 by patch_embeddings.0
      lowerCAmelCase_      :				List[str]      =       key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
      lowerCAmelCase_      :				List[Any]      =       key.replace(f'patch_embed{idx}'							,   f'patch_embeddings.{int(A__ )-1}' )
    if "norm" in key:
      lowerCAmelCase_      :				Any      =       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_      :				int      =       key.replace(f'layer_norm{idx}'							,   f'layer_norm.{int(A__ )-1}' )
    if "layer_norm1" in key:
      lowerCAmelCase_      :				str      =       key.replace("""layer_norm1"""							,   """layer_norm_1""" )
    if "layer_norm2" in key:
      lowerCAmelCase_      :				Union[str, Any]      =       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_      :				str      =       key.replace(f'block{idx}'							,   f'block.{int(A__ )-1}' )
    if "attn.q" in key:
      lowerCAmelCase_      :				List[Any]      =       key.replace("""attn.q"""							,   """attention.self.query""" )
    if "attn.proj" in key:
      lowerCAmelCase_      :				Optional[int]      =       key.replace("""attn.proj"""							,   """attention.output.dense""" )
    if "attn" in key:
      lowerCAmelCase_      :				str      =       key.replace("""attn"""							,   """attention.self""" )
    if "fc1" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""fc1"""							,   """dense1""" )
    if "fc2" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""fc2"""							,   """dense2""" )
    if "linear_pred" in key:
      lowerCAmelCase_      :				List[Any]      =       key.replace("""linear_pred"""							,   """classifier""" )
    if "linear_fuse" in key:
      lowerCAmelCase_      :				Optional[Any]      =       key.replace("""linear_fuse.conv"""							,   """linear_fuse""" )
      lowerCAmelCase_      :				Any      =       key.replace("""linear_fuse.bn"""							,   """batch_norm""" )
    if "linear_c" in key:
      # replace for example linear_c4 by linear_c.3
      lowerCAmelCase_      :				str      =       key[key.find("""linear_c""" ) + len("""linear_c""" )]
      lowerCAmelCase_      :				Dict      =       key.replace(f'linear_c{idx}'							,   f'linear_c.{int(A__ )-1}' )
    if key.startswith("""head""" ):
      lowerCAmelCase_      :				int      =       key.replace("""head"""							,   """classifier""" )
    lowerCAmelCase_      :				int      =       value

  return new_state_dict







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Union[str, 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)
      lowerCAmelCase_      :				int      =       state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight' )
      lowerCAmelCase_      :				Optional[int]      =       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_      :				List[str]      =       kv_weight[
          : config.hidden_sizes[i], :
      ]
      lowerCAmelCase_      :				Optional[Any]      =       kv_bias[: config.hidden_sizes[i]]
      lowerCAmelCase_      :				Union[str, Any]      =       kv_weight[
          config.hidden_sizes[i] :, :
      ]
      lowerCAmelCase_      :				str      =       kv_bias[
          config.hidden_sizes[i] :
      ]







def     UpperCamelCase_      (   ):
  '''simple docstring'''





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

  return image







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       List[Any]							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       SegformerConfig()
  lowerCAmelCase_      :				Optional[Any]      =       False

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

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

  # load image processor (only resize + normalize)
  lowerCAmelCase_      :				List[Any]      =       SegformerImageProcessor(
      image_scale=(5_12, 5_12)							,   keep_ratio=A__							,   align=A__							,   do_random_crop=A__ )

  # prepare image
  lowerCAmelCase_      :				Optional[Any]      =       prepare_img()
  lowerCAmelCase_      :				Union[str, Any]      =       image_processor(images=A__							,   return_tensors="""pt""" ).pixel_values

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

  # load original state dict
  if encoder_only:
    lowerCAmelCase_      :				str      =       torch.load(A__							,   map_location=torch.device("""cpu""" ) )
  else:
    lowerCAmelCase_      :				List[str]      =       torch.load(A__							,   map_location=torch.device("""cpu""" ) )["""state_dict"""]

  # rename keys
  lowerCAmelCase_      :				Dict      =       rename_keys(A__							,   encoder_only=A__ )
  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(A__							,   A__ )

  # create HuggingFace model and load state dict
  if encoder_only:
    lowerCAmelCase_      :				Dict      =       False
    lowerCAmelCase_      :				List[Any]      =       SegformerForImageClassification(A__ )
  else:
    lowerCAmelCase_      :				str      =       SegformerForSemanticSegmentation(A__ )
  model.load_state_dict(A__ )
  model.eval()

  # forward pass
  lowerCAmelCase_      :				Tuple      =       model(A__ )
  lowerCAmelCase_      :				Union[str, Any]      =       outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    lowerCAmelCase_      :				Tuple      =       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":
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				List[str]      =       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":
    lowerCAmelCase_      :				List[str]      =       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":
    lowerCAmelCase_      :				Dict      =       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":
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				Dict      =       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":
    lowerCAmelCase_      :				str      =       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":
    lowerCAmelCase_      :				Optional[int]      =       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":
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				int      =       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":
    lowerCAmelCase_      :				Union[str, Any]      =       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":
    lowerCAmelCase_      :				List[Any]      =       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:
    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]							,   A__							,   atol=1E-2 )

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


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



