



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

      return new_state_dict
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # for each of the encoder blocks:
      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)
                  UpperCAmelCase_    				=							state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""   )
                  UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase_    				=							kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase_    				=							kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase_    				=							kv_bias[
                      config.hidden_sizes[i] :
                  ]
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )

      return image
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SegformerConfig()
      UpperCAmelCase_    				=							False

      # set attributes based on model_name
      UpperCAmelCase_    				=							"huggingface/label-files"
      if "segformer" in model_name:
            UpperCAmelCase_    				=							model_name[len("segformer."   ) : len("segformer."   ) + 2]
            if "ade" in model_name:
                  UpperCAmelCase_    				=							150
                  UpperCAmelCase_    				=							"ade20k-id2label.json"
                  UpperCAmelCase_    				=							(1, 150, 128, 128)
            elif "city" in model_name:
                  UpperCAmelCase_    				=							19
                  UpperCAmelCase_    				=							"cityscapes-id2label.json"
                  UpperCAmelCase_    				=							(1, 19, 128, 128)
            else:
                  raise ValueError(f"""Model {model_name} not supported"""   )
      elif "mit" in model_name:
            UpperCAmelCase_    				=							True
            UpperCAmelCase_    				=							model_name[4:6]
            UpperCAmelCase_    				=							1000
            UpperCAmelCase_    				=							"imagenet-1k-id2label.json"
            UpperCAmelCase_    				=							(1, 1000)
      else:
            raise ValueError(f"""Model {model_name} not supported"""   )

      # set config attributes
      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      UpperCAmelCase_    				=							idalabel
      UpperCAmelCase_    				=							{v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							256
      elif size == "b2":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 4, 6, 3]
      elif size == "b3":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 4, 18, 3]
      elif size == "b4":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 8, 27, 3]
      elif size == "b5":
            UpperCAmelCase_    				=							[64, 128, 320, 512]
            UpperCAmelCase_    				=							768
            UpperCAmelCase_    				=							[3, 6, 40, 3]
      else:
            raise ValueError(f"""Size {size} not supported"""   )

      # load image processor (only resize + normalize)
      UpperCAmelCase_    				=							SegformerImageProcessor(
          image_scale=(512, 512)			,    keep_ratio=lowerCAmelCase__			,    align=lowerCAmelCase__			,    do_random_crop=lowerCAmelCase__   )

      # prepare image
      UpperCAmelCase_    				=							prepare_img()
      UpperCAmelCase_    				=							image_processor(images=lowerCAmelCase__			,    return_tensors="pt"   ).pixel_values

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

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

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

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

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









"""simple docstring"""





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SwinConfig.from_pretrained(
          "microsoft/swin-tiny-patch4-window7-224"			,    out_features=["stage1", "stage2", "stage3", "stage4"]   )
      UpperCAmelCase_    				=							MaskFormerConfig(backbone_config=lowerCAmelCase__   )

      UpperCAmelCase_    				=							"huggingface/label-files"
      if "ade20k-full" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							847
            UpperCAmelCase_    				=							"maskformer-ade20k-full-id2label.json"
      elif "ade" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							150
            UpperCAmelCase_    				=							"ade20k-id2label.json"
      elif "coco-stuff" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							171
            UpperCAmelCase_    				=							"maskformer-coco-stuff-id2label.json"
      elif "coco" in model_name:
            # TODO
            UpperCAmelCase_    				=							133
            UpperCAmelCase_    				=							"coco-panoptic-id2label.json"
      elif "cityscapes" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							19
            UpperCAmelCase_    				=							"cityscapes-id2label.json"
      elif "vistas" in model_name:
            # this should be ok
            UpperCAmelCase_    				=							65
            UpperCAmelCase_    				=							"mapillary-vistas-id2label.json"

      UpperCAmelCase_    				=							json.load(open(hf_hub_download(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

      return config
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      # stem
      # fmt: off
      rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")   )
      rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")   )
      rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")   )
      rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")   )
      # stages
      for i in range(len(config.backbone_config.depths   )   ):
            for j in range(config.backbone_config.depths[i]   ):
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")   )

            if i < 3:
                  rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")   )
                  rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")   )
            rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")   )
            rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")   )

      # FPN
      rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")   )
      rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")   )
      rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")   )
      for source_index, target_index in zip(range(3			,    0			,    -1   )			,    range(0			,    3   )   ):
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")   )
            rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")   )
            rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")   )
      rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")   )
      rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")   )

      # Transformer decoder
      for idx in range(config.decoder_config.decoder_layers   ):
            # self-attention out projection
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")   )
            # cross-attention out projection
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")   )
            # MLP 1
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")   )
            # MLP 2
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")   )
            # layernorm 1 (self-attention layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")   )
            # layernorm 2 (cross-attention layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")   )
            # layernorm 3 (final layernorm)
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")   )

      rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")   )
      rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")   )

      # heads on top
      rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")   )

      rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")   )
      rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")   )

      rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")   )
      rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")   )

      for i in range(3   ):
            rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")   )
            rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")   )
      # fmt: on

      return rename_keys
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCAmelCase_    				=							num_features[i]
            for j in range(backbone_config.depths[i]   ):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""   )
                  UpperCAmelCase_    				=							state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""   )
                  # next, add query, keys and values (in that order) to the state dict
                  UpperCAmelCase_    				=							in_proj_weight[:dim, :]
                  UpperCAmelCase_    				=							in_proj_bias[: dim]
                  UpperCAmelCase_    				=							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_    				=							in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_    				=							in_proj_bias[-dim :]
                  # fmt: on
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # fmt: off
      UpperCAmelCase_    				=							config.decoder_config.hidden_size
      for idx in range(config.decoder_config.decoder_layers   ):
            # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
            # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""   )
            UpperCAmelCase_    				=							state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_    				=							in_proj_weight[: hidden_size, :]
            UpperCAmelCase_    				=							in_proj_bias[:config.hidden_size]
            UpperCAmelCase_    				=							in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCAmelCase_    				=							in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_    				=							in_proj_weight[-hidden_size :, :]
            UpperCAmelCase_    				=							in_proj_bias[-hidden_size :]
      # fmt: on
def      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							get_maskformer_config(lowerCAmelCase__   )

      # load original state_dict
      with open(lowerCAmelCase__			,    "rb"   ) as f:
            UpperCAmelCase_    				=							pickle.load(lowerCAmelCase__   )
      UpperCAmelCase_    				=							data["model"]

      # for name, param in state_dict.items():
      #     print(name, param.shape)

      # rename keys
      UpperCAmelCase_    				=							create_rename_keys(lowerCAmelCase__   )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
      read_in_swin_q_k_v(lowerCAmelCase__			,    config.backbone_config   )
      read_in_decoder_q_k_v(lowerCAmelCase__			,    lowerCAmelCase__   )

      # update to torch tensors
      for key, value in state_dict.items():
            UpperCAmelCase_    				=							torch.from_numpy(lowerCAmelCase__   )

      # load 🤗 model
      UpperCAmelCase_    				=							MaskFormerForInstanceSegmentation(lowerCAmelCase__   )
      model.eval()

      for name, param in model.named_parameters():
            print(lowerCAmelCase__			,    param.shape   )

      UpperCAmelCase_				,					UpperCAmelCase_    				=							model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(lowerCAmelCase__   ) == 0, f"""Unexpected keys: {unexpected_keys}"""

      # verify results
      UpperCAmelCase_    				=							prepare_img()
      if "vistas" in model_name:
            UpperCAmelCase_    				=							65
      elif "cityscapes" in model_name:
            UpperCAmelCase_    				=							65535
      else:
            UpperCAmelCase_    				=							255
      UpperCAmelCase_    				=							True if "ade" in model_name else False
      UpperCAmelCase_    				=							MaskFormerImageProcessor(ignore_index=lowerCAmelCase__			,    reduce_labels=lowerCAmelCase__   )

      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

      print("Logits:"			,    outputs.class_queries_logits[0, :3, :3]   )

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase_    				=							torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]   )
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,    lowerCAmelCase__			,    atol=1e-4   )
      print("Looks ok!"   )

      if pytorch_dump_folder_path is not None:
            print(f"""Saving 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 push_to_hub:
            print("Pushing model and image processor to the hub..."   )
            model.push_to_hub(f"""nielsr/{model_name}"""   )
            image_processor.push_to_hub(f"""nielsr/{model_name}"""   )


if __name__ == "__main__":
  lowerCamelCase      =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""maskformer-swin-tiny-ade""",
      type=str,
      help=("""Name of the MaskFormer model you'd like to convert""",),
  )
  parser.add_argument(
      """--checkpoint_path""",
      default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
      type=str,
      help="""Path to the original state dict (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  lowerCamelCase      =		parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )




