





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

   return new_state_dict
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : Union[str, Any]			):
   # 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)
         lowercase_      :Any         =			state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'			)
         lowercase_      :Union[str, Any]         =			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_      :List[Any]         =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowercase_      :Optional[Any]         =			kv_bias[: config.hidden_sizes[i]]
         lowercase_      :Any         =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowercase_      :str         =			kv_bias[
             config.hidden_sizes[i] :
         ]
def 							UpperCAmelCase_  (						):
   lowercase_      :str         =			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_      :Optional[Any]         =			Image.open(requests.get(__lowerCamelCase     ,stream=__lowerCamelCase			).raw			)

   return image
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : List[str]     ,__lowerCamelCase : List[str]			):
   lowercase_      :str         =			SegformerConfig()
   lowercase_      :List[Any]         =			False

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

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

   # load image processor (only resize + normalize)
   lowercase_      :Tuple         =			SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,keep_ratio=__lowerCamelCase     ,align=__lowerCamelCase     ,do_random_crop=__lowerCamelCase			)

   # prepare image
   lowercase_      :List[str]         =			prepare_img()
   lowercase_      :Optional[int]         =			image_processor(images=__lowerCamelCase     ,return_tensors="pt"			).pixel_values

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

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

   # rename keys
   lowercase_      :Dict         =			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:
      lowercase_      :int         =			False
      lowercase_      :Optional[int]         =			SegformerForImageClassification(__lowerCamelCase			)
   else:
      lowercase_      :Any         =			SegformerForSemanticSegmentation(__lowerCamelCase			)
   model.load_state_dict(__lowerCamelCase			)
   model.eval()

   # forward pass
   lowercase_      :int         =			model(__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			outputs.logits

   # set expected_slice based on model name
   # ADE20k checkpoints
   if model_name == "segformer.b0.512x512.ade.160k":
      lowercase_      :List[Any]         =			torch.tensor(
          [
              [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
              [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
              [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
          ]			)
   elif model_name == "segformer.b1.512x512.ade.160k":
      lowercase_      :Optional[Any]         =			torch.tensor(
          [
              [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
              [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
              [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
          ]			)
   elif model_name == "segformer.b2.512x512.ade.160k":
      lowercase_      :Any         =			torch.tensor(
          [
              [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
              [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
              [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
          ]			)
   elif model_name == "segformer.b3.512x512.ade.160k":
      lowercase_      :str         =			torch.tensor(
          [
              [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
              [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
              [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
          ]			)
   elif model_name == "segformer.b4.512x512.ade.160k":
      lowercase_      :Optional[Any]         =			torch.tensor(
          [
              [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
              [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
              [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
          ]			)
   elif model_name == "segformer.b5.640x640.ade.160k":
      lowercase_      :List[str]         =			torch.tensor(
          [
              [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
              [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
              [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
          ]			)
   # Cityscapes checkpoints
   elif model_name == "segformer.b0.1024x1024.city.160k":
      lowercase_      :Union[str, Any]         =			torch.tensor(
          [
              [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
              [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
              [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
          ]			)
   elif model_name == "segformer.b0.512x1024.city.160k":
      lowercase_      :List[Any]         =			torch.tensor(
          [
              [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
              [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
              [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
          ]			)
   elif model_name == "segformer.b0.640x1280.city.160k":
      lowercase_      :Optional[int]         =			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":
      lowercase_      :Any         =			torch.tensor(
          [
              [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
              [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
              [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
          ]			)
   elif model_name == "segformer.b1.1024x1024.city.160k":
      lowercase_      :Optional[Any]         =			torch.tensor(
          [
              [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
              [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
              [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
          ]			)
   elif model_name == "segformer.b2.1024x1024.city.160k":
      lowercase_      :List[str]         =			torch.tensor(
          [
              [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
              [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
              [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
          ]			)
   elif model_name == "segformer.b3.1024x1024.city.160k":
      lowercase_      :List[Any]         =			torch.tensor(
          [
              [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
              [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
              [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
          ]			)
   elif model_name == "segformer.b4.1024x1024.city.160k":
      lowercase_      :Optional[Any]         =			torch.tensor(
          [
              [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
              [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
              [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
          ]			)
   elif model_name == "segformer.b5.1024x1024.city.160k":
      lowercase_      :Union[str, Any]         =			torch.tensor(
          [
              [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
              [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
              [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
          ]			)
   else:
      lowercase_      :Optional[int]         =			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 :					str 							=argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''segformer.b0.512x512.ade.160k''',
     type=str,
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 lowerCAmelCase :					List[str] 							=parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


