
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        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = SwinConfig.from_pretrained(
		    '''microsoft/swin-tiny-patch4-window7-224''',							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
		SCREAMING_SNAKE_CASE_						 = MaskFormerConfig(backbone_config=__a    )

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

		SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(__a,							__a,							repo_type='''dataset'''    ),							'''r'''    )    )
		SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}

		return config



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = []
		# 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        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = dct.pop(__a    )
		SCREAMING_SNAKE_CASE_						 = val



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
		for i in range(len(backbone_config.depths    )    ):
				SCREAMING_SNAKE_CASE_						 = 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)
						SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'    )
						SCREAMING_SNAKE_CASE_						 = 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
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[:dim, :]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[: dim]
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[
						    dim : dim * 2, :
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[
						    dim : dim * 2
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_weight[
						    -dim :, :
						]
						SCREAMING_SNAKE_CASE_						 = in_proj_bias[-dim :]
						# fmt: on



def        _lowerCamelCase ( __a,							__a    ):
		# fmt: off
		SCREAMING_SNAKE_CASE_						 = 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)
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'    )
				SCREAMING_SNAKE_CASE_						 = 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
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE_						 = 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)
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'    )
				SCREAMING_SNAKE_CASE_						 = 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
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[: hidden_size, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[:config.hidden_size]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[hidden_size : hidden_size * 2, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE_						 = in_proj_weight[-hidden_size :, :]
				SCREAMING_SNAKE_CASE_						 = in_proj_bias[-hidden_size :]
		# fmt: on



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a,							__a = False    ):
		SCREAMING_SNAKE_CASE_						 = get_maskformer_config(__a    )

		# load original state_dict
		with open(__a,							'''rb'''    ) as f:
				SCREAMING_SNAKE_CASE_						 = pickle.load(__a    )
		SCREAMING_SNAKE_CASE_						 = data['''model''']

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

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

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

		# load 🤗 model
		SCREAMING_SNAKE_CASE_						 = MaskFormerForInstanceSegmentation(__a    )
		model.eval()

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

		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = model.load_state_dict(__a,							strict=__a    )
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(__a    ) == 0, F'Unexpected keys: {unexpected_keys}'

		# verify results
		SCREAMING_SNAKE_CASE_						 = prepare_img()
		if "vistas" in model_name:
				SCREAMING_SNAKE_CASE_						 = 65
		elif "cityscapes" in model_name:
				SCREAMING_SNAKE_CASE_						 = 65_535
		else:
				SCREAMING_SNAKE_CASE_						 = 255
		SCREAMING_SNAKE_CASE_						 = True if '''ade''' in model_name else False
		SCREAMING_SNAKE_CASE_						 = MaskFormerImageProcessor(ignore_index=__a,							reduce_labels=__a    )

		SCREAMING_SNAKE_CASE_						 = image_processor(__a,							return_tensors='''pt'''    )

		SCREAMING_SNAKE_CASE_						 = model(**__a    )

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

		if model_name == "maskformer-swin-tiny-ade":
				SCREAMING_SNAKE_CASE_						 = torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]    )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3],							__a,							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(__a    ).mkdir(exist_ok=__a    )
				model.save_pretrained(__a    )
				image_processor.save_pretrained(__a    )

		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
						)

"""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__							=				{
    '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__							=				logging.get_logger(__name__)





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''maskformer'''
							UpperCAmelCase__						     =      {'''hidden_size''': '''mask_feature_size'''}
							UpperCAmelCase__						     =      ['''resnet''', '''swin''']
							UpperCAmelCase__						     =      ['''detr''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 0.1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 0.02       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 20.0       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE_						 = SwinConfig(
											    image_size=3_84       ,       in_channels=3       ,       patch_size=4       ,       embed_dim=1_28       ,       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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = backbone_config.pop('''model_type'''    )
											SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									# 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
											SCREAMING_SNAKE_CASE_						 = DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE_						 = (
											    decoder_config.pop('''model_type'''    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) 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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = backbone_config
									SCREAMING_SNAKE_CASE_						 = decoder_config
									# main feature dimension for the model
									SCREAMING_SNAKE_CASE_						 = fpn_feature_size
									SCREAMING_SNAKE_CASE_						 = mask_feature_size
									# initializer
									SCREAMING_SNAKE_CASE_						 = init_std
									SCREAMING_SNAKE_CASE_						 = init_xavier_std
									# Hungarian matcher && loss
									SCREAMING_SNAKE_CASE_						 = cross_entropy_weight
									SCREAMING_SNAKE_CASE_						 = dice_weight
									SCREAMING_SNAKE_CASE_						 = mask_weight
									SCREAMING_SNAKE_CASE_						 = use_auxiliary_loss
									SCREAMING_SNAKE_CASE_						 = no_object_weight
									SCREAMING_SNAKE_CASE_						 = output_auxiliary_logits

									SCREAMING_SNAKE_CASE_						 = self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.num_hidden_layers
									super().__init__(**SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = copy.deepcopy(self.__dict__    )
									SCREAMING_SNAKE_CASE_						 = self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.__class__.model_type
									return output