
'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: int=1							):



						"""simple docstring"""





						if n_shave_prefix_segments >= 0:
												return ".".join(path.split('''.'''							)[n_shave_prefix_segments:]							)
						else:
												return ".".join(path.split('''.'''							)[:n_shave_prefix_segments]							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: int=0							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       []
						for old_item in old_list:
												lowercase_	:     Optional[Any]													=       old_item.replace('''in_layers.0'''      ,      '''norm1'''							)
												lowercase_	:     Dict													=       new_item.replace('''in_layers.2'''      ,      '''conv1'''							)

												lowercase_	:     int													=       new_item.replace('''out_layers.0'''      ,      '''norm2'''							)
												lowercase_	:     Union[str, Any]													=       new_item.replace('''out_layers.3'''      ,      '''conv2'''							)

												lowercase_	:     List[Any]													=       new_item.replace('''emb_layers.1'''      ,      '''time_emb_proj'''							)
												lowercase_	:     int													=       new_item.replace('''skip_connection'''      ,      '''conv_shortcut'''							)

												lowercase_	:     Union[str, Any]													=       shave_segments(__SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)

												mapping.append({'''old''': old_item, '''new''': new_item}							)

						return mapping
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]=0							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       []
						for old_item in old_list:
												lowercase_	:     Union[str, Any]													=       old_item

												lowercase_	:     Optional[Any]													=       new_item.replace('''norm.weight'''      ,      '''group_norm.weight'''							)
												lowercase_	:     List[str]													=       new_item.replace('''norm.bias'''      ,      '''group_norm.bias'''							)

												lowercase_	:     Tuple													=       new_item.replace('''proj_out.weight'''      ,      '''proj_attn.weight'''							)
												lowercase_	:     List[Any]													=       new_item.replace('''proj_out.bias'''      ,      '''proj_attn.bias'''							)

												lowercase_	:     Tuple													=       shave_segments(__SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=__SCREAMING_SNAKE_CASE							)

												mapping.append({'''old''': old_item, '''new''': new_item}							)

						return mapping
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str=None      ,      __SCREAMING_SNAKE_CASE					: Dict=None      ,      __SCREAMING_SNAKE_CASE					: Dict=None							):



						"""simple docstring"""





						assert isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							), "Paths should be a list of dicts containing 'old' and 'new' keys."

						# Splits the attention layers into three variables.
						if attention_paths_to_split is not None:
												for path, path_map in attention_paths_to_split.items():
																		lowercase_	:     Tuple													=       old_checkpoint[path]
																		lowercase_	:     str													=       old_tensor.shape[0] // 3

																		lowercase_	:     List[str]													=       (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

																		lowercase_	:     List[Any]													=       old_tensor.shape[0] // config['''num_head_channels'''] // 3

																		lowercase_	:     int													=       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
																		lowercase_						, lowercase_						, lowercase_	:     Optional[int]													=       old_tensor.split(channels // num_heads      ,      dim=1							)

																		lowercase_	:     Tuple													=       query.reshape(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     Any													=       key.reshape(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     int													=       value.reshape(__SCREAMING_SNAKE_CASE							)

						for path in paths:
												lowercase_	:     List[str]													=       path['''new''']

												# These have already been assigned
												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												# Global renaming happens here
												lowercase_	:     str													=       new_path.replace('''middle_block.0'''      ,      '''mid_block.resnets.0'''							)
												lowercase_	:     List[Any]													=       new_path.replace('''middle_block.1'''      ,      '''mid_block.attentions.0'''							)
												lowercase_	:     Dict													=       new_path.replace('''middle_block.2'''      ,      '''mid_block.resnets.1'''							)

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								lowercase_	:     Dict													=       new_path.replace(replacement['''old''']      ,      replacement['''new''']							)

        # proj_attn.weight has to be converted from conv 1D to linear
												if "proj_attn.weight" in new_path:
																		lowercase_	:     Optional[Any]													=       old_checkpoint[path['''old''']][:, :, 0]
												else:
																		lowercase_	:     Optional[Any]													=       old_checkpoint[path['''old''']]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       {}

						lowercase_	:     Optional[int]													=       checkpoint['''time_embed.0.weight''']
						lowercase_	:     Optional[Any]													=       checkpoint['''time_embed.0.bias''']
						lowercase_	:     Optional[Any]													=       checkpoint['''time_embed.2.weight''']
						lowercase_	:     int													=       checkpoint['''time_embed.2.bias''']

						lowercase_	:     Optional[Any]													=       checkpoint['''input_blocks.0.0.weight''']
						lowercase_	:     List[str]													=       checkpoint['''input_blocks.0.0.bias''']

						lowercase_	:     int													=       checkpoint['''out.0.weight''']
						lowercase_	:     Tuple													=       checkpoint['''out.0.bias''']
						lowercase_	:     Dict													=       checkpoint['''out.2.weight''']
						lowercase_	:     Any													=       checkpoint['''out.2.bias''']

						# Retrieves the keys for the input blocks only
						lowercase_	:     int													=       len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''input_blocks''' in layer}							)
						lowercase_	:     Union[str, Any]													=       {
						    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
						    for layer_id in range(__SCREAMING_SNAKE_CASE							)
						}

						# Retrieves the keys for the middle blocks only
						lowercase_	:     int													=       len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''middle_block''' in layer}							)
						lowercase_	:     str													=       {
						    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
						    for layer_id in range(__SCREAMING_SNAKE_CASE							)
						}

						# Retrieves the keys for the output blocks only
						lowercase_	:     List[Any]													=       len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''output_blocks''' in layer}							)
						lowercase_	:     str													=       {
						    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
						    for layer_id in range(__SCREAMING_SNAKE_CASE							)
						}

						for i in range(1      ,      __SCREAMING_SNAKE_CASE							):
												lowercase_	:     Any													=       (i - 1) // (config['''num_res_blocks'''] + 1)
												lowercase_	:     Any													=       (i - 1) % (config['''num_res_blocks'''] + 1)

												lowercase_	:     List[str]													=       [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
												lowercase_	:     List[Any]													=       [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

												if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
																		lowercase_	:     List[Any]													=       checkpoint[
																		    F'''input_blocks.{i}.0.op.weight'''
																		]
																		lowercase_	:     List[str]													=       checkpoint[
																		    F'''input_blocks.{i}.0.op.bias'''
																		]
																		continue

												lowercase_	:     Union[str, Any]													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Optional[int]													=       {'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												lowercase_	:     List[Any]													=       {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
												assign_to_checkpoint(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path, resnet_op]      ,      config=__SCREAMING_SNAKE_CASE							)

												if len(__SCREAMING_SNAKE_CASE							):
																		lowercase_	:     Optional[Any]													=       renew_attention_paths(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     List[str]													=       {
																		    '''old''': F'''input_blocks.{i}.1''',
																		    '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
																		}
																		lowercase_	:     Dict													=       {
																		    F'''input_blocks.{i}.1.qkv.bias''': {
																		        '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																		        '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																		        '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																		    },
																		    F'''input_blocks.{i}.1.qkv.weight''': {
																		        '''key''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																		        '''query''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																		        '''value''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																		    },
																		}
																		assign_to_checkpoint(
																		    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=__SCREAMING_SNAKE_CASE      ,      config=__SCREAMING_SNAKE_CASE      ,      )

						lowercase_	:     int													=       middle_blocks[0]
						lowercase_	:     Union[str, Any]													=       middle_blocks[1]
						lowercase_	:     Optional[Any]													=       middle_blocks[2]

						lowercase_	:     Dict													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
						assign_to_checkpoint(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      config=__SCREAMING_SNAKE_CASE							)

						lowercase_	:     List[str]													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
						assign_to_checkpoint(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      config=__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Dict													=       renew_attention_paths(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[Any]													=       {
						    '''middle_block.1.qkv.bias''': {
						        '''key''': '''mid_block.attentions.0.key.bias''',
						        '''query''': '''mid_block.attentions.0.query.bias''',
						        '''value''': '''mid_block.attentions.0.value.bias''',
						    },
						    '''middle_block.1.qkv.weight''': {
						        '''key''': '''mid_block.attentions.0.key.weight''',
						        '''query''': '''mid_block.attentions.0.query.weight''',
						        '''value''': '''mid_block.attentions.0.value.weight''',
						    },
						}
						assign_to_checkpoint(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      attention_paths_to_split=__SCREAMING_SNAKE_CASE      ,      config=__SCREAMING_SNAKE_CASE							)

						for i in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     List[Any]													=       i // (config['''num_res_blocks'''] + 1)
												lowercase_	:     int													=       i % (config['''num_res_blocks'''] + 1)
												lowercase_	:     Optional[Any]													=       [shave_segments(__SCREAMING_SNAKE_CASE      ,      2							) for name in output_blocks[i]]
												lowercase_	:     Any													=       {}

												for layer in output_block_layers:
																		lowercase_						, lowercase_	:     Any													=       layer.split('''.'''							)[0], shave_segments(__SCREAMING_SNAKE_CASE      ,      1							)
																		if layer_id in output_block_list:
																								output_block_list[layer_id].append(__SCREAMING_SNAKE_CASE							)
																		else:
																								lowercase_	:     Optional[Any]													=       [layer_name]

												if len(__SCREAMING_SNAKE_CASE							) > 1:
																		lowercase_	:     List[Any]													=       [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																		lowercase_	:     Union[str, Any]													=       [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

																		lowercase_	:     Optional[Any]													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE							)
																		lowercase_	:     List[Any]													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE							)

																		lowercase_	:     List[Any]													=       {'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
																		assign_to_checkpoint(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path]      ,      config=__SCREAMING_SNAKE_CASE							)

																		if ["conv.weight", "conv.bias"] in output_block_list.values():
																								lowercase_	:     Any													=       list(output_block_list.values()							).index(['''conv.weight''', '''conv.bias''']							)
																								lowercase_	:     str													=       checkpoint[
																								    F'''output_blocks.{i}.{index}.conv.weight'''
																								]
																								lowercase_	:     Union[str, Any]													=       checkpoint[
																								    F'''output_blocks.{i}.{index}.conv.bias'''
																								]

																								# Clear attentions as they have been attributed above.
																								if len(__SCREAMING_SNAKE_CASE							) == 2:
																														lowercase_	:     Tuple													=       []

																		if len(__SCREAMING_SNAKE_CASE							):
																								lowercase_	:     Union[str, Any]													=       renew_attention_paths(__SCREAMING_SNAKE_CASE							)
																								lowercase_	:     Any													=       {
																								    '''old''': F'''output_blocks.{i}.1''',
																								    '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																								}
																								lowercase_	:     List[str]													=       {
																								    F'''output_blocks.{i}.1.qkv.bias''': {
																								        '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																								        '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																								        '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																								    },
																								    F'''output_blocks.{i}.1.qkv.weight''': {
																								        '''key''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																								        '''query''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																								        '''value''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																								    },
																								}
																								assign_to_checkpoint(
																								    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      additional_replacements=[meta_path]      ,      attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions							) else None      ,      config=__SCREAMING_SNAKE_CASE      ,      )
												else:
																		lowercase_	:     int													=       renew_resnet_paths(__SCREAMING_SNAKE_CASE      ,      n_shave_prefix_segments=1							)
																		for path in resnet_0_paths:
																								lowercase_	:     Optional[int]													=       '''.'''.join(['''output_blocks''', str(__SCREAMING_SNAKE_CASE							), path['''old''']]							)
																								lowercase_	:     Union[str, Any]													=       '''.'''.join(['''up_blocks''', str(__SCREAMING_SNAKE_CASE							), '''resnets''', str(__SCREAMING_SNAKE_CASE							), path['''new''']]							)

																								lowercase_	:     Optional[Any]													=       checkpoint[old_path]

						return new_checkpoint


if __name__ == "__main__":
							_lowercase :  Union[str, Any]    							=       argparse.ArgumentParser()

							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
							)

							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the architecture.",
							)

							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

							_lowercase :  int    							=       parser.parse_args()

							_lowercase :  Dict    							=       torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														_lowercase :  str    							=       json.loads(f.read())

							_lowercase :  Union[str, Any]    							=       convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							_lowercase :  Any    							=       UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														_lowercase :  str    							=       DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
														_lowercase :  List[str]    							=       VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

														_lowercase :  Optional[Any]    							=       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_      : Optional[int]  					=       {
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase_      : List[str]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mask2former'''
      snake_case__				:     Any        =						['''swin''']
      snake_case__				:     str        =						{'''hidden_size''': '''hidden_dim'''}
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict] = None     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       str = "relu"     ,							SCREAMING_SNAKE_CASE__				:       int = 6     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0     ,							SCREAMING_SNAKE_CASE__				:       int = 8     ,							SCREAMING_SNAKE_CASE__				:       float = 0.0     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       int = 4     ,							SCREAMING_SNAKE_CASE__				:       int = 2_5_5     ,							SCREAMING_SNAKE_CASE__				:       int = 1_0_0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.1     ,							SCREAMING_SNAKE_CASE__				:       float = 2.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       float = 5.0     ,							SCREAMING_SNAKE_CASE__				:       int = 1_2_5_4_4     ,							SCREAMING_SNAKE_CASE__				:       float = 3.0     ,							SCREAMING_SNAKE_CASE__				:       float = 0.75     ,							SCREAMING_SNAKE_CASE__				:       float = 0.02     ,							SCREAMING_SNAKE_CASE__				:       float = 1.0     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       List[int] = [4, 8, 1_6, 3_2]     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'     )
                    a_					:		Dict							=  CONFIG_MAPPING['swin'](
                        image_size=2_2_4     ,							in_channels=3     ,							patch_size=4     ,							embed_dim=9_6     ,							depths=[2, 2, 1_8, 2]     ,							num_heads=[3, 6, 1_2, 2_4]     ,							window_size=7     ,							drop_path_rate=0.3     ,							use_absolute_embeddings=SCREAMING_SNAKE_CASE__     ,							out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,							)

             if isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Any							=  backbone_config.pop('model_type'     )
                    a_					:		Optional[Any]							=  CONFIG_MAPPING[backbone_model_type]
                    a_					:		List[str]							=  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 Mask2Former. """
                        F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

             a_					:		Dict							=  backbone_config
             a_					:		List[str]							=  feature_size
             a_					:		List[str]							=  mask_feature_size
             a_					:		int							=  hidden_dim
             a_					:		Dict							=  encoder_feedforward_dim
             a_					:		str							=  activation_function
             a_					:		List[str]							=  encoder_layers
             a_					:		List[str]							=  decoder_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		str							=  dropout
             a_					:		Tuple							=  dim_feedforward
             a_					:		List[str]							=  pre_norm
             a_					:		Optional[int]							=  enforce_input_projection
             a_					:		Any							=  common_stride
             a_					:		Optional[int]							=  ignore_value
             a_					:		int							=  num_queries
             a_					:		Tuple							=  no_object_weight
             a_					:		Dict							=  class_weight
             a_					:		Optional[int]							=  mask_weight
             a_					:		Optional[int]							=  dice_weight
             a_					:		str							=  train_num_points
             a_					:		List[str]							=  oversample_ratio
             a_					:		List[Any]							=  importance_sample_ratio
             a_					:		Any							=  init_std
             a_					:		Union[str, Any]							=  init_xavier_std
             a_					:		Union[str, Any]							=  use_auxiliary_loss
             a_					:		Dict							=  feature_strides
             a_					:		List[str]							=  output_auxiliary_logits
             a_					:		Dict							=  decoder_layers

             super().__init__(**SCREAMING_SNAKE_CASE__     )
      @classmethod
      def        SCREAMING_SNAKE_CASE							(       cls				:       str     ,							SCREAMING_SNAKE_CASE__				:       PretrainedConfig     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			List[Any]:
             return cls(
                 backbone_config=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict[str, any]:
             a_					:		Optional[int]							=  copy.deepcopy(self.__dict__     )
             a_					:		List[Any]							=  self.backbone_config.to_dict()
             a_					:		Optional[Any]							=  self.__class__.model_type
             return output


