

import copy
from typing import Dict, Optional

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


_lowerCamelCase							:  str     =				logging.get_logger(__name__)

_lowerCamelCase							:  Any     =				{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class    __UpperCAmelCase	(			lowercase_		):
   UpperCamelCase  	=	"""oneformer"""
   UpperCamelCase  	=	{"""hidden_size""": """hidden_dim"""}



   def __init__(				self				:	Any,						__A				:	Optional[Dict] = None,						__A				:	int = 2_5_5,						__A				:	int = 1_5_0,						__A				:	int = 0.1,						__A				:	float = 2.0,						__A				:	float = 5.0,						__A				:	float = 5.0,						__A				:	float = 0.5,						__A				:	float = 0.0_7,						__A				:	int = 1_2_5_4_4,						__A				:	float = 3.0,						__A				:	float = 0.7_5,						__A				:	float = 0.0_2,						__A				:	float = 1.0,						__A				:	float = 1E-05,						__A				:	bool = False,						__A				:	bool = True,						__A				:	bool = True,						__A				:	Optional[list] = [4, 8, 1_6, 3_2],						__A				:	int = 7_7,						__A				:	int = 2_5_6,						__A				:	int = 7_7,						__A				:	int = 6,						__A				:	int = 4_9_4_0_8,						__A				:	int = 2,						__A				:	int = 1_6,						__A				:	int = 2_5_6,						__A				:	int = 2_5_6,						__A				:	int = 2_5_6,						__A				:	int = 1_0_2_4,						__A				:	str = "GN",						__A				:	int = 6,						__A				:	int = 1_0,						__A				:	bool = True,						__A				:	int = 8,						__A				:	float = 0.1,						__A				:	int = 2_0_4_8,						__A				:	bool = False,						__A				:	bool = False,						__A				:	int = 2,						__A				:	int = 4,						**__A				:	List[str],						):
     if backbone_config is None:
       logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''		)
       UpperCAmelCase			:				List[Any]										=					CONFIG_MAPPING['''swin'''](
           image_size=2_2_4,						in_channels=3,						patch_size=4,						embed_dim=9_6,						depths=[2, 2, 6, 2],						num_heads=[3, 6, 1_2, 2_4],						window_size=7,						drop_path_rate=0.3,						use_absolute_embeddings=__A,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''],						)
     elif isinstance(__A,						__A		):
       UpperCAmelCase			:				Any										=					backbone_config.get('''model_type'''		)
       UpperCAmelCase			:				str										=					CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase			:				int										=					config_class.from_dict(__A		)

     UpperCAmelCase			:				List[Any]										=					backbone_config

     UpperCAmelCase			:				Any										=					ignore_value
     UpperCAmelCase			:				List[Any]										=					num_queries
     UpperCAmelCase			:				Union[str, Any]										=					no_object_weight
     UpperCAmelCase			:				int										=					class_weight
     UpperCAmelCase			:				Union[str, Any]										=					mask_weight
     UpperCAmelCase			:				Dict										=					dice_weight
     UpperCAmelCase			:				Dict										=					contrastive_weight
     UpperCAmelCase			:				Dict										=					contrastive_temperature
     UpperCAmelCase			:				Union[str, Any]										=					train_num_points
     UpperCAmelCase			:				Union[str, Any]										=					oversample_ratio
     UpperCAmelCase			:				List[Any]										=					importance_sample_ratio
     UpperCAmelCase			:				Any										=					init_std
     UpperCAmelCase			:				List[str]										=					init_xavier_std
     UpperCAmelCase			:				Any										=					layer_norm_eps
     UpperCAmelCase			:				List[str]										=					is_training
     UpperCAmelCase			:				Any										=					use_auxiliary_loss
     UpperCAmelCase			:				Tuple										=					output_auxiliary_logits
     UpperCAmelCase			:				str										=					strides
     UpperCAmelCase			:				Union[str, Any]										=					task_seq_len
     UpperCAmelCase			:				List[str]										=					text_encoder_width
     UpperCAmelCase			:				Any										=					text_encoder_context_length
     UpperCAmelCase			:				Any										=					text_encoder_num_layers
     UpperCAmelCase			:				Tuple										=					text_encoder_vocab_size
     UpperCAmelCase			:				Optional[Any]										=					text_encoder_proj_layers
     UpperCAmelCase			:				List[Any]										=					text_encoder_n_ctx
     UpperCAmelCase			:				Optional[int]										=					conv_dim
     UpperCAmelCase			:				int										=					mask_dim
     UpperCAmelCase			:				Optional[int]										=					hidden_dim
     UpperCAmelCase			:				Union[str, Any]										=					encoder_feedforward_dim
     UpperCAmelCase			:				Tuple										=					norm
     UpperCAmelCase			:				List[Any]										=					encoder_layers
     UpperCAmelCase			:				Optional[int]										=					decoder_layers
     UpperCAmelCase			:				str										=					use_task_norm
     UpperCAmelCase			:				int										=					num_attention_heads
     UpperCAmelCase			:				Any										=					dropout
     UpperCAmelCase			:				Dict										=					dim_feedforward
     UpperCAmelCase			:				str										=					pre_norm
     UpperCAmelCase			:				Dict										=					enforce_input_proj
     UpperCAmelCase			:				List[str]										=					query_dec_layers
     UpperCAmelCase			:				Tuple										=					common_stride
     UpperCAmelCase			:				int										=					decoder_layers

     super().__init__(**__A		)





   def 			__magic_name__       (				self				:	Dict		):
     UpperCAmelCase			:				Any										=					copy.deepcopy(self.__dict__		)
     UpperCAmelCase			:				Optional[Any]										=					self.backbone_config.to_dict()
     UpperCAmelCase			:				Dict										=					self.__class__.model_type
     return output











"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


