


"""simple docstring"""







import copy
from typing import Dict, Optional

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


lowerCamelCase_  :  Optional[int]     		=    logging.get_logger(__name__)

lowerCamelCase_  :  Optional[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  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "oneformer"
 __lowerCAmelCase					   =    {"hidden_size": "hidden_dim"}


 def __init__(						self					,       __A = None					,       __A = 255					,       __A = 150					,       __A = 0.1					,       __A = 2.0					,       __A = 5.0					,       __A = 5.0					,       __A = 0.5					,       __A = 0.07					,       __A = 1_2544					,       __A = 3.0					,       __A = 0.75					,       __A = 0.02					,       __A = 1.0					,       __A = 1E-0_5					,       __A = False					,       __A = True					,       __A = True					,       __A = [4, 8, 16, 32]					,       __A = 77					,       __A = 256					,       __A = 77					,       __A = 6					,       __A = 4_9408					,       __A = 2					,       __A = 16					,       __A = 256					,       __A = 256					,       __A = 256					,       __A = 1024					,       __A = "GN"					,       __A = 6					,       __A = 10					,       __A = True					,       __A = 8					,       __A = 0.1					,       __A = 2048					,       __A = False					,       __A = False					,       __A = 2					,       __A = 4					,       **__A					,       )   ->	Optional[int]:
     if backbone_config is None:
         logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''		)
         a											=CONFIG_MAPPING['''swin'''](
             image_size=224					,       in_channels=3					,       patch_size=4					,       embed_dim=96					,       depths=[2, 2, 6, 2]					,       num_heads=[3, 6, 12, 24]					,       window_size=7					,       drop_path_rate=0.3					,       use_absolute_embeddings=__A					,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					,       )
     elif isinstance(__A					,       __A		):
         a											=backbone_config.get('''model_type'''		)
         a											=CONFIG_MAPPING[backbone_model_type]
         a											=config_class.from_dict(__A		)

     a											=backbone_config

     a											=ignore_value
     a											=num_queries
     a											=no_object_weight
     a											=class_weight
     a											=mask_weight
     a											=dice_weight
     a											=contrastive_weight
     a											=contrastive_temperature
     a											=train_num_points
     a											=oversample_ratio
     a											=importance_sample_ratio
     a											=init_std
     a											=init_xavier_std
     a											=layer_norm_eps
     a											=is_training
     a											=use_auxiliary_loss
     a											=output_auxiliary_logits
     a											=strides
     a											=task_seq_len
     a											=text_encoder_width
     a											=text_encoder_context_length
     a											=text_encoder_num_layers
     a											=text_encoder_vocab_size
     a											=text_encoder_proj_layers
     a											=text_encoder_n_ctx
     a											=conv_dim
     a											=mask_dim
     a											=hidden_dim
     a											=encoder_feedforward_dim
     a											=norm
     a											=encoder_layers
     a											=decoder_layers
     a											=use_task_norm
     a											=num_attention_heads
     a											=dropout
     a											=dim_feedforward
     a											=pre_norm
     a											=enforce_input_proj
     a											=query_dec_layers
     a											=common_stride
     a											=decoder_layers

     super().__init__(**__A		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Dict[str, any]:
     a											=copy.deepcopy(self.__dict__		)
     a											=self.backbone_config.to_dict()
     a											=self.__class__.model_type
     return output



"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask