code: 
  '''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  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase			):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @register_to_config
    def __init__(self  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : float	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : int	, lowerCAmelCase_  : str	, lowerCAmelCase_  : bool = False	, )   ->       Tuple:
          super().__init__()
  
          snake_case_						       =							nn.Linear(lowerCAmelCase_	, lowerCAmelCase_	, bias=lowerCAmelCase_    )
  
          snake_case_						       =							nn.Embedding(lowerCAmelCase_	, lowerCAmelCase_    )
          snake_case_						       =							False
  
          snake_case_						       =							nn.Dropout(p=lowerCAmelCase_    )
  
          snake_case_						       =							TaConfig(
              d_model=lowerCAmelCase_	, num_heads=lowerCAmelCase_	, d_kv=lowerCAmelCase_	, d_ff=lowerCAmelCase_	, feed_forward_proj=lowerCAmelCase_	, dropout_rate=lowerCAmelCase_	, is_decoder=lowerCAmelCase_	, is_encoder_decoder=lowerCAmelCase_	, )
          snake_case_						       =							nn.ModuleList()
          for lyr_num in range(lowerCAmelCase_    ):
                snake_case_						       =							TaBlock(lowerCAmelCase_    )
                self.encoders.append(lowerCAmelCase_    )
  
          snake_case_						       =							TaLayerNorm(lowerCAmelCase_    )
          snake_case_						       =							nn.Dropout(p=lowerCAmelCase_    )
  
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any    )   ->       str:
          snake_case_						       =							self.input_proj(lowerCAmelCase_    )
  
          # terminal relative positional encodings
          snake_case_						       =							encoder_inputs.shape[1]
          snake_case_						       =							torch.arange(lowerCAmelCase_	, device=encoder_inputs.device    )
  
          snake_case_						       =							encoder_inputs_mask.sum(-1    )
          snake_case_						       =							torch.roll(input_positions.unsqueeze(0    )	, tuple(seq_lens.tolist()    )	, dims=0    )
          x += self.position_encoding(lowerCAmelCase_    )
  
          snake_case_						       =							self.dropout_pre(lowerCAmelCase_    )
  
          # inverted the attention mask
          snake_case_						       =							encoder_inputs.size()
          snake_case_						       =							self.get_extended_attention_mask(lowerCAmelCase_	, lowerCAmelCase_    )
  
          for lyr in self.encoders:
                snake_case_						       =							lyr(lowerCAmelCase_	, lowerCAmelCase_    )[0]
          snake_case_						       =							self.layer_norm(lowerCAmelCase_    )
  
          return self.dropout_post(lowerCAmelCase_    ), encoder_inputs_mask
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  