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