
'''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



'''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__)

