






"""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    (  _a							,	_a							,	_a  ):

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

       UpperCamelCase__         =    nn.Embedding(snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    nn.Embedding(snake_case_ ,				snake_case_      )
       UpperCamelCase__         =    False

       UpperCamelCase__         =    nn.Dropout(p=snake_case_      )

       UpperCamelCase__         =    TaConfig(
           vocab_size=snake_case_ ,				d_model=snake_case_ ,				num_heads=snake_case_ ,				d_kv=snake_case_ ,				d_ff=snake_case_ ,				dropout_rate=snake_case_ ,				feed_forward_proj=snake_case_ ,				is_decoder=snake_case_ ,				is_encoder_decoder=snake_case_ ,				)

       UpperCamelCase__         =    nn.ModuleList()
       for lyr_num in range(snake_case_      ):
           UpperCamelCase__         =    TaBlock(snake_case_      )
           self.encoders.append(snake_case_      )

       UpperCamelCase__         =    TaLayerNorm(snake_case_      )
       UpperCamelCase__         =    nn.Dropout(p=snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    self.token_embedder(snake_case_      )

       UpperCamelCase__         =    encoder_input_tokens.shape[1]
       UpperCamelCase__         =    torch.arange(snake_case_ ,				device=encoder_input_tokens.device      )
       x += self.position_encoding(snake_case_      )

       UpperCamelCase__         =    self.dropout_pre(snake_case_      )

       # inverted the attention mask
       UpperCamelCase__         =    encoder_input_tokens.size()
       UpperCamelCase__         =    self.get_extended_attention_mask(snake_case_ ,				snake_case_      )

       for lyr in self.encoders:
           UpperCamelCase__         =    lyr(snake_case_ ,				snake_case_      )[0]
       UpperCamelCase__         =    self.layer_norm(snake_case_      )

       return self.dropout_post(snake_case_      ), encoder_inputs_mask












"""simple docstring"""





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


A__					: List[str]=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a  ):
   a      :				Optional[int] 						="""timm_backbone"""

   def __init__(					self ,				snake_case_=None ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=None ,				**snake_case_ ,				)		->       Dict:
       super().__init__(**snake_case_      )
       UpperCamelCase__         =    backbone
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    features_only
       UpperCamelCase__         =    use_pretrained_backbone
       UpperCamelCase__         =    True
       UpperCamelCase__         =    out_indices if out_indices is not None else (-1,)




