
"""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      (       lowerCAmelCase_					,						lowerCAmelCase_					,						lowerCAmelCase_      ):







       @register_to_config
       def __init__(       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_ = False				,):



          '''simple docstring'''

          super().__init__()

          _UpperCAmelCase 						=       nn.Linear(lowerCamelCase_				,lowerCamelCase_				,bias=lowerCamelCase_		)

          _UpperCAmelCase 						=       nn.Embedding(lowerCamelCase_				,lowerCamelCase_		)
          _UpperCAmelCase 						=       False

          _UpperCAmelCase 						=       nn.Dropout(p=lowerCamelCase_		)

          _UpperCAmelCase 						=       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_				,)
          _UpperCAmelCase 						=       nn.ModuleList()
          for lyr_num in range(lowerCamelCase_		):
             _UpperCAmelCase 						=       TaBlock(lowerCamelCase_		)
             self.encoders.append(lowerCamelCase_		)

          _UpperCAmelCase 						=       TaLayerNorm(lowerCamelCase_		)
          _UpperCAmelCase 						=       nn.Dropout(p=lowerCamelCase_		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.input_proj(lowerCamelCase_		)

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

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

          _UpperCAmelCase 						=       self.dropout_pre(lowerCamelCase_		)

          # inverted the attention mask
          _UpperCAmelCase 						=       encoder_inputs.size()
          _UpperCAmelCase 						=       self.get_extended_attention_mask(lowerCamelCase_				,lowerCamelCase_		)

          for lyr in self.encoders:
             _UpperCAmelCase 						=       lyr(lowerCamelCase_				,lowerCamelCase_		)[0]
          _UpperCAmelCase 						=       self.layer_norm(lowerCamelCase_		)

          return self.dropout_post(lowerCamelCase_		), encoder_inputs_mask



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[int]					)  ->							int:

   # validate:
   if not isinstance(a_     ,	a_					) or not all(
       isinstance(a_     ,	a_					) and item >= 0 for item in citations					):
      raise ValueError("The citations should be a list of non negative integers."					)

   citations.sort()
   _UpperCAmelCase 						=       len(a_					)

   for i in range(a_					):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
  import doctest

  doctest.testmod()

