





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   __snake_case				(			_a       ,				_a       ,				_a   ):






    '''simple docstring'''





    @register_to_config
    def __init__(   self						:		str			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		float			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		int			,						snake_case						:		str			,						snake_case						:		bool = False			,						):

      """simple docstring"""





      super().__init__()

      __lowerCAmelCase			=				nn.Linear(snake_case			,						snake_case			,						bias=snake_case							)

      __lowerCAmelCase			=				nn.Embedding(snake_case			,						snake_case							)
      __lowerCAmelCase			=				False

      __lowerCAmelCase			=				nn.Dropout(p=snake_case							)

      __lowerCAmelCase			=				TaConfig(
          d_model=snake_case			,						num_heads=snake_case			,						d_kv=snake_case			,						d_ff=snake_case			,						feed_forward_proj=snake_case			,						dropout_rate=snake_case			,						is_decoder=snake_case			,						is_encoder_decoder=snake_case			,						)
      __lowerCAmelCase			=				nn.ModuleList()
      for lyr_num in range(snake_case							):
        __lowerCAmelCase			=				TaBlock(snake_case							)
        self.encoders.append(snake_case							)

      __lowerCAmelCase			=				TaLayerNorm(snake_case							)
      __lowerCAmelCase			=				nn.Dropout(p=snake_case							)





    def 	A		(   self						:		Dict			,						snake_case						:		List[str]			,						snake_case						:		Dict							):

      """simple docstring"""





      __lowerCAmelCase			=				self.input_proj(snake_case							)

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

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

      __lowerCAmelCase			=				self.dropout_pre(snake_case							)

      # inverted the attention mask
      __lowerCAmelCase			=				encoder_inputs.size()
      __lowerCAmelCase			=				self.get_extended_attention_mask(snake_case			,						snake_case							)

      for lyr in self.encoders:
        __lowerCAmelCase			=				lyr(snake_case			,						snake_case							)[0]
      __lowerCAmelCase			=				self.layer_norm(snake_case							)

      return self.dropout_post(snake_case							), encoder_inputs_mask











def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list):



  '''simple docstring'''






  if len(__lowercase) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]),
    ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
