





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(						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_):

  """simple docstring"""







  @register_to_config
  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase = False  ,    )-> List[Any]:
     super().__init__()

     __A				 =       nn.Linear(lowercase_  ,    lowercase_  ,    bias=lowercase_     )

     __A				 =       nn.Embedding(lowercase_  ,    lowercase_     )
     __A				 =       False

     __A				 =       nn.Dropout(p=lowercase_     )

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

     __A				 =       TaLayerNorm(lowercase_     )
     __A				 =       nn.Dropout(p=lowercase_     )





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Union[str, Any]:
     __A				 =       self.input_proj(lowercase_     )

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

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

     __A				 =       self.dropout_pre(lowercase_     )

     # inverted the attention mask
     __A				 =       encoder_inputs.size()
     __A				 =       self.get_extended_attention_mask(lowercase_  ,    lowercase_     )

     for lyr in self.encoders:
        __A				 =       lyr(lowercase_  ,    lowercase_     )[0]
     __A				 =       self.layer_norm(lowercase_     )

     return self.dropout_post(lowercase_     ), encoder_inputs_mask








from __future__ import annotations
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						list:


   '''simple docstring'''







   __A				 =       [None] * (len(snake_case     ) + len(snake_case     ))

   __A				 =       0  # pointer to current index for left Half
   __A				 =       0  # pointer to current index for the right Half
   __A				 =       0  # pointer to current index for the sorted array Half

   while pointera < len(snake_case     ) and pointera < len(snake_case     ):
      if left_half[pointera] < right_half[pointera]:
         __A				 =       left_half[pointera]
         pointera += 1
         index += 1
      else:
         __A				 =       right_half[pointera]
         pointera += 1
         index += 1
   while pointera < len(snake_case     ):
      __A				 =       left_half[pointera]
      pointera += 1
      index += 1

   while pointera < len(snake_case     ):
      __A				 =       right_half[pointera]
      pointera += 1
      index += 1

   return sorted_array
def 				__UpperCamelCase  (		snake_case     )  ->						list:


   '''simple docstring'''







   if len(snake_case     ) <= 1:
      return array
   # the actual formula to calculate the middle element = left + (right - left) // 2
   # this avoids integer overflow in case of large N
   __A				 =       0 + (len(snake_case     ) - 0) // 2

   # Split the array into halves till the array length becomes equal to One
   # merge the arrays of single length returned by mergeSort function and
   # pass them into the merge arrays function which merges the array
   __A				 =       array[:middle]
   __A				 =       array[middle:]

   return merge(merge_sort(snake_case     )    ,	merge_sort(snake_case     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

