
"""simple docstring"""

def       lowercase__  (   lowerCAmelCase			:   list	)	-> list:






    """simple docstring"""




    UpperCAmelCase       =   len(lowerCAmelCase	)
    for cycle_start in range(0	,			array_len - 1	):
        UpperCAmelCase       =   array[cycle_start]

        UpperCAmelCase       =   cycle_start
        for i in range(cycle_start + 1	,			lowerCAmelCase	):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        UpperCAmelCase     ,							UpperCAmelCase       =   item, array[pos]
        while pos != cycle_start:
            UpperCAmelCase       =   cycle_start
            for i in range(cycle_start + 1	,			lowerCAmelCase	):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            UpperCAmelCase     ,							UpperCAmelCase       =   item, array[pos]

    return array


if __name__ == "__main__":
   assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
   assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]








"""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        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




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

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

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

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

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

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




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

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

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

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

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

          return self.dropout_post(lowercase_							), encoder_inputs_mask



