


"""simple docstring"""





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput



@dataclass
class 						snake_case_(				a__	):
     __UpperCamelCase     =    None
     __UpperCamelCase     =    None
     __UpperCamelCase     =    None
     __UpperCamelCase     =    None



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    Optional[int]=1    , UpperCamelCase_     :    Tuple=0    , UpperCamelCase_     :    Optional[int]=2    , UpperCamelCase_     :    Union[str, Any]=5_1_2    , UpperCamelCase_     :    str="cls"    , UpperCamelCase_     :    List[Any]=False    , UpperCamelCase_     :    Optional[Any]=True    , **UpperCamelCase_     :    Optional[int]    , ):
           super().__init__(pad_token_id=UpperCamelCase_    , bos_token_id=UpperCamelCase_    , eos_token_id=UpperCamelCase_    , **UpperCamelCase_ )
           lowerCAmelCase    :		str				=      project_dim
           lowerCAmelCase    :		Dict				=      pooler_fn
           lowerCAmelCase    :		str				=      learn_encoder
           lowerCAmelCase    :		str				=      use_attention_mask



class 						snake_case_(				a__	):
     __UpperCamelCase     =    [R'''pooler''', R'''logit_scale''']
     __UpperCamelCase     =    [R'''position_ids''', R'''predictions.decoder.bias''']
     __UpperCamelCase     =    '''roberta'''
     __UpperCamelCase     =    RobertaSeriesConfig






     def __init__(		self     :    Any    , UpperCamelCase_     :    List[str] ):
           super().__init__(UpperCamelCase_ )
           lowerCAmelCase    :		Union[str, Any]				=      XLMRobertaModel(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      nn.Linear(config.hidden_size    , config.project_dim )
           lowerCAmelCase    :		Union[str, Any]				=      getattr(UpperCamelCase_    , '''has_pre_transformation'''    , UpperCamelCase_ )
           if self.has_pre_transformation:
                 lowerCAmelCase    :		List[Any]				=      nn.Linear(config.hidden_size    , config.project_dim )
                 lowerCAmelCase    :		Any				=      nn.LayerNorm(config.hidden_size    , eps=config.layer_norm_eps )
           self.post_init()






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[torch.Tensor] = None    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Optional[bool] = None    , UpperCamelCase_     :    Optional[bool] = None    , ):
           lowerCAmelCase    :		Optional[Any]				=      return_dict if return_dict is not None else self.config.use_return_dict

           lowerCAmelCase    :		List[Any]				=      self.base_model(
               input_ids=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , position_ids=UpperCamelCase_    , head_mask=UpperCamelCase_    , inputs_embeds=UpperCamelCase_    , encoder_hidden_states=UpperCamelCase_    , encoder_attention_mask=UpperCamelCase_    , output_attentions=UpperCamelCase_    , output_hidden_states=True if self.has_pre_transformation else output_hidden_states    , return_dict=UpperCamelCase_    , )

           if self.has_pre_transformation:
                 lowerCAmelCase    :		Optional[Any]				=      outputs['''hidden_states'''][-2]
                 lowerCAmelCase    :		List[str]				=      self.pre_LN(UpperCamelCase_ )
                 lowerCAmelCase    :		Optional[int]				=      self.transformation_pre(UpperCamelCase_ )

                 return TransformationModelOutput(
                     projection_state=UpperCamelCase_    , last_hidden_state=outputs.last_hidden_state    , hidden_states=outputs.hidden_states    , attentions=outputs.attentions    , )
           else:
                 lowerCAmelCase    :		str				=      self.transformation(outputs.last_hidden_state )
                 return TransformationModelOutput(
                     projection_state=UpperCamelCase_    , last_hidden_state=outputs.last_hidden_state    , hidden_states=outputs.hidden_states    , attentions=outputs.attentions    , )






"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


