





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''',	_a,	)
class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	List[Any]			     = RobertaConfig
			lowerCamelCase_	:	Union[str, Any]			     = '''roberta'''






			def __init__(self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							super().__init__(__magic_name__       )

							snake_case_		:       Optional[Any]           =    RobertaEmbeddings(__magic_name__       )
							self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. ''',	_a,	)
class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[Any]			     = RobertaConfig
			lowerCamelCase_	:	Optional[Any]			     = '''roberta'''






			def __init__(self   ,				__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							super().__init__(__magic_name__       )
							snake_case_		:       Any           =    config.num_labels
							snake_case_		:       int           =    config.num_hidden_layers

							snake_case_		:       List[Any]           =    DeeRobertaModel(__magic_name__       )
							snake_case_		:       Optional[int]           =    nn.Dropout(config.hidden_dropout_prob       )
							snake_case_		:       Union[str, Any]           =    nn.Linear(config.hidden_size   ,				self.config.num_labels       )






			@add_start_docstrings_to_model_forward(__magic_name__       )
			def      lowerCamelCase    (self   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=None   ,				__magic_name__=-1   ,				__magic_name__=False   ,				)	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.num_layers
							try:
											snake_case_		:       Any           =    self.roberta(
											    __magic_name__   ,				attention_mask=__magic_name__   ,				token_type_ids=__magic_name__   ,				position_ids=__magic_name__   ,				head_mask=__magic_name__   ,				inputs_embeds=__magic_name__   ,				)

											snake_case_		:       Optional[int]           =    outputs[1]

											snake_case_		:       Any           =    self.dropout(__magic_name__       )
											snake_case_		:       Any           =    self.classifier(__magic_name__       )
											snake_case_		:       Union[str, Any]           =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
											snake_case_		:       Tuple           =    e.message
											snake_case_		:       int           =    e.exit_layer
											snake_case_		:       List[Any]           =    outputs[0]

							if not self.training:
											snake_case_		:       int           =    entropy(__magic_name__       )
											snake_case_		:       Dict           =    []
											snake_case_		:       List[Any]           =    []
							if labels is not None:
											if self.num_labels == 1:
															#  We are doing regression
															snake_case_		:       Union[str, Any]           =    MSELoss()
															snake_case_		:       List[Any]           =    loss_fct(logits.view(-1       )   ,				labels.view(-1       )       )
											else:
															snake_case_		:       int           =    CrossEntropyLoss()
															snake_case_		:       Tuple           =    loss_fct(logits.view(-1   ,				self.num_labels       )   ,				labels.view(-1       )       )

											# work with highway exits
											snake_case_		:       Dict           =    []
											for highway_exit in outputs[-1]:
															snake_case_		:       int           =    highway_exit[0]
															if not self.training:
																			highway_logits_all.append(__magic_name__       )
																			highway_entropy.append(highway_exit[2]       )
															if self.num_labels == 1:
																			#  We are doing regression
																			snake_case_		:       Dict           =    MSELoss()
																			snake_case_		:       Optional[Any]           =    loss_fct(highway_logits.view(-1       )   ,				labels.view(-1       )       )
															else:
																			snake_case_		:       Optional[Any]           =    CrossEntropyLoss()
																			snake_case_		:       Dict           =    loss_fct(highway_logits.view(-1   ,				self.num_labels       )   ,				labels.view(-1       )       )
															highway_losses.append(__magic_name__       )

											if train_highway:
															snake_case_		:       Any           =    (sum(highway_losses[:-1]       ),) + outputs
															# exclude the final highway, of course
											else:
															snake_case_		:       int           =    (loss,) + outputs
							if not self.training:
											snake_case_		:       Tuple           =    outputs + ((original_entropy, highway_entropy), exit_layer)
											if output_layer >= 0:
															snake_case_		:       List[Any]           =    (
															    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
															)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), entropy





def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))