






"""simple docstring"""

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__			):





		'''simple docstring'''






		a_	:				List[Any]            =							RobertaConfig
		a_	:				Optional[Any]            =							"""roberta"""





		def __init__(   self							:	Optional[Any]						,			a_							:	Optional[Any]	):
				super().__init__(__a	)

				lowerCAmelCase_   :							Optional[Any]									=			RobertaEmbeddings(__a	)
				self.init_weights()







@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. """ ,							A__ ,							)
class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		a_	:				str            =							RobertaConfig
		a_	:				int            =							"""roberta"""





		def __init__(   self							:	str						,			a_							:	Union[str, Any]	):
				super().__init__(__a	)
				lowerCAmelCase_   :							Optional[int]									=			config.num_labels
				lowerCAmelCase_   :							Optional[int]									=			config.num_hidden_layers

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





		@add_start_docstrings_to_model_forward(__a	)
		def    lowerCamelCase       (   self							:	int						,			a_							:	Optional[Any]=None						,			a_							:	List[Any]=None						,			a_							:	Optional[Any]=None						,			a_							:	Any=None						,			a_							:	Dict=None						,			a_							:	int=None						,			a_							:	Tuple=None						,			a_							:	Tuple=-1						,			a_							:	Any=False						,			):
				lowerCAmelCase_   :							Union[str, Any]									=			self.num_layers
				try:
						lowerCAmelCase_   :							List[Any]									=			self.roberta(
						    __a						,			attention_mask=__a						,			token_type_ids=__a						,			position_ids=__a						,			head_mask=__a						,			inputs_embeds=__a						,			)

						lowerCAmelCase_   :							List[Any]									=			outputs[1]

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

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

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

						if train_highway:
								lowerCAmelCase_   :							int									=			(sum(highway_losses[:-1]	),) + outputs
								# exclude the final highway, of course
						else:
								lowerCAmelCase_   :							Any									=			(loss,) + outputs
				if not self.training:
						lowerCAmelCase_   :							Optional[Any]									=			outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
								lowerCAmelCase_   :							Optional[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





from __future__ import annotations

import bisect






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							int    =    len(_lowerCamelCase  )

							while lo < hi:
														_lowerCAmelCase     :							Optional[Any]    =    lo + (hi - lo) // 2
														if sorted_collection[mid] < item:
																					_lowerCAmelCase     :							Union[str, Any]    =    mid + 1
														else:
																					_lowerCAmelCase     :							str    =    mid

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							if hi < 0:
														_lowerCAmelCase     :							str    =    len(_lowerCamelCase  )

							while lo < hi:
														_lowerCAmelCase     :							Tuple    =    lo + (hi - lo) // 2
														if sorted_collection[mid] <= item:
																					_lowerCAmelCase     :							Dict    =    mid + 1
														else:
																					_lowerCAmelCase     :							str    =    mid

							return lo






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_left(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 0				, _lowerCamelCase = -1  ):





							'''simple docstring'''




							sorted_collection.insert(bisect_right(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    0
							_lowerCAmelCase     :							Union[str, Any]    =    len(_lowerCamelCase  ) - 1

							while left <= right:
														_lowerCAmelCase     :							int    =    left + (right - left) // 2
														_lowerCAmelCase     :							int    =    sorted_collection[midpoint]
														if current_item == item:
																					return midpoint
														elif item < current_item:
																					_lowerCAmelCase     :							str    =    midpoint - 1
														else:
																					_lowerCAmelCase     :							Any    =    midpoint + 1
							return None






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    bisect.bisect_left(_lowerCamelCase				, _lowerCamelCase  )
							if index != len(_lowerCamelCase  ) and sorted_collection[index] == item:
														return index
							return None






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if right < left:
														return None

							_lowerCAmelCase     :							Optional[int]    =    left + (right - left) // 2

							if sorted_collection[midpoint] == item:
														return midpoint
							elif sorted_collection[midpoint] > item:
														return binary_search_by_recursion(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, midpoint - 1  )
							else:
														return binary_search_by_recursion(_lowerCamelCase				, _lowerCamelCase				, midpoint + 1				, _lowerCamelCase  )


if __name__ == "__main__":
						_snake_case		=  input("Enter numbers separated by comma:\n").strip()
						_snake_case		=  sorted(int(item) for item in user_input.split(","))
						_snake_case		=  int(input("Enter a single number to be found in the list:\n"))
						_snake_case		=  binary_search(collection, target)
						if result is None:
												print(f'''{target} was not found in {collection}.''')
						else:
												print(f'''{target} was found at position {result} in {collection}.''')
