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). """	,							UpperCamelCase_	,							)
class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		RobertaConfig
						UpperCamelCase_     =		"""roberta"""


						def __init__(       self	:				Optional[int]    ,		UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





												super().__init__(UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							RobertaEmbeddings(UpperCamelCase__						)
												self.init_weights()






@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """	,							UpperCamelCase_	,							)
class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		RobertaConfig
						UpperCamelCase_     =		"""roberta"""


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												super().__init__(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							config.num_labels
												SCREAMING_SNAKE_CASE							:    int							=							config.num_hidden_layers

												SCREAMING_SNAKE_CASE							:    List[Any]							=							DeeRobertaModel(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							nn.Dropout(config.hidden_dropout_prob						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							nn.Linear(config.hidden_size    ,		self.config.num_labels						)



						@add_start_docstrings_to_model_forward(UpperCamelCase__						)
						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				str=None    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Tuple=None    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Tuple=None    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Tuple=-1    ,		UpperCamelCase__	:				str=False    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.num_layers
												try:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.roberta(
																		    UpperCamelCase__    ,		attention_mask=UpperCamelCase__    ,		token_type_ids=UpperCamelCase__    ,		position_ids=UpperCamelCase__    ,		head_mask=UpperCamelCase__    ,		inputs_embeds=UpperCamelCase__    ,		)

																		SCREAMING_SNAKE_CASE							:    List[str]							=							outputs[1]

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

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

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

																		if train_highway:
																								SCREAMING_SNAKE_CASE							:    Dict							=							(sum(highway_losses[:-1]						),) + outputs
																								# exclude the final highway, of course
																		else:
																								SCREAMING_SNAKE_CASE							:    int							=							(loss,) + outputs
												if not self.training:
																		SCREAMING_SNAKE_CASE							:    Any							=							outputs + ((original_entropy, highway_entropy), exit_layer)
																		if output_layer >= 0:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							(
																								    (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	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





