



"""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). """      ,		_lowerCAmelCase      ,		)
class 			_snake_case     (   _lowerCAmelCase):
				UpperCamelCase__			:   Tuple       						=RobertaConfig
				UpperCamelCase__			:   Optional[int]       						="""roberta"""




				def __init__(  self   :  Optional[int],   __lowercase   :  Any  ):
							super().__init__(SCREAMING_SNAKE_CASE_  )

							lowercase__   					=			RobertaEmbeddings(SCREAMING_SNAKE_CASE_  )
							self.init_weights()
@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """      ,		_lowerCAmelCase      ,		)
class 			_snake_case     (   _lowerCAmelCase):
				UpperCamelCase__			:   Optional[Any]       						=RobertaConfig
				UpperCamelCase__			:   Union[str, Any]       						="""roberta"""




				def __init__(  self   :  Optional[int],   __lowercase   :  Union[str, Any]  ):
							super().__init__(SCREAMING_SNAKE_CASE_  )
							lowercase__   					=			config.num_labels
							lowercase__   					=			config.num_hidden_layers

							lowercase__   					=			DeeRobertaModel(SCREAMING_SNAKE_CASE_  )
							lowercase__   					=			nn.Dropout(config.hidden_dropout_prob  )
							lowercase__   					=			nn.Linear(config.hidden_size,   self.config.num_labels  )




				@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_  )
				def 							A__ (  self   :  List[str],   __lowercase   :  Dict=None,   __lowercase   :  List[str]=None,   __lowercase   :  int=None,   __lowercase   :  int=None,   __lowercase   :  str=None,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Optional[Any]=None,   __lowercase   :  str=-1,   __lowercase   :  Any=False,   ):
							lowercase__   					=			self.num_layers
							try:
										lowercase__   					=			self.roberta(
										    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   position_ids=SCREAMING_SNAKE_CASE_,   head_mask=SCREAMING_SNAKE_CASE_,   inputs_embeds=SCREAMING_SNAKE_CASE_,   )

										lowercase__   					=			outputs[1]

										lowercase__   					=			self.dropout(SCREAMING_SNAKE_CASE_  )
										lowercase__   					=			self.classifier(SCREAMING_SNAKE_CASE_  )
										lowercase__   					=			(logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										lowercase__   					=			e.message
										lowercase__   					=			e.exit_layer
										lowercase__   					=			outputs[0]

							if not self.training:
										lowercase__   					=			entropy(SCREAMING_SNAKE_CASE_  )
										lowercase__   					=			[]
										lowercase__   					=			[]
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													lowercase__   					=			MSELoss()
													lowercase__   					=			loss_fct(logits.view(-1  ),   labels.view(-1  )  )
										else:
													lowercase__   					=			CrossEntropyLoss()
													lowercase__   					=			loss_fct(logits.view(-1,   self.num_labels  ),   labels.view(-1  )  )

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

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

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




