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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[int]						=      RobertaConfig
    a__	:						List[str]						=      """roberta"""







    def __init__(	self , __lowercase)       ->	str:
        super().__init__(__lowercase)

        __UpperCamelCase     :Dict			  =		RobertaEmbeddings(__lowercase)
        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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[Any]						=      RobertaConfig
    a__	:						str						=      """roberta"""







    def __init__(	self , __lowercase)       ->	Any:
        super().__init__(__lowercase)
        __UpperCamelCase     :List[str]			  =		config.num_labels
        __UpperCamelCase     :List[Any]			  =		config.num_hidden_layers

        __UpperCamelCase     :Optional[Any]			  =		DeeRobertaModel(__lowercase)
        __UpperCamelCase     :Any			  =		nn.Dropout(config.hidden_dropout_prob)
        __UpperCamelCase     :str			  =		nn.Linear(config.hidden_size , self.config.num_labels)







    @add_start_docstrings_to_model_forward(__lowercase)
    def 			UpperCamelCase__   (	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=-1 , __lowercase=False , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		self.num_layers
        try:
            __UpperCamelCase     :Tuple			  =		self.roberta(
                __lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , position_ids=__lowercase , head_mask=__lowercase , inputs_embeds=__lowercase , )

            __UpperCamelCase     :int			  =		outputs[1]

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

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

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

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






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str  ,  **lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						Union[str, Any] =					AutoConfig.from_pretrained(lowerCamelCase  ,  **lowerCamelCase	)
 UpperCamelCase_					:						str =					AutoModelForSeqaSeqLM.from_config(lowerCamelCase	)
 model.save_pretrained(lowerCamelCase	)
 AutoTokenizer.from_pretrained(lowerCamelCase	).save_pretrained(lowerCamelCase	)
 return model


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



