





'''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). '						,						__magic_name__						,						)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					List[Any]						    =   RobertaConfig
       _snake_case     :					str						    =   'roberta'





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__					)

            _UpperCamelCase       =	RobertaEmbeddings(lowerCAmelCase__					)
            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. '						,						__magic_name__						,						)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Any						    =   RobertaConfig
       _snake_case     :					int						    =   'roberta'





       def __init__(							self       :		str	,			lowerCAmelCase__       :		int					)   ->					Optional[int]:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__					)
            _UpperCamelCase       =	config.num_labels
            _UpperCamelCase       =	config.num_hidden_layers

            _UpperCamelCase       =	DeeRobertaModel(lowerCAmelCase__					)
            _UpperCamelCase       =	nn.Dropout(config.hidden_dropout_prob					)
            _UpperCamelCase       =	nn.Linear(config.hidden_size	,			self.config.num_labels					)







       @add_start_docstrings_to_model_forward(lowerCAmelCase__					)
       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		Any=None	,			lowerCAmelCase__       :		Union[str, Any]=None	,			lowerCAmelCase__       :		int=None	,			lowerCAmelCase__       :		List[Any]=None	,			lowerCAmelCase__       :		List[str]=None	,			lowerCAmelCase__       :		str=None	,			lowerCAmelCase__       :		Any=None	,			lowerCAmelCase__       :		int=-1	,			lowerCAmelCase__       :		Optional[int]=False	,			)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.num_layers
            try:
                 _UpperCamelCase       =	self.roberta(
                     lowerCAmelCase__	,			attention_mask=lowerCAmelCase__	,			token_type_ids=lowerCAmelCase__	,			position_ids=lowerCAmelCase__	,			head_mask=lowerCAmelCase__	,			inputs_embeds=lowerCAmelCase__	,			)

                 _UpperCamelCase       =	outputs[1]

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

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

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

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

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










'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey







def  a__    (    lowercase :					str				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(lowercase				):
          for j in range(lowercase				):
               _UpperCamelCase       =	[255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__						:      Optional[int]	=    imread('image_data/lena.jpg', 1)

   # convert to its negative
   lowercase__						:      Union[str, Any]	=    convert_to_negative(img)

   # show result image
   imshow('negative of original image', img)
   waitKey(0)
   destroyAllWindows()



