




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 __snake_case			(			a		):
   UpperCAmelCase__  :    List[str]          =  RobertaConfig
   UpperCAmelCase__  :    str          =  '''roberta'''







   def __init__(      self		:						Dict					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        super().__init__(_snake_case)

        UpperCAmelCase_          =							RobertaEmbeddings(_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. ''' ,  a ,  )
class __snake_case			(			a		):
   UpperCAmelCase__  :    Any          =  RobertaConfig
   UpperCAmelCase__  :    Dict          =  '''roberta'''







   def __init__(      self		:						int					,						_snake_case		:						Tuple):

        """simple docstring"""


        super().__init__(_snake_case)
        UpperCAmelCase_          =							config.num_labels
        UpperCAmelCase_          =							config.num_hidden_layers

        UpperCAmelCase_          =							DeeRobertaModel(_snake_case)
        UpperCAmelCase_          =							nn.Dropout(config.hidden_dropout_prob)
        UpperCAmelCase_          =							nn.Linear(config.hidden_size					,						self.config.num_labels)







   @add_start_docstrings_to_model_forward(_snake_case)
   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Union[str, Any]=None					,						_snake_case		:						Optional[int]=None					,						_snake_case		:						Tuple=None					,						_snake_case		:						Any=None					,						_snake_case		:						List[str]=None					,						_snake_case		:						int=None					,						_snake_case		:						int=None					,						_snake_case		:						Union[str, Any]=-1					,						_snake_case		:						List[str]=False					,						):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_layers
        try:
             UpperCAmelCase_          =							self.roberta(
                 _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						position_ids=_snake_case					,						head_mask=_snake_case					,						inputs_embeds=_snake_case					,						)

             UpperCAmelCase_          =							outputs[1]

             UpperCAmelCase_          =							self.dropout(_snake_case)
             UpperCAmelCase_          =							self.classifier(_snake_case)
             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(_snake_case)
             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(_snake_case)
                       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(_snake_case)

             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"""
import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt







class 			UpperCamelCase_       :





	def __init__(      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					""
				UpperCAmelCase_     : int			     =					""
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : int			     =					0
				UpperCAmelCase_     : List[Any]			     =					256
				UpperCAmelCase_     : Dict			     =					0
				UpperCAmelCase_     : List[Any]			     =					0
				UpperCAmelCase_     : str			     =					0
				UpperCAmelCase_     : List[str]			     =					0





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Dict						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					cva.imread(lowerCAmelCase_	,	0						)
				UpperCAmelCase_     : Union[str, Any]			     =					copy.deepcopy(self.img						)
				UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					plt.hist(self.img.ravel()	,	256	,	[0, 256]	,	label="x"						)
				UpperCAmelCase_     : List[Any]			     =					np.sum(lowerCAmelCase_						)
				for i in range(len(lowerCAmelCase_						)						):
							UpperCAmelCase_     : List[Any]			     =					x[i] / self.k
							self.sk += prk
							UpperCAmelCase_     : Optional[Any]			     =					(self.L - 1) * self.sk
							if self.rem != 0:
										UpperCAmelCase_     : Any			     =					int(last % last						)
							UpperCAmelCase_     : List[str]			     =					int(last + 1 if self.rem >= 0.5 else last						)
							self.last_list.append(lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[Any]			     =					int(np.ma.count(self.img						) / self.img[1].size						)
							UpperCAmelCase_     : Dict			     =					self.img[1].size
				for i in range(self.number_of_cols						):
							for j in range(self.number_of_rows						):
										UpperCAmelCase_     : Any			     =					self.img[j][i]
										if num != self.last_list[num]:
													UpperCAmelCase_     : Tuple			     =					self.last_list[num]
				cva.imwrite("output_data/output.jpg"	,	self.img						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Union[str, Any]:
				plt.hist(self.img.ravel()	,	256	,	[0, 256]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				cva.imshow("Output-Image"	,	self.img						)
				cva.imshow("Input-Image"	,	self.original_image						)
				cva.waitKey(5_000						)
				cva.destroyAllWindows()


if __name__ == "__main__":
			lowerCamelCase_					=      os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
			lowerCamelCase_					=      ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()



