



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). "	,    SCREAMING_SNAKE_CASE	,    )
class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       RobertaConfig
	SCREAMING_SNAKE_CASE       =       "roberta"



	def __init__(						self							:    int				,	snake_case_							:    str)->							Optional[Any]:
						super().__init__(snake_case_)

						__lowerCAmelCase	   =RobertaEmbeddings(snake_case_)
						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. "	,    SCREAMING_SNAKE_CASE	,    )
class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       RobertaConfig
	SCREAMING_SNAKE_CASE       =       "roberta"



	def __init__(						self							:    int				,	snake_case_							:    Dict)->							Any:
						super().__init__(snake_case_)
						__lowerCAmelCase	   =config.num_labels
						__lowerCAmelCase	   =config.num_hidden_layers

						__lowerCAmelCase	   =DeeRobertaModel(snake_case_)
						__lowerCAmelCase	   =nn.Dropout(config.hidden_dropout_prob)
						__lowerCAmelCase	   =nn.Linear(config.hidden_size				,	self.config.num_labels)



	@add_start_docstrings_to_model_forward(snake_case_)
	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Any=None				,	snake_case_							:    Tuple=None				,	snake_case_							:    int=None				,	snake_case_							:    List[Any]=None				,	snake_case_							:    List[Any]=None				,	snake_case_							:    Tuple=None				,	snake_case_							:    Dict=None				,	snake_case_							:    Dict=-1				,	snake_case_							:    Any=False				,	)->							Optional[Any]:
						__lowerCAmelCase	   =self.num_layers
						try:
											__lowerCAmelCase	   =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_				,	)

											__lowerCAmelCase	   =outputs[1]

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

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

											# work with highway exits
											__lowerCAmelCase	   =[]
											for highway_exit in outputs[-1]:
																__lowerCAmelCase	   =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
																					__lowerCAmelCase	   =MSELoss()
																					__lowerCAmelCase	   =loss_fct(highway_logits.view(-1)				,	labels.view(-1))
																else:
																					__lowerCAmelCase	   =CrossEntropyLoss()
																					__lowerCAmelCase	   =loss_fct(highway_logits.view(-1				,	self.num_labels)				,	labels.view(-1))
																highway_losses.append(snake_case_)

											if train_highway:
																__lowerCAmelCase	   =(sum(highway_losses[:-1]),) + outputs
																# exclude the final highway, of course
											else:
																__lowerCAmelCase	   =(loss,) + outputs
						if not self.training:
											__lowerCAmelCase	   =outputs + ((original_entropy, highway_entropy), exit_layer)
											if output_layer >= 0:
																__lowerCAmelCase	   =(
																    (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 PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
				lowercase_	       =			{
				    '''linear''': PIL.Image.Resampling.BILINEAR,
				    '''bilinear''': PIL.Image.Resampling.BILINEAR,
				    '''bicubic''': PIL.Image.Resampling.BICUBIC,
				    '''lanczos''': PIL.Image.Resampling.LANCZOS,
				    '''nearest''': PIL.Image.Resampling.NEAREST,
				}
else:
				lowercase_	       =			{
				    '''linear''': PIL.Image.LINEAR,
				    '''bilinear''': PIL.Image.BILINEAR,
				    '''bicubic''': PIL.Image.BICUBIC,
				    '''lanczos''': PIL.Image.LANCZOS,
				    '''nearest''': PIL.Image.NEAREST,
				}






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						int:
					__lowerCAmelCase	   =(images / 2 + 0.5).clamp(0				,   1	)
					__lowerCAmelCase	   =images.cpu().permute(0				,   2				,   3				,   1	).float().numpy()
					__lowerCAmelCase	   =numpy_to_pil(__lowerCamelCase	)
					return images






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					if images.ndim == 3:
										__lowerCAmelCase	   =images[None, ...]
					__lowerCAmelCase	   =(images * 255).round().astype("""uint8"""	)
					if images.shape[-1] == 1:
										# special case for grayscale (single channel) images
										__lowerCAmelCase	   =[Image.fromarray(image.squeeze()				,   mode="""L"""	) for image in images]
					else:
										__lowerCAmelCase	   =[Image.fromarray(__lowerCamelCase	) for image in images]

					return pil_images




