





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       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       RobertaConfig
			__lowerCamelCase      :			Optional[Any]							     =       '''roberta'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]  )      ->      List[str]:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )

					A__     =  RobertaEmbeddings(__lowerCAmelCase  )
					self.init_weights()


@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''  ,	SCREAMING_SNAKE_CASE  ,	)
class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       RobertaConfig
			__lowerCamelCase      :			List[Any]							     =       '''roberta'''






			def __init__(     self  :       Dict				,      __lowerCAmelCase  :       Any  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )
					A__     =  config.num_labels
					A__     =  config.num_hidden_layers

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







			@add_start_docstrings_to_model_forward(__lowerCAmelCase  )
			def   a_			(     self  :       Dict				,      __lowerCAmelCase  :       Dict=None				,      __lowerCAmelCase  :       List[Any]=None				,      __lowerCAmelCase  :       Dict=None				,      __lowerCAmelCase  :       Optional[int]=None				,      __lowerCAmelCase  :       Any=None				,      __lowerCAmelCase  :       Optional[Any]=None				,      __lowerCAmelCase  :       int=None				,      __lowerCAmelCase  :       Any=-1				,      __lowerCAmelCase  :       Optional[Any]=False				,      )      ->      List[Any]:







					"""simple docstring"""






					A__     =  self.num_layers
					try:
							A__     =  self.roberta(
							    __lowerCAmelCase				,      attention_mask=__lowerCAmelCase				,      token_type_ids=__lowerCAmelCase				,      position_ids=__lowerCAmelCase				,      head_mask=__lowerCAmelCase				,      inputs_embeds=__lowerCAmelCase				,      )

							A__     =  outputs[1]

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

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

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

							if train_highway:
									A__     =  (sum(highway_losses[:-1]  ),) + outputs
									# exclude the final highway, of course
							else:
									A__     =  (loss,) + outputs
					if not self.training:
							A__     =  outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
									A__     =  (
									    (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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A       :   Optional[int]					= logging.get_logger(__name__)

A       :   Dict					= {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  num_channels
					A__     =  num_encoder_blocks
					A__     =  depths
					A__     =  sr_ratios
					A__     =  hidden_sizes
					A__     =  patch_sizes
					A__     =  strides
					A__     =  mlp_ratios
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  initializer_range
					A__     =  drop_path_rate
					A__     =  layer_norm_eps
					A__     =  decoder_hidden_size
					A__     =  max_depth
					A__     =  head_in_index



