
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a__							:	Any  						=			logging.get_logger(__name__)

a__							:	Dict  						=			{
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class   a_    (       UpperCamelCase__				):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE    : Union[str, Any]		=   'dpt'






       def __init__(						self							,    _lowerCamelCase=768							,    _lowerCamelCase=12							,    _lowerCamelCase=12							,    _lowerCamelCase=3072							,    _lowerCamelCase="gelu"							,    _lowerCamelCase=0.0							,    _lowerCamelCase=0.0							,    _lowerCamelCase=0.0_2							,    _lowerCamelCase=1e-12							,    _lowerCamelCase=384							,    _lowerCamelCase=16							,    _lowerCamelCase=3							,    _lowerCamelCase=False							,    _lowerCamelCase=True							,    _lowerCamelCase=[2, 5, 8, 11]							,    _lowerCamelCase="project"							,    _lowerCamelCase=[4, 2, 1, 0.5]							,    _lowerCamelCase=[96, 192, 384, 768]							,    _lowerCamelCase=256							,    _lowerCamelCase=-1							,    _lowerCamelCase=False							,    _lowerCamelCase=True							,    _lowerCamelCase=0.4							,    _lowerCamelCase=255							,    _lowerCamelCase=0.1							,    _lowerCamelCase=[1, 1024, 24, 24]							,    _lowerCamelCase=[0, 1]							,    _lowerCamelCase=None							,    **_lowerCamelCase							,    ) ->Optional[Any]:
           super().__init__(**_a					)

           SCREAMING_SNAKE_CASE							:							Any								=      hidden_size
           SCREAMING_SNAKE_CASE							:							Optional[int]								=      is_hybrid

           if self.is_hybrid:
               if backbone_config is None:
                   logger.info('''Initializing the config with a `BiT` backbone.'''					)
                   SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      {
                       """global_padding""": """same""",
                       """layer_type""": """bottleneck""",
                       """depths""": [3, 4, 9],
                       """out_features""": ["""stage1""", """stage2""", """stage3"""],
                       """embedding_dynamic_padding""": True,
                   }
                   SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      BitConfig(**_a					)
               elif isinstance(_a							,    _a					):
                   logger.info('''Initializing the config with a `BiT` backbone.'''					)
                   SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      BitConfig(**_a					)
               elif isinstance(_a							,    _a					):
                   SCREAMING_SNAKE_CASE							:							Optional[Any]								=      backbone_config
               else:
                   raise ValueError(
                       F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""					)

               SCREAMING_SNAKE_CASE							:							Any								=      backbone_featmap_shape
               SCREAMING_SNAKE_CASE							:							Optional[Any]								=      neck_ignore_stages

               if readout_type != "project":
                   raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''					)
           else:
               SCREAMING_SNAKE_CASE							:							Tuple								=      None
               SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      None
               SCREAMING_SNAKE_CASE							:							Dict								=      []

           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      num_hidden_layers
           SCREAMING_SNAKE_CASE							:							List[str]								=      num_attention_heads
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      intermediate_size
           SCREAMING_SNAKE_CASE							:							Tuple								=      hidden_act
           SCREAMING_SNAKE_CASE							:							int								=      hidden_dropout_prob
           SCREAMING_SNAKE_CASE							:							Tuple								=      attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE							:							List[str]								=      initializer_range
           SCREAMING_SNAKE_CASE							:							List[Any]								=      layer_norm_eps
           SCREAMING_SNAKE_CASE							:							str								=      image_size
           SCREAMING_SNAKE_CASE							:							str								=      patch_size
           SCREAMING_SNAKE_CASE							:							List[str]								=      num_channels
           SCREAMING_SNAKE_CASE							:							int								=      qkv_bias
           SCREAMING_SNAKE_CASE							:							Dict								=      backbone_out_indices
           if readout_type not in ["ignore", "add", "project"]:
               raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''					)
           SCREAMING_SNAKE_CASE							:							int								=      readout_type
           SCREAMING_SNAKE_CASE							:							List[str]								=      reassemble_factors
           SCREAMING_SNAKE_CASE							:							str								=      neck_hidden_sizes
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      fusion_hidden_size
           SCREAMING_SNAKE_CASE							:							List[Any]								=      head_in_index
           SCREAMING_SNAKE_CASE							:							List[Any]								=      use_batch_norm_in_fusion_residual
           # auxiliary head attributes (semantic segmentation)
           SCREAMING_SNAKE_CASE							:							Tuple								=      use_auxiliary_head
           SCREAMING_SNAKE_CASE							:							Union[str, Any]								=      auxiliary_loss_weight
           SCREAMING_SNAKE_CASE							:							Tuple								=      semantic_loss_ignore_index
           SCREAMING_SNAKE_CASE							:							List[Any]								=      semantic_classifier_dropout






       def      __lowerCAmelCase			(						self					) ->Union[str, Any]:
           SCREAMING_SNAKE_CASE							:							str								=      copy.deepcopy(self.__dict__					)

           if output["backbone_config"] is not None:
               SCREAMING_SNAKE_CASE							:							Tuple								=      self.backbone_config.to_dict()

           SCREAMING_SNAKE_CASE							:							Tuple								=      self.__class__.model_type
           return output






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 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           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. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

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

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

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

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

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

