
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):
      _UpperCAmelCase				:    Union[str, Any]           =			RobertaConfig
      _UpperCAmelCase				:    List[str]           =			"""roberta"""
      def __init__(      self    ,  __magic_name__      ):
       super().__init__(__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      RobertaEmbeddings(__magic_name__      )
       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):
      _UpperCAmelCase				:    Dict           =			RobertaConfig
      _UpperCAmelCase				:    List[str]           =			"""roberta"""
      def __init__(      self    ,  __magic_name__      ):
       super().__init__(__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      config.num_labels
       lowerCamelCase		:	List[str]							=      config.num_hidden_layers

       lowerCamelCase		:	Union[str, Any]							=      DeeRobertaModel(__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      nn.Dropout(config.hidden_dropout_prob      )
       lowerCamelCase		:	int							=      nn.Linear(config.hidden_size    ,  self.config.num_labels      )






      @add_start_docstrings_to_model_forward(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=None    ,  __magic_name__=-1    ,  __magic_name__=False    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      self.num_layers
       try:
        lowerCamelCase		:	Tuple							=      self.roberta(
            __magic_name__    ,  attention_mask=__magic_name__    ,  token_type_ids=__magic_name__    ,  position_ids=__magic_name__    ,  head_mask=__magic_name__    ,  inputs_embeds=__magic_name__    ,  )

        lowerCamelCase		:	str							=      outputs[1]

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

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

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

        if train_highway:
         lowerCamelCase		:	str							=      (sum(highway_losses[:-1]      ),) + outputs
         # exclude the final highway, of course
        else:
         lowerCamelCase		:	Dict							=      (loss,) + outputs
       if not self.training:
        lowerCamelCase		:	Dict							=      outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
         lowerCamelCase		:	Tuple							=      (
             (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 copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




