
'''simple docstring'''






from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin




@dataclass
class      __snake_case		( a__):
					_lowerCAmelCase        =						42





class      __snake_case		( a__     , a__):
					@register_to_config
					def __init__(  self,     A = 16,     A = 88,     A = None,     A = None,     A = 1,     A = 0.0,     A = 32,     A = None,     A = False,     A = None,     A = "geglu",     A = True,     A = True,     ):

									"""simple docstring"""


									super().__init__()
									lowerCamelCase       :      List[Any]          =			num_attention_heads
									lowerCamelCase       :      List[str]          =			attention_head_dim
									lowerCamelCase       :      Any          =			num_attention_heads * attention_head_dim

									lowerCamelCase       :      Union[str, Any]          =			in_channels

									lowerCamelCase       :      Dict          =			torch.nn.GroupNorm(num_groups=lowerCamelCase_,     num_channels=lowerCamelCase_,     eps=1e-6,     affine=lowerCamelCase_   )
									lowerCamelCase       :      List[str]          =			nn.Linear(lowerCamelCase_,     lowerCamelCase_   )

									# 3. Define transformers blocks
									lowerCamelCase       :      List[Any]          =			nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     dropout=lowerCamelCase_,     cross_attention_dim=lowerCamelCase_,     activation_fn=lowerCamelCase_,     attention_bias=lowerCamelCase_,     double_self_attention=lowerCamelCase_,     norm_elementwise_affine=lowerCamelCase_,     )
									        for d in range(lowerCamelCase_   )
									    ]   )

									lowerCamelCase       :      Tuple          =			nn.Linear(lowerCamelCase_,     lowerCamelCase_   )




					def      UpperCAmelCase_				(  self,     A,     A=None,     A=None,     A=None,     A=1,     A=None,     A = True,     ):

									"""simple docstring"""


									lowerCamelCase       :      Tuple          =			hidden_states.shape
									lowerCamelCase       :      Dict          =			batch_frames // num_frames

									lowerCamelCase       :      Optional[Any]          =			hidden_states

									lowerCamelCase       :      Optional[int]          =			hidden_states[None, :].reshape(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_   )
									lowerCamelCase       :      Dict          =			hidden_states.permute(0,     2,     1,     3,     4   )

									lowerCamelCase       :      Union[str, Any]          =			self.norm(lowerCamelCase_   )
									lowerCamelCase       :      Any          =			hidden_states.permute(0,     3,     4,     2,     1   ).reshape(batch_size * height * width,     lowerCamelCase_,     lowerCamelCase_   )

									lowerCamelCase       :      List[str]          =			self.proj_in(lowerCamelCase_   )

									# 2. Blocks
									for block in self.transformer_blocks:
													lowerCamelCase       :      Tuple          =			block(
													    lowerCamelCase_,     encoder_hidden_states=lowerCamelCase_,     timestep=lowerCamelCase_,     cross_attention_kwargs=lowerCamelCase_,     class_labels=lowerCamelCase_,     )

									# 3. Output
									lowerCamelCase       :      Any          =			self.proj_out(lowerCamelCase_   )
									lowerCamelCase       :      Optional[int]          =			(
									    hidden_states[None, None, :]
									    .reshape(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_   )
									    .permute(0,     3,     4,     1,     2   )
									    .contiguous()
									)
									lowerCamelCase       :      Optional[Any]          =			hidden_states.reshape(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_   )

									lowerCamelCase       :      Optional[int]          =			hidden_states + residual

									if not return_dict:
													return (output,)

									return TransformerTemporalModelOutput(sample=lowerCamelCase_   )

'''simple docstring'''

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). '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= RobertaConfig
      UpperCAmelCase_   :   Tuple   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	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. '''		,lowerCAmelCase		,)
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Tuple   				= RobertaConfig
      UpperCAmelCase_   :   Optional[Any]   				= '''roberta'''




      def __init__(					self       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         super().__init__(lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	config.num_labels
         UpperCAmelCase__				:					Union[str, Any] 					=	config.num_hidden_layers

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




      @add_start_docstrings_to_model_forward(lowerCamelCase_				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=None       ,lowerCamelCase_=-1       ,lowerCamelCase_=False       ,)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.num_layers
         try:
            UpperCAmelCase__				:					Union[str, Any] 					=	self.roberta(
                lowerCamelCase_       ,attention_mask=lowerCamelCase_       ,token_type_ids=lowerCamelCase_       ,position_ids=lowerCamelCase_       ,head_mask=lowerCamelCase_       ,inputs_embeds=lowerCamelCase_       ,)

            UpperCAmelCase__				:					List[Any] 					=	outputs[1]

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

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

            # work with highway exits
            UpperCAmelCase__				:					List[str] 					=	[]
            for highway_exit in outputs[-1]:
               UpperCAmelCase__				:					Optional[int] 					=	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
                  UpperCAmelCase__				:					Any 					=	MSELoss()
                  UpperCAmelCase__				:					Optional[Any] 					=	loss_fct(highway_logits.view(-1				)       ,labels.view(-1				)				)
               else:
                  UpperCAmelCase__				:					Any 					=	CrossEntropyLoss()
                  UpperCAmelCase__				:					int 					=	loss_fct(highway_logits.view(-1       ,self.num_labels				)       ,labels.view(-1				)				)
               highway_losses.append(lowerCamelCase_				)

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

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




