


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). '   ,snake_case_   ,)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       RobertaConfig
     __lowercase				:							List[str]	   =       'roberta'

     def __init__(					self:List[str]     ,		_a:Union[str, Any]   ):
       super().__init__(_a   )

       snake_case__						= 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. '   ,snake_case_   ,)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[str]	   =       RobertaConfig
     __lowercase				:							int	   =       'roberta'

     def __init__(					self:str     ,		_a:Optional[Any]   ):
       super().__init__(_a   )
       snake_case__						= config.num_labels
       snake_case__						= config.num_hidden_layers

       snake_case__						= DeeRobertaModel(_a   )
       snake_case__						= nn.Dropout(config.hidden_dropout_prob   )
       snake_case__						= nn.Linear(config.hidden_size     ,		self.config.num_labels   )







     @add_start_docstrings_to_model_forward(_a   )
     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Any=None     ,		_a:Any=None     ,		_a:str=None     ,		_a:Optional[Any]=None     ,		_a:Union[str, Any]=None     ,		_a:Optional[Any]=None     ,		_a:Dict=None     ,		_a:str=-1     ,		_a:Optional[int]=False     ,		):
       snake_case__						= self.num_layers
       try:
         snake_case__						= self.roberta(
             _a     ,		attention_mask=_a     ,		token_type_ids=_a     ,		position_ids=_a     ,		head_mask=_a     ,		inputs_embeds=_a     ,		)

         snake_case__						= outputs[1]

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

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

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

         if train_highway:
           snake_case__						= (sum(highway_losses[:-1]   ),) + outputs
           # exclude the final highway, of course
         else:
           snake_case__						= (loss,) + outputs
       if not self.training:
         snake_case__						= outputs + ((original_entropy, highway_entropy), exit_layer)
         if output_layer >= 0:
           snake_case__						= (
               (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 torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       self.assertEqual(output_a.shape     ,		sample.shape   )
       self.assertEqual(output_a.shape     ,		output_a.shape   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

       with self.assertRaises(_a     ,		msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''   ):
         scheduler.set_timesteps(num_inference_steps=_a     ,		timesteps=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
         scheduler.set_timesteps(timesteps=_a   )

