





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). ''',	_a,	)
class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	List[Any]			     = RobertaConfig
			lowerCamelCase_	:	Union[str, Any]			     = '''roberta'''






			def __init__(self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							super().__init__(__magic_name__       )

							snake_case_		:       Optional[Any]           =    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. ''',	_a,	)
class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[Any]			     = RobertaConfig
			lowerCamelCase_	:	Optional[Any]			     = '''roberta'''






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


							'''simple docstring'''



							super().__init__(__magic_name__       )
							snake_case_		:       Any           =    config.num_labels
							snake_case_		:       int           =    config.num_hidden_layers

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






			@add_start_docstrings_to_model_forward(__magic_name__       )
			def      lowerCamelCase    (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   ,				)	->  List[str]:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.num_layers
							try:
											snake_case_		:       Any           =    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__   ,				)

											snake_case_		:       Optional[int]           =    outputs[1]

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

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

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

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






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

